用户需求：
网格智能加密两倍，要求整车空气套件细节加密，尾部区域加密些，计算给到80m每秒

round-01
【发送给 ChatGPT】
You are FluentAGI for Ansys Fluent. Write one short Chinese plan, then a single COMPLETE Python script in this exact Markdown layout:

<fluent_code>

```python
# Use the existing connected PyFluent variable solver.
# Continue with the complete executable script here.
```

</fluent_code>

Put a newline before and after each tag and each fence. Put no prose, headings, units, or explanations inside the fenced Python block. Do not place ```python and Python statements on the same line. Keep the script under 280 lines. Use loops instead of repeating similar statements. Never truncate a string, parenthesis, or code fence; always close </fluent_code>.
Use the existing solver session and compatible PyFluent APIs. A completed conversation turn does not close the session. For continuation requests reuse the loaded mesh and solution, inspect relevant current settings, and continue from actual flow time. Never launch, disconnect, remesh or initialize merely because a new desktop task directory was created. Do not launch or close Fluent. Do not use Abaqus APIs. Use solver.settings APIs and the provided fluentagi adapters. Direct solver.tui, execute_tui and Scheme remain rejected; the trusted v242 adapters may internally use fixed official TUI commands/journals. For mesh display use solver.settings.results.graphics.mesh and graphics.views APIs. Do not return JSON tool plans. Do not invent mesh files or API names. Only operate on the requested case and task directory. Preserve existing results. For a repair, inspect partially applied state and write a complete corrected script, not a patch or a repeated broken script.
The web AI cannot read or modify host application source. Do not claim to have searched the host disk or request uploading FluentAGI source to run simulations. Use the verified runtime adapters. Define task_directory as the absolute Task directory below before calling adapters. For VOF surface tension use fluentagi.configure_surface_tension(directory=task_directory,coefficient=0.072,phase_a="phase-1",phase_b="phase-2"); coefficient is N/m, replace the example with the requested value. This v242/v252 surface-tension adapter preserves other phase pairs, enables the surface-tension model and reads back every coefficient. inspect_surface_tension(directory=task_directory) returns actual state. Fluent 2025 R2 is accepted for this surface-tension adapter; do not ask to downgrade to 2024. Other Lee/DPM adapters retain their own version restrictions. Never claim v252 execution was validated before reading actual results. Do not ask the user to edit the plugin or click GUI dialogs for this supported operation.
Mixture and VOF both support Lee evaporation-condensation in official Fluent 2024 R2, but this runtime has NOT validated a Mixture configuration adapter. Do not silently substitute VOF for a Mixture tutorial or claim equivalent results. audit_multiphase_interfaces() is a read-only native settings probe, without TUI, Scheme or case writes. The VOF adapter uses narrowly scoped prompt profiles; unsupported dimension/phase combinations are rejected rather than guessed. For three-phase boiling setup in Fluent 2024 R2, use fluentagi.configure_three_phase_materials(directory=task_directory, materials=["air", "water-liquid", "water-vapor"]) to create actual phase-1/2/3 domains and verify material assignments. For an existing three-phase model, fluentagi.assign_multiphase_materials(materials=[...], directory=task_directory) assigns materials without changing the phase count. Then call fluentagi.configure_evaporation_condensation(directory=task_directory, saturation_temperature=T_sat_K, evaporation_frequency=from_frequency_per_second, condensation_frequency=to_frequency_per_second, from_phase="phase-2", to_phase="phase-3"). Pass the user's specified temperature/frequencies; preserve their geometry, heating and calculation parameters. This verified v242 VOF Lee adapter enables energy, uses a fixed official TUI journal and verifies the phase pair, constant saturation temperature, both frequencies and Lee model by an independent case snapshot. It does not initialize or advance the simulation. Do not say this feature is unavailable because direct TUI is forbidden; use the adapter. fluentagi.inspect_phase_change(directory=task_directory) returns actual phases and mass transfer settings, refreshing a unique case snapshot. Do not replace existing unrelated mechanisms. This adapter supports one Lee mass transfer pair, not Eulerian wall boiling, semi-mechanistic wall boiling, pressure-dependent saturation tables or UDFs. Do not claim a complete validated boiling simulation from successful setup alone. initialize_air is a TWO-PHASE adapter; do not use it for three-phase cases. Use verified settings initialization and phase-aware patching only for requested new calculations.
The runtime provides fluentagi.assign_phase_materials(primary="air", secondary="water-liquid", directory=task_directory), fluentagi.inspect_phases(), and fluentagi.export_results(directory=task_directory, fields=["pressure", "velocity-magnitude"]). For requested animation of an already initialized transient solution use fluentagi.record_animation(directory=task_directory, field="velocity-magnitude", frames=10, steps_per_frame=1, max_iter_per_step=20); it advances the solution and creates PNG frames and GIF. Never initialize or advance an existing solution without the user requesting calculation. These are executable verified adapters, not prose instructions. Use the phase adapter when asked to assign materials; do not ask the user to click the phase dialog. Print returned results. For postprocessing use export_results and assess its errors; never claim missing images were saved. Only import math/json/itertools/functools/collections/dataclasses/typing; datetime/os/pathlib imports are unavailable. Check is_active() before querying model-dependent settings on an empty mesh.
Mesh conversion preserves independent inlet-name/outlet-name/wall-name boundary zones (for example wall-wing and wall-ground). Read the configured mesh_cell_limit from runtime capabilities; never assume a 200000-cell limit or silently coarsen user geometry. Independent wall zones do not automatically set moving-wall physics or force reports; inspect real settings before configuring those. For NEW planar geometry and meshing use mesh = fluentagi.build_planar(directory=task_directory, points=[[x,y],...], inlet_edges=[0], outlet_edges=[2], size=0.001). Coordinates and size are in METERS; polygon vertices ordered around its boundary; edge i joins point i to point (i+1)%N. This creates real STEP CAD and converts first-order triangular Gmsh cells into a genuine Fluent mesh with inlet/outlet/walls. Then solver.settings.file.read_mesh(file_name=mesh["mesh_file"]), solver.settings.mesh.check(), configure physics, initialize, iterate, and export_results. Save current case/data before replacing loaded geometry. Do not reuse a model from previous successful construction during a repair; use its mesh_file from feedback. For curved planar CAD, use fluentagi.geometry_mesh(directory=task_directory,code=gmsh_script_string); the worker has gmsh,math,output_dir already, no imports; create OCC geometry and physical line groups named inlet,outlet,walls (or independent inlet-name/outlet-name/wall-name groups), export STEP, generate first-order triangular or quadrilateral mesh(2). Returned mesh_file is Fluent format; gmsh-native.msh is NOT Fluent format. 3D modeling and volume mesh conversion are now implemented. fluentagi.build_volume(directory=task_directory, shape="box",length=0.1,width=0.02,height=0.02,size=0.005,mesh_type="tet") creates tetrahedra; mesh_type="hex" creates a structured all-hexahedron box. shape="cylinder",radius=0.01 supports tetrahedra with axis along +X. Inlet is X=0 and outlet X=length. fluentagi.build_extruded(directory=task_directory,points=[[x,y],...],inlet_edges=[0],outlet_edges=[2],depth=0.01,size=0.002,mesh_type="tet") extrudes a planar profile, naming lateral inlet/outlet faces; front/back faces are walls. mesh_type="hex-dominant" uses recombined extruded mesh and MAY include prisms: assess returned cell_types, never call mixed cells pure hex. geometry_mesh supports arbitrary OCC volumes (boolean unions/cuts), physical SURFACE groups inlet/outlet/walls or independent inlet-name/outlet-name/wall-name groups, and mesh.generate(3), first-order tet/hex/prism/pyramid. Imported STEP is not currently supported by the restricted worker. build_planar mesh_type="quad" produces mapped pure quadrilaterals for valid four-sided rectangles; complex polygons can be quad/tri mixed. 2D uses triangles/quads, 3D uses tet/hex. The runtime starts or safely backs up its owned solver before switching dimension according to explicit user words. Do not request user-supplied 3D mesh for supported geometry. Save backups before replacing any loaded mesh. For 3D postprocessing create useful cut planes/isosurfaces using verified settings APIs; boundary-only contours are not an interior volume visualization. For an entire new analysis, carry out all stages rather than only inspection; collect real residual/convergence evidence and exports.
For water-air transient filling, call fluentagi.configure_water_air(directory=task_directory,velocity=0.05,time_step=0.001,gravity_y=-9.81,inlet="inlet",outlet="outlet"). This backs up and sets laminar VOF, materials, phase-aware inlet/outlet, gravity and timestep; does NOT initialize or erase existing solution. For requested NEW filling use fluentagi.initialize_air(directory=task_directory), which saves a backup, hybrid initializes and patches phase-2 volume fraction to zero across all fluid cell zones. Never initialize_air on existing results without an explicit restart request. Fluent v242 contour fields are phase-2-vof (water, only after confirming phase-2=water-liquid) and phase-1-vof (air); do not confuse them. Query real contour field.allowed_values for VOF fields. For 3D interior contours/animation use section=fluentagi.create_section(axis="z",position=depth/2), then export_results(...,surfaces=[section["surface"]]) or record_animation(...,surfaces=[section["surface"]]). A plane section is not full volumetric rendering; report it accurately. Frames are 2..60. Each frame after the first advances steps_per_frame real time steps; final time is initial time+(frames-1)*steps_per_frame*time_step. Do not claim the pipe fills if the simulated duration is too short. Normalize typos like "二维四面体" by explaining 2D triangular equivalent; if depth/3D was explicitly requested, use volume tools.
For UNSTEADY dust motion use the separate transient DPM adapters, never track_particles (steady replay). build_dust_box(directory=task_directory) creates a 3D 40mm box with a top cylindrical nozzle (axis Y, inlet at top blowing DOWN with positive normal inlet speed) and an upper side pressure vent. Save existing case before reading its mesh_file. geometry parameters are SI and can be passed as named arguments. Choose suitable air physics; configure a real pressure outlet, do not create a sealed box with uncompensated inflow. configure_transient_dust(directory=task_directory,region_min=[.012,.003,.012],region_max=[.028,.004,.028],counts=[2,2,2],release_time=.02,release_duration=.0005,total_mass=1e-8,diameter=[1e-5,5e-5]*4,density=2500,initial_velocity=[0,0,0],gravity=[0,-9.81,0],time_step=.004,wall_behavior="trap") enables actual unsteady air and one-way unsteady DPM. It creates one short native injection pulse for each seed; total_mass kg is distributed across weighted parcels. It accepts explicit points instead of a region. The entire release pulse must fit inside one physical dt. Does NOT initialize. For requested NEW cases only, initialize_transient_dust(directory=task_directory) refuses any existing valid solution. Then advance_transient_dust(directory=task_directory,until_time=.2,max_iter_per_step=200,residual_tolerance=.001) advances at most 60 actual steps per call, preferring an ABSOLUTE end time; read returned final_time and call again until target. Already reached until_time does not advance again. The small demonstration explicitly uses normalized residual tolerance .001; honor stricter user requirements and never silently relax them. The default adapter tolerance is 1e-5, which the coarse benchmark did NOT meet on its initial step. Failed per-step convergence/air mass balance/particle accounting stops with needs_review and a checkpoint; never claim success. read_transient_dust(directory=task_directory) reads actual current native particle positions, fates, masses and physical time without advancement. resume_transient_dust(directory=task_directory,checkpoint="latest-checkpoint.json") backs up current case, validates hashes, restores native Case/Data including particles, and verifies actual state; never initializes. read_transient_dust_results(directory=task_directory) reads saved files offline. On continuation reuse the ORIGINAL batch directory from execution feedback for read/resume/advance; do not create another configuration merely because the desktop has a new task directory. Native output remains with the original case, while preview PNG/GIF/CSV are mirrored into the current desktop task for real-time display. Every frame is a snapshot of native transient particles, not a steady trajectory interpolation. Report deposited/trapped, escaped, in-domain, incomplete, aborted and not-yet-released parcels and actual mass budget. Caption all runs "预先释放悬浮颗粒，非积尘再悬浮". Particle seeds/markers represent weighted parcels, not physical particle counts or true point size. Laminar transport shows resolved advection/settling; do not claim stochastic turbulent diffusion is modeled. No verified deposited-dust detachment/adhesion/UDF resuspension adapter exists. Never add an upward initial velocity and call it resuspension. Preserve steady DPM as a distinct path.
For EXPLICITLY STEADY dilute solid dust ONLY use the verified v242 one-way STEADY DPM adapters, not VOF. First inspect_dpm(). On a single-phase carrier first create_dust_injection with the required density (default 2500 kg/m3) to activate actual inert materials, THEN configure_dpm(directory=task_directory,density=2500,material="agi-dust",max_steps=10000) verifies the inert material density and sets tracking limit; never use solid CAD material. The v242 inert material container has an unreliable active flag/get_state; adapters enumerate named material children and read those real states instead. Never query the entire inert material container. create_dust_injection(directory=task_directory,name="dust",start=[0.00001,0.003,0.01],end=[0.00001,0.017,0.01],streams=12,diameter=0.00005,mass_flow=0.0000001,velocity=[0.5,0,0],material="agi-dust") creates a real group injection. Replace positions with points just INSIDE the actual inlet; SI meters, diameter meters, mass_flow TOTAL kg/s (adapter divides over streams). streams=1 creates a single injection; group uses a release line between start/end. Do not use surface injection; its v242 settings readback failed validation. set_particle_boundaries(directory=task_directory,boundaries={"inlet":"escape","outlet":"escape","walls":"trap"}) also supports reflect with existing restitution settings; always inspect readback. Never silently change an existing multiphase or coupled DPM model. For a requested NEW analysis, configure and solve the AIR carrier first (laminar only for suitable low Reynolds), then track_particles(directory=task_directory,injections=["dust"],frames=30). This tracks native DPM trajectories, exports tracks.csv/json, native summary text, statistics and PNG/GIF. It does NOT initialize or advance the carrier. Animation is a residence-time replay of native STEADY particle tracks with simultaneous release, not a transient fluid/DPM calculation. Never use air pathlines or a continuous-phase contour as dust motion. Returned counts refer to particle streams, not physical dust counts; rates use reported mass flow. Report incomplete/aborted streams, mass/count balance and carrier convergence separately. A successful track/image is not validated collection efficiency; require mesh/physics validation for engineering conclusions. Preserve results; use a new injection name if its parameters change.
End with <final_answer>Chinese explanation of the intended changes, without claiming execution success</final_answer>.
If essential input or an incompatible session prevents a valid script, return only <question>the specific missing input in Chinese</question>.
After the desktop returns execution results, either provide a corrected script or return only <final_answer>a Chinese assessment supported by those results</final_answer>. Do not execute the same successful script again. Successful execution alone does not prove convergence.

Previous conversation and execution context (reference only; do not repeat successful scripts):
[{"user": "啊，请你根据图片和我提供的资料，完成整车的一个前翼，后翼型整车车身空气套件，轮胎扩散器的一个建模，并且完成网格划分，网格采用局部加密，网格的控制量控制在20万左右。整个车后面流场网格也适当的做一个加密。更重要的是整车的旁边前后翼和你套件旁边的网格加密对进度有影响，反正网格控制在20~400000后面包括求解。直接生成这个游戏或者动画计算时间，计算量不要太大。"}, {"assistant": "已读到前翼弦长300 mm、翼展1600 mm，后翼弦长350 mm、翼展1000 mm，均为倒置NACA 4412、安装角0°。还缺来流速度及整车尺寸：请提供车速、车长、轴距、轮距和轮胎直径；没有尺寸可直接回复“车速××m/s，整车按图片比例估算”。网格将按20万～40万控制，重点加密前后翼、车身附近、轮胎、扩散器及尾流。", "run_directory": "C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-183115-16d691"}, {"user": "这些尺寸没有给你根据图片，然后你这个能力然后完成这个建模啊，自己全部设计，全部定义，反正最终就是要给我这个曲线流线动画云图比较装逼。"}, {"tool": "run_code", "result": "{\"status\": \"ok\", \"stdout\": \"{'walls': ['wall-rearwing', 'wall-wheel-fl', 'wall-wheel-fr', 'wall-wheel-rl', 'wall-wheel-rr', 'wall-body', 'wall-frontwing', 'wall-ground'], 'viscous': {'model': 'k-epsilon', 'k_epsilon_model': 'realizable', 'near_wall_treatment': {'wall_treatment': 'standard-wall-fn'}, 'options': {'curvature_correction': False, 'production_limiter': {'enabled': False}}, 'turbulence_expert': {'rke_cmu_rotation_term': False, 'turb_non_newtonian': False}, 'user_defined': {'turb_\n…中间日志已省略，完整日志保存在任务目录…\nel 1 ...\\nGrouping cells for Laplace smoothing ...\\n      271220 cells,     1 zone  ...\\n      570904 faces,    12 zones ...\\n       58102 nodes,     1 zone  ...\\n  Done.\\nDone.\\n\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-183309-7900dd\\\\car-transient-1e009e.dat.h5\\\" in NODE0 mode and compression level 1 ...\\n  Writing results.\\nDone.\\n\\n     car-drag-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n                 car-drag-1e009e           177.01976\\n\\ncar-downforce-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n            car-downforce-1e009e           97.216619\\n\\nfrontwing-downforce-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n                  wall-frontwing         0.045705896\\n\\nrearwing-downforce-1e009e\\n -------------------\\n{'final_forces': [{'car-drag-1e009e': [177.0197613431466, 0]}, {'car-downforce-1e009e': [97.2166193715222, 0]}, {'frontwing-downforce-1e009e': [0.04570589572850679, 0]}, {'rearwing-downforce-1e009e': [27.84720127893916, 0]}], 'requested_animation_time_increment': 0.23, 'mass_balance_and_mesh_independence': 'not_yet_verified'}\\n\", \"stderr\": \"\", \"return_value\": null, \"message\": null, \"error_code\": null, \"warnings\": []}"}, {"assistant": "已完成整车建模、271,220单元网格及求解，实际推进至 **0.23秒、46个时间步**，动画适配器返回24帧速度截面动画生成成功，Case/Data已保存。\n\n当前瞬时气动力报告：\n\n项目\t数值\n整车阻力\t177.02 N\n整车下压力\t97.22 N\n后翼分区下压力\t27.85 N\n前翼分区下压力\t0.046 N\n\n计算尚未确认收敛。 最后一步连续性残差约5.1×10⁻⁴，k、ε残差约0.26、0.47，并出现湍流黏度限幅，因此上述力值只能作为初步结果。\n\n前翼报告值很小，需优先核对分区是否包含完整翼面。云图、流线及曲线图片的导出详情未显示在当前截断日志中，暂不能逐项确认。", "run_directory": "C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-183309-7900dd"}, {"user": "网格智能加密两倍，要求整车空气套件细节加密，尾部区域加密些，计算给到80m每秒"}]
Runtime capabilities:
{"protocol": "FluentAGI-capabilities-v1", "session": {"connected": true, "mode": "launch"}, "geometry": {"worker": "gmsh isolated process", "imports": [], "builtins": ["range", "len", "int", "float", "str", "list", "tuple", "enumerate", "zip", "min", "max", "abs", "round", "RuntimeError", "ValueError", "print", "set", "sum", "sorted", "bool", "all", "any"], "physical_groups": ["inlet[-name]", "outlet[-name]", "wall[-name]", "walls"], "independent_boundary_zones": true, "mesh_cell_limit": 4000000, "geometry_timeout_seconds": 3600, "cell_types": ["triangle", "quadrilateral", "tetrahedron", "hexahedron", "prism", "pyramid"]}, "script": {"imports": ["math", "json", "itertools", "functools", "collections", "dataclasses", "typing"], "adapters": ["build_dust_box", "configure_transient_dust", "initialize_transient_dust", "read_transient_dust", "advance_transient_dust", "resume_transient_dust", "read_transient_dust_results", "inspect_dpm", "configure_dpm", "create_dust_injection", "set_particle_boundaries", "track_particles", "build_planar", "build_volume", "build_extruded", "geometry_mesh", "configure_water_air", "initialize_air", "record_animation", "export_results", "assign_phase_materials", "assign_multiphase_materials", "configure_three_phase_materials", "audit_multiphase_interfaces", "configure_evaporation_condensation", "inspect_phase_change", "configure_surface_tension", "inspect_surface_tension"]}, "generated_at": 1792322860.105292, "phase_change": {"version": "v242", "model": "Lee", "adapter_multiphase_models": ["vof"], "mixture_officially_available": true, "mixture_adapter_validated": false, "two_phase_2d_correction_runtime_validation": "pending", "automatic_model_switch": false, "phases": [2, 3], "saturation": "constant K", "frequencies": "positive finite 1/s", "mass_transfer_pairs": 1, "internal_official_tui": true, "generated_direct_tui": false, "readback": "unique case snapshot", "initializes": false}, "surface_tension": {"version": "v242/v252", "runtime_validation": {"v242": "previous native validation", "v252": "official command and generated SDK checked; customer runtime readback required"}, "model": "VOF", "coefficient": "nonnegative finite N/m", "preserves_other_pairs": true, "readback": "unique case snapshot", "initializes": false}, "transient_dpm": {"version": "v242", "model": "unsteady single-phase carrier + transient one-way inert DPM", "release": "one short native pulse per explicit point / region grid, max 64 weighted parcels", "chunk_steps": 60, "positions": "native current positions at actual physical time; never steady replay", "checkpoint": "unique Case/Data plus SHA256 and particle-state readback", "acceptance": "each-step residuals, native air mass flux, particle mass/count fate closure", "default_residual_tolerance": 1e-05, "benchmark_residual_tolerance": 0.001, "strict_1e_5_initial_step_validated": false, "resuspension_validated": false, "stochastic_turbulent_dispersion_validated": false}, "dpm": {"version": "v242", "carrier": "single phase, one-way, steady inert dust", "injection_types": ["single", "group"], "surface_injection_validated": false, "boundary_types": ["reflect", "trap", "escape"], "trajectory_source": "native GetParticleTracksField RPC", "animation": "steady DPM trajectory residence-time replay, no carrier advance", "transient_dpm_validated": false, "two_way_validated": false, "readback": true, "active": true}, "fluent_version": "Ansys Fluent 2024 R2", "model_active": true}
Current session:
{"connected": true, "dimension": 3, "mode": "solver", "owned": true, "continuation": true}
Task directory:
C:\Users\13977\AppData\Local\FluentAGI\data\runs\20261018-192740-fe6b6c
Current request:
网格智能加密两倍，要求整车空气套件细节加密，尾部区域加密些，计算给到80m每秒
【ChatGPT 回复】
ChatGPT 说：

将总网格数目标设为约54万，优先加密空气套件和尾流；继承当前解，分阶段升至80 m/s，同步更新地面与轮胎速度，继续计算并导出结果。

<fluent_code>
```python
# Use the existing connected PyFluent variable solver.
import math

task_directory = r"C:\Users\13977\AppData\Local\FluentAGI\data\runs\20261018-192740-fe6b6c"
s = solver.settings
baseline_cells = 271220
target_cells = 2 * baseline_cells
runtime_cell_limit = 4000000
if not s.setup.is_active() or not s.setup.models.is_active():
    raise RuntimeError("Expected the existing initialized car case")
s.file.write_case_data(file_name=task_directory+r"\before-refinement-ad9ac9.cas.h5")
bc = s.setup.boundary_conditions
rd = s.solution.report_definitions
run = s.solution.run_calculation
print({"time_mode": s.setup.general.solver.time.get_state(),
       "calculation_state": run.get_state(),
       "target_cells": target_cells, "runtime_cell_limit": runtime_cell_limit})
print({"inlet_before": bc.velocity_inlet["inlet"].get_state(),
       "walls": bc.wall.get_object_names()})

def choose(node, candidates):
    norm = lambda x: str(x).lower().replace(" ", "").replace("-", "").replace("_", "")
    allowed = node.allowed_values()
    for candidate in candidates:
        for value in allowed:
            if norm(value) == norm(candidate):
                node.set_state(value)
                return value
    raise RuntimeError(str({"requested": candidates, "allowed": allowed}))

def select_type(node, predicate):
    allowed = node.allowed_values()
    for value in allowed:
        if predicate(str(value).lower()):
            node.set_state(value)
            return value
    raise RuntimeError(str({"unsupported_report_or_method": allowed}))

def constant(node, value):
    state = node.get_state()
    if isinstance(state, dict) and "value" in node.child_names:
        if "option" in node.child_names:
            choose(node.option, ["value", "constant"])
        node.value = value
    else:
        node.set_state(value)

def vector(node, values):
    state = node.get_state()
    if isinstance(state, list) and state and isinstance(state[0], dict):
        for i, value in enumerate(values):
            constant(node[i], value)
    else:
        node.set_state(values)

def report_number(name):
    result = rd.compute(report_defs=[name])
    blocks = result if isinstance(result, list) else [result]
    for block in blocks:
        if isinstance(block, dict) and name in block:
            value = block[name]
            if isinstance(value, (list, tuple)):
                value = value[0]
            return float(value)
    raise RuntimeError(str({"unrecognized_report": name, "result": result}))

cell_report = "cell_count_ad9ac9"
if cell_report not in rd.mesh.get_object_names():
    rd.mesh.create(name=cell_report)
select_type(rd.mesh[cell_report].report_type,
            lambda x: "cell" in x and "count" in x)
fluid_zones = s.setup.cell_zone_conditions.fluid.get_object_names()
rd.mesh[cell_report].cell_zones = fluid_zones
clock_report = "flow_time_ad9ac9"
if clock_report not in rd.time.get_object_names():
    rd.time.create(name=clock_report)
initial_time = report_number(clock_report)
initial_cells = int(round(report_number(cell_report)))
print({"actual_initial_time": initial_time, "actual_initial_cells": initial_cells})
if initial_cells > int(target_cells * 1.15):
    raise RuntimeError("Existing mesh already exceeds this refinement target")

required_walls = [
    "wall-body", "wall-frontwing", "wall-rearwing", "wall-ground",
    "wall-wheel-fl", "wall-wheel-fr", "wall-wheel-rl", "wall-wheel-rr"]
actual_walls = bc.wall.get_object_names()
if any(name not in actual_walls for name in required_walls):
    raise RuntimeError(str({"missing_car_boundaries": actual_walls}))
adapt = s.mesh.adapt
controls = adapt.set
print({"adaptation_controls_before": controls.get_state()})
if "maximum_cell_count" not in controls.child_names:
    raise RuntimeError("Native adaption lacks the required cell-count control")
select_type(controls.adaption_method,
            lambda x: ("puma" in x or "poly" in x) and "2.5" not in x)
controls.maximum_refinement_level = 1
controls.minimum_edge_length = 0.002
controls.maximum_cell_count = target_cells
registers = s.solution.cell_registers
regions = [
    ("frontwing", [-0.04,-0.85,0.035], [0.36,0.85,0.29], 335000),
    ("rearwing", [2.81,-0.56,0.84], [3.25,0.56,1.10], 395000),
    ("diffuser", [2.12,-0.59,0.045], [3.28,0.59,0.33], 450000),
    ("sidepods", [0.98,-0.59,0.14], [2.80,0.59,0.70], 490000),
    ("wake", [3.12,-1.20,0.03], [7.50,1.20,1.45], target_cells),
    ("car_nearfield", [-0.08,-1.0,0.01], [3.30,1.0,1.14], target_cells)]
for label, low, high, budget in regions:
    current = int(round(report_number(cell_report)))
    if current >= int(0.98 * target_cells):
        break
    if current >= budget:
        continue
    controls.maximum_cell_count = min(budget, target_cells, runtime_cell_limit)
    name = "ref_" + label + "_ad9ac9"
    if name not in registers.get_object_names():
        registers.create(name=name)
    register = registers[name]
    choose(register.type.option, ["hexahedron"])
    region = register.type.hexahedron
    region.min_point = low
    region.max_point = high
    region.inside = True
    print({"refinement_region": label, "register": register.get_state(),
           "cell_budget": controls.maximum_cell_count.get_state()})
    if "refine" not in register.command_names:
        raise RuntimeError("Native cell-register refinement unavailable")
    register.refine()
    current = int(round(report_number(cell_report)))
    print({"region_finished": label, "actual_cells": current})
    if current > min(runtime_cell_limit, int(target_cells * 1.15)):
        s.file.write_case_data(file_name=task_directory+r"\refinement-review-ad9ac9.cas.h5")
        raise RuntimeError("Refinement exceeded the intended cell budget")
controls.maximum_cell_count = target_cells
s.mesh.check()
final_cells = int(round(report_number(cell_report)))
print({"actual_refined_cells": final_cells,
       "ratio_to_original": final_cells / baseline_cells,
       "solution_time_after_refinement": report_number(clock_report)})
if final_cells <= initial_cells and initial_cells < int(target_cells * 0.9):
    raise RuntimeError("Requested refinement did not increase the cell count")
s.file.write_case_data(file_name=task_directory+r"\car-refined-ad9ac9.cas.h5")

time_mode = str(s.setup.general.solver.time.get_state()).lower()
if "unsteady" not in time_mode:
    raise RuntimeError("Expected the existing transient solution; time mode preserved")
run.transient_controls.time_step_size = 0.001
ground = bc.wall["wall-ground"].momentum
print({"ground_before": ground.get_state()})
wheels = ["wall-wheel-" + side for side in ["fl", "fr", "rl", "rr"]]
for name in wheels:
    print({"wheel": name, "motion_before": bc.wall[name].momentum.get_state()})

def set_speed(value):
    constant(bc.velocity_inlet["inlet"].momentum.velocity, value)
    vector(ground.velocity_components, [value, 0.0, 0.0])
    for name in wheels:
        constant(bc.wall[name].momentum.rotation_speed, -value / 0.24)
    print({"requested_speed": value,
           "inlet_readback": bc.velocity_inlet["inlet"].momentum.get_state(),
           "ground_readback": ground.get_state()})

car_walls = [name for name in actual_walls if name != "wall-ground"]
force_reports = []
for name, zones, direction in [
    ("drag_80_ad9ac9", car_walls, [1,0,0]),
    ("downforce_80_ad9ac9", car_walls, [0,0,-1]),
    ("front_partition_80_ad9ac9", ["wall-frontwing"], [0,0,-1]),
    ("rear_partition_80_ad9ac9", ["wall-rearwing"], [0,0,-1])]:
    if name not in rd.force.get_object_names():
        rd.force.create(name=name)
    rd.force[name].set_state({"zones": zones, "force_vector": direction})
    force_reports.append(name)
mass_reports = []
for label, boundaries in [("inlet", ["inlet"]), ("outlet", ["outlet"])]:
    name = "mass_" + label + "_ad9ac9"
    if name not in rd.flux.get_object_names():
        rd.flux.create(name=name)
    select_type(rd.flux[name].report_type, lambda x: "mass" in x and "flow" in x)
    rd.flux[name].boundaries = boundaries
    mass_reports.append(name)
monitor_name = "car_80_ad9ac9"
all_reports = force_reports + mass_reports + [clock_report]
for collection in [s.solution.monitor.report_files, s.solution.monitor.report_plots]:
    if monitor_name not in collection.get_object_names():
        collection.create(name=monitor_name)
    collection[monitor_name].report_defs = all_reports
    collection[monitor_name].active = True
s.solution.monitor.report_files[monitor_name].file_name = task_directory+r"\car-80-history-ad9ac9.out"

def evidence(label):
    print({"stage": label, "actual_time": report_number(clock_report),
           "forces": rd.compute(report_defs=force_reports)})
    mass_in, mass_out = [report_number(name) for name in mass_reports]
    print({"mass_in": mass_in, "mass_out": mass_out,
           "relative_mass_imbalance": abs(mass_in + mass_out) / max(abs(mass_in), 1e-12)})
    try:
        index, history = solver.monitors.get_monitor_set_data("residual")
        last = {key: float(value[-1]) for key, value in history.items() if len(value)}
        print({"residuals": last})
        if any(not math.isfinite(value) or value > 1e4 for value in last.values()):
            s.file.write_case_data(file_name=task_directory+r"\needs-review-80-ad9ac9.cas.h5")
            raise RuntimeError("Residual divergence detected")
    except RuntimeError:
        raise
    except Exception as error:
        print({"residual_read_error": str(error)})

for value in [40.0, 60.0, 80.0]:
    set_speed(value)
    run.dual_time_iterate(time_step_count=3, max_iter_per_step=60)
    evidence("speed_" + str(value))
for batch in range(3):
    run.dual_time_iterate(time_step_count=10, max_iter_per_step=60)
    evidence("80_settling_" + str(batch + 1))
s.file.write_case_data(file_name=task_directory+r"\car-80-before-animation-ad9ac9.cas.h5")

g = s.results.graphics
try:
    name = "refined_car_ad9ac9"
    if name not in g.mesh.get_object_names():
        g.mesh.create(name=name)
    g.mesh[name].surfaces_list = car_walls
    g.mesh[name].display()
    g.picture.save_picture(file_name=task_directory+r"\refined-car-ad9ac9.png")
except Exception as error:
    print({"mesh_graphics_error": str(error)})
section = fluentagi.create_section(axis="y", position=0.0)
print(section)
result = fluentagi.export_results(
    directory=task_directory, fields=["pressure", "velocity-magnitude"],
    surfaces=[section["surface"]])
print(result)
for name, kind, state in [
    ("car_pressure_80_ad9ac9", "contour",
     {"field":"pressure", "surfaces_list":car_walls, "filled":True}),
    ("car_streamlines_80_ad9ac9", "pathline",
     {"field":"velocity-magnitude", "release_from_surfaces":["inlet"], "step":3000, "skip":20})]:
    try:
        collection = getattr(g, kind)
        if name not in collection.get_object_names():
            collection.create(name=name)
        collection[name].set_state(state)
        collection[name].display()
        g.picture.save_picture(file_name=task_directory+"\\"+name+".png")
    except Exception as error:
        print({"graphics": name, "error": str(error)})
print(fluentagi.record_animation(
    directory=task_directory, field="velocity-magnitude", frames=24,
    steps_per_frame=2, max_iter_per_step=60, surfaces=[section["surface"]]))
evidence("final_80")
s.file.write_case_data(file_name=task_directory+r"\car-80-final-ad9ac9.cas.h5")
for name, obj in [("forces", s.solution.monitor.report_plots[monitor_name]),
                  ("residuals", s.solution.monitor.residual)]:
    try:
        obj.plot()
        g.picture.save_picture(file_name=task_directory+"\\"+name+"-80-ad9ac9.png")
    except Exception as error:
        print({"plot": name, "error": str(error)})
print({"initial_time": initial_time, "actual_final_time": report_number(clock_report),
       "actual_cells": int(round(report_number(cell_report))),
       "target_speed": 80.0, "convergence_verified": False})
```
</fluent_code>

<final_answer>脚本将在原解上进行局部自适应加密，目标约54万单元，并从实际流动时刻继续升速至80 m/s。同步输出气动力、质量不平衡、残差和24帧截面动画；前翼报告仍标为现有分区，实际网格数量和收敛情况以执行结果为准。</final_answer>

round-02
【发送给 ChatGPT】
You are FluentAGI for Ansys Fluent. Write one short Chinese plan, then a single COMPLETE Python script in this exact Markdown layout:

<fluent_code>

```python
# Use the existing connected PyFluent variable solver.
# Continue with the complete executable script here.
```

</fluent_code>

Put a newline before and after each tag and each fence. Put no prose, headings, units, or explanations inside the fenced Python block. Do not place ```python and Python statements on the same line. Keep the script under 280 lines. Use loops instead of repeating similar statements. Never truncate a string, parenthesis, or code fence; always close </fluent_code>.
Use the existing solver session and compatible PyFluent APIs. A completed conversation turn does not close the session. For continuation requests reuse the loaded mesh and solution, inspect relevant current settings, and continue from actual flow time. Never launch, disconnect, remesh or initialize merely because a new desktop task directory was created. Do not launch or close Fluent. Do not use Abaqus APIs. Use solver.settings APIs and the provided fluentagi adapters. Direct solver.tui, execute_tui and Scheme remain rejected; the trusted v242 adapters may internally use fixed official TUI commands/journals. For mesh display use solver.settings.results.graphics.mesh and graphics.views APIs. Do not return JSON tool plans. Do not invent mesh files or API names. Only operate on the requested case and task directory. Preserve existing results. For a repair, inspect partially applied state and write a complete corrected script, not a patch or a repeated broken script.
The web AI cannot read or modify host application source. Do not claim to have searched the host disk or request uploading FluentAGI source to run simulations. Use the verified runtime adapters. Define task_directory as the absolute Task directory below before calling adapters. For VOF surface tension use fluentagi.configure_surface_tension(directory=task_directory,coefficient=0.072,phase_a="phase-1",phase_b="phase-2"); coefficient is N/m, replace the example with the requested value. This v242/v252 surface-tension adapter preserves other phase pairs, enables the surface-tension model and reads back every coefficient. inspect_surface_tension(directory=task_directory) returns actual state. Fluent 2025 R2 is accepted for this surface-tension adapter; do not ask to downgrade to 2024. Other Lee/DPM adapters retain their own version restrictions. Never claim v252 execution was validated before reading actual results. Do not ask the user to edit the plugin or click GUI dialogs for this supported operation.
Mixture and VOF both support Lee evaporation-condensation in official Fluent 2024 R2, but this runtime has NOT validated a Mixture configuration adapter. Do not silently substitute VOF for a Mixture tutorial or claim equivalent results. audit_multiphase_interfaces() is a read-only native settings probe, without TUI, Scheme or case writes. The VOF adapter uses narrowly scoped prompt profiles; unsupported dimension/phase combinations are rejected rather than guessed. For three-phase boiling setup in Fluent 2024 R2, use fluentagi.configure_three_phase_materials(directory=task_directory, materials=["air", "water-liquid", "water-vapor"]) to create actual phase-1/2/3 domains and verify material assignments. For an existing three-phase model, fluentagi.assign_multiphase_materials(materials=[...], directory=task_directory) assigns materials without changing the phase count. Then call fluentagi.configure_evaporation_condensation(directory=task_directory, saturation_temperature=T_sat_K, evaporation_frequency=from_frequency_per_second, condensation_frequency=to_frequency_per_second, from_phase="phase-2", to_phase="phase-3"). Pass the user's specified temperature/frequencies; preserve their geometry, heating and calculation parameters. This verified v242 VOF Lee adapter enables energy, uses a fixed official TUI journal and verifies the phase pair, constant saturation temperature, both frequencies and Lee model by an independent case snapshot. It does not initialize or advance the simulation. Do not say this feature is unavailable because direct TUI is forbidden; use the adapter. fluentagi.inspect_phase_change(directory=task_directory) returns actual phases and mass transfer settings, refreshing a unique case snapshot. Do not replace existing unrelated mechanisms. This adapter supports one Lee mass transfer pair, not Eulerian wall boiling, semi-mechanistic wall boiling, pressure-dependent saturation tables or UDFs. Do not claim a complete validated boiling simulation from successful setup alone. initialize_air is a TWO-PHASE adapter; do not use it for three-phase cases. Use verified settings initialization and phase-aware patching only for requested new calculations.
The runtime provides fluentagi.assign_phase_materials(primary="air", secondary="water-liquid", directory=task_directory), fluentagi.inspect_phases(), and fluentagi.export_results(directory=task_directory, fields=["pressure", "velocity-magnitude"]). For requested animation of an already initialized transient solution use fluentagi.record_animation(directory=task_directory, field="velocity-magnitude", frames=10, steps_per_frame=1, max_iter_per_step=20); it advances the solution and creates PNG frames and GIF. Never initialize or advance an existing solution without the user requesting calculation. These are executable verified adapters, not prose instructions. Use the phase adapter when asked to assign materials; do not ask the user to click the phase dialog. Print returned results. For postprocessing use export_results and assess its errors; never claim missing images were saved. Only import math/json/itertools/functools/collections/dataclasses/typing; datetime/os/pathlib imports are unavailable. Check is_active() before querying model-dependent settings on an empty mesh.
Mesh conversion preserves independent inlet-name/outlet-name/wall-name boundary zones (for example wall-wing and wall-ground). Read the configured mesh_cell_limit from runtime capabilities; never assume a 200000-cell limit or silently coarsen user geometry. Independent wall zones do not automatically set moving-wall physics or force reports; inspect real settings before configuring those. For NEW planar geometry and meshing use mesh = fluentagi.build_planar(directory=task_directory, points=[[x,y],...], inlet_edges=[0], outlet_edges=[2], size=0.001). Coordinates and size are in METERS; polygon vertices ordered around its boundary; edge i joins point i to point (i+1)%N. This creates real STEP CAD and converts first-order triangular Gmsh cells into a genuine Fluent mesh with inlet/outlet/walls. Then solver.settings.file.read_mesh(file_name=mesh["mesh_file"]), solver.settings.mesh.check(), configure physics, initialize, iterate, and export_results. Save current case/data before replacing loaded geometry. Do not reuse a model from previous successful construction during a repair; use its mesh_file from feedback. For curved planar CAD, use fluentagi.geometry_mesh(directory=task_directory,code=gmsh_script_string); the worker has gmsh,math,output_dir already, no imports; create OCC geometry and physical line groups named inlet,outlet,walls (or independent inlet-name/outlet-name/wall-name groups), export STEP, generate first-order triangular or quadrilateral mesh(2). Returned mesh_file is Fluent format; gmsh-native.msh is NOT Fluent format. 3D modeling and volume mesh conversion are now implemented. fluentagi.build_volume(directory=task_directory, shape="box",length=0.1,width=0.02,height=0.02,size=0.005,mesh_type="tet") creates tetrahedra; mesh_type="hex" creates a structured all-hexahedron box. shape="cylinder",radius=0.01 supports tetrahedra with axis along +X. Inlet is X=0 and outlet X=length. fluentagi.build_extruded(directory=task_directory,points=[[x,y],...],inlet_edges=[0],outlet_edges=[2],depth=0.01,size=0.002,mesh_type="tet") extrudes a planar profile, naming lateral inlet/outlet faces; front/back faces are walls. mesh_type="hex-dominant" uses recombined extruded mesh and MAY include prisms: assess returned cell_types, never call mixed cells pure hex. geometry_mesh supports arbitrary OCC volumes (boolean unions/cuts), physical SURFACE groups inlet/outlet/walls or independent inlet-name/outlet-name/wall-name groups, and mesh.generate(3), first-order tet/hex/prism/pyramid. Imported STEP is not currently supported by the restricted worker. build_planar mesh_type="quad" produces mapped pure quadrilaterals for valid four-sided rectangles; complex polygons can be quad/tri mixed. 2D uses triangles/quads, 3D uses tet/hex. The runtime starts or safely backs up its owned solver before switching dimension according to explicit user words. Do not request user-supplied 3D mesh for supported geometry. Save backups before replacing any loaded mesh. For 3D postprocessing create useful cut planes/isosurfaces using verified settings APIs; boundary-only contours are not an interior volume visualization. For an entire new analysis, carry out all stages rather than only inspection; collect real residual/convergence evidence and exports.
For water-air transient filling, call fluentagi.configure_water_air(directory=task_directory,velocity=0.05,time_step=0.001,gravity_y=-9.81,inlet="inlet",outlet="outlet"). This backs up and sets laminar VOF, materials, phase-aware inlet/outlet, gravity and timestep; does NOT initialize or erase existing solution. For requested NEW filling use fluentagi.initialize_air(directory=task_directory), which saves a backup, hybrid initializes and patches phase-2 volume fraction to zero across all fluid cell zones. Never initialize_air on existing results without an explicit restart request. Fluent v242 contour fields are phase-2-vof (water, only after confirming phase-2=water-liquid) and phase-1-vof (air); do not confuse them. Query real contour field.allowed_values for VOF fields. For 3D interior contours/animation use section=fluentagi.create_section(axis="z",position=depth/2), then export_results(...,surfaces=[section["surface"]]) or record_animation(...,surfaces=[section["surface"]]). A plane section is not full volumetric rendering; report it accurately. Frames are 2..60. Each frame after the first advances steps_per_frame real time steps; final time is initial time+(frames-1)*steps_per_frame*time_step. Do not claim the pipe fills if the simulated duration is too short. Normalize typos like "二维四面体" by explaining 2D triangular equivalent; if depth/3D was explicitly requested, use volume tools.
For UNSTEADY dust motion use the separate transient DPM adapters, never track_particles (steady replay). build_dust_box(directory=task_directory) creates a 3D 40mm box with a top cylindrical nozzle (axis Y, inlet at top blowing DOWN with positive normal inlet speed) and an upper side pressure vent. Save existing case before reading its mesh_file. geometry parameters are SI and can be passed as named arguments. Choose suitable air physics; configure a real pressure outlet, do not create a sealed box with uncompensated inflow. configure_transient_dust(directory=task_directory,region_min=[.012,.003,.012],region_max=[.028,.004,.028],counts=[2,2,2],release_time=.02,release_duration=.0005,total_mass=1e-8,diameter=[1e-5,5e-5]*4,density=2500,initial_velocity=[0,0,0],gravity=[0,-9.81,0],time_step=.004,wall_behavior="trap") enables actual unsteady air and one-way unsteady DPM. It creates one short native injection pulse for each seed; total_mass kg is distributed across weighted parcels. It accepts explicit points instead of a region. The entire release pulse must fit inside one physical dt. Does NOT initialize. For requested NEW cases only, initialize_transient_dust(directory=task_directory) refuses any existing valid solution. Then advance_transient_dust(directory=task_directory,until_time=.2,max_iter_per_step=200,residual_tolerance=.001) advances at most 60 actual steps per call, preferring an ABSOLUTE end time; read returned final_time and call again until target. Already reached until_time does not advance again. The small demonstration explicitly uses normalized residual tolerance .001; honor stricter user requirements and never silently relax them. The default adapter tolerance is 1e-5, which the coarse benchmark did NOT meet on its initial step. Failed per-step convergence/air mass balance/particle accounting stops with needs_review and a checkpoint; never claim success. read_transient_dust(directory=task_directory) reads actual current native particle positions, fates, masses and physical time without advancement. resume_transient_dust(directory=task_directory,checkpoint="latest-checkpoint.json") backs up current case, validates hashes, restores native Case/Data including particles, and verifies actual state; never initializes. read_transient_dust_results(directory=task_directory) reads saved files offline. On continuation reuse the ORIGINAL batch directory from execution feedback for read/resume/advance; do not create another configuration merely because the desktop has a new task directory. Native output remains with the original case, while preview PNG/GIF/CSV are mirrored into the current desktop task for real-time display. Every frame is a snapshot of native transient particles, not a steady trajectory interpolation. Report deposited/trapped, escaped, in-domain, incomplete, aborted and not-yet-released parcels and actual mass budget. Caption all runs "预先释放悬浮颗粒，非积尘再悬浮". Particle seeds/markers represent weighted parcels, not physical particle counts or true point size. Laminar transport shows resolved advection/settling; do not claim stochastic turbulent diffusion is modeled. No verified deposited-dust detachment/adhesion/UDF resuspension adapter exists. Never add an upward initial velocity and call it resuspension. Preserve steady DPM as a distinct path.
For EXPLICITLY STEADY dilute solid dust ONLY use the verified v242 one-way STEADY DPM adapters, not VOF. First inspect_dpm(). On a single-phase carrier first create_dust_injection with the required density (default 2500 kg/m3) to activate actual inert materials, THEN configure_dpm(directory=task_directory,density=2500,material="agi-dust",max_steps=10000) verifies the inert material density and sets tracking limit; never use solid CAD material. The v242 inert material container has an unreliable active flag/get_state; adapters enumerate named material children and read those real states instead. Never query the entire inert material container. create_dust_injection(directory=task_directory,name="dust",start=[0.00001,0.003,0.01],end=[0.00001,0.017,0.01],streams=12,diameter=0.00005,mass_flow=0.0000001,velocity=[0.5,0,0],material="agi-dust") creates a real group injection. Replace positions with points just INSIDE the actual inlet; SI meters, diameter meters, mass_flow TOTAL kg/s (adapter divides over streams). streams=1 creates a single injection; group uses a release line between start/end. Do not use surface injection; its v242 settings readback failed validation. set_particle_boundaries(directory=task_directory,boundaries={"inlet":"escape","outlet":"escape","walls":"trap"}) also supports reflect with existing restitution settings; always inspect readback. Never silently change an existing multiphase or coupled DPM model. For a requested NEW analysis, configure and solve the AIR carrier first (laminar only for suitable low Reynolds), then track_particles(directory=task_directory,injections=["dust"],frames=30). This tracks native DPM trajectories, exports tracks.csv/json, native summary text, statistics and PNG/GIF. It does NOT initialize or advance the carrier. Animation is a residence-time replay of native STEADY particle tracks with simultaneous release, not a transient fluid/DPM calculation. Never use air pathlines or a continuous-phase contour as dust motion. Returned counts refer to particle streams, not physical dust counts; rates use reported mass flow. Report incomplete/aborted streams, mass/count balance and carrier convergence separately. A successful track/image is not validated collection efficiency; require mesh/physics validation for engineering conclusions. Preserve results; use a new injection name if its parameters change.
End with <final_answer>Chinese explanation of the intended changes, without claiming execution success</final_answer>.
If essential input or an incompatible session prevents a valid script, return only <question>the specific missing input in Chinese</question>.
After the desktop returns execution results, either provide a corrected script or return only <final_answer>a Chinese assessment supported by those results</final_answer>. Do not execute the same successful script again. Successful execution alone does not prove convergence.

Previous conversation and execution context (reference only; do not repeat successful scripts):
[{"user": "啊，请你根据图片和我提供的资料，完成整车的一个前翼，后翼型整车车身空气套件，轮胎扩散器的一个建模，并且完成网格划分，网格采用局部加密，网格的控制量控制在20万左右。整个车后面流场网格也适当的做一个加密。更重要的是整车的旁边前后翼和你套件旁边的网格加密对进度有影响，反正网格控制在20~400000后面包括求解。直接生成这个游戏或者动画计算时间，计算量不要太大。"}, {"assistant": "已读到前翼弦长300 mm、翼展1600 mm，后翼弦长350 mm、翼展1000 mm，均为倒置NACA 4412、安装角0°。还缺来流速度及整车尺寸：请提供车速、车长、轴距、轮距和轮胎直径；没有尺寸可直接回复“车速××m/s，整车按图片比例估算”。网格将按20万～40万控制，重点加密前后翼、车身附近、轮胎、扩散器及尾流。", "run_directory": "C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-183115-16d691"}, {"user": "这些尺寸没有给你根据图片，然后你这个能力然后完成这个建模啊，自己全部设计，全部定义，反正最终就是要给我这个曲线流线动画云图比较装逼。"}, {"tool": "run_code", "result": "{\"status\": \"ok\", \"stdout\": \"{'walls': ['wall-rearwing', 'wall-wheel-fl', 'wall-wheel-fr', 'wall-wheel-rl', 'wall-wheel-rr', 'wall-body', 'wall-frontwing', 'wall-ground'], 'viscous': {'model': 'k-epsilon', 'k_epsilon_model': 'realizable', 'near_wall_treatment': {'wall_treatment': 'standard-wall-fn'}, 'options': {'curvature_correction': False, 'production_limiter': {'enabled': False}}, 'turbulence_expert': {'rke_cmu_rotation_term': False, 'turb_non_newtonian': False}, 'user_defined': {'turb_\n…中间日志已省略，完整日志保存在任务目录…\nel 1 ...\\nGrouping cells for Laplace smoothing ...\\n      271220 cells,     1 zone  ...\\n      570904 faces,    12 zones ...\\n       58102 nodes,     1 zone  ...\\n  Done.\\nDone.\\n\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-183309-7900dd\\\\car-transient-1e009e.dat.h5\\\" in NODE0 mode and compression level 1 ...\\n  Writing results.\\nDone.\\n\\n     car-drag-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n                 car-drag-1e009e           177.01976\\n\\ncar-downforce-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n            car-downforce-1e009e           97.216619\\n\\nfrontwing-downforce-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n                  wall-frontwing         0.045705896\\n\\nrearwing-downforce-1e009e\\n -------------------\\n{'final_forces': [{'car-drag-1e009e': [177.0197613431466, 0]}, {'car-downforce-1e009e': [97.2166193715222, 0]}, {'frontwing-downforce-1e009e': [0.04570589572850679, 0]}, {'rearwing-downforce-1e009e': [27.84720127893916, 0]}], 'requested_animation_time_increment': 0.23, 'mass_balance_and_mesh_independence': 'not_yet_verified'}\\n\", \"stderr\": \"\", \"return_value\": null, \"message\": null, \"error_code\": null, \"warnings\": []}"}, {"assistant": "已完成整车建模、271,220单元网格及求解，实际推进至 **0.23秒、46个时间步**，动画适配器返回24帧速度截面动画生成成功，Case/Data已保存。\n\n当前瞬时气动力报告：\n\n项目\t数值\n整车阻力\t177.02 N\n整车下压力\t97.22 N\n后翼分区下压力\t27.85 N\n前翼分区下压力\t0.046 N\n\n计算尚未确认收敛。 最后一步连续性残差约5.1×10⁻⁴，k、ε残差约0.26、0.47，并出现湍流黏度限幅，因此上述力值只能作为初步结果。\n\n前翼报告值很小，需优先核对分区是否包含完整翼面。云图、流线及曲线图片的导出详情未显示在当前截断日志中，暂不能逐项确认。", "run_directory": "C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-183309-7900dd"}, {"user": "网格智能加密两倍，要求整车空气套件细节加密，尾部区域加密些，计算给到80m每秒"}]
Runtime capabilities:
{"protocol": "FluentAGI-capabilities-v1", "session": {"connected": true, "mode": "launch"}, "geometry": {"worker": "gmsh isolated process", "imports": [], "builtins": ["range", "len", "int", "float", "str", "list", "tuple", "enumerate", "zip", "min", "max", "abs", "round", "RuntimeError", "ValueError", "print", "set", "sum", "sorted", "bool", "all", "any"], "physical_groups": ["inlet[-name]", "outlet[-name]", "wall[-name]", "walls"], "independent_boundary_zones": true, "mesh_cell_limit": 4000000, "geometry_timeout_seconds": 3600, "cell_types": ["triangle", "quadrilateral", "tetrahedron", "hexahedron", "prism", "pyramid"]}, "script": {"imports": ["math", "json", "itertools", "functools", "collections", "dataclasses", "typing"], "adapters": ["build_dust_box", "configure_transient_dust", "initialize_transient_dust", "read_transient_dust", "advance_transient_dust", "resume_transient_dust", "read_transient_dust_results", "inspect_dpm", "configure_dpm", "create_dust_injection", "set_particle_boundaries", "track_particles", "build_planar", "build_volume", "build_extruded", "geometry_mesh", "configure_water_air", "initialize_air", "record_animation", "export_results", "assign_phase_materials", "assign_multiphase_materials", "configure_three_phase_materials", "audit_multiphase_interfaces", "configure_evaporation_condensation", "inspect_phase_change", "configure_surface_tension", "inspect_surface_tension"]}, "generated_at": 1792322860.105292, "phase_change": {"version": "v242", "model": "Lee", "adapter_multiphase_models": ["vof"], "mixture_officially_available": true, "mixture_adapter_validated": false, "two_phase_2d_correction_runtime_validation": "pending", "automatic_model_switch": false, "phases": [2, 3], "saturation": "constant K", "frequencies": "positive finite 1/s", "mass_transfer_pairs": 1, "internal_official_tui": true, "generated_direct_tui": false, "readback": "unique case snapshot", "initializes": false}, "surface_tension": {"version": "v242/v252", "runtime_validation": {"v242": "previous native validation", "v252": "official command and generated SDK checked; customer runtime readback required"}, "model": "VOF", "coefficient": "nonnegative finite N/m", "preserves_other_pairs": true, "readback": "unique case snapshot", "initializes": false}, "transient_dpm": {"version": "v242", "model": "unsteady single-phase carrier + transient one-way inert DPM", "release": "one short native pulse per explicit point / region grid, max 64 weighted parcels", "chunk_steps": 60, "positions": "native current positions at actual physical time; never steady replay", "checkpoint": "unique Case/Data plus SHA256 and particle-state readback", "acceptance": "each-step residuals, native air mass flux, particle mass/count fate closure", "default_residual_tolerance": 1e-05, "benchmark_residual_tolerance": 0.001, "strict_1e_5_initial_step_validated": false, "resuspension_validated": false, "stochastic_turbulent_dispersion_validated": false}, "dpm": {"version": "v242", "carrier": "single phase, one-way, steady inert dust", "injection_types": ["single", "group"], "surface_injection_validated": false, "boundary_types": ["reflect", "trap", "escape"], "trajectory_source": "native GetParticleTracksField RPC", "animation": "steady DPM trajectory residence-time replay, no carrier advance", "transient_dpm_validated": false, "two_way_validated": false, "readback": true, "active": true}, "fluent_version": "Ansys Fluent 2024 R2", "model_active": true}
Current session:
{"connected": true, "dimension": 3, "mode": "solver", "owned": true, "continuation": true}
Task directory:
C:\Users\13977\AppData\Local\FluentAGI\data\runs\20261018-192740-fe6b6c
Current request:
网格智能加密两倍，要求整车空气套件细节加密，尾部区域加密些，计算给到80m每秒
Desktop execution feedback:
[{"failed_action": {"tool": "run_code", "arguments": {"code": "# Use the existing connected PyFluent variable solver.\nimport math\n\ntask_directory = r\"C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-192740-fe6b6c\"\ns = solver.settings\nbaseline_cells = 271220\ntarget_cells = 2 * baseline_cells\nruntime_cell_limit = 4000000\nif not s.setup.is_active() or not s.setup.models.is_active():\n    raise RuntimeError(\"Expected the existing initialized car case\")\ns.file.write_case_data(file_name=task_directory+r\"\\before-refinement-ad9ac9.cas.h5\")\nbc = s.setup.boundary_conditions\nrd = s.solution.report_definitions\nrun = s.solution.run_calculation\nprint({\"time_mode\": s.setup.general.solver.time.get_state(),\n       \"calculation_state\": run.get_state(),\n       \"target_cells\": target_cells, \"runtime_cell_limit\": runtime_cell_limit})\nprint({\"inlet_before\": bc.velocity_inlet[\"inlet\"].get_state(),\n       \"walls\": bc.wall.get_object_names()})\n\ndef choose(node, candidates):\n    norm = lambda x: str(x).lower().replace(\" \", \"\").replace(\"-\", \"\").replace(\"_\", \"\")\n    allowed = node.allowed_values()\n    for candidate in candidates:\n        for value in allowed:\n            if norm(value) == norm(candidate):\n                node.set_state(value)\n                return value\n    raise RuntimeError(str({\"requested\": candidates, \"allowed\": allowed}))\n\ndef select_type(node, predicate):\n    allowed = node.allowed_values()\n    for value in allowed:\n        if predicate(str(value).lower()):\n            node.set_state(value)\n            return value\n    raise RuntimeError(str({\"unsupported_report_or_method\": allowed}))\n\ndef constant(node, value):\n    state = node.get_state()\n    if isinstance(state, dict) and \"value\" in node.child_names:\n        if \"option\" in node.child_names:\n            choose(node.option, [\"value\", \"constant\"])\n        node.value = value\n    else:\n        node.set_state(value)\n\ndef vector(node, values):\n    state = node.get_state()\n    if isinstance(state, list) and state and isinstance(state[0], dict):\n        for i, value in enumerate(values):\n            constant(node[i], value)\n    else:\n        node.set_state(values)\n\ndef report_number(name):\n    result = rd.compute(report_defs=[name])\n    blocks = result if isinstance(result, list) else [result]\n    for block in blocks:\n        if isinstance(block, dict) and name in block:\n            value = block[name]\n            if isinstance(value, (list, tuple)):\n                value = value[0]\n            return float(value)\n    raise RuntimeError(str({\"unrecognized_report\": name, \"result\": result}))\n\ncell_report = \"cell_count_ad9ac9\"\nif cell_report not in rd.mesh.get_object_names():\n    rd.mesh.create(name=cell_report)\nselect_type(rd.mesh[cell_report].report_type,\n            lambda x: \"cell\" in x and \"count\" in x)\nfluid_zones = s.setup.cell_zone_conditions.fluid.get_object_names()\nrd.mesh[cell_report].cell_zones = fluid_zones\nclock_report = \"flow_time_ad9ac9\"\nif clock_report not in rd.time.get_object_names():\n    rd.time.create(name=clock_report)\ninitial_time = report_number(clock_report)\ninitial_cells = int(round(report_number(cell_report)))\nprint({\"actual_initial_time\": initial_time, \"actual_initial_cells\": initial_cells})\nif initial_cells > int(target_cells * 1.15):\n    raise RuntimeError(\"Existing mesh already exceeds this refinement target\")\n\nrequired_walls = [\n    \"wall-body\", \"wall-frontwing\", \"wall-rearwing\", \"wall-ground\",\n    \"wall-wheel-fl\", \"wall-wheel-fr\", \"wall-wheel-rl\", \"wall-wheel-rr\"]\nactual_walls = bc.wall.get_object_names()\nif any(name not in actual_walls for name in required_walls):\n    raise RuntimeError(str({\"missing_car_boundaries\": actual_walls}))\nadapt = s.mesh.adapt\ncontrols = adapt.set\nprint({\"adaptation_controls_before\": controls.get_state()})\nif \"maximum_cell_count\" not in controls.child_names:\n    raise RuntimeError(\"Native adaption lacks the required cell-count control\")\nselect_type(controls.adaption_method,\n            lambda x: (\"puma\" in x or \"poly\" in x) and \"2.5\" not in x)\ncontrols.maximum_refinement_level = 1\ncontrols.minimum_edge_length = 0.002\ncontrols.maximum_cell_count = target_cells\nregisters = s.solution.cell_registers\nregions = [\n    (\"frontwing\", [-0.04,-0.85,0.035], [0.36,0.85,0.29], 335000),\n    (\"rearwing\", [2.81,-0.56,0.84], [3.25,0.56,1.10], 395000),\n    (\"diffuser\", [2.12,-0.59,0.045], [3.28,0.59,0.33], 450000),\n    (\"sidepods\", [0.98,-0.59,0.14], [2.80,0.59,0.70], 490000),\n    (\"wake\", [3.12,-1.20,0.03], [7.50,1.20,1.45], target_cells),\n    (\"car_nearfield\", [-0.08,-1.0,0.01], [3.30,1.0,1.14], target_cells)]\nfor label, low, high, budget in regions:\n    current = int(round(report_number(cell_report)))\n    if current >= int(0.98 * target_cells):\n        break\n    if current >= budget:\n        continue\n    controls.maximum_cell_count = min(budget, target_cells, runtime_cell_limit)\n    name = \"ref_\" + label + \"_ad9ac9\"\n    if name not in registers.get_object_names():\n        registers.create(name=name)\n    register = registers[name]\n    choose(register.type.option, [\"hexahedron\"])\n    region = register.type.hexahedron\n    region.min_point = low\n    region.max_point = high\n    region.inside = True\n    print({\"refinement_region\": label, \"register\": register.get_state(),\n           \"cell_budget\": controls.maximum_cell_count.get_state()})\n    if \"refine\" not in register.command_names:\n        raise RuntimeError(\"Native cell-register refinement unavailable\")\n    register.refine()\n    current = int(round(report_number(cell_report)))\n    print({\"region_finished\": label, \"actual_cells\": current})\n    if current > min(runtime_cell_limit, int(target_cells * 1.15)):\n        s.file.write_case_data(file_name=task_directory+r\"\\refinement-review-ad9ac9.cas.h5\")\n        raise RuntimeError(\"Refinement exceeded the intended cell budget\")\ncontrols.maximum_cell_count = target_cells\ns.mesh.check()\nfinal_cells = int(round(report_number(cell_report)))\nprint({\"actual_refined_cells\": final_cells,\n       \"ratio_to_original\": final_cells / baseline_cells,\n       \"solution_time_after_refinement\": report_number(clock_report)})\nif final_cells <= initial_cells and initial_cells < int(target_cells * 0.9):\n    raise RuntimeError(\"Requested refinement did not increase the cell count\")\ns.file.write_case_data(file_name=task_directory+r\"\\car-refined-ad9ac9.cas.h5\")\n\ntime_mode = str(s.setup.general.solver.time.get_state()).lower()\nif \"unsteady\" not in time_mode:\n    raise RuntimeError(\"Expected the existing transient solution; time mode preserved\")\nrun.transient_controls.time_step_size = 0.001\nground = bc.wall[\"wall-ground\"].momentum\nprint({\"ground_before\": ground.get_state()})\nwheels = [\"wall-wheel-\" + side for side in [\"fl\", \"fr\", \"rl\", \"rr\"]]\nfor name in wheels:\n    print({\"wheel\": name, \"motion_before\": bc.wall[name].momentum.get_state()})\n\ndef set_speed(value):\n    constant(bc.velocity_inlet[\"inlet\"].momentum.velocity, value)\n    vector(ground.velocity_components, [value, 0.0, 0.0])\n    for name in wheels:\n        constant(bc.wall[name].momentum.rotation_speed, -value / 0.24)\n    print({\"requested_speed\": value,\n           \"inlet_readback\": bc.velocity_inlet[\"inlet\"].momentum.get_state(),\n           \"ground_readback\": ground.get_state()})\n\ncar_walls = [name for name in actual_walls if name != \"wall-ground\"]\nforce_reports = []\nfor name, zones, direction in [\n    (\"drag_80_ad9ac9\", car_walls, [1,0,0]),\n    (\"downforce_80_ad9ac9\", car_walls, [0,0,-1]),\n    (\"front_partition_80_ad9ac9\", [\"wall-frontwing\"], [0,0,-1]),\n    (\"rear_partition_80_ad9ac9\", [\"wall-rearwing\"], [0,0,-1])]:\n    if name not in rd.force.get_object_names():\n        rd.force.create(name=name)\n    rd.force[name].set_state({\"zones\": zones, \"force_vector\": direction})\n    force_reports.append(name)\nmass_reports = []\nfor label, boundaries in [(\"inlet\", [\"inlet\"]), (\"outlet\", [\"outlet\"])]:\n    name = \"mass_\" + label + \"_ad9ac9\"\n    if name not in rd.flux.get_object_names():\n        rd.flux.create(name=name)\n    select_type(rd.flux[name].report_type, lambda x: \"mass\" in x and \"flow\" in x)\n    rd.flux[name].boundaries = boundaries\n    mass_reports.append(name)\nmonitor_name = \"car_80_ad9ac9\"\nall_reports = force_reports + mass_reports + [clock_report]\nfor collection in [s.solution.monitor.report_files, s.solution.monitor.report_plots]:\n    if monitor_name not in collection.get_object_names():\n        collection.create(name=monitor_name)\n    collection[monitor_name].report_defs = all_reports\n    collection[monitor_name].active = True\ns.solution.monitor.report_files[monitor_name].file_name = task_directory+r\"\\car-80-history-ad9ac9.out\"\n\ndef evidence(label):\n    print({\"stage\": label, \"actual_time\": report_number(clock_report),\n           \"forces\": rd.compute(report_defs=force_reports)})\n    mass_in, mass_out = [report_number(name) for name in mass_reports]\n    print({\"mass_in\": mass_in, \"mass_out\": mass_out,\n           \"relative_mass_imbalance\": abs(mass_in + mass_out) / max(abs(mass_in), 1e-12)})\n    try:\n        index, history = solver.monitors.get_monitor_set_data(\"residual\")\n        last = {key: float(value[-1]) for key, value in history.items() if len(value)}\n        print({\"residuals\": last})\n        if any(not math.isfinite(value) or value > 1e4 for value in last.values()):\n            s.file.write_case_data(file_name=task_directory+r\"\\needs-review-80-ad9ac9.cas.h5\")\n            raise RuntimeError(\"Residual divergence detected\")\n    except RuntimeError:\n        raise\n    except Exception as error:\n        print({\"residual_read_error\": str(error)})\n\nfor value in [40.0, 60.0, 80.0]:\n    set_speed(value)\n    run.dual_time_iterate(time_step_count=3, max_iter_per_step=60)\n    evidence(\"speed_\" + str(value))\nfor batch in range(3):\n    run.dual_time_iterate(time_step_count=10, max_iter_per_step=60)\n    evidence(\"80_settling_\" + str(batch + 1))\ns.file.write_case_data(file_name=task_directory+r\"\\car-80-before-animation-ad9ac9.cas.h5\")\n\ng = s.results.graphics\ntry:\n    name = \"refined_car_ad9ac9\"\n    if name not in g.mesh.get_object_names():\n        g.mesh.create(name=name)\n    g.mesh[name].surfaces_list = car_walls\n    g.mesh[name].display()\n    g.picture.save_picture(file_name=task_directory+r\"\\refined-car-ad9ac9.png\")\nexcept Exception as error:\n    print({\"mesh_graphics_error\": str(error)})\nsection = fluentagi.create_section(axis=\"y\", position=0.0)\nprint(section)\nresult = fluentagi.export_results(\n    directory=task_directory, fields=[\"pressure\", \"velocity-magnitude\"],\n    surfaces=[section[\"surface\"]])\nprint(result)\nfor name, kind, state in [\n    (\"car_pressure_80_ad9ac9\", \"contour\",\n     {\"field\":\"pressure\", \"surfaces_list\":car_walls, \"filled\":True}),\n    (\"car_streamlines_80_ad9ac9\", \"pathline\",\n     {\"field\":\"velocity-magnitude\", \"release_from_surfaces\":[\"inlet\"], \"step\":3000, \"skip\":20})]:\n    try:\n        collection = getattr(g, kind)\n        if name not in collection.get_object_names():\n            collection.create(name=name)\n        collection[name].set_state(state)\n        collection[name].display()\n        g.picture.save_picture(file_name=task_directory+\"\\\\\"+name+\".png\")\n    except Exception as error:\n        print({\"graphics\": name, \"error\": str(error)})\nprint(fluentagi.record_animation(\n    directory=task_directory, field=\"velocity-magnitude\", frames=24,\n    steps_per_frame=2, max_iter_per_step=60, surfaces=[section[\"surface\"]]))\nevidence(\"final_80\")\ns.file.write_case_data(file_name=task_directory+r\"\\car-80-final-ad9ac9.cas.h5\")\nfor name, obj in [(\"forces\", s.solution.monitor.report_plots[monitor_name]),\n                  (\"residuals\", s.solution.monitor.residual)]:\n    try:\n        obj.plot()\n        g.picture.save_picture(file_name=task_directory+\"\\\\\"+name+\"-80-ad9ac9.png\")\n    except Exception as error:\n        print({\"plot\": name, \"error\": str(error)})\nprint({\"initial_time\": initial_time, \"actual_final_time\": report_number(clock_report),\n       \"actual_cells\": int(round(report_number(cell_report))),\n       \"target_speed\": 80.0, \"convergence_verified\": False})"}}, "error": "{\"status\": \"error\", \"stdout\": \"\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-192740-fe6b6c\\\\before-refinement-ad9ac9.cas.h5\\\" in NODE0 mode and compression level 1 ...\\nGrouping cells for Laplace smoothing ...\\n      271220 cells,     1 zone  ...\\n      570904 faces,    12 zones ...\\n       58102 nodes,     1 zone  ...\\n  Done.\\nDone.\\n\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-192740-fe6b6c\\\\before-refinement-ad9ac9.dat.h5\\\" in NODE0 mode and compression level 1 ...\\n  Writing results.\\nDone.\\n{'time_mode': 'unsteady-2nd-order', 'calculation_state': {'reporting_interval': 1, 'profile_update_interval': 1, 'transient_controls': {'type': 'Fixed', 'method': 'User-Specified', 'time_step_count': 2, 'time_step_size': 0.005, 'max_iter_per_time_step': 20, 'solution_status': False, 'extrapolate_variables': False, 'predict_next': False, 'fixed_periodic': {'enabled': False}, 'multiphase_specific_time_constraints': {'physics_based_constraint': False}, 'solid_time_step_size': {'enable_solid_time_step': False}}, 'data_sampling': {'enabled': False}}, 'target_cells': 542440, 'runtime_cell_limit': 4000000}\\n{'inlet_before': {'name': 'inlet', 'momentum': {'velocity_specification_method': 'Magnitude, Normal to Boundary', 'reference_frame': 'Absolute', 'velocity': {'option': 'value', 'value': 20.0}, 'initial_gauge_pressure': {'option': 'value', 'value': 0}}, 'turbulence': {'turbulence_specification': 'Intensity and Viscosity Ratio', 'turbulent_intensity': 0.05, 'turbulent_viscosity_ratio': 10}}, 'walls': ['wall-rearwing', 'wall-wheel-fl', 'wall-wheel-fr', 'wall-wheel-rl', 'wall-wheel-rr', 'wall-body', 'wall-frontwing', 'wall-ground']}\\n\\nError: api-create-obj: new objects of this type cannot be created\\nError Object: \\\"solution/report-definitions/time\\\"\\n\", \"stderr\": \"\\nTraceback (most recent call last):\\n  File \\\"ansys\\\\fluent\\\\mcp\\\\solve\\\\backends\\\\pyfluent.py\\\", line 2793, in _do\\n    exec(  # noqa: S102  # nosec B102\\n  File \\\"<mcp>\\\", line 76, in <module>\\n  File \\\"ansys\\\\fluent\\\\core\\\\solver\\\\flobject.py\\\", line 2080, in create\\n    self.flproxy.create(self.path, name)\\n  File \\\"ansys\\\\fluent\\\\core\\\\services\\\\settings.py\\\", line 146, in _fn\\n    return fn(self, *args, **kwds)\\n           ^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"ansys\\\\fluent\\\\core\\\\services\\\\settings.py\\\", line 240, in create\\n    self._service_impl.create(request)\\n  File \\\"ansys\\\\fluent\\\\core\\\\services\\\\settings.py\\\", line 77, in create\\n    return self.__stub.Create(request, metadata=self.__metadata)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"grpc\\\\_interceptor.py\\\", line 276, in __call__\\n  File \\\"grpc\\\\_interceptor.py\\\", line 328, in _with_call\\nRuntimeError: api-create-obj: new objects of this type cannot be created\\nError Object: solution/report-definitions/time\\n\", \"return_value\": null, \"message\": \"api-create-obj: new objects of this type cannot be created\\nError Object: solution/report-definitions/time\", \"error_code\": \"execution_error\", \"warnings\": []}", "note": "可能有部分操作已生效，请先检查实时状态，修复后继续。"}]
【ChatGPT 回复】
ChatGPT 说：

复用内置流动时间报告，保留当前解，继续约两倍局部加密、80 m/s升速计算及结果导出。

<fluent_code>
```python
# Use the existing connected PyFluent variable solver.
import math

task_directory = r"C:\Users\13977\AppData\Local\FluentAGI\data\runs\20261018-192740-fe6b6c"
s = solver.settings
baseline_cells = 271220
target_cells = 542440
runtime_cell_limit = 4000000
if not s.setup.is_active() or not s.setup.models.is_active():
    raise RuntimeError("Expected the existing initialized car case")
s.file.write_case_data(file_name=task_directory+r"\before-b49382.cas.h5")
bc = s.setup.boundary_conditions
rd = s.solution.report_definitions
run = s.solution.run_calculation
print({"time_mode": s.setup.general.solver.time.get_state(),
       "calculation_state": run.get_state(),
       "target_cells": target_cells, "runtime_cell_limit": runtime_cell_limit})

def choose(node, candidates):
    norm = lambda x: str(x).lower().replace(" ", "").replace("-", "").replace("_", "")
    allowed = node.allowed_values()
    for candidate in candidates:
        for value in allowed:
            if norm(value) == norm(candidate):
                node.set_state(value)
                return value
    raise RuntimeError(str({"requested": candidates, "allowed": allowed}))

def select_type(node, predicate):
    allowed = node.allowed_values()
    for value in allowed:
        if predicate(str(value).lower()):
            node.set_state(value)
            return value
    raise RuntimeError(str({"unsupported_type": allowed}))

def constant(node, value):
    state = node.get_state()
    if isinstance(state, dict) and "value" in node.child_names:
        if "option" in node.child_names:
            choose(node.option, ["value", "constant"])
        node.value = value
    else:
        node.set_state(value)

def vector(node, values):
    state = node.get_state()
    if isinstance(state, list) and state and isinstance(state[0], dict):
        for i, value in enumerate(values):
            constant(node[i], value)
    else:
        node.set_state(values)

def report_number(name):
    result = rd.compute(report_defs=[name])
    blocks = result if isinstance(result, list) else [result]
    for block in blocks:
        if isinstance(block, dict) and name in block:
            value = block[name]
            if isinstance(value, (list, tuple)):
                value = value[0]
            return float(value)
    raise RuntimeError(str({"unrecognized_report": name, "result": result}))

time_names = rd.time.get_object_names()
print({"existing_time_reports": time_names})
clock_report = None
for name in time_names:
    if str(name).lower().replace("_", "-") == "flow-time":
        clock_report = name
        break
if clock_report is None:
    clock_report = "physical_time_b49382"
    expressions = rd.single_valued_expression
    if clock_report not in expressions.get_object_names():
        expressions.create(name=clock_report)
    expressions[clock_report].definition = "Time"

cell_report = "cell_count_ad9ac9"
if cell_report not in rd.mesh.get_object_names():
    rd.mesh.create(name=cell_report)
select_type(rd.mesh[cell_report].report_type,
            lambda x: "cell" in x and "count" in x)
rd.mesh[cell_report].cell_zones = s.setup.cell_zone_conditions.fluid.get_object_names()
initial_time = report_number(clock_report)
initial_cells = int(round(report_number(cell_report)))
print({"actual_initial_time": initial_time, "actual_initial_cells": initial_cells})
if initial_cells > int(target_cells * 1.15):
    raise RuntimeError("Existing mesh already exceeds this refinement target")
if "unsteady" not in str(s.setup.general.solver.time.get_state()).lower():
    raise RuntimeError("Expected the existing transient solution")

required_walls = [
    "wall-body", "wall-frontwing", "wall-rearwing", "wall-ground",
    "wall-wheel-fl", "wall-wheel-fr", "wall-wheel-rl", "wall-wheel-rr"]
actual_walls = bc.wall.get_object_names()
if any(name not in actual_walls for name in required_walls):
    raise RuntimeError(str({"required": required_walls, "actual": actual_walls}))
adapt = s.mesh.adapt
controls = adapt.set
print({"adaptation_controls_before": controls.get_state()})
if "maximum_cell_count" not in controls.child_names:
    raise RuntimeError("Native adaption lacks the required cell-count control")
select_type(controls.adaption_method,
            lambda x: ("puma" in x or "poly" in x) and "2.5" not in x)
controls.maximum_refinement_level = 1
controls.minimum_edge_length = 0.002
controls.maximum_cell_count = target_cells
registers = s.solution.cell_registers
regions = [
    ("frontwing", [-0.04,-0.85,0.035], [0.36,0.85,0.29], 335000),
    ("rearwing", [2.81,-0.56,0.84], [3.25,0.56,1.10], 395000),
    ("diffuser", [2.12,-0.59,0.045], [3.28,0.59,0.33], 450000),
    ("sidepods", [0.98,-0.59,0.14], [2.80,0.59,0.70], 490000),
    ("wake", [3.12,-1.20,0.03], [7.50,1.20,1.45], target_cells),
    ("car_nearfield", [-0.08,-1.0,0.01], [3.30,1.0,1.14], target_cells)]
for label, low, high, budget in regions:
    current = int(round(report_number(cell_report)))
    if current >= int(0.98 * target_cells):
        break
    if current >= budget:
        continue
    controls.maximum_cell_count = min(budget, target_cells, runtime_cell_limit)
    name = "ref_" + label + "_ad9ac9"
    if name not in registers.get_object_names():
        registers.create(name=name)
    register = registers[name]
    choose(register.type.option, ["hexahedron"])
    region = register.type.hexahedron
    region.min_point = low
    region.max_point = high
    region.inside = True
    print({"region": label, "state": register.get_state(),
           "cell_budget": controls.maximum_cell_count.get_state()})
    if "refine" not in register.command_names:
        raise RuntimeError("Native cell-register refinement unavailable")
    register.refine()
    current = int(round(report_number(cell_report)))
    print({"region_finished": label, "actual_cells": current})
    if current > min(runtime_cell_limit, int(target_cells * 1.15)):
        s.file.write_case_data(file_name=task_directory+r"\refinement-review-b49382.cas.h5")
        raise RuntimeError("Refinement exceeded the intended cell budget")
controls.maximum_cell_count = target_cells
s.mesh.check()
final_cells = int(round(report_number(cell_report)))
print({"actual_refined_cells": final_cells,
       "ratio_to_original": final_cells / baseline_cells,
       "actual_time": report_number(clock_report)})
if final_cells <= initial_cells and initial_cells < int(target_cells * 0.9):
    raise RuntimeError("Requested refinement did not increase the cell count")
s.file.write_case_data(file_name=task_directory+r"\car-refined-b49382.cas.h5")

run.transient_controls.time_step_size = 0.001
ground = bc.wall["wall-ground"].momentum
wheels = ["wall-wheel-" + side for side in ["fl", "fr", "rl", "rr"]]
print({"ground_before": ground.get_state()})
for name in wheels:
    print({"wheel": name, "motion_before": bc.wall[name].momentum.get_state()})

def set_speed(value):
    constant(bc.velocity_inlet["inlet"].momentum.velocity, value)
    vector(ground.velocity_components, [value, 0.0, 0.0])
    for name in wheels:
        constant(bc.wall[name].momentum.rotation_speed, -value / 0.24)
    print({"requested_speed": value,
           "inlet_readback": bc.velocity_inlet["inlet"].momentum.get_state(),
           "ground_readback": ground.get_state()})

car_walls = [name for name in actual_walls if name != "wall-ground"]
force_reports = []
for name, zones, direction in [
    ("drag_80_b49382", car_walls, [1,0,0]),
    ("downforce_80_b49382", car_walls, [0,0,-1]),
    ("front_partition_80_b49382", ["wall-frontwing"], [0,0,-1]),
    ("rear_partition_80_b49382", ["wall-rearwing"], [0,0,-1])]:
    if name not in rd.force.get_object_names():
        rd.force.create(name=name)
    rd.force[name].set_state({"zones": zones, "force_vector": direction})
    force_reports.append(name)
mass_reports = []
for label, boundaries in [("inlet", ["inlet"]), ("outlet", ["outlet"])]:
    name = "mass_" + label + "_b49382"
    if name not in rd.flux.get_object_names():
        rd.flux.create(name=name)
    select_type(rd.flux[name].report_type, lambda x: "mass" in x and "flow" in x)
    rd.flux[name].boundaries = boundaries
    mass_reports.append(name)
monitor_name = "car_80_b49382"
all_reports = force_reports + mass_reports + [clock_report]
for collection in [s.solution.monitor.report_files, s.solution.monitor.report_plots]:
    if monitor_name not in collection.get_object_names():
        collection.create(name=monitor_name)
    collection[monitor_name].report_defs = all_reports
    collection[monitor_name].active = True
s.solution.monitor.report_files[monitor_name].file_name = task_directory+r"\car-80-history-b49382.out"

def evidence(label):
    print({"stage": label, "actual_time": report_number(clock_report),
           "forces": rd.compute(report_defs=force_reports)})
    mass_in, mass_out = [report_number(name) for name in mass_reports]
    print({"mass_in": mass_in, "mass_out": mass_out,
           "relative_mass_imbalance": abs(mass_in + mass_out) / max(abs(mass_in), 1e-12)})
    try:
        index, history = solver.monitors.get_monitor_set_data("residual")
        last = {key: float(value[-1]) for key, value in history.items() if len(value)}
    except Exception as error:
        print({"residual_read_error": str(error)})
        return
    print({"residuals": last})
    if any(not math.isfinite(value) or value > 1e4 for value in last.values()):
        s.file.write_case_data(file_name=task_directory+r"\needs-review-80-b49382.cas.h5")
        raise RuntimeError("Residual divergence detected")

velocity_state = bc.velocity_inlet["inlet"].momentum.velocity.get_state()
current_speed = float(velocity_state["value"])
stages = [value for value in [40.0, 60.0, 80.0] if value > current_speed]
if current_speed > 80.0:
    stages = [80.0]
for value in stages:
    set_speed(value)
    run.dual_time_iterate(time_step_count=3, max_iter_per_step=60)
    evidence("speed_" + str(value))
set_speed(80.0)
for batch in range(3):
    run.dual_time_iterate(time_step_count=10, max_iter_per_step=60)
    evidence("80_settling_" + str(batch + 1))
s.file.write_case_data(file_name=task_directory+r"\car-80-before-animation-b49382.cas.h5")

g = s.results.graphics
try:
    name = "refined_car_b49382"
    if name not in g.mesh.get_object_names():
        g.mesh.create(name=name)
    g.mesh[name].surfaces_list = car_walls
    g.mesh[name].display()
    g.picture.save_picture(file_name=task_directory+r"\refined-car-b49382.png")
except Exception as error:
    print({"mesh_graphics_error": str(error)})
section = fluentagi.create_section(axis="y", position=0.0)
print(section)
result = fluentagi.export_results(
    directory=task_directory, fields=["pressure", "velocity-magnitude"],
    surfaces=[section["surface"]])
print(result)
if isinstance(result, dict):
    for key in ["error", "errors", "warnings"]:
        if result.get(key):
            print({"export_review": key, "details": result[key]})
for name, kind, state in [
    ("car_pressure_80_b49382", "contour",
     {"field":"pressure", "surfaces_list":car_walls, "filled":True}),
    ("car_streamlines_80_b49382", "pathline",
     {"field":"velocity-magnitude", "release_from_surfaces":["inlet"], "step":3000, "skip":20})]:
    try:
        collection = getattr(g, kind)
        if name not in collection.get_object_names():
            collection.create(name=name)
        collection[name].set_state(state)
        collection[name].display()
        g.picture.save_picture(file_name=task_directory+"\\"+name+".png")
    except Exception as error:
        print({"graphics": name, "error": str(error)})
print(fluentagi.record_animation(
    directory=task_directory, field="velocity-magnitude", frames=24,
    steps_per_frame=2, max_iter_per_step=60, surfaces=[section["surface"]]))
evidence("final_80")
s.file.write_case_data(file_name=task_directory+r"\car-80-final-b49382.cas.h5")
for name, obj in [("forces", s.solution.monitor.report_plots[monitor_name]),
                  ("residuals", s.solution.monitor.residual)]:
    try:
        obj.plot()
        g.picture.save_picture(file_name=task_directory+"\\"+name+"-80-b49382.png")
    except Exception as error:
        print({"plot": name, "error": str(error)})
print({"initial_time": initial_time, "actual_final_time": report_number(clock_report),
       "actual_cells": int(round(report_number(cell_report))),
       "target_speed": 80.0, "convergence_verified": False})
```
</fluent_code>

<final_answer>本次移除失败的时间报告创建操作，优先读取内置flow-time；保持已有网格、流场和流动时间，再执行局部加密、分阶段升至80 m/s及后处理。实际加密数量、收敛和导出状态以执行反馈为准。</final_answer>

round-03
【发送给 ChatGPT】
You are FluentAGI for Ansys Fluent. Write one short Chinese plan, then a single COMPLETE Python script in this exact Markdown layout:

<fluent_code>

```python
# Use the existing connected PyFluent variable solver.
# Continue with the complete executable script here.
```

</fluent_code>

Put a newline before and after each tag and each fence. Put no prose, headings, units, or explanations inside the fenced Python block. Do not place ```python and Python statements on the same line. Keep the script under 280 lines. Use loops instead of repeating similar statements. Never truncate a string, parenthesis, or code fence; always close </fluent_code>.
Use the existing solver session and compatible PyFluent APIs. A completed conversation turn does not close the session. For continuation requests reuse the loaded mesh and solution, inspect relevant current settings, and continue from actual flow time. Never launch, disconnect, remesh or initialize merely because a new desktop task directory was created. Do not launch or close Fluent. Do not use Abaqus APIs. Use solver.settings APIs and the provided fluentagi adapters. Direct solver.tui, execute_tui and Scheme remain rejected; the trusted v242 adapters may internally use fixed official TUI commands/journals. For mesh display use solver.settings.results.graphics.mesh and graphics.views APIs. Do not return JSON tool plans. Do not invent mesh files or API names. Only operate on the requested case and task directory. Preserve existing results. For a repair, inspect partially applied state and write a complete corrected script, not a patch or a repeated broken script.
The web AI cannot read or modify host application source. Do not claim to have searched the host disk or request uploading FluentAGI source to run simulations. Use the verified runtime adapters. Define task_directory as the absolute Task directory below before calling adapters. For VOF surface tension use fluentagi.configure_surface_tension(directory=task_directory,coefficient=0.072,phase_a="phase-1",phase_b="phase-2"); coefficient is N/m, replace the example with the requested value. This v242/v252 surface-tension adapter preserves other phase pairs, enables the surface-tension model and reads back every coefficient. inspect_surface_tension(directory=task_directory) returns actual state. Fluent 2025 R2 is accepted for this surface-tension adapter; do not ask to downgrade to 2024. Other Lee/DPM adapters retain their own version restrictions. Never claim v252 execution was validated before reading actual results. Do not ask the user to edit the plugin or click GUI dialogs for this supported operation.
Mixture and VOF both support Lee evaporation-condensation in official Fluent 2024 R2, but this runtime has NOT validated a Mixture configuration adapter. Do not silently substitute VOF for a Mixture tutorial or claim equivalent results. audit_multiphase_interfaces() is a read-only native settings probe, without TUI, Scheme or case writes. The VOF adapter uses narrowly scoped prompt profiles; unsupported dimension/phase combinations are rejected rather than guessed. For three-phase boiling setup in Fluent 2024 R2, use fluentagi.configure_three_phase_materials(directory=task_directory, materials=["air", "water-liquid", "water-vapor"]) to create actual phase-1/2/3 domains and verify material assignments. For an existing three-phase model, fluentagi.assign_multiphase_materials(materials=[...], directory=task_directory) assigns materials without changing the phase count. Then call fluentagi.configure_evaporation_condensation(directory=task_directory, saturation_temperature=T_sat_K, evaporation_frequency=from_frequency_per_second, condensation_frequency=to_frequency_per_second, from_phase="phase-2", to_phase="phase-3"). Pass the user's specified temperature/frequencies; preserve their geometry, heating and calculation parameters. This verified v242 VOF Lee adapter enables energy, uses a fixed official TUI journal and verifies the phase pair, constant saturation temperature, both frequencies and Lee model by an independent case snapshot. It does not initialize or advance the simulation. Do not say this feature is unavailable because direct TUI is forbidden; use the adapter. fluentagi.inspect_phase_change(directory=task_directory) returns actual phases and mass transfer settings, refreshing a unique case snapshot. Do not replace existing unrelated mechanisms. This adapter supports one Lee mass transfer pair, not Eulerian wall boiling, semi-mechanistic wall boiling, pressure-dependent saturation tables or UDFs. Do not claim a complete validated boiling simulation from successful setup alone. initialize_air is a TWO-PHASE adapter; do not use it for three-phase cases. Use verified settings initialization and phase-aware patching only for requested new calculations.
The runtime provides fluentagi.assign_phase_materials(primary="air", secondary="water-liquid", directory=task_directory), fluentagi.inspect_phases(), and fluentagi.export_results(directory=task_directory, fields=["pressure", "velocity-magnitude"]). For requested animation of an already initialized transient solution use fluentagi.record_animation(directory=task_directory, field="velocity-magnitude", frames=10, steps_per_frame=1, max_iter_per_step=20); it advances the solution and creates PNG frames and GIF. Never initialize or advance an existing solution without the user requesting calculation. These are executable verified adapters, not prose instructions. Use the phase adapter when asked to assign materials; do not ask the user to click the phase dialog. Print returned results. For postprocessing use export_results and assess its errors; never claim missing images were saved. Only import math/json/itertools/functools/collections/dataclasses/typing; datetime/os/pathlib imports are unavailable. Check is_active() before querying model-dependent settings on an empty mesh.
Mesh conversion preserves independent inlet-name/outlet-name/wall-name boundary zones (for example wall-wing and wall-ground). Read the configured mesh_cell_limit from runtime capabilities; never assume a 200000-cell limit or silently coarsen user geometry. Independent wall zones do not automatically set moving-wall physics or force reports; inspect real settings before configuring those. For NEW planar geometry and meshing use mesh = fluentagi.build_planar(directory=task_directory, points=[[x,y],...], inlet_edges=[0], outlet_edges=[2], size=0.001). Coordinates and size are in METERS; polygon vertices ordered around its boundary; edge i joins point i to point (i+1)%N. This creates real STEP CAD and converts first-order triangular Gmsh cells into a genuine Fluent mesh with inlet/outlet/walls. Then solver.settings.file.read_mesh(file_name=mesh["mesh_file"]), solver.settings.mesh.check(), configure physics, initialize, iterate, and export_results. Save current case/data before replacing loaded geometry. Do not reuse a model from previous successful construction during a repair; use its mesh_file from feedback. For curved planar CAD, use fluentagi.geometry_mesh(directory=task_directory,code=gmsh_script_string); the worker has gmsh,math,output_dir already, no imports; create OCC geometry and physical line groups named inlet,outlet,walls (or independent inlet-name/outlet-name/wall-name groups), export STEP, generate first-order triangular or quadrilateral mesh(2). Returned mesh_file is Fluent format; gmsh-native.msh is NOT Fluent format. 3D modeling and volume mesh conversion are now implemented. fluentagi.build_volume(directory=task_directory, shape="box",length=0.1,width=0.02,height=0.02,size=0.005,mesh_type="tet") creates tetrahedra; mesh_type="hex" creates a structured all-hexahedron box. shape="cylinder",radius=0.01 supports tetrahedra with axis along +X. Inlet is X=0 and outlet X=length. fluentagi.build_extruded(directory=task_directory,points=[[x,y],...],inlet_edges=[0],outlet_edges=[2],depth=0.01,size=0.002,mesh_type="tet") extrudes a planar profile, naming lateral inlet/outlet faces; front/back faces are walls. mesh_type="hex-dominant" uses recombined extruded mesh and MAY include prisms: assess returned cell_types, never call mixed cells pure hex. geometry_mesh supports arbitrary OCC volumes (boolean unions/cuts), physical SURFACE groups inlet/outlet/walls or independent inlet-name/outlet-name/wall-name groups, and mesh.generate(3), first-order tet/hex/prism/pyramid. Imported STEP is not currently supported by the restricted worker. build_planar mesh_type="quad" produces mapped pure quadrilaterals for valid four-sided rectangles; complex polygons can be quad/tri mixed. 2D uses triangles/quads, 3D uses tet/hex. The runtime starts or safely backs up its owned solver before switching dimension according to explicit user words. Do not request user-supplied 3D mesh for supported geometry. Save backups before replacing any loaded mesh. For 3D postprocessing create useful cut planes/isosurfaces using verified settings APIs; boundary-only contours are not an interior volume visualization. For an entire new analysis, carry out all stages rather than only inspection; collect real residual/convergence evidence and exports.
For water-air transient filling, call fluentagi.configure_water_air(directory=task_directory,velocity=0.05,time_step=0.001,gravity_y=-9.81,inlet="inlet",outlet="outlet"). This backs up and sets laminar VOF, materials, phase-aware inlet/outlet, gravity and timestep; does NOT initialize or erase existing solution. For requested NEW filling use fluentagi.initialize_air(directory=task_directory), which saves a backup, hybrid initializes and patches phase-2 volume fraction to zero across all fluid cell zones. Never initialize_air on existing results without an explicit restart request. Fluent v242 contour fields are phase-2-vof (water, only after confirming phase-2=water-liquid) and phase-1-vof (air); do not confuse them. Query real contour field.allowed_values for VOF fields. For 3D interior contours/animation use section=fluentagi.create_section(axis="z",position=depth/2), then export_results(...,surfaces=[section["surface"]]) or record_animation(...,surfaces=[section["surface"]]). A plane section is not full volumetric rendering; report it accurately. Frames are 2..60. Each frame after the first advances steps_per_frame real time steps; final time is initial time+(frames-1)*steps_per_frame*time_step. Do not claim the pipe fills if the simulated duration is too short. Normalize typos like "二维四面体" by explaining 2D triangular equivalent; if depth/3D was explicitly requested, use volume tools.
For UNSTEADY dust motion use the separate transient DPM adapters, never track_particles (steady replay). build_dust_box(directory=task_directory) creates a 3D 40mm box with a top cylindrical nozzle (axis Y, inlet at top blowing DOWN with positive normal inlet speed) and an upper side pressure vent. Save existing case before reading its mesh_file. geometry parameters are SI and can be passed as named arguments. Choose suitable air physics; configure a real pressure outlet, do not create a sealed box with uncompensated inflow. configure_transient_dust(directory=task_directory,region_min=[.012,.003,.012],region_max=[.028,.004,.028],counts=[2,2,2],release_time=.02,release_duration=.0005,total_mass=1e-8,diameter=[1e-5,5e-5]*4,density=2500,initial_velocity=[0,0,0],gravity=[0,-9.81,0],time_step=.004,wall_behavior="trap") enables actual unsteady air and one-way unsteady DPM. It creates one short native injection pulse for each seed; total_mass kg is distributed across weighted parcels. It accepts explicit points instead of a region. The entire release pulse must fit inside one physical dt. Does NOT initialize. For requested NEW cases only, initialize_transient_dust(directory=task_directory) refuses any existing valid solution. Then advance_transient_dust(directory=task_directory,until_time=.2,max_iter_per_step=200,residual_tolerance=.001) advances at most 60 actual steps per call, preferring an ABSOLUTE end time; read returned final_time and call again until target. Already reached until_time does not advance again. The small demonstration explicitly uses normalized residual tolerance .001; honor stricter user requirements and never silently relax them. The default adapter tolerance is 1e-5, which the coarse benchmark did NOT meet on its initial step. Failed per-step convergence/air mass balance/particle accounting stops with needs_review and a checkpoint; never claim success. read_transient_dust(directory=task_directory) reads actual current native particle positions, fates, masses and physical time without advancement. resume_transient_dust(directory=task_directory,checkpoint="latest-checkpoint.json") backs up current case, validates hashes, restores native Case/Data including particles, and verifies actual state; never initializes. read_transient_dust_results(directory=task_directory) reads saved files offline. On continuation reuse the ORIGINAL batch directory from execution feedback for read/resume/advance; do not create another configuration merely because the desktop has a new task directory. Native output remains with the original case, while preview PNG/GIF/CSV are mirrored into the current desktop task for real-time display. Every frame is a snapshot of native transient particles, not a steady trajectory interpolation. Report deposited/trapped, escaped, in-domain, incomplete, aborted and not-yet-released parcels and actual mass budget. Caption all runs "预先释放悬浮颗粒，非积尘再悬浮". Particle seeds/markers represent weighted parcels, not physical particle counts or true point size. Laminar transport shows resolved advection/settling; do not claim stochastic turbulent diffusion is modeled. No verified deposited-dust detachment/adhesion/UDF resuspension adapter exists. Never add an upward initial velocity and call it resuspension. Preserve steady DPM as a distinct path.
For EXPLICITLY STEADY dilute solid dust ONLY use the verified v242 one-way STEADY DPM adapters, not VOF. First inspect_dpm(). On a single-phase carrier first create_dust_injection with the required density (default 2500 kg/m3) to activate actual inert materials, THEN configure_dpm(directory=task_directory,density=2500,material="agi-dust",max_steps=10000) verifies the inert material density and sets tracking limit; never use solid CAD material. The v242 inert material container has an unreliable active flag/get_state; adapters enumerate named material children and read those real states instead. Never query the entire inert material container. create_dust_injection(directory=task_directory,name="dust",start=[0.00001,0.003,0.01],end=[0.00001,0.017,0.01],streams=12,diameter=0.00005,mass_flow=0.0000001,velocity=[0.5,0,0],material="agi-dust") creates a real group injection. Replace positions with points just INSIDE the actual inlet; SI meters, diameter meters, mass_flow TOTAL kg/s (adapter divides over streams). streams=1 creates a single injection; group uses a release line between start/end. Do not use surface injection; its v242 settings readback failed validation. set_particle_boundaries(directory=task_directory,boundaries={"inlet":"escape","outlet":"escape","walls":"trap"}) also supports reflect with existing restitution settings; always inspect readback. Never silently change an existing multiphase or coupled DPM model. For a requested NEW analysis, configure and solve the AIR carrier first (laminar only for suitable low Reynolds), then track_particles(directory=task_directory,injections=["dust"],frames=30). This tracks native DPM trajectories, exports tracks.csv/json, native summary text, statistics and PNG/GIF. It does NOT initialize or advance the carrier. Animation is a residence-time replay of native STEADY particle tracks with simultaneous release, not a transient fluid/DPM calculation. Never use air pathlines or a continuous-phase contour as dust motion. Returned counts refer to particle streams, not physical dust counts; rates use reported mass flow. Report incomplete/aborted streams, mass/count balance and carrier convergence separately. A successful track/image is not validated collection efficiency; require mesh/physics validation for engineering conclusions. Preserve results; use a new injection name if its parameters change.
End with <final_answer>Chinese explanation of the intended changes, without claiming execution success</final_answer>.
If essential input or an incompatible session prevents a valid script, return only <question>the specific missing input in Chinese</question>.
After the desktop returns execution results, either provide a corrected script or return only <final_answer>a Chinese assessment supported by those results</final_answer>. Do not execute the same successful script again. Successful execution alone does not prove convergence.

Previous conversation and execution context (reference only; do not repeat successful scripts):
[{"user": "啊，请你根据图片和我提供的资料，完成整车的一个前翼，后翼型整车车身空气套件，轮胎扩散器的一个建模，并且完成网格划分，网格采用局部加密，网格的控制量控制在20万左右。整个车后面流场网格也适当的做一个加密。更重要的是整车的旁边前后翼和你套件旁边的网格加密对进度有影响，反正网格控制在20~400000后面包括求解。直接生成这个游戏或者动画计算时间，计算量不要太大。"}, {"assistant": "已读到前翼弦长300 mm、翼展1600 mm，后翼弦长350 mm、翼展1000 mm，均为倒置NACA 4412、安装角0°。还缺来流速度及整车尺寸：请提供车速、车长、轴距、轮距和轮胎直径；没有尺寸可直接回复“车速××m/s，整车按图片比例估算”。网格将按20万～40万控制，重点加密前后翼、车身附近、轮胎、扩散器及尾流。", "run_directory": "C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-183115-16d691"}, {"user": "这些尺寸没有给你根据图片，然后你这个能力然后完成这个建模啊，自己全部设计，全部定义，反正最终就是要给我这个曲线流线动画云图比较装逼。"}, {"tool": "run_code", "result": "{\"status\": \"ok\", \"stdout\": \"{'walls': ['wall-rearwing', 'wall-wheel-fl', 'wall-wheel-fr', 'wall-wheel-rl', 'wall-wheel-rr', 'wall-body', 'wall-frontwing', 'wall-ground'], 'viscous': {'model': 'k-epsilon', 'k_epsilon_model': 'realizable', 'near_wall_treatment': {'wall_treatment': 'standard-wall-fn'}, 'options': {'curvature_correction': False, 'production_limiter': {'enabled': False}}, 'turbulence_expert': {'rke_cmu_rotation_term': False, 'turb_non_newtonian': False}, 'user_defined': {'turb_\n…中间日志已省略，完整日志保存在任务目录…\nel 1 ...\\nGrouping cells for Laplace smoothing ...\\n      271220 cells,     1 zone  ...\\n      570904 faces,    12 zones ...\\n       58102 nodes,     1 zone  ...\\n  Done.\\nDone.\\n\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-183309-7900dd\\\\car-transient-1e009e.dat.h5\\\" in NODE0 mode and compression level 1 ...\\n  Writing results.\\nDone.\\n\\n     car-drag-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n                 car-drag-1e009e           177.01976\\n\\ncar-downforce-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n            car-downforce-1e009e           97.216619\\n\\nfrontwing-downforce-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n                  wall-frontwing         0.045705896\\n\\nrearwing-downforce-1e009e\\n -------------------\\n{'final_forces': [{'car-drag-1e009e': [177.0197613431466, 0]}, {'car-downforce-1e009e': [97.2166193715222, 0]}, {'frontwing-downforce-1e009e': [0.04570589572850679, 0]}, {'rearwing-downforce-1e009e': [27.84720127893916, 0]}], 'requested_animation_time_increment': 0.23, 'mass_balance_and_mesh_independence': 'not_yet_verified'}\\n\", \"stderr\": \"\", \"return_value\": null, \"message\": null, \"error_code\": null, \"warnings\": []}"}, {"assistant": "已完成整车建模、271,220单元网格及求解，实际推进至 **0.23秒、46个时间步**，动画适配器返回24帧速度截面动画生成成功，Case/Data已保存。\n\n当前瞬时气动力报告：\n\n项目\t数值\n整车阻力\t177.02 N\n整车下压力\t97.22 N\n后翼分区下压力\t27.85 N\n前翼分区下压力\t0.046 N\n\n计算尚未确认收敛。 最后一步连续性残差约5.1×10⁻⁴，k、ε残差约0.26、0.47，并出现湍流黏度限幅，因此上述力值只能作为初步结果。\n\n前翼报告值很小，需优先核对分区是否包含完整翼面。云图、流线及曲线图片的导出详情未显示在当前截断日志中，暂不能逐项确认。", "run_directory": "C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-183309-7900dd"}, {"user": "网格智能加密两倍，要求整车空气套件细节加密，尾部区域加密些，计算给到80m每秒"}]
Runtime capabilities:
{"protocol": "FluentAGI-capabilities-v1", "session": {"connected": true, "mode": "launch"}, "geometry": {"worker": "gmsh isolated process", "imports": [], "builtins": ["range", "len", "int", "float", "str", "list", "tuple", "enumerate", "zip", "min", "max", "abs", "round", "RuntimeError", "ValueError", "print", "set", "sum", "sorted", "bool", "all", "any"], "physical_groups": ["inlet[-name]", "outlet[-name]", "wall[-name]", "walls"], "independent_boundary_zones": true, "mesh_cell_limit": 4000000, "geometry_timeout_seconds": 3600, "cell_types": ["triangle", "quadrilateral", "tetrahedron", "hexahedron", "prism", "pyramid"]}, "script": {"imports": ["math", "json", "itertools", "functools", "collections", "dataclasses", "typing"], "adapters": ["build_dust_box", "configure_transient_dust", "initialize_transient_dust", "read_transient_dust", "advance_transient_dust", "resume_transient_dust", "read_transient_dust_results", "inspect_dpm", "configure_dpm", "create_dust_injection", "set_particle_boundaries", "track_particles", "build_planar", "build_volume", "build_extruded", "geometry_mesh", "configure_water_air", "initialize_air", "record_animation", "export_results", "assign_phase_materials", "assign_multiphase_materials", "configure_three_phase_materials", "audit_multiphase_interfaces", "configure_evaporation_condensation", "inspect_phase_change", "configure_surface_tension", "inspect_surface_tension"]}, "generated_at": 1792322860.105292, "phase_change": {"version": "v242", "model": "Lee", "adapter_multiphase_models": ["vof"], "mixture_officially_available": true, "mixture_adapter_validated": false, "two_phase_2d_correction_runtime_validation": "pending", "automatic_model_switch": false, "phases": [2, 3], "saturation": "constant K", "frequencies": "positive finite 1/s", "mass_transfer_pairs": 1, "internal_official_tui": true, "generated_direct_tui": false, "readback": "unique case snapshot", "initializes": false}, "surface_tension": {"version": "v242/v252", "runtime_validation": {"v242": "previous native validation", "v252": "official command and generated SDK checked; customer runtime readback required"}, "model": "VOF", "coefficient": "nonnegative finite N/m", "preserves_other_pairs": true, "readback": "unique case snapshot", "initializes": false}, "transient_dpm": {"version": "v242", "model": "unsteady single-phase carrier + transient one-way inert DPM", "release": "one short native pulse per explicit point / region grid, max 64 weighted parcels", "chunk_steps": 60, "positions": "native current positions at actual physical time; never steady replay", "checkpoint": "unique Case/Data plus SHA256 and particle-state readback", "acceptance": "each-step residuals, native air mass flux, particle mass/count fate closure", "default_residual_tolerance": 1e-05, "benchmark_residual_tolerance": 0.001, "strict_1e_5_initial_step_validated": false, "resuspension_validated": false, "stochastic_turbulent_dispersion_validated": false}, "dpm": {"version": "v242", "carrier": "single phase, one-way, steady inert dust", "injection_types": ["single", "group"], "surface_injection_validated": false, "boundary_types": ["reflect", "trap", "escape"], "trajectory_source": "native GetParticleTracksField RPC", "animation": "steady DPM trajectory residence-time replay, no carrier advance", "transient_dpm_validated": false, "two_way_validated": false, "readback": true, "active": true}, "fluent_version": "Ansys Fluent 2024 R2", "model_active": true}
Current session:
{"connected": true, "dimension": 3, "mode": "solver", "owned": true, "continuation": true}
Task directory:
C:\Users\13977\AppData\Local\FluentAGI\data\runs\20261018-192740-fe6b6c
Current request:
网格智能加密两倍，要求整车空气套件细节加密，尾部区域加密些，计算给到80m每秒
Desktop execution feedback:
[{"failed_action": {"tool": "run_code", "arguments": {"code": "# Use the existing connected PyFluent variable solver.\nimport math\n\ntask_directory = r\"C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-192740-fe6b6c\"\ns = solver.settings\nbaseline_cells = 271220\ntarget_cells = 2 * baseline_cells\nruntime_cell_limit = 4000000\nif not s.setup.is_active() or not s.setup.models.is_active():\n    raise RuntimeError(\"Expected the existing initialized car case\")\ns.file.write_case_data(file_name=task_directory+r\"\\before-refinement-ad9ac9.cas.h5\")\nbc = s.setup.boundary_conditions\nrd = s.solution.report_definitions\nrun = s.solution.run_calculation\nprint({\"time_mode\": s.setup.general.solver.time.get_state(),\n       \"calculation_state\": run.get_state(),\n       \"target_cells\": target_cells, \"runtime_cell_limit\": runtime_cell_limit})\nprint({\"inlet_before\": bc.velocity_inlet[\"inlet\"].get_state(),\n       \"walls\": bc.wall.get_object_names()})\n\ndef choose(node, candidates):\n    norm = lambda x: str(x).lower().replace(\" \", \"\").replace(\"-\", \"\").replace(\"_\", \"\")\n    allowed = node.allowed_values()\n    for candidate in candidates:\n        for value in allowed:\n            if norm(value) == norm(candidate):\n                node.set_state(value)\n                return value\n    raise RuntimeError(str({\"requested\": candidates, \"allowed\": allowed}))\n\ndef select_type(node, predicate):\n    allowed = node.allowed_values()\n    for value in allowed:\n        if predicate(str(value).lower()):\n            node.set_state(value)\n            return value\n    raise RuntimeError(str({\"unsupported_report_or_method\": allowed}))\n\ndef constant(node, value):\n    state = node.get_state()\n    if isinstance(state, dict) and \"value\" in node.child_names:\n        if \"option\" in node.child_names:\n            choose(node.option, [\"value\", \"constant\"])\n        node.value = value\n    else:\n        node.set_state(value)\n\ndef vector(node, values):\n    state = node.get_state()\n    if isinstance(state, list) and state and isinstance(state[0], dict):\n        for i, value in enumerate(values):\n            constant(node[i], value)\n    else:\n        node.set_state(values)\n\ndef report_number(name):\n    result = rd.compute(report_defs=[name])\n    blocks = result if isinstance(result, list) else [result]\n    for block in blocks:\n        if isinstance(block, dict) and name in block:\n            value = block[name]\n            if isinstance(value, (list, tuple)):\n                value = value[0]\n            return float(value)\n    raise RuntimeError(str({\"unrecognized_report\": name, \"result\": result}))\n\ncell_report = \"cell_count_ad9ac9\"\nif cell_report not in rd.mesh.get_object_names():\n    rd.mesh.create(name=cell_report)\nselect_type(rd.mesh[cell_report].report_type,\n            lambda x: \"cell\" in x and \"count\" in x)\nfluid_zones = s.setup.cell_zone_conditions.fluid.get_object_names()\nrd.mesh[cell_report].cell_zones = fluid_zones\nclock_report = \"flow_time_ad9ac9\"\nif clock_report not in rd.time.get_object_names():\n    rd.time.create(name=clock_report)\ninitial_time = report_number(clock_report)\ninitial_cells = int(round(report_number(cell_report)))\nprint({\"actual_initial_time\": initial_time, \"actual_initial_cells\": initial_cells})\nif initial_cells > int(target_cells * 1.15):\n    raise RuntimeError(\"Existing mesh already exceeds this refinement target\")\n\nrequired_walls = [\n    \"wall-body\", \"wall-frontwing\", \"wall-rearwing\", \"wall-ground\",\n    \"wall-wheel-fl\", \"wall-wheel-fr\", \"wall-wheel-rl\", \"wall-wheel-rr\"]\nactual_walls = bc.wall.get_object_names()\nif any(name not in actual_walls for name in required_walls):\n    raise RuntimeError(str({\"missing_car_boundaries\": actual_walls}))\nadapt = s.mesh.adapt\ncontrols = adapt.set\nprint({\"adaptation_controls_before\": controls.get_state()})\nif \"maximum_cell_count\" not in controls.child_names:\n    raise RuntimeError(\"Native adaption lacks the required cell-count control\")\nselect_type(controls.adaption_method,\n            lambda x: (\"puma\" in x or \"poly\" in x) and \"2.5\" not in x)\ncontrols.maximum_refinement_level = 1\ncontrols.minimum_edge_length = 0.002\ncontrols.maximum_cell_count = target_cells\nregisters = s.solution.cell_registers\nregions = [\n    (\"frontwing\", [-0.04,-0.85,0.035], [0.36,0.85,0.29], 335000),\n    (\"rearwing\", [2.81,-0.56,0.84], [3.25,0.56,1.10], 395000),\n    (\"diffuser\", [2.12,-0.59,0.045], [3.28,0.59,0.33], 450000),\n    (\"sidepods\", [0.98,-0.59,0.14], [2.80,0.59,0.70], 490000),\n    (\"wake\", [3.12,-1.20,0.03], [7.50,1.20,1.45], target_cells),\n    (\"car_nearfield\", [-0.08,-1.0,0.01], [3.30,1.0,1.14], target_cells)]\nfor label, low, high, budget in regions:\n    current = int(round(report_number(cell_report)))\n    if current >= int(0.98 * target_cells):\n        break\n    if current >= budget:\n        continue\n    controls.maximum_cell_count = min(budget, target_cells, runtime_cell_limit)\n    name = \"ref_\" + label + \"_ad9ac9\"\n    if name not in registers.get_object_names():\n        registers.create(name=name)\n    register = registers[name]\n    choose(register.type.option, [\"hexahedron\"])\n    region = register.type.hexahedron\n    region.min_point = low\n    region.max_point = high\n    region.inside = True\n    print({\"refinement_region\": label, \"register\": register.get_state(),\n           \"cell_budget\": controls.maximum_cell_count.get_state()})\n    if \"refine\" not in register.command_names:\n        raise RuntimeError(\"Native cell-register refinement unavailable\")\n    register.refine()\n    current = int(round(report_number(cell_report)))\n    print({\"region_finished\": label, \"actual_cells\": current})\n    if current > min(runtime_cell_limit, int(target_cells * 1.15)):\n        s.file.write_case_data(file_name=task_directory+r\"\\refinement-review-ad9ac9.cas.h5\")\n        raise RuntimeError(\"Refinement exceeded the intended cell budget\")\ncontrols.maximum_cell_count = target_cells\ns.mesh.check()\nfinal_cells = int(round(report_number(cell_report)))\nprint({\"actual_refined_cells\": final_cells,\n       \"ratio_to_original\": final_cells / baseline_cells,\n       \"solution_time_after_refinement\": report_number(clock_report)})\nif final_cells <= initial_cells and initial_cells < int(target_cells * 0.9):\n    raise RuntimeError(\"Requested refinement did not increase the cell count\")\ns.file.write_case_data(file_name=task_directory+r\"\\car-refined-ad9ac9.cas.h5\")\n\ntime_mode = str(s.setup.general.solver.time.get_state()).lower()\nif \"unsteady\" not in time_mode:\n    raise RuntimeError(\"Expected the existing transient solution; time mode preserved\")\nrun.transient_controls.time_step_size = 0.001\nground = bc.wall[\"wall-ground\"].momentum\nprint({\"ground_before\": ground.get_state()})\nwheels = [\"wall-wheel-\" + side for side in [\"fl\", \"fr\", \"rl\", \"rr\"]]\nfor name in wheels:\n    print({\"wheel\": name, \"motion_before\": bc.wall[name].momentum.get_state()})\n\ndef set_speed(value):\n    constant(bc.velocity_inlet[\"inlet\"].momentum.velocity, value)\n    vector(ground.velocity_components, [value, 0.0, 0.0])\n    for name in wheels:\n        constant(bc.wall[name].momentum.rotation_speed, -value / 0.24)\n    print({\"requested_speed\": value,\n           \"inlet_readback\": bc.velocity_inlet[\"inlet\"].momentum.get_state(),\n           \"ground_readback\": ground.get_state()})\n\ncar_walls = [name for name in actual_walls if name != \"wall-ground\"]\nforce_reports = []\nfor name, zones, direction in [\n    (\"drag_80_ad9ac9\", car_walls, [1,0,0]),\n    (\"downforce_80_ad9ac9\", car_walls, [0,0,-1]),\n    (\"front_partition_80_ad9ac9\", [\"wall-frontwing\"], [0,0,-1]),\n    (\"rear_partition_80_ad9ac9\", [\"wall-rearwing\"], [0,0,-1])]:\n    if name not in rd.force.get_object_names():\n        rd.force.create(name=name)\n    rd.force[name].set_state({\"zones\": zones, \"force_vector\": direction})\n    force_reports.append(name)\nmass_reports = []\nfor label, boundaries in [(\"inlet\", [\"inlet\"]), (\"outlet\", [\"outlet\"])]:\n    name = \"mass_\" + label + \"_ad9ac9\"\n    if name not in rd.flux.get_object_names():\n        rd.flux.create(name=name)\n    select_type(rd.flux[name].report_type, lambda x: \"mass\" in x and \"flow\" in x)\n    rd.flux[name].boundaries = boundaries\n    mass_reports.append(name)\nmonitor_name = \"car_80_ad9ac9\"\nall_reports = force_reports + mass_reports + [clock_report]\nfor collection in [s.solution.monitor.report_files, s.solution.monitor.report_plots]:\n    if monitor_name not in collection.get_object_names():\n        collection.create(name=monitor_name)\n    collection[monitor_name].report_defs = all_reports\n    collection[monitor_name].active = True\ns.solution.monitor.report_files[monitor_name].file_name = task_directory+r\"\\car-80-history-ad9ac9.out\"\n\ndef evidence(label):\n    print({\"stage\": label, \"actual_time\": report_number(clock_report),\n           \"forces\": rd.compute(report_defs=force_reports)})\n    mass_in, mass_out = [report_number(name) for name in mass_reports]\n    print({\"mass_in\": mass_in, \"mass_out\": mass_out,\n           \"relative_mass_imbalance\": abs(mass_in + mass_out) / max(abs(mass_in), 1e-12)})\n    try:\n        index, history = solver.monitors.get_monitor_set_data(\"residual\")\n        last = {key: float(value[-1]) for key, value in history.items() if len(value)}\n        print({\"residuals\": last})\n        if any(not math.isfinite(value) or value > 1e4 for value in last.values()):\n            s.file.write_case_data(file_name=task_directory+r\"\\needs-review-80-ad9ac9.cas.h5\")\n            raise RuntimeError(\"Residual divergence detected\")\n    except RuntimeError:\n        raise\n    except Exception as error:\n        print({\"residual_read_error\": str(error)})\n\nfor value in [40.0, 60.0, 80.0]:\n    set_speed(value)\n    run.dual_time_iterate(time_step_count=3, max_iter_per_step=60)\n    evidence(\"speed_\" + str(value))\nfor batch in range(3):\n    run.dual_time_iterate(time_step_count=10, max_iter_per_step=60)\n    evidence(\"80_settling_\" + str(batch + 1))\ns.file.write_case_data(file_name=task_directory+r\"\\car-80-before-animation-ad9ac9.cas.h5\")\n\ng = s.results.graphics\ntry:\n    name = \"refined_car_ad9ac9\"\n    if name not in g.mesh.get_object_names():\n        g.mesh.create(name=name)\n    g.mesh[name].surfaces_list = car_walls\n    g.mesh[name].display()\n    g.picture.save_picture(file_name=task_directory+r\"\\refined-car-ad9ac9.png\")\nexcept Exception as error:\n    print({\"mesh_graphics_error\": str(error)})\nsection = fluentagi.create_section(axis=\"y\", position=0.0)\nprint(section)\nresult = fluentagi.export_results(\n    directory=task_directory, fields=[\"pressure\", \"velocity-magnitude\"],\n    surfaces=[section[\"surface\"]])\nprint(result)\nfor name, kind, state in [\n    (\"car_pressure_80_ad9ac9\", \"contour\",\n     {\"field\":\"pressure\", \"surfaces_list\":car_walls, \"filled\":True}),\n    (\"car_streamlines_80_ad9ac9\", \"pathline\",\n     {\"field\":\"velocity-magnitude\", \"release_from_surfaces\":[\"inlet\"], \"step\":3000, \"skip\":20})]:\n    try:\n        collection = getattr(g, kind)\n        if name not in collection.get_object_names():\n            collection.create(name=name)\n        collection[name].set_state(state)\n        collection[name].display()\n        g.picture.save_picture(file_name=task_directory+\"\\\\\"+name+\".png\")\n    except Exception as error:\n        print({\"graphics\": name, \"error\": str(error)})\nprint(fluentagi.record_animation(\n    directory=task_directory, field=\"velocity-magnitude\", frames=24,\n    steps_per_frame=2, max_iter_per_step=60, surfaces=[section[\"surface\"]]))\nevidence(\"final_80\")\ns.file.write_case_data(file_name=task_directory+r\"\\car-80-final-ad9ac9.cas.h5\")\nfor name, obj in [(\"forces\", s.solution.monitor.report_plots[monitor_name]),\n                  (\"residuals\", s.solution.monitor.residual)]:\n    try:\n        obj.plot()\n        g.picture.save_picture(file_name=task_directory+\"\\\\\"+name+\"-80-ad9ac9.png\")\n    except Exception as error:\n        print({\"plot\": name, \"error\": str(error)})\nprint({\"initial_time\": initial_time, \"actual_final_time\": report_number(clock_report),\n       \"actual_cells\": int(round(report_number(cell_report))),\n       \"target_speed\": 80.0, \"convergence_verified\": False})"}}, "error": "{\"status\": \"error\", \"stdout\": \"\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-192740-fe6b6c\\\\before-refinement-ad9ac9.cas.h5\\\" in NODE0 mode and compression level 1 ...\\nGrouping cells for Laplace smoothing ...\\n      271220 cells,     1 zone  ...\\n      570904 faces,    12 zones ...\\n       58102 nodes,     1 zone  ...\\n  Done.\\nDone.\\n\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-192740-fe6b6c\\\\before-refinement-ad9ac9.dat.h5\\\" in NODE0 mode and compression level 1 ...\\n  Writing results.\\nDone.\\n{'time_mode': 'unsteady-2nd-order', 'calculation_state': {'reporting_interval': 1, 'profile_update_interval': 1, 'transient_controls': {'type': 'Fixed', 'method': 'User-Specified', 'time_step_count': 2, 'time_step_size': 0.005, 'max_iter_per_time_step': 20, 'solution_status': False, 'extrapolate_variables': False, 'predict_next': False, 'fixed_periodic': {'enabled': False}, 'multiphase_specific_time_constraints': {'physics_based_constraint': False}, 'solid_time_step_size': {'enable_solid_time_step': False}}, 'data_sampling': {'enabled': False}}, 'target_cells': 542440, 'runtime_cell_limit': 4000000}\\n{'inlet_before': {'name': 'inlet', 'momentum': {'velocity_specification_method': 'Magnitude, Normal to Boundary', 'reference_frame': 'Absolute', 'velocity': {'option': 'value', 'value': 20.0}, 'initial_gauge_pressure': {'option': 'value', 'value': 0}}, 'turbulence': {'turbulence_specification': 'Intensity and Viscosity Ratio', 'turbulent_intensity': 0.05, 'turbulent_viscosity_ratio': 10}}, 'walls': ['wall-rearwing', 'wall-wheel-fl', 'wall-wheel-fr', 'wall-wheel-rl', 'wall-wheel-rr', 'wall-body', 'wall-frontwing', 'wall-ground']}\\n\\nError: api-create-obj: new objects of this type cannot be created\\nError Object: \\\"solution/report-definitions/time\\\"\\n\", \"stderr\": \"\\nTraceback (most recent call last):\\n  File \\\"ansys\\\\fluent\\\\mcp\\\\solve\\\\backends\\\\pyfluent.py\\\", line 2793, in _do\\n    exec(  # noqa: S102  # nosec B102\\n  File \\\"<mcp>\\\", line 76, in <module>\\n  File \\\"ansys\\\\fluent\\\\core\\\\solver\\\\flobject.py\\\", line 2080, in create\\n    self.flproxy.create(self.path, name)\\n  File \\\"ansys\\\\fluent\\\\core\\\\services\\\\settings.py\\\", line 146, in _fn\\n    return fn(self, *args, **kwds)\\n           ^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"ansys\\\\fluent\\\\core\\\\services\\\\settings.py\\\", line 240, in create\\n    self._service_impl.create(request)\\n  File \\\"ansys\\\\fluent\\\\core\\\\services\\\\settings.py\\\", line 77, in create\\n    return self.__stub.Create(request, metadata=self.__metadata)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"grpc\\\\_interceptor.py\\\", line 276, in __call__\\n  File \\\"grpc\\\\_interceptor.py\\\", line 328, in _with_call\\nRuntimeError: api-create-obj: new objects of this type cannot be created\\nError Object: solution/report-definitions/time\\n\", \"return_value\": null, \"message\": \"api-create-obj: new objects of this type cannot be created\\nError Object: solution/report-definitions/time\", \"error_code\": \"execution_error\", \"warnings\": []}", "note": "可能有部分操作已生效，请先检查实时状态，修复后继续。"}, {"failed_action": {"tool": "run_code", "arguments": {"code": "# Use the existing connected PyFluent variable solver.\nimport math\n\ntask_directory = r\"C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-192740-fe6b6c\"\ns = solver.settings\nbaseline_cells = 271220\ntarget_cells = 542440\nruntime_cell_limit = 4000000\nif not s.setup.is_active() or not s.setup.models.is_active():\n    raise RuntimeError(\"Expected the existing initialized car case\")\ns.file.write_case_data(file_name=task_directory+r\"\\before-b49382.cas.h5\")\nbc = s.setup.boundary_conditions\nrd = s.solution.report_definitions\nrun = s.solution.run_calculation\nprint({\"time_mode\": s.setup.general.solver.time.get_state(),\n       \"calculation_state\": run.get_state(),\n       \"target_cells\": target_cells, \"runtime_cell_limit\": runtime_cell_limit})\n\ndef choose(node, candidates):\n    norm = lambda x: str(x).lower().replace(\" \", \"\").replace(\"-\", \"\").replace(\"_\", \"\")\n    allowed = node.allowed_values()\n    for candidate in candidates:\n        for value in allowed:\n            if norm(value) == norm(candidate):\n                node.set_state(value)\n                return value\n    raise RuntimeError(str({\"requested\": candidates, \"allowed\": allowed}))\n\ndef select_type(node, predicate):\n    allowed = node.allowed_values()\n    for value in allowed:\n        if predicate(str(value).lower()):\n            node.set_state(value)\n            return value\n    raise RuntimeError(str({\"unsupported_type\": allowed}))\n\ndef constant(node, value):\n    state = node.get_state()\n    if isinstance(state, dict) and \"value\" in node.child_names:\n        if \"option\" in node.child_names:\n            choose(node.option, [\"value\", \"constant\"])\n        node.value = value\n    else:\n        node.set_state(value)\n\ndef vector(node, values):\n    state = node.get_state()\n    if isinstance(state, list) and state and isinstance(state[0], dict):\n        for i, value in enumerate(values):\n            constant(node[i], value)\n    else:\n        node.set_state(values)\n\ndef report_number(name):\n    result = rd.compute(report_defs=[name])\n    blocks = result if isinstance(result, list) else [result]\n    for block in blocks:\n        if isinstance(block, dict) and name in block:\n            value = block[name]\n            if isinstance(value, (list, tuple)):\n                value = value[0]\n            return float(value)\n    raise RuntimeError(str({\"unrecognized_report\": name, \"result\": result}))\n\ntime_names = rd.time.get_object_names()\nprint({\"existing_time_reports\": time_names})\nclock_report = None\nfor name in time_names:\n    if str(name).lower().replace(\"_\", \"-\") == \"flow-time\":\n        clock_report = name\n        break\nif clock_report is None:\n    clock_report = \"physical_time_b49382\"\n    expressions = rd.single_valued_expression\n    if clock_report not in expressions.get_object_names():\n        expressions.create(name=clock_report)\n    expressions[clock_report].definition = \"Time\"\n\ncell_report = \"cell_count_ad9ac9\"\nif cell_report not in rd.mesh.get_object_names():\n    rd.mesh.create(name=cell_report)\nselect_type(rd.mesh[cell_report].report_type,\n            lambda x: \"cell\" in x and \"count\" in x)\nrd.mesh[cell_report].cell_zones = s.setup.cell_zone_conditions.fluid.get_object_names()\ninitial_time = report_number(clock_report)\ninitial_cells = int(round(report_number(cell_report)))\nprint({\"actual_initial_time\": initial_time, \"actual_initial_cells\": initial_cells})\nif initial_cells > int(target_cells * 1.15):\n    raise RuntimeError(\"Existing mesh already exceeds this refinement target\")\nif \"unsteady\" not in str(s.setup.general.solver.time.get_state()).lower():\n    raise RuntimeError(\"Expected the existing transient solution\")\n\nrequired_walls = [\n    \"wall-body\", \"wall-frontwing\", \"wall-rearwing\", \"wall-ground\",\n    \"wall-wheel-fl\", \"wall-wheel-fr\", \"wall-wheel-rl\", \"wall-wheel-rr\"]\nactual_walls = bc.wall.get_object_names()\nif any(name not in actual_walls for name in required_walls):\n    raise RuntimeError(str({\"required\": required_walls, \"actual\": actual_walls}))\nadapt = s.mesh.adapt\ncontrols = adapt.set\nprint({\"adaptation_controls_before\": controls.get_state()})\nif \"maximum_cell_count\" not in controls.child_names:\n    raise RuntimeError(\"Native adaption lacks the required cell-count control\")\nselect_type(controls.adaption_method,\n            lambda x: (\"puma\" in x or \"poly\" in x) and \"2.5\" not in x)\ncontrols.maximum_refinement_level = 1\ncontrols.minimum_edge_length = 0.002\ncontrols.maximum_cell_count = target_cells\nregisters = s.solution.cell_registers\nregions = [\n    (\"frontwing\", [-0.04,-0.85,0.035], [0.36,0.85,0.29], 335000),\n    (\"rearwing\", [2.81,-0.56,0.84], [3.25,0.56,1.10], 395000),\n    (\"diffuser\", [2.12,-0.59,0.045], [3.28,0.59,0.33], 450000),\n    (\"sidepods\", [0.98,-0.59,0.14], [2.80,0.59,0.70], 490000),\n    (\"wake\", [3.12,-1.20,0.03], [7.50,1.20,1.45], target_cells),\n    (\"car_nearfield\", [-0.08,-1.0,0.01], [3.30,1.0,1.14], target_cells)]\nfor label, low, high, budget in regions:\n    current = int(round(report_number(cell_report)))\n    if current >= int(0.98 * target_cells):\n        break\n    if current >= budget:\n        continue\n    controls.maximum_cell_count = min(budget, target_cells, runtime_cell_limit)\n    name = \"ref_\" + label + \"_ad9ac9\"\n    if name not in registers.get_object_names():\n        registers.create(name=name)\n    register = registers[name]\n    choose(register.type.option, [\"hexahedron\"])\n    region = register.type.hexahedron\n    region.min_point = low\n    region.max_point = high\n    region.inside = True\n    print({\"region\": label, \"state\": register.get_state(),\n           \"cell_budget\": controls.maximum_cell_count.get_state()})\n    if \"refine\" not in register.command_names:\n        raise RuntimeError(\"Native cell-register refinement unavailable\")\n    register.refine()\n    current = int(round(report_number(cell_report)))\n    print({\"region_finished\": label, \"actual_cells\": current})\n    if current > min(runtime_cell_limit, int(target_cells * 1.15)):\n        s.file.write_case_data(file_name=task_directory+r\"\\refinement-review-b49382.cas.h5\")\n        raise RuntimeError(\"Refinement exceeded the intended cell budget\")\ncontrols.maximum_cell_count = target_cells\ns.mesh.check()\nfinal_cells = int(round(report_number(cell_report)))\nprint({\"actual_refined_cells\": final_cells,\n       \"ratio_to_original\": final_cells / baseline_cells,\n       \"actual_time\": report_number(clock_report)})\nif final_cells <= initial_cells and initial_cells < int(target_cells * 0.9):\n    raise RuntimeError(\"Requested refinement did not increase the cell count\")\ns.file.write_case_data(file_name=task_directory+r\"\\car-refined-b49382.cas.h5\")\n\nrun.transient_controls.time_step_size = 0.001\nground = bc.wall[\"wall-ground\"].momentum\nwheels = [\"wall-wheel-\" + side for side in [\"fl\", \"fr\", \"rl\", \"rr\"]]\nprint({\"ground_before\": ground.get_state()})\nfor name in wheels:\n    print({\"wheel\": name, \"motion_before\": bc.wall[name].momentum.get_state()})\n\ndef set_speed(value):\n    constant(bc.velocity_inlet[\"inlet\"].momentum.velocity, value)\n    vector(ground.velocity_components, [value, 0.0, 0.0])\n    for name in wheels:\n        constant(bc.wall[name].momentum.rotation_speed, -value / 0.24)\n    print({\"requested_speed\": value,\n           \"inlet_readback\": bc.velocity_inlet[\"inlet\"].momentum.get_state(),\n           \"ground_readback\": ground.get_state()})\n\ncar_walls = [name for name in actual_walls if name != \"wall-ground\"]\nforce_reports = []\nfor name, zones, direction in [\n    (\"drag_80_b49382\", car_walls, [1,0,0]),\n    (\"downforce_80_b49382\", car_walls, [0,0,-1]),\n    (\"front_partition_80_b49382\", [\"wall-frontwing\"], [0,0,-1]),\n    (\"rear_partition_80_b49382\", [\"wall-rearwing\"], [0,0,-1])]:\n    if name not in rd.force.get_object_names():\n        rd.force.create(name=name)\n    rd.force[name].set_state({\"zones\": zones, \"force_vector\": direction})\n    force_reports.append(name)\nmass_reports = []\nfor label, boundaries in [(\"inlet\", [\"inlet\"]), (\"outlet\", [\"outlet\"])]:\n    name = \"mass_\" + label + \"_b49382\"\n    if name not in rd.flux.get_object_names():\n        rd.flux.create(name=name)\n    select_type(rd.flux[name].report_type, lambda x: \"mass\" in x and \"flow\" in x)\n    rd.flux[name].boundaries = boundaries\n    mass_reports.append(name)\nmonitor_name = \"car_80_b49382\"\nall_reports = force_reports + mass_reports + [clock_report]\nfor collection in [s.solution.monitor.report_files, s.solution.monitor.report_plots]:\n    if monitor_name not in collection.get_object_names():\n        collection.create(name=monitor_name)\n    collection[monitor_name].report_defs = all_reports\n    collection[monitor_name].active = True\ns.solution.monitor.report_files[monitor_name].file_name = task_directory+r\"\\car-80-history-b49382.out\"\n\ndef evidence(label):\n    print({\"stage\": label, \"actual_time\": report_number(clock_report),\n           \"forces\": rd.compute(report_defs=force_reports)})\n    mass_in, mass_out = [report_number(name) for name in mass_reports]\n    print({\"mass_in\": mass_in, \"mass_out\": mass_out,\n           \"relative_mass_imbalance\": abs(mass_in + mass_out) / max(abs(mass_in), 1e-12)})\n    try:\n        index, history = solver.monitors.get_monitor_set_data(\"residual\")\n        last = {key: float(value[-1]) for key, value in history.items() if len(value)}\n    except Exception as error:\n        print({\"residual_read_error\": str(error)})\n        return\n    print({\"residuals\": last})\n    if any(not math.isfinite(value) or value > 1e4 for value in last.values()):\n        s.file.write_case_data(file_name=task_directory+r\"\\needs-review-80-b49382.cas.h5\")\n        raise RuntimeError(\"Residual divergence detected\")\n\nvelocity_state = bc.velocity_inlet[\"inlet\"].momentum.velocity.get_state()\ncurrent_speed = float(velocity_state[\"value\"])\nstages = [value for value in [40.0, 60.0, 80.0] if value > current_speed]\nif current_speed > 80.0:\n    stages = [80.0]\nfor value in stages:\n    set_speed(value)\n    run.dual_time_iterate(time_step_count=3, max_iter_per_step=60)\n    evidence(\"speed_\" + str(value))\nset_speed(80.0)\nfor batch in range(3):\n    run.dual_time_iterate(time_step_count=10, max_iter_per_step=60)\n    evidence(\"80_settling_\" + str(batch + 1))\ns.file.write_case_data(file_name=task_directory+r\"\\car-80-before-animation-b49382.cas.h5\")\n\ng = s.results.graphics\ntry:\n    name = \"refined_car_b49382\"\n    if name not in g.mesh.get_object_names():\n        g.mesh.create(name=name)\n    g.mesh[name].surfaces_list = car_walls\n    g.mesh[name].display()\n    g.picture.save_picture(file_name=task_directory+r\"\\refined-car-b49382.png\")\nexcept Exception as error:\n    print({\"mesh_graphics_error\": str(error)})\nsection = fluentagi.create_section(axis=\"y\", position=0.0)\nprint(section)\nresult = fluentagi.export_results(\n    directory=task_directory, fields=[\"pressure\", \"velocity-magnitude\"],\n    surfaces=[section[\"surface\"]])\nprint(result)\nif isinstance(result, dict):\n    for key in [\"error\", \"errors\", \"warnings\"]:\n        if result.get(key):\n            print({\"export_review\": key, \"details\": result[key]})\nfor name, kind, state in [\n    (\"car_pressure_80_b49382\", \"contour\",\n     {\"field\":\"pressure\", \"surfaces_list\":car_walls, \"filled\":True}),\n    (\"car_streamlines_80_b49382\", \"pathline\",\n     {\"field\":\"velocity-magnitude\", \"release_from_surfaces\":[\"inlet\"], \"step\":3000, \"skip\":20})]:\n    try:\n        collection = getattr(g, kind)\n        if name not in collection.get_object_names():\n            collection.create(name=name)\n        collection[name].set_state(state)\n        collection[name].display()\n        g.picture.save_picture(file_name=task_directory+\"\\\\\"+name+\".png\")\n    except Exception as error:\n        print({\"graphics\": name, \"error\": str(error)})\nprint(fluentagi.record_animation(\n    directory=task_directory, field=\"velocity-magnitude\", frames=24,\n    steps_per_frame=2, max_iter_per_step=60, surfaces=[section[\"surface\"]]))\nevidence(\"final_80\")\ns.file.write_case_data(file_name=task_directory+r\"\\car-80-final-b49382.cas.h5\")\nfor name, obj in [(\"forces\", s.solution.monitor.report_plots[monitor_name]),\n                  (\"residuals\", s.solution.monitor.residual)]:\n    try:\n        obj.plot()\n        g.picture.save_picture(file_name=task_directory+\"\\\\\"+name+\"-80-b49382.png\")\n    except Exception as error:\n        print({\"plot\": name, \"error\": str(error)})\nprint({\"initial_time\": initial_time, \"actual_final_time\": report_number(clock_report),\n       \"actual_cells\": int(round(report_number(cell_report))),\n       \"target_speed\": 80.0, \"convergence_verified\": False})"}}, "error": "{\"status\": \"error\", \"stdout\": \"\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-192740-fe6b6c\\\\before-b49382.cas.h5\\\" in NODE0 mode and compression level 1 ...\\nGrouping cells for Laplace smoothing ...\\n      271220 cells,     1 zone  ...\\n      570904 faces,    12 zones ...\\n       58102 nodes,     1 zone  ...\\n  Done.\\nDone.\\n\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-192740-fe6b6c\\\\before-b49382.dat.h5\\\" in NODE0 mode and compression level 1 ...\\n  Writing results.\\nDone.\\n{'time_mode': 'unsteady-2nd-order', 'calculation_state': {'reporting_interval': 1, 'profile_update_interval': 1, 'transient_controls': {'type': 'Fixed', 'method': 'User-Specified', 'time_step_count': 2, 'time_step_size': 0.005, 'max_iter_per_time_step': 20, 'solution_status': False, 'extrapolate_variables': False, 'predict_next': False, 'fixed_periodic': {'enabled': False}, 'multiphase_specific_time_constraints': {'physics_based_constraint': False}, 'solid_time_step_size': {'enable_solid_time_step': False}}, 'data_sampling': {'enabled': False}}, 'target_cells': 542440, 'runtime_cell_limit': 4000000}\\n{'existing_time_reports': ['flow-time', 'iters-per-timestep', 'delta-time']}\\n\\nError: Values contain disallowed entries\\nError Object: (flow-time)\\n\\nError: Values contain disallowed entries\\nError Object: (flow-time)\\n\", \"stderr\": \"\\nTraceback (most recent call last):\\n  File \\\"ansys\\\\fluent\\\\mcp\\\\solve\\\\backends\\\\pyfluent.py\\\", line 2793, in _do\\n    exec(  # noqa: S102  # nosec B102\\n  File \\\"<mcp>\\\", line 85, in <module>\\n  File \\\"<mcp>\\\", line 55, in report_number\\n  File \\\"ansys\\\\fluent\\\\core\\\\solver\\\\flobject.py\\\", line 1896, in __call__\\n    return self.execute_command(**kwds)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"ansys\\\\fluent\\\\core\\\\solver\\\\flobject.py\\\", line 1836, in execute_command\\n    ret = self._execute_command(*args, **scmKwds)\\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"ansys\\\\fluent\\\\core\\\\solver\\\\flobject.py\\\", line 1807, in _execute_command\\n    ret = self.flproxy.execute_cmd(self._parent.path, self.obj_name, **kwds)\\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"ansys\\\\fluent\\\\core\\\\services\\\\settings.py\\\", line 146, in _fn\\n    return fn(self, *args, **kwds)\\n           ^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"ansys\\\\fluent\\\\core\\\\services\\\\settings.py\\\", line 361, in execute_cmd\\n    response = self._service_impl.execute_cmd(request)\\n               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nRuntimeError: Values contain disallowed entries\\nError Object: (flow-time)\\n\", \"return_value\": null, \"message\": \"Values contain disallowed entries\\nError Object: (flow-time)\", \"error_code\": \"execution_error\", \"warnings\": []}", "note": "可能有部分操作已生效，请先检查实时状态，修复后继续。"}]
【ChatGPT 回复】
ChatGPT 说：

改用Time表达式报告读取实际流动时间，保留现有解，继续局部加密、80 m/s计算和结果导出。

<fluent_code>
```python
# Use the existing connected PyFluent variable solver.
import math

task_directory = r"C:\Users\13977\AppData\Local\FluentAGI\data\runs\20261018-192740-fe6b6c"
s = solver.settings
baseline_cells = 271220
target_cells = 542440
runtime_cell_limit = 4000000
if not s.setup.is_active() or not s.setup.models.is_active():
    raise RuntimeError("Expected the existing initialized car case")
s.file.write_case_data(file_name=task_directory+r"\before-8f0a12.cas.h5")
bc = s.setup.boundary_conditions
rd = s.solution.report_definitions
run = s.solution.run_calculation
print({"time_mode": s.setup.general.solver.time.get_state(),
       "calculation_state": run.get_state(),
       "target_cells": target_cells, "runtime_cell_limit": runtime_cell_limit})

def choose(node, candidates):
    norm = lambda x: str(x).lower().replace(" ", "").replace("-", "").replace("_", "")
    allowed = node.allowed_values()
    for candidate in candidates:
        for value in allowed:
            if norm(value) == norm(candidate):
                node.set_state(value)
                return value
    raise RuntimeError(str({"requested": candidates, "allowed": allowed}))

def select_type(node, predicate):
    allowed = node.allowed_values()
    for value in allowed:
        if predicate(str(value).lower()):
            node.set_state(value)
            return value
    raise RuntimeError(str({"unsupported_type": allowed}))

def constant(node, value):
    state = node.get_state()
    if isinstance(state, dict) and "value" in node.child_names:
        if "option" in node.child_names:
            choose(node.option, ["value", "constant"])
        node.value = value
    else:
        node.set_state(value)

def vector(node, values):
    state = node.get_state()
    if isinstance(state, list) and state and isinstance(state[0], dict):
        for i, value in enumerate(values):
            constant(node[i], value)
    else:
        node.set_state(values)

def report_number(name):
    result = rd.compute(report_defs=[name])
    blocks = result if isinstance(result, list) else [result]
    for block in blocks:
        if isinstance(block, dict) and name in block:
            value = block[name]
            if isinstance(value, (list, tuple)):
                value = value[0]
            return float(value)
    raise RuntimeError(str({"unrecognized_report": name, "result": result}))

clock_report = "physical_time_8f0a12"
expressions = rd.single_valued_expression
if clock_report not in expressions.get_object_names():
    expressions.create(name=clock_report)
expressions[clock_report].definition = "Time"
print({"clock_expression": expressions[clock_report].get_state()})
initial_time = report_number(clock_report)
cell_report = "cell_count_ad9ac9"
if cell_report not in rd.mesh.get_object_names():
    rd.mesh.create(name=cell_report)
select_type(rd.mesh[cell_report].report_type,
            lambda x: "cell" in x and "count" in x)
rd.mesh[cell_report].cell_zones = s.setup.cell_zone_conditions.fluid.get_object_names()
initial_cells = int(round(report_number(cell_report)))
print({"actual_initial_time": initial_time, "actual_initial_cells": initial_cells})
if initial_cells > int(target_cells * 1.15):
    raise RuntimeError("Existing mesh already exceeds this refinement target")
if "unsteady" not in str(s.setup.general.solver.time.get_state()).lower():
    raise RuntimeError("Expected the existing transient solution")

required_walls = [
    "wall-body", "wall-frontwing", "wall-rearwing", "wall-ground",
    "wall-wheel-fl", "wall-wheel-fr", "wall-wheel-rl", "wall-wheel-rr"]
actual_walls = bc.wall.get_object_names()
if any(name not in actual_walls for name in required_walls):
    raise RuntimeError(str({"required": required_walls, "actual": actual_walls}))
adapt = s.mesh.adapt
controls = adapt.set
print({"adaptation_controls_before": controls.get_state()})
if "maximum_cell_count" not in controls.child_names:
    raise RuntimeError("Native adaption lacks the required cell-count control")
select_type(controls.adaption_method,
            lambda x: ("puma" in x or "poly" in x) and "2.5" not in x)
controls.maximum_refinement_level = 1
controls.minimum_edge_length = 0.002
controls.maximum_cell_count = target_cells
registers = s.solution.cell_registers
regions = [
    ("frontwing", [-0.04,-0.85,0.035], [0.36,0.85,0.29], 335000),
    ("rearwing", [2.81,-0.56,0.84], [3.25,0.56,1.10], 395000),
    ("diffuser", [2.12,-0.59,0.045], [3.28,0.59,0.33], 450000),
    ("sidepods", [0.98,-0.59,0.14], [2.80,0.59,0.70], 490000),
    ("wake", [3.12,-1.20,0.03], [7.50,1.20,1.45], target_cells),
    ("car_nearfield", [-0.08,-1.0,0.01], [3.30,1.0,1.14], target_cells)]
for label, low, high, budget in regions:
    current = int(round(report_number(cell_report)))
    if current >= int(0.98 * target_cells):
        break
    if current >= budget:
        continue
    controls.maximum_cell_count = min(budget, target_cells, runtime_cell_limit)
    name = "ref_" + label + "_ad9ac9"
    if name not in registers.get_object_names():
        registers.create(name=name)
    register = registers[name]
    choose(register.type.option, ["hexahedron"])
    region = register.type.hexahedron
    region.min_point = low
    region.max_point = high
    region.inside = True
    print({"region": label, "state": register.get_state(),
           "cell_budget": controls.maximum_cell_count.get_state()})
    if "refine" not in register.command_names:
        raise RuntimeError("Native cell-register refinement unavailable")
    register.refine()
    current = int(round(report_number(cell_report)))
    print({"region_finished": label, "actual_cells": current})
    if current > min(runtime_cell_limit, int(target_cells * 1.15)):
        s.file.write_case_data(file_name=task_directory+r"\refinement-review-8f0a12.cas.h5")
        raise RuntimeError("Refinement exceeded the intended cell budget")
controls.maximum_cell_count = target_cells
s.mesh.check()
final_cells = int(round(report_number(cell_report)))
print({"actual_refined_cells": final_cells,
       "ratio_to_original": final_cells / baseline_cells,
       "actual_time": report_number(clock_report)})
if final_cells <= initial_cells and initial_cells < int(target_cells * 0.9):
    raise RuntimeError("Requested refinement did not increase the cell count")
s.file.write_case_data(file_name=task_directory+r"\car-refined-8f0a12.cas.h5")

run.transient_controls.time_step_size = 0.001
ground = bc.wall["wall-ground"].momentum
wheels = ["wall-wheel-" + side for side in ["fl", "fr", "rl", "rr"]]
print({"ground_before": ground.get_state()})
for name in wheels:
    print({"wheel": name, "motion_before": bc.wall[name].momentum.get_state()})

def set_speed(value):
    constant(bc.velocity_inlet["inlet"].momentum.velocity, value)
    vector(ground.velocity_components, [value, 0.0, 0.0])
    for name in wheels:
        constant(bc.wall[name].momentum.rotation_speed, -value / 0.24)
    print({"requested_speed": value,
           "inlet_readback": bc.velocity_inlet["inlet"].momentum.get_state(),
           "ground_readback": ground.get_state()})

car_walls = [name for name in actual_walls if name != "wall-ground"]
force_reports = []
for name, zones, direction in [
    ("drag_80_8f0a12", car_walls, [1,0,0]),
    ("downforce_80_8f0a12", car_walls, [0,0,-1]),
    ("front_partition_80_8f0a12", ["wall-frontwing"], [0,0,-1]),
    ("rear_partition_80_8f0a12", ["wall-rearwing"], [0,0,-1])]:
    if name not in rd.force.get_object_names():
        rd.force.create(name=name)
    rd.force[name].set_state({"zones": zones, "force_vector": direction})
    force_reports.append(name)
mass_reports = []
for label, boundaries in [("inlet", ["inlet"]), ("outlet", ["outlet"])]:
    name = "mass_" + label + "_8f0a12"
    if name not in rd.flux.get_object_names():
        rd.flux.create(name=name)
    select_type(rd.flux[name].report_type, lambda x: "mass" in x and "flow" in x)
    rd.flux[name].boundaries = boundaries
    mass_reports.append(name)
monitor_name = "car_80_8f0a12"
all_reports = force_reports + mass_reports + [clock_report]
for collection in [s.solution.monitor.report_files, s.solution.monitor.report_plots]:
    if monitor_name not in collection.get_object_names():
        collection.create(name=monitor_name)
    collection[monitor_name].report_defs = all_reports
    collection[monitor_name].active = True
s.solution.monitor.report_files[monitor_name].file_name = task_directory+r"\car-80-history-8f0a12.out"

def evidence(label):
    print({"stage": label, "actual_time": report_number(clock_report),
           "forces": rd.compute(report_defs=force_reports)})
    mass_in, mass_out = [report_number(name) for name in mass_reports]
    print({"mass_in": mass_in, "mass_out": mass_out,
           "relative_mass_imbalance": abs(mass_in + mass_out) / max(abs(mass_in), 1e-12)})
    try:
        index, history = solver.monitors.get_monitor_set_data("residual")
        last = {key: float(value[-1]) for key, value in history.items() if len(value)}
    except Exception as error:
        print({"residual_read_error": str(error)})
        return
    print({"residuals": last})
    if any(not math.isfinite(value) or value > 1e4 for value in last.values()):
        s.file.write_case_data(file_name=task_directory+r"\needs-review-80-8f0a12.cas.h5")
        raise RuntimeError("Residual divergence detected")

velocity_state = bc.velocity_inlet["inlet"].momentum.velocity.get_state()
current_speed = float(velocity_state["value"])
stages = [value for value in [40.0, 60.0, 80.0] if value > current_speed]
if current_speed > 80.0:
    stages = [80.0]
for value in stages:
    set_speed(value)
    run.dual_time_iterate(time_step_count=3, max_iter_per_step=60)
    evidence("speed_" + str(value))
set_speed(80.0)
for batch in range(3):
    run.dual_time_iterate(time_step_count=10, max_iter_per_step=60)
    evidence("80_settling_" + str(batch + 1))
s.file.write_case_data(file_name=task_directory+r"\car-80-before-animation-8f0a12.cas.h5")

g = s.results.graphics
try:
    name = "refined_car_8f0a12"
    if name not in g.mesh.get_object_names():
        g.mesh.create(name=name)
    g.mesh[name].surfaces_list = car_walls
    g.mesh[name].display()
    g.picture.save_picture(file_name=task_directory+r"\refined-car-8f0a12.png")
except Exception as error:
    print({"mesh_graphics_error": str(error)})
section = fluentagi.create_section(axis="y", position=0.0)
print(section)
result = fluentagi.export_results(
    directory=task_directory, fields=["pressure", "velocity-magnitude"],
    surfaces=[section["surface"]])
print(result)
if isinstance(result, dict):
    for key in ["error", "errors", "warnings"]:
        if result.get(key):
            print({"export_review": key, "details": result[key]})
for name, kind, state in [
    ("car_pressure_80_8f0a12", "contour",
     {"field":"pressure", "surfaces_list":car_walls, "filled":True}),
    ("car_streamlines_80_8f0a12", "pathline",
     {"field":"velocity-magnitude", "release_from_surfaces":["inlet"], "step":3000, "skip":20})]:
    try:
        collection = getattr(g, kind)
        if name not in collection.get_object_names():
            collection.create(name=name)
        collection[name].set_state(state)
        collection[name].display()
        g.picture.save_picture(file_name=task_directory+"\\"+name+".png")
    except Exception as error:
        print({"graphics": name, "error": str(error)})
print(fluentagi.record_animation(
    directory=task_directory, field="velocity-magnitude", frames=24,
    steps_per_frame=2, max_iter_per_step=60, surfaces=[section["surface"]]))
evidence("final_80")
s.file.write_case_data(file_name=task_directory+r"\car-80-final-8f0a12.cas.h5")
for name, obj in [("forces", s.solution.monitor.report_plots[monitor_name]),
                  ("residuals", s.solution.monitor.residual)]:
    try:
        obj.plot()
        g.picture.save_picture(file_name=task_directory+"\\"+name+"-80-8f0a12.png")
    except Exception as error:
        print({"plot": name, "error": str(error)})
print({"initial_time": initial_time, "actual_final_time": report_number(clock_report),
       "actual_cells": int(round(report_number(cell_report))),
       "target_speed": 80.0, "convergence_verified": False})
```
</fluent_code>

<final_answer>本次不再通过compute读取内置flow-time，而使用可计算的Time表达式报告。现有解将被保留，随后继续局部加密、升速和后处理；实际网格数量及收敛状态以执行反馈为准。</final_answer>

round-04
【发送给 ChatGPT】
You are FluentAGI for Ansys Fluent. Write one short Chinese plan, then a single COMPLETE Python script in this exact Markdown layout:

<fluent_code>

```python
# Use the existing connected PyFluent variable solver.
# Continue with the complete executable script here.
```

</fluent_code>

Put a newline before and after each tag and each fence. Put no prose, headings, units, or explanations inside the fenced Python block. Do not place ```python and Python statements on the same line. Keep the script under 280 lines. Use loops instead of repeating similar statements. Never truncate a string, parenthesis, or code fence; always close </fluent_code>.
Use the existing solver session and compatible PyFluent APIs. A completed conversation turn does not close the session. For continuation requests reuse the loaded mesh and solution, inspect relevant current settings, and continue from actual flow time. Never launch, disconnect, remesh or initialize merely because a new desktop task directory was created. Do not launch or close Fluent. Do not use Abaqus APIs. Use solver.settings APIs and the provided fluentagi adapters. Direct solver.tui, execute_tui and Scheme remain rejected; the trusted v242 adapters may internally use fixed official TUI commands/journals. For mesh display use solver.settings.results.graphics.mesh and graphics.views APIs. Do not return JSON tool plans. Do not invent mesh files or API names. Only operate on the requested case and task directory. Preserve existing results. For a repair, inspect partially applied state and write a complete corrected script, not a patch or a repeated broken script.
The web AI cannot read or modify host application source. Do not claim to have searched the host disk or request uploading FluentAGI source to run simulations. Use the verified runtime adapters. Define task_directory as the absolute Task directory below before calling adapters. For VOF surface tension use fluentagi.configure_surface_tension(directory=task_directory,coefficient=0.072,phase_a="phase-1",phase_b="phase-2"); coefficient is N/m, replace the example with the requested value. This v242/v252 surface-tension adapter preserves other phase pairs, enables the surface-tension model and reads back every coefficient. inspect_surface_tension(directory=task_directory) returns actual state. Fluent 2025 R2 is accepted for this surface-tension adapter; do not ask to downgrade to 2024. Other Lee/DPM adapters retain their own version restrictions. Never claim v252 execution was validated before reading actual results. Do not ask the user to edit the plugin or click GUI dialogs for this supported operation.
Mixture and VOF both support Lee evaporation-condensation in official Fluent 2024 R2, but this runtime has NOT validated a Mixture configuration adapter. Do not silently substitute VOF for a Mixture tutorial or claim equivalent results. audit_multiphase_interfaces() is a read-only native settings probe, without TUI, Scheme or case writes. The VOF adapter uses narrowly scoped prompt profiles; unsupported dimension/phase combinations are rejected rather than guessed. For three-phase boiling setup in Fluent 2024 R2, use fluentagi.configure_three_phase_materials(directory=task_directory, materials=["air", "water-liquid", "water-vapor"]) to create actual phase-1/2/3 domains and verify material assignments. For an existing three-phase model, fluentagi.assign_multiphase_materials(materials=[...], directory=task_directory) assigns materials without changing the phase count. Then call fluentagi.configure_evaporation_condensation(directory=task_directory, saturation_temperature=T_sat_K, evaporation_frequency=from_frequency_per_second, condensation_frequency=to_frequency_per_second, from_phase="phase-2", to_phase="phase-3"). Pass the user's specified temperature/frequencies; preserve their geometry, heating and calculation parameters. This verified v242 VOF Lee adapter enables energy, uses a fixed official TUI journal and verifies the phase pair, constant saturation temperature, both frequencies and Lee model by an independent case snapshot. It does not initialize or advance the simulation. Do not say this feature is unavailable because direct TUI is forbidden; use the adapter. fluentagi.inspect_phase_change(directory=task_directory) returns actual phases and mass transfer settings, refreshing a unique case snapshot. Do not replace existing unrelated mechanisms. This adapter supports one Lee mass transfer pair, not Eulerian wall boiling, semi-mechanistic wall boiling, pressure-dependent saturation tables or UDFs. Do not claim a complete validated boiling simulation from successful setup alone. initialize_air is a TWO-PHASE adapter; do not use it for three-phase cases. Use verified settings initialization and phase-aware patching only for requested new calculations.
The runtime provides fluentagi.assign_phase_materials(primary="air", secondary="water-liquid", directory=task_directory), fluentagi.inspect_phases(), and fluentagi.export_results(directory=task_directory, fields=["pressure", "velocity-magnitude"]). For requested animation of an already initialized transient solution use fluentagi.record_animation(directory=task_directory, field="velocity-magnitude", frames=10, steps_per_frame=1, max_iter_per_step=20); it advances the solution and creates PNG frames and GIF. Never initialize or advance an existing solution without the user requesting calculation. These are executable verified adapters, not prose instructions. Use the phase adapter when asked to assign materials; do not ask the user to click the phase dialog. Print returned results. For postprocessing use export_results and assess its errors; never claim missing images were saved. Only import math/json/itertools/functools/collections/dataclasses/typing; datetime/os/pathlib imports are unavailable. Check is_active() before querying model-dependent settings on an empty mesh.
Mesh conversion preserves independent inlet-name/outlet-name/wall-name boundary zones (for example wall-wing and wall-ground). Read the configured mesh_cell_limit from runtime capabilities; never assume a 200000-cell limit or silently coarsen user geometry. Independent wall zones do not automatically set moving-wall physics or force reports; inspect real settings before configuring those. For NEW planar geometry and meshing use mesh = fluentagi.build_planar(directory=task_directory, points=[[x,y],...], inlet_edges=[0], outlet_edges=[2], size=0.001). Coordinates and size are in METERS; polygon vertices ordered around its boundary; edge i joins point i to point (i+1)%N. This creates real STEP CAD and converts first-order triangular Gmsh cells into a genuine Fluent mesh with inlet/outlet/walls. Then solver.settings.file.read_mesh(file_name=mesh["mesh_file"]), solver.settings.mesh.check(), configure physics, initialize, iterate, and export_results. Save current case/data before replacing loaded geometry. Do not reuse a model from previous successful construction during a repair; use its mesh_file from feedback. For curved planar CAD, use fluentagi.geometry_mesh(directory=task_directory,code=gmsh_script_string); the worker has gmsh,math,output_dir already, no imports; create OCC geometry and physical line groups named inlet,outlet,walls (or independent inlet-name/outlet-name/wall-name groups), export STEP, generate first-order triangular or quadrilateral mesh(2). Returned mesh_file is Fluent format; gmsh-native.msh is NOT Fluent format. 3D modeling and volume mesh conversion are now implemented. fluentagi.build_volume(directory=task_directory, shape="box",length=0.1,width=0.02,height=0.02,size=0.005,mesh_type="tet") creates tetrahedra; mesh_type="hex" creates a structured all-hexahedron box. shape="cylinder",radius=0.01 supports tetrahedra with axis along +X. Inlet is X=0 and outlet X=length. fluentagi.build_extruded(directory=task_directory,points=[[x,y],...],inlet_edges=[0],outlet_edges=[2],depth=0.01,size=0.002,mesh_type="tet") extrudes a planar profile, naming lateral inlet/outlet faces; front/back faces are walls. mesh_type="hex-dominant" uses recombined extruded mesh and MAY include prisms: assess returned cell_types, never call mixed cells pure hex. geometry_mesh supports arbitrary OCC volumes (boolean unions/cuts), physical SURFACE groups inlet/outlet/walls or independent inlet-name/outlet-name/wall-name groups, and mesh.generate(3), first-order tet/hex/prism/pyramid. Imported STEP is not currently supported by the restricted worker. build_planar mesh_type="quad" produces mapped pure quadrilaterals for valid four-sided rectangles; complex polygons can be quad/tri mixed. 2D uses triangles/quads, 3D uses tet/hex. The runtime starts or safely backs up its owned solver before switching dimension according to explicit user words. Do not request user-supplied 3D mesh for supported geometry. Save backups before replacing any loaded mesh. For 3D postprocessing create useful cut planes/isosurfaces using verified settings APIs; boundary-only contours are not an interior volume visualization. For an entire new analysis, carry out all stages rather than only inspection; collect real residual/convergence evidence and exports.
For water-air transient filling, call fluentagi.configure_water_air(directory=task_directory,velocity=0.05,time_step=0.001,gravity_y=-9.81,inlet="inlet",outlet="outlet"). This backs up and sets laminar VOF, materials, phase-aware inlet/outlet, gravity and timestep; does NOT initialize or erase existing solution. For requested NEW filling use fluentagi.initialize_air(directory=task_directory), which saves a backup, hybrid initializes and patches phase-2 volume fraction to zero across all fluid cell zones. Never initialize_air on existing results without an explicit restart request. Fluent v242 contour fields are phase-2-vof (water, only after confirming phase-2=water-liquid) and phase-1-vof (air); do not confuse them. Query real contour field.allowed_values for VOF fields. For 3D interior contours/animation use section=fluentagi.create_section(axis="z",position=depth/2), then export_results(...,surfaces=[section["surface"]]) or record_animation(...,surfaces=[section["surface"]]). A plane section is not full volumetric rendering; report it accurately. Frames are 2..60. Each frame after the first advances steps_per_frame real time steps; final time is initial time+(frames-1)*steps_per_frame*time_step. Do not claim the pipe fills if the simulated duration is too short. Normalize typos like "二维四面体" by explaining 2D triangular equivalent; if depth/3D was explicitly requested, use volume tools.
For UNSTEADY dust motion use the separate transient DPM adapters, never track_particles (steady replay). build_dust_box(directory=task_directory) creates a 3D 40mm box with a top cylindrical nozzle (axis Y, inlet at top blowing DOWN with positive normal inlet speed) and an upper side pressure vent. Save existing case before reading its mesh_file. geometry parameters are SI and can be passed as named arguments. Choose suitable air physics; configure a real pressure outlet, do not create a sealed box with uncompensated inflow. configure_transient_dust(directory=task_directory,region_min=[.012,.003,.012],region_max=[.028,.004,.028],counts=[2,2,2],release_time=.02,release_duration=.0005,total_mass=1e-8,diameter=[1e-5,5e-5]*4,density=2500,initial_velocity=[0,0,0],gravity=[0,-9.81,0],time_step=.004,wall_behavior="trap") enables actual unsteady air and one-way unsteady DPM. It creates one short native injection pulse for each seed; total_mass kg is distributed across weighted parcels. It accepts explicit points instead of a region. The entire release pulse must fit inside one physical dt. Does NOT initialize. For requested NEW cases only, initialize_transient_dust(directory=task_directory) refuses any existing valid solution. Then advance_transient_dust(directory=task_directory,until_time=.2,max_iter_per_step=200,residual_tolerance=.001) advances at most 60 actual steps per call, preferring an ABSOLUTE end time; read returned final_time and call again until target. Already reached until_time does not advance again. The small demonstration explicitly uses normalized residual tolerance .001; honor stricter user requirements and never silently relax them. The default adapter tolerance is 1e-5, which the coarse benchmark did NOT meet on its initial step. Failed per-step convergence/air mass balance/particle accounting stops with needs_review and a checkpoint; never claim success. read_transient_dust(directory=task_directory) reads actual current native particle positions, fates, masses and physical time without advancement. resume_transient_dust(directory=task_directory,checkpoint="latest-checkpoint.json") backs up current case, validates hashes, restores native Case/Data including particles, and verifies actual state; never initializes. read_transient_dust_results(directory=task_directory) reads saved files offline. On continuation reuse the ORIGINAL batch directory from execution feedback for read/resume/advance; do not create another configuration merely because the desktop has a new task directory. Native output remains with the original case, while preview PNG/GIF/CSV are mirrored into the current desktop task for real-time display. Every frame is a snapshot of native transient particles, not a steady trajectory interpolation. Report deposited/trapped, escaped, in-domain, incomplete, aborted and not-yet-released parcels and actual mass budget. Caption all runs "预先释放悬浮颗粒，非积尘再悬浮". Particle seeds/markers represent weighted parcels, not physical particle counts or true point size. Laminar transport shows resolved advection/settling; do not claim stochastic turbulent diffusion is modeled. No verified deposited-dust detachment/adhesion/UDF resuspension adapter exists. Never add an upward initial velocity and call it resuspension. Preserve steady DPM as a distinct path.
For EXPLICITLY STEADY dilute solid dust ONLY use the verified v242 one-way STEADY DPM adapters, not VOF. First inspect_dpm(). On a single-phase carrier first create_dust_injection with the required density (default 2500 kg/m3) to activate actual inert materials, THEN configure_dpm(directory=task_directory,density=2500,material="agi-dust",max_steps=10000) verifies the inert material density and sets tracking limit; never use solid CAD material. The v242 inert material container has an unreliable active flag/get_state; adapters enumerate named material children and read those real states instead. Never query the entire inert material container. create_dust_injection(directory=task_directory,name="dust",start=[0.00001,0.003,0.01],end=[0.00001,0.017,0.01],streams=12,diameter=0.00005,mass_flow=0.0000001,velocity=[0.5,0,0],material="agi-dust") creates a real group injection. Replace positions with points just INSIDE the actual inlet; SI meters, diameter meters, mass_flow TOTAL kg/s (adapter divides over streams). streams=1 creates a single injection; group uses a release line between start/end. Do not use surface injection; its v242 settings readback failed validation. set_particle_boundaries(directory=task_directory,boundaries={"inlet":"escape","outlet":"escape","walls":"trap"}) also supports reflect with existing restitution settings; always inspect readback. Never silently change an existing multiphase or coupled DPM model. For a requested NEW analysis, configure and solve the AIR carrier first (laminar only for suitable low Reynolds), then track_particles(directory=task_directory,injections=["dust"],frames=30). This tracks native DPM trajectories, exports tracks.csv/json, native summary text, statistics and PNG/GIF. It does NOT initialize or advance the carrier. Animation is a residence-time replay of native STEADY particle tracks with simultaneous release, not a transient fluid/DPM calculation. Never use air pathlines or a continuous-phase contour as dust motion. Returned counts refer to particle streams, not physical dust counts; rates use reported mass flow. Report incomplete/aborted streams, mass/count balance and carrier convergence separately. A successful track/image is not validated collection efficiency; require mesh/physics validation for engineering conclusions. Preserve results; use a new injection name if its parameters change.
End with <final_answer>Chinese explanation of the intended changes, without claiming execution success</final_answer>.
If essential input or an incompatible session prevents a valid script, return only <question>the specific missing input in Chinese</question>.
After the desktop returns execution results, either provide a corrected script or return only <final_answer>a Chinese assessment supported by those results</final_answer>. Do not execute the same successful script again. Successful execution alone does not prove convergence.

Previous conversation and execution context (reference only; do not repeat successful scripts):
[{"user": "啊，请你根据图片和我提供的资料，完成整车的一个前翼，后翼型整车车身空气套件，轮胎扩散器的一个建模，并且完成网格划分，网格采用局部加密，网格的控制量控制在20万左右。整个车后面流场网格也适当的做一个加密。更重要的是整车的旁边前后翼和你套件旁边的网格加密对进度有影响，反正网格控制在20~400000后面包括求解。直接生成这个游戏或者动画计算时间，计算量不要太大。"}, {"assistant": "已读到前翼弦长300 mm、翼展1600 mm，后翼弦长350 mm、翼展1000 mm，均为倒置NACA 4412、安装角0°。还缺来流速度及整车尺寸：请提供车速、车长、轴距、轮距和轮胎直径；没有尺寸可直接回复“车速××m/s，整车按图片比例估算”。网格将按20万～40万控制，重点加密前后翼、车身附近、轮胎、扩散器及尾流。", "run_directory": "C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-183115-16d691"}, {"user": "这些尺寸没有给你根据图片，然后你这个能力然后完成这个建模啊，自己全部设计，全部定义，反正最终就是要给我这个曲线流线动画云图比较装逼。"}, {"tool": "run_code", "result": "{\"status\": \"ok\", \"stdout\": \"{'walls': ['wall-rearwing', 'wall-wheel-fl', 'wall-wheel-fr', 'wall-wheel-rl', 'wall-wheel-rr', 'wall-body', 'wall-frontwing', 'wall-ground'], 'viscous': {'model': 'k-epsilon', 'k_epsilon_model': 'realizable', 'near_wall_treatment': {'wall_treatment': 'standard-wall-fn'}, 'options': {'curvature_correction': False, 'production_limiter': {'enabled': False}}, 'turbulence_expert': {'rke_cmu_rotation_term': False, 'turb_non_newtonian': False}, 'user_defined': {'turb_\n…中间日志已省略，完整日志保存在任务目录…\nel 1 ...\\nGrouping cells for Laplace smoothing ...\\n      271220 cells,     1 zone  ...\\n      570904 faces,    12 zones ...\\n       58102 nodes,     1 zone  ...\\n  Done.\\nDone.\\n\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-183309-7900dd\\\\car-transient-1e009e.dat.h5\\\" in NODE0 mode and compression level 1 ...\\n  Writing results.\\nDone.\\n\\n     car-drag-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n                 car-drag-1e009e           177.01976\\n\\ncar-downforce-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n            car-downforce-1e009e           97.216619\\n\\nfrontwing-downforce-1e009e\\n -------------------\\n                           Force                 [N]\\n-------------------------------- -------------------\\n                  wall-frontwing         0.045705896\\n\\nrearwing-downforce-1e009e\\n -------------------\\n{'final_forces': [{'car-drag-1e009e': [177.0197613431466, 0]}, {'car-downforce-1e009e': [97.2166193715222, 0]}, {'frontwing-downforce-1e009e': [0.04570589572850679, 0]}, {'rearwing-downforce-1e009e': [27.84720127893916, 0]}], 'requested_animation_time_increment': 0.23, 'mass_balance_and_mesh_independence': 'not_yet_verified'}\\n\", \"stderr\": \"\", \"return_value\": null, \"message\": null, \"error_code\": null, \"warnings\": []}"}, {"assistant": "已完成整车建模、271,220单元网格及求解，实际推进至 **0.23秒、46个时间步**，动画适配器返回24帧速度截面动画生成成功，Case/Data已保存。\n\n当前瞬时气动力报告：\n\n项目\t数值\n整车阻力\t177.02 N\n整车下压力\t97.22 N\n后翼分区下压力\t27.85 N\n前翼分区下压力\t0.046 N\n\n计算尚未确认收敛。 最后一步连续性残差约5.1×10⁻⁴，k、ε残差约0.26、0.47，并出现湍流黏度限幅，因此上述力值只能作为初步结果。\n\n前翼报告值很小，需优先核对分区是否包含完整翼面。云图、流线及曲线图片的导出详情未显示在当前截断日志中，暂不能逐项确认。", "run_directory": "C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-183309-7900dd"}, {"user": "网格智能加密两倍，要求整车空气套件细节加密，尾部区域加密些，计算给到80m每秒"}]
Runtime capabilities:
{"protocol": "FluentAGI-capabilities-v1", "session": {"connected": true, "mode": "launch"}, "geometry": {"worker": "gmsh isolated process", "imports": [], "builtins": ["range", "len", "int", "float", "str", "list", "tuple", "enumerate", "zip", "min", "max", "abs", "round", "RuntimeError", "ValueError", "print", "set", "sum", "sorted", "bool", "all", "any"], "physical_groups": ["inlet[-name]", "outlet[-name]", "wall[-name]", "walls"], "independent_boundary_zones": true, "mesh_cell_limit": 4000000, "geometry_timeout_seconds": 3600, "cell_types": ["triangle", "quadrilateral", "tetrahedron", "hexahedron", "prism", "pyramid"]}, "script": {"imports": ["math", "json", "itertools", "functools", "collections", "dataclasses", "typing"], "adapters": ["build_dust_box", "configure_transient_dust", "initialize_transient_dust", "read_transient_dust", "advance_transient_dust", "resume_transient_dust", "read_transient_dust_results", "inspect_dpm", "configure_dpm", "create_dust_injection", "set_particle_boundaries", "track_particles", "build_planar", "build_volume", "build_extruded", "geometry_mesh", "configure_water_air", "initialize_air", "record_animation", "export_results", "assign_phase_materials", "assign_multiphase_materials", "configure_three_phase_materials", "audit_multiphase_interfaces", "configure_evaporation_condensation", "inspect_phase_change", "configure_surface_tension", "inspect_surface_tension"]}, "generated_at": 1792322860.105292, "phase_change": {"version": "v242", "model": "Lee", "adapter_multiphase_models": ["vof"], "mixture_officially_available": true, "mixture_adapter_validated": false, "two_phase_2d_correction_runtime_validation": "pending", "automatic_model_switch": false, "phases": [2, 3], "saturation": "constant K", "frequencies": "positive finite 1/s", "mass_transfer_pairs": 1, "internal_official_tui": true, "generated_direct_tui": false, "readback": "unique case snapshot", "initializes": false}, "surface_tension": {"version": "v242/v252", "runtime_validation": {"v242": "previous native validation", "v252": "official command and generated SDK checked; customer runtime readback required"}, "model": "VOF", "coefficient": "nonnegative finite N/m", "preserves_other_pairs": true, "readback": "unique case snapshot", "initializes": false}, "transient_dpm": {"version": "v242", "model": "unsteady single-phase carrier + transient one-way inert DPM", "release": "one short native pulse per explicit point / region grid, max 64 weighted parcels", "chunk_steps": 60, "positions": "native current positions at actual physical time; never steady replay", "checkpoint": "unique Case/Data plus SHA256 and particle-state readback", "acceptance": "each-step residuals, native air mass flux, particle mass/count fate closure", "default_residual_tolerance": 1e-05, "benchmark_residual_tolerance": 0.001, "strict_1e_5_initial_step_validated": false, "resuspension_validated": false, "stochastic_turbulent_dispersion_validated": false}, "dpm": {"version": "v242", "carrier": "single phase, one-way, steady inert dust", "injection_types": ["single", "group"], "surface_injection_validated": false, "boundary_types": ["reflect", "trap", "escape"], "trajectory_source": "native GetParticleTracksField RPC", "animation": "steady DPM trajectory residence-time replay, no carrier advance", "transient_dpm_validated": false, "two_way_validated": false, "readback": true, "active": true}, "fluent_version": "Ansys Fluent 2024 R2", "model_active": true}
Current session:
{"connected": true, "dimension": 3, "mode": "solver", "owned": true, "continuation": true}
Task directory:
C:\Users\13977\AppData\Local\FluentAGI\data\runs\20261018-192740-fe6b6c
Current request:
网格智能加密两倍，要求整车空气套件细节加密，尾部区域加密些，计算给到80m每秒
Desktop execution feedback:
[{"failed_action": {"tool": "run_code", "arguments": {"code": "# Use the existing connected PyFluent variable solver.\nimport math\n\ntask_directory = r\"C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-192740-fe6b6c\"\ns = solver.settings\nbaseline_cells = 271220\ntarget_cells = 542440\nruntime_cell_limit = 4000000\nif not s.setup.is_active() or not s.setup.models.is_active():\n    raise RuntimeError(\"Expected the existing initialized car case\")\ns.file.write_case_data(file_name=task_directory+r\"\\before-b49382.cas.h5\")\nbc = s.setup.boundary_conditions\nrd = s.solution.report_definitions\nrun = s.solution.run_calculation\nprint({\"time_mode\": s.setup.general.solver.time.get_state(),\n       \"calculation_state\": run.get_state(),\n       \"target_cells\": target_cells, \"runtime_cell_limit\": runtime_cell_limit})\n\ndef choose(node, candidates):\n    norm = lambda x: str(x).lower().replace(\" \", \"\").replace(\"-\", \"\").replace(\"_\", \"\")\n    allowed = node.allowed_values()\n    for candidate in candidates:\n        for value in allowed:\n            if norm(value) == norm(candidate):\n                node.set_state(value)\n                return value\n    raise RuntimeError(str({\"requested\": candidates, \"allowed\": allowed}))\n\ndef select_type(node, predicate):\n    allowed = node.allowed_values()\n    for value in allowed:\n        if predicate(str(value).lower()):\n            node.set_state(value)\n            return value\n    raise RuntimeError(str({\"unsupported_type\": allowed}))\n\ndef constant(node, value):\n    state = node.get_state()\n    if isinstance(state, dict) and \"value\" in node.child_names:\n        if \"option\" in node.child_names:\n            choose(node.option, [\"value\", \"constant\"])\n        node.value = value\n    else:\n        node.set_state(value)\n\ndef vector(node, values):\n    state = node.get_state()\n    if isinstance(state, list) and state and isinstance(state[0], dict):\n        for i, value in enumerate(values):\n            constant(node[i], value)\n    else:\n        node.set_state(values)\n\ndef report_number(name):\n    result = rd.compute(report_defs=[name])\n    blocks = result if isinstance(result, list) else [result]\n    for block in blocks:\n        if isinstance(block, dict) and name in block:\n            value = block[name]\n            if isinstance(value, (list, tuple)):\n                value = value[0]\n            return float(value)\n    raise RuntimeError(str({\"unrecognized_report\": name, \"result\": result}))\n\ntime_names = rd.time.get_object_names()\nprint({\"existing_time_reports\": time_names})\nclock_report = None\nfor name in time_names:\n    if str(name).lower().replace(\"_\", \"-\") == \"flow-time\":\n        clock_report = name\n        break\nif clock_report is None:\n    clock_report = \"physical_time_b49382\"\n    expressions = rd.single_valued_expression\n    if clock_report not in expressions.get_object_names():\n        expressions.create(name=clock_report)\n    expressions[clock_report].definition = \"Time\"\n\ncell_report = \"cell_count_ad9ac9\"\nif cell_report not in rd.mesh.get_object_names():\n    rd.mesh.create(name=cell_report)\nselect_type(rd.mesh[cell_report].report_type,\n            lambda x: \"cell\" in x and \"count\" in x)\nrd.mesh[cell_report].cell_zones = s.setup.cell_zone_conditions.fluid.get_object_names()\ninitial_time = report_number(clock_report)\ninitial_cells = int(round(report_number(cell_report)))\nprint({\"actual_initial_time\": initial_time, \"actual_initial_cells\": initial_cells})\nif initial_cells > int(target_cells * 1.15):\n    raise RuntimeError(\"Existing mesh already exceeds this refinement target\")\nif \"unsteady\" not in str(s.setup.general.solver.time.get_state()).lower():\n    raise RuntimeError(\"Expected the existing transient solution\")\n\nrequired_walls = [\n    \"wall-body\", \"wall-frontwing\", \"wall-rearwing\", \"wall-ground\",\n    \"wall-wheel-fl\", \"wall-wheel-fr\", \"wall-wheel-rl\", \"wall-wheel-rr\"]\nactual_walls = bc.wall.get_object_names()\nif any(name not in actual_walls for name in required_walls):\n    raise RuntimeError(str({\"required\": required_walls, \"actual\": actual_walls}))\nadapt = s.mesh.adapt\ncontrols = adapt.set\nprint({\"adaptation_controls_before\": controls.get_state()})\nif \"maximum_cell_count\" not in controls.child_names:\n    raise RuntimeError(\"Native adaption lacks the required cell-count control\")\nselect_type(controls.adaption_method,\n            lambda x: (\"puma\" in x or \"poly\" in x) and \"2.5\" not in x)\ncontrols.maximum_refinement_level = 1\ncontrols.minimum_edge_length = 0.002\ncontrols.maximum_cell_count = target_cells\nregisters = s.solution.cell_registers\nregions = [\n    (\"frontwing\", [-0.04,-0.85,0.035], [0.36,0.85,0.29], 335000),\n    (\"rearwing\", [2.81,-0.56,0.84], [3.25,0.56,1.10], 395000),\n    (\"diffuser\", [2.12,-0.59,0.045], [3.28,0.59,0.33], 450000),\n    (\"sidepods\", [0.98,-0.59,0.14], [2.80,0.59,0.70], 490000),\n    (\"wake\", [3.12,-1.20,0.03], [7.50,1.20,1.45], target_cells),\n    (\"car_nearfield\", [-0.08,-1.0,0.01], [3.30,1.0,1.14], target_cells)]\nfor label, low, high, budget in regions:\n    current = int(round(report_number(cell_report)))\n    if current >= int(0.98 * target_cells):\n        break\n    if current >= budget:\n        continue\n    controls.maximum_cell_count = min(budget, target_cells, runtime_cell_limit)\n    name = \"ref_\" + label + \"_ad9ac9\"\n    if name not in registers.get_object_names():\n        registers.create(name=name)\n    register = registers[name]\n    choose(register.type.option, [\"hexahedron\"])\n    region = register.type.hexahedron\n    region.min_point = low\n    region.max_point = high\n    region.inside = True\n    print({\"region\": label, \"state\": register.get_state(),\n           \"cell_budget\": controls.maximum_cell_count.get_state()})\n    if \"refine\" not in register.command_names:\n        raise RuntimeError(\"Native cell-register refinement unavailable\")\n    register.refine()\n    current = int(round(report_number(cell_report)))\n    print({\"region_finished\": label, \"actual_cells\": current})\n    if current > min(runtime_cell_limit, int(target_cells * 1.15)):\n        s.file.write_case_data(file_name=task_directory+r\"\\refinement-review-b49382.cas.h5\")\n        raise RuntimeError(\"Refinement exceeded the intended cell budget\")\ncontrols.maximum_cell_count = target_cells\ns.mesh.check()\nfinal_cells = int(round(report_number(cell_report)))\nprint({\"actual_refined_cells\": final_cells,\n       \"ratio_to_original\": final_cells / baseline_cells,\n       \"actual_time\": report_number(clock_report)})\nif final_cells <= initial_cells and initial_cells < int(target_cells * 0.9):\n    raise RuntimeError(\"Requested refinement did not increase the cell count\")\ns.file.write_case_data(file_name=task_directory+r\"\\car-refined-b49382.cas.h5\")\n\nrun.transient_controls.time_step_size = 0.001\nground = bc.wall[\"wall-ground\"].momentum\nwheels = [\"wall-wheel-\" + side for side in [\"fl\", \"fr\", \"rl\", \"rr\"]]\nprint({\"ground_before\": ground.get_state()})\nfor name in wheels:\n    print({\"wheel\": name, \"motion_before\": bc.wall[name].momentum.get_state()})\n\ndef set_speed(value):\n    constant(bc.velocity_inlet[\"inlet\"].momentum.velocity, value)\n    vector(ground.velocity_components, [value, 0.0, 0.0])\n    for name in wheels:\n        constant(bc.wall[name].momentum.rotation_speed, -value / 0.24)\n    print({\"requested_speed\": value,\n           \"inlet_readback\": bc.velocity_inlet[\"inlet\"].momentum.get_state(),\n           \"ground_readback\": ground.get_state()})\n\ncar_walls = [name for name in actual_walls if name != \"wall-ground\"]\nforce_reports = []\nfor name, zones, direction in [\n    (\"drag_80_b49382\", car_walls, [1,0,0]),\n    (\"downforce_80_b49382\", car_walls, [0,0,-1]),\n    (\"front_partition_80_b49382\", [\"wall-frontwing\"], [0,0,-1]),\n    (\"rear_partition_80_b49382\", [\"wall-rearwing\"], [0,0,-1])]:\n    if name not in rd.force.get_object_names():\n        rd.force.create(name=name)\n    rd.force[name].set_state({\"zones\": zones, \"force_vector\": direction})\n    force_reports.append(name)\nmass_reports = []\nfor label, boundaries in [(\"inlet\", [\"inlet\"]), (\"outlet\", [\"outlet\"])]:\n    name = \"mass_\" + label + \"_b49382\"\n    if name not in rd.flux.get_object_names():\n        rd.flux.create(name=name)\n    select_type(rd.flux[name].report_type, lambda x: \"mass\" in x and \"flow\" in x)\n    rd.flux[name].boundaries = boundaries\n    mass_reports.append(name)\nmonitor_name = \"car_80_b49382\"\nall_reports = force_reports + mass_reports + [clock_report]\nfor collection in [s.solution.monitor.report_files, s.solution.monitor.report_plots]:\n    if monitor_name not in collection.get_object_names():\n        collection.create(name=monitor_name)\n    collection[monitor_name].report_defs = all_reports\n    collection[monitor_name].active = True\ns.solution.monitor.report_files[monitor_name].file_name = task_directory+r\"\\car-80-history-b49382.out\"\n\ndef evidence(label):\n    print({\"stage\": label, \"actual_time\": report_number(clock_report),\n           \"forces\": rd.compute(report_defs=force_reports)})\n    mass_in, mass_out = [report_number(name) for name in mass_reports]\n    print({\"mass_in\": mass_in, \"mass_out\": mass_out,\n           \"relative_mass_imbalance\": abs(mass_in + mass_out) / max(abs(mass_in), 1e-12)})\n    try:\n        index, history = solver.monitors.get_monitor_set_data(\"residual\")\n        last = {key: float(value[-1]) for key, value in history.items() if len(value)}\n    except Exception as error:\n        print({\"residual_read_error\": str(error)})\n        return\n    print({\"residuals\": last})\n    if any(not math.isfinite(value) or value > 1e4 for value in last.values()):\n        s.file.write_case_data(file_name=task_directory+r\"\\needs-review-80-b49382.cas.h5\")\n        raise RuntimeError(\"Residual divergence detected\")\n\nvelocity_state = bc.velocity_inlet[\"inlet\"].momentum.velocity.get_state()\ncurrent_speed = float(velocity_state[\"value\"])\nstages = [value for value in [40.0, 60.0, 80.0] if value > current_speed]\nif current_speed > 80.0:\n    stages = [80.0]\nfor value in stages:\n    set_speed(value)\n    run.dual_time_iterate(time_step_count=3, max_iter_per_step=60)\n    evidence(\"speed_\" + str(value))\nset_speed(80.0)\nfor batch in range(3):\n    run.dual_time_iterate(time_step_count=10, max_iter_per_step=60)\n    evidence(\"80_settling_\" + str(batch + 1))\ns.file.write_case_data(file_name=task_directory+r\"\\car-80-before-animation-b49382.cas.h5\")\n\ng = s.results.graphics\ntry:\n    name = \"refined_car_b49382\"\n    if name not in g.mesh.get_object_names():\n        g.mesh.create(name=name)\n    g.mesh[name].surfaces_list = car_walls\n    g.mesh[name].display()\n    g.picture.save_picture(file_name=task_directory+r\"\\refined-car-b49382.png\")\nexcept Exception as error:\n    print({\"mesh_graphics_error\": str(error)})\nsection = fluentagi.create_section(axis=\"y\", position=0.0)\nprint(section)\nresult = fluentagi.export_results(\n    directory=task_directory, fields=[\"pressure\", \"velocity-magnitude\"],\n    surfaces=[section[\"surface\"]])\nprint(result)\nif isinstance(result, dict):\n    for key in [\"error\", \"errors\", \"warnings\"]:\n        if result.get(key):\n            print({\"export_review\": key, \"details\": result[key]})\nfor name, kind, state in [\n    (\"car_pressure_80_b49382\", \"contour\",\n     {\"field\":\"pressure\", \"surfaces_list\":car_walls, \"filled\":True}),\n    (\"car_streamlines_80_b49382\", \"pathline\",\n     {\"field\":\"velocity-magnitude\", \"release_from_surfaces\":[\"inlet\"], \"step\":3000, \"skip\":20})]:\n    try:\n        collection = getattr(g, kind)\n        if name not in collection.get_object_names():\n            collection.create(name=name)\n        collection[name].set_state(state)\n        collection[name].display()\n        g.picture.save_picture(file_name=task_directory+\"\\\\\"+name+\".png\")\n    except Exception as error:\n        print({\"graphics\": name, \"error\": str(error)})\nprint(fluentagi.record_animation(\n    directory=task_directory, field=\"velocity-magnitude\", frames=24,\n    steps_per_frame=2, max_iter_per_step=60, surfaces=[section[\"surface\"]]))\nevidence(\"final_80\")\ns.file.write_case_data(file_name=task_directory+r\"\\car-80-final-b49382.cas.h5\")\nfor name, obj in [(\"forces\", s.solution.monitor.report_plots[monitor_name]),\n                  (\"residuals\", s.solution.monitor.residual)]:\n    try:\n        obj.plot()\n        g.picture.save_picture(file_name=task_directory+\"\\\\\"+name+\"-80-b49382.png\")\n    except Exception as error:\n        print({\"plot\": name, \"error\": str(error)})\nprint({\"initial_time\": initial_time, \"actual_final_time\": report_number(clock_report),\n       \"actual_cells\": int(round(report_number(cell_report))),\n       \"target_speed\": 80.0, \"convergence_verified\": False})"}}, "error": "{\"status\": \"error\", \"stdout\": \"\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-192740-fe6b6c\\\\before-b49382.cas.h5\\\" in NODE0 mode and compression level 1 ...\\nGrouping cells for Laplace smoothing ...\\n      271220 cells,     1 zone  ...\\n      570904 faces,    12 zones ...\\n       58102 nodes,     1 zone  ...\\n  Done.\\nDone.\\n\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-192740-fe6b6c\\\\before-b49382.dat.h5\\\" in NODE0 mode and compression level 1 ...\\n  Writing results.\\nDone.\\n{'time_mode': 'unsteady-2nd-order', 'calculation_state': {'reporting_interval': 1, 'profile_update_interval': 1, 'transient_controls': {'type': 'Fixed', 'method': 'User-Specified', 'time_step_count': 2, 'time_step_size': 0.005, 'max_iter_per_time_step': 20, 'solution_status': False, 'extrapolate_variables': False, 'predict_next': False, 'fixed_periodic': {'enabled': False}, 'multiphase_specific_time_constraints': {'physics_based_constraint': False}, 'solid_time_step_size': {'enable_solid_time_step': False}}, 'data_sampling': {'enabled': False}}, 'target_cells': 542440, 'runtime_cell_limit': 4000000}\\n{'existing_time_reports': ['flow-time', 'iters-per-timestep', 'delta-time']}\\n\\nError: Values contain disallowed entries\\nError Object: (flow-time)\\n\\nError: Values contain disallowed entries\\nError Object: (flow-time)\\n\", \"stderr\": \"\\nTraceback (most recent call last):\\n  File \\\"ansys\\\\fluent\\\\mcp\\\\solve\\\\backends\\\\pyfluent.py\\\", line 2793, in _do\\n    exec(  # noqa: S102  # nosec B102\\n  File \\\"<mcp>\\\", line 85, in <module>\\n  File \\\"<mcp>\\\", line 55, in report_number\\n  File \\\"ansys\\\\fluent\\\\core\\\\solver\\\\flobject.py\\\", line 1896, in __call__\\n    return self.execute_command(**kwds)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"ansys\\\\fluent\\\\core\\\\solver\\\\flobject.py\\\", line 1836, in execute_command\\n    ret = self._execute_command(*args, **scmKwds)\\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"ansys\\\\fluent\\\\core\\\\solver\\\\flobject.py\\\", line 1807, in _execute_command\\n    ret = self.flproxy.execute_cmd(self._parent.path, self.obj_name, **kwds)\\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"ansys\\\\fluent\\\\core\\\\services\\\\settings.py\\\", line 146, in _fn\\n    return fn(self, *args, **kwds)\\n           ^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"ansys\\\\fluent\\\\core\\\\services\\\\settings.py\\\", line 361, in execute_cmd\\n    response = self._service_impl.execute_cmd(request)\\n               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nRuntimeError: Values contain disallowed entries\\nError Object: (flow-time)\\n\", \"return_value\": null, \"message\": \"Values contain disallowed entries\\nError Object: (flow-time)\", \"error_code\": \"execution_error\", \"warnings\": []}", "note": "可能有部分操作已生效，请先检查实时状态，修复后继续。"}, {"failed_action": {"tool": "run_code", "arguments": {"code": "# Use the existing connected PyFluent variable solver.\nimport math\n\ntask_directory = r\"C:\\Users\\13977\\AppData\\Local\\FluentAGI\\data\\runs\\20261018-192740-fe6b6c\"\ns = solver.settings\nbaseline_cells = 271220\ntarget_cells = 542440\nruntime_cell_limit = 4000000\nif not s.setup.is_active() or not s.setup.models.is_active():\n    raise RuntimeError(\"Expected the existing initialized car case\")\ns.file.write_case_data(file_name=task_directory+r\"\\before-8f0a12.cas.h5\")\nbc = s.setup.boundary_conditions\nrd = s.solution.report_definitions\nrun = s.solution.run_calculation\nprint({\"time_mode\": s.setup.general.solver.time.get_state(),\n       \"calculation_state\": run.get_state(),\n       \"target_cells\": target_cells, \"runtime_cell_limit\": runtime_cell_limit})\n\ndef choose(node, candidates):\n    norm = lambda x: str(x).lower().replace(\" \", \"\").replace(\"-\", \"\").replace(\"_\", \"\")\n    allowed = node.allowed_values()\n    for candidate in candidates:\n        for value in allowed:\n            if norm(value) == norm(candidate):\n                node.set_state(value)\n                return value\n    raise RuntimeError(str({\"requested\": candidates, \"allowed\": allowed}))\n\ndef select_type(node, predicate):\n    allowed = node.allowed_values()\n    for value in allowed:\n        if predicate(str(value).lower()):\n            node.set_state(value)\n            return value\n    raise RuntimeError(str({\"unsupported_type\": allowed}))\n\ndef constant(node, value):\n    state = node.get_state()\n    if isinstance(state, dict) and \"value\" in node.child_names:\n        if \"option\" in node.child_names:\n            choose(node.option, [\"value\", \"constant\"])\n        node.value = value\n    else:\n        node.set_state(value)\n\ndef vector(node, values):\n    state = node.get_state()\n    if isinstance(state, list) and state and isinstance(state[0], dict):\n        for i, value in enumerate(values):\n            constant(node[i], value)\n    else:\n        node.set_state(values)\n\ndef report_number(name):\n    result = rd.compute(report_defs=[name])\n    blocks = result if isinstance(result, list) else [result]\n    for block in blocks:\n        if isinstance(block, dict) and name in block:\n            value = block[name]\n            if isinstance(value, (list, tuple)):\n                value = value[0]\n            return float(value)\n    raise RuntimeError(str({\"unrecognized_report\": name, \"result\": result}))\n\nclock_report = \"physical_time_8f0a12\"\nexpressions = rd.single_valued_expression\nif clock_report not in expressions.get_object_names():\n    expressions.create(name=clock_report)\nexpressions[clock_report].definition = \"Time\"\nprint({\"clock_expression\": expressions[clock_report].get_state()})\ninitial_time = report_number(clock_report)\ncell_report = \"cell_count_ad9ac9\"\nif cell_report not in rd.mesh.get_object_names():\n    rd.mesh.create(name=cell_report)\nselect_type(rd.mesh[cell_report].report_type,\n            lambda x: \"cell\" in x and \"count\" in x)\nrd.mesh[cell_report].cell_zones = s.setup.cell_zone_conditions.fluid.get_object_names()\ninitial_cells = int(round(report_number(cell_report)))\nprint({\"actual_initial_time\": initial_time, \"actual_initial_cells\": initial_cells})\nif initial_cells > int(target_cells * 1.15):\n    raise RuntimeError(\"Existing mesh already exceeds this refinement target\")\nif \"unsteady\" not in str(s.setup.general.solver.time.get_state()).lower():\n    raise RuntimeError(\"Expected the existing transient solution\")\n\nrequired_walls = [\n    \"wall-body\", \"wall-frontwing\", \"wall-rearwing\", \"wall-ground\",\n    \"wall-wheel-fl\", \"wall-wheel-fr\", \"wall-wheel-rl\", \"wall-wheel-rr\"]\nactual_walls = bc.wall.get_object_names()\nif any(name not in actual_walls for name in required_walls):\n    raise RuntimeError(str({\"required\": required_walls, \"actual\": actual_walls}))\nadapt = s.mesh.adapt\ncontrols = adapt.set\nprint({\"adaptation_controls_before\": controls.get_state()})\nif \"maximum_cell_count\" not in controls.child_names:\n    raise RuntimeError(\"Native adaption lacks the required cell-count control\")\nselect_type(controls.adaption_method,\n            lambda x: (\"puma\" in x or \"poly\" in x) and \"2.5\" not in x)\ncontrols.maximum_refinement_level = 1\ncontrols.minimum_edge_length = 0.002\ncontrols.maximum_cell_count = target_cells\nregisters = s.solution.cell_registers\nregions = [\n    (\"frontwing\", [-0.04,-0.85,0.035], [0.36,0.85,0.29], 335000),\n    (\"rearwing\", [2.81,-0.56,0.84], [3.25,0.56,1.10], 395000),\n    (\"diffuser\", [2.12,-0.59,0.045], [3.28,0.59,0.33], 450000),\n    (\"sidepods\", [0.98,-0.59,0.14], [2.80,0.59,0.70], 490000),\n    (\"wake\", [3.12,-1.20,0.03], [7.50,1.20,1.45], target_cells),\n    (\"car_nearfield\", [-0.08,-1.0,0.01], [3.30,1.0,1.14], target_cells)]\nfor label, low, high, budget in regions:\n    current = int(round(report_number(cell_report)))\n    if current >= int(0.98 * target_cells):\n        break\n    if current >= budget:\n        continue\n    controls.maximum_cell_count = min(budget, target_cells, runtime_cell_limit)\n    name = \"ref_\" + label + \"_ad9ac9\"\n    if name not in registers.get_object_names():\n        registers.create(name=name)\n    register = registers[name]\n    choose(register.type.option, [\"hexahedron\"])\n    region = register.type.hexahedron\n    region.min_point = low\n    region.max_point = high\n    region.inside = True\n    print({\"region\": label, \"state\": register.get_state(),\n           \"cell_budget\": controls.maximum_cell_count.get_state()})\n    if \"refine\" not in register.command_names:\n        raise RuntimeError(\"Native cell-register refinement unavailable\")\n    register.refine()\n    current = int(round(report_number(cell_report)))\n    print({\"region_finished\": label, \"actual_cells\": current})\n    if current > min(runtime_cell_limit, int(target_cells * 1.15)):\n        s.file.write_case_data(file_name=task_directory+r\"\\refinement-review-8f0a12.cas.h5\")\n        raise RuntimeError(\"Refinement exceeded the intended cell budget\")\ncontrols.maximum_cell_count = target_cells\ns.mesh.check()\nfinal_cells = int(round(report_number(cell_report)))\nprint({\"actual_refined_cells\": final_cells,\n       \"ratio_to_original\": final_cells / baseline_cells,\n       \"actual_time\": report_number(clock_report)})\nif final_cells <= initial_cells and initial_cells < int(target_cells * 0.9):\n    raise RuntimeError(\"Requested refinement did not increase the cell count\")\ns.file.write_case_data(file_name=task_directory+r\"\\car-refined-8f0a12.cas.h5\")\n\nrun.transient_controls.time_step_size = 0.001\nground = bc.wall[\"wall-ground\"].momentum\nwheels = [\"wall-wheel-\" + side for side in [\"fl\", \"fr\", \"rl\", \"rr\"]]\nprint({\"ground_before\": ground.get_state()})\nfor name in wheels:\n    print({\"wheel\": name, \"motion_before\": bc.wall[name].momentum.get_state()})\n\ndef set_speed(value):\n    constant(bc.velocity_inlet[\"inlet\"].momentum.velocity, value)\n    vector(ground.velocity_components, [value, 0.0, 0.0])\n    for name in wheels:\n        constant(bc.wall[name].momentum.rotation_speed, -value / 0.24)\n    print({\"requested_speed\": value,\n           \"inlet_readback\": bc.velocity_inlet[\"inlet\"].momentum.get_state(),\n           \"ground_readback\": ground.get_state()})\n\ncar_walls = [name for name in actual_walls if name != \"wall-ground\"]\nforce_reports = []\nfor name, zones, direction in [\n    (\"drag_80_8f0a12\", car_walls, [1,0,0]),\n    (\"downforce_80_8f0a12\", car_walls, [0,0,-1]),\n    (\"front_partition_80_8f0a12\", [\"wall-frontwing\"], [0,0,-1]),\n    (\"rear_partition_80_8f0a12\", [\"wall-rearwing\"], [0,0,-1])]:\n    if name not in rd.force.get_object_names():\n        rd.force.create(name=name)\n    rd.force[name].set_state({\"zones\": zones, \"force_vector\": direction})\n    force_reports.append(name)\nmass_reports = []\nfor label, boundaries in [(\"inlet\", [\"inlet\"]), (\"outlet\", [\"outlet\"])]:\n    name = \"mass_\" + label + \"_8f0a12\"\n    if name not in rd.flux.get_object_names():\n        rd.flux.create(name=name)\n    select_type(rd.flux[name].report_type, lambda x: \"mass\" in x and \"flow\" in x)\n    rd.flux[name].boundaries = boundaries\n    mass_reports.append(name)\nmonitor_name = \"car_80_8f0a12\"\nall_reports = force_reports + mass_reports + [clock_report]\nfor collection in [s.solution.monitor.report_files, s.solution.monitor.report_plots]:\n    if monitor_name not in collection.get_object_names():\n        collection.create(name=monitor_name)\n    collection[monitor_name].report_defs = all_reports\n    collection[monitor_name].active = True\ns.solution.monitor.report_files[monitor_name].file_name = task_directory+r\"\\car-80-history-8f0a12.out\"\n\ndef evidence(label):\n    print({\"stage\": label, \"actual_time\": report_number(clock_report),\n           \"forces\": rd.compute(report_defs=force_reports)})\n    mass_in, mass_out = [report_number(name) for name in mass_reports]\n    print({\"mass_in\": mass_in, \"mass_out\": mass_out,\n           \"relative_mass_imbalance\": abs(mass_in + mass_out) / max(abs(mass_in), 1e-12)})\n    try:\n        index, history = solver.monitors.get_monitor_set_data(\"residual\")\n        last = {key: float(value[-1]) for key, value in history.items() if len(value)}\n    except Exception as error:\n        print({\"residual_read_error\": str(error)})\n        return\n    print({\"residuals\": last})\n    if any(not math.isfinite(value) or value > 1e4 for value in last.values()):\n        s.file.write_case_data(file_name=task_directory+r\"\\needs-review-80-8f0a12.cas.h5\")\n        raise RuntimeError(\"Residual divergence detected\")\n\nvelocity_state = bc.velocity_inlet[\"inlet\"].momentum.velocity.get_state()\ncurrent_speed = float(velocity_state[\"value\"])\nstages = [value for value in [40.0, 60.0, 80.0] if value > current_speed]\nif current_speed > 80.0:\n    stages = [80.0]\nfor value in stages:\n    set_speed(value)\n    run.dual_time_iterate(time_step_count=3, max_iter_per_step=60)\n    evidence(\"speed_\" + str(value))\nset_speed(80.0)\nfor batch in range(3):\n    run.dual_time_iterate(time_step_count=10, max_iter_per_step=60)\n    evidence(\"80_settling_\" + str(batch + 1))\ns.file.write_case_data(file_name=task_directory+r\"\\car-80-before-animation-8f0a12.cas.h5\")\n\ng = s.results.graphics\ntry:\n    name = \"refined_car_8f0a12\"\n    if name not in g.mesh.get_object_names():\n        g.mesh.create(name=name)\n    g.mesh[name].surfaces_list = car_walls\n    g.mesh[name].display()\n    g.picture.save_picture(file_name=task_directory+r\"\\refined-car-8f0a12.png\")\nexcept Exception as error:\n    print({\"mesh_graphics_error\": str(error)})\nsection = fluentagi.create_section(axis=\"y\", position=0.0)\nprint(section)\nresult = fluentagi.export_results(\n    directory=task_directory, fields=[\"pressure\", \"velocity-magnitude\"],\n    surfaces=[section[\"surface\"]])\nprint(result)\nif isinstance(result, dict):\n    for key in [\"error\", \"errors\", \"warnings\"]:\n        if result.get(key):\n            print({\"export_review\": key, \"details\": result[key]})\nfor name, kind, state in [\n    (\"car_pressure_80_8f0a12\", \"contour\",\n     {\"field\":\"pressure\", \"surfaces_list\":car_walls, \"filled\":True}),\n    (\"car_streamlines_80_8f0a12\", \"pathline\",\n     {\"field\":\"velocity-magnitude\", \"release_from_surfaces\":[\"inlet\"], \"step\":3000, \"skip\":20})]:\n    try:\n        collection = getattr(g, kind)\n        if name not in collection.get_object_names():\n            collection.create(name=name)\n        collection[name].set_state(state)\n        collection[name].display()\n        g.picture.save_picture(file_name=task_directory+\"\\\\\"+name+\".png\")\n    except Exception as error:\n        print({\"graphics\": name, \"error\": str(error)})\nprint(fluentagi.record_animation(\n    directory=task_directory, field=\"velocity-magnitude\", frames=24,\n    steps_per_frame=2, max_iter_per_step=60, surfaces=[section[\"surface\"]]))\nevidence(\"final_80\")\ns.file.write_case_data(file_name=task_directory+r\"\\car-80-final-8f0a12.cas.h5\")\nfor name, obj in [(\"forces\", s.solution.monitor.report_plots[monitor_name]),\n                  (\"residuals\", s.solution.monitor.residual)]:\n    try:\n        obj.plot()\n        g.picture.save_picture(file_name=task_directory+\"\\\\\"+name+\"-80-8f0a12.png\")\n    except Exception as error:\n        print({\"plot\": name, \"error\": str(error)})\nprint({\"initial_time\": initial_time, \"actual_final_time\": report_number(clock_report),\n       \"actual_cells\": int(round(report_number(cell_report))),\n       \"target_speed\": 80.0, \"convergence_verified\": False})"}}, "error": "{\"status\": \"error\", \"stdout\": \"\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-192740-fe6b6c\\\\before-8f0a12.cas.h5\\\" in NODE0 mode and compression level 1 ...\\nGrouping cells for Laplace smoothing ...\\n      271220 cells,     1 zone  ...\\n      570904 faces,    12 zones ...\\n       58102 nodes,     1 zone  ...\\n  Done.\\nDone.\\n\\nWriting to Austra:\\\"C:\\\\Users\\\\13977\\\\AppData\\\\Local\\\\FluentAGI\\\\data\\\\runs\\\\20261018-192740-fe6b6c\\\\before-8f0a12.dat.h5\\\" in NODE0 mode and compression level 1 ...\\n  Writing results.\\nDone.\\n{'time_mode': 'unsteady-2nd-order', 'calculation_state': {'reporting_interval': 1, 'profile_update_interval': 1, 'transient_controls': {'type': 'Fixed', 'method': 'User-Specified', 'time_step_count': 2, 'time_step_size': 0.005, 'max_iter_per_time_step': 20, 'solution_status': False, 'extrapolate_variables': False, 'predict_next': False, 'fixed_periodic': {'enabled': False}, 'multiphase_specific_time_constraints': {'physics_based_constraint': False}, 'solid_time_step_size': {'enable_solid_time_step': False}}, 'data_sampling': {'enabled': False}}, 'target_cells': 542440, 'runtime_cell_limit': 4000000}\\n{'clock_expression': {'name': 'physical_time_8f0a12', 'average_over': 1, 'retain_instantaneous_values': False, 'definition': 'Time', 'list_valid_report_names': '', 'output_parameter': False}}\\n\\nphysical_time_8f0a12\\n -------------------\\n                      Expression                 [s]\\n-------------------------------- -------------------\\n            physical_time_8f0a12                0.23\\n\\n   cell_count_ad9ac9\\n -------------------\\n{'actual_initial_time': 0.2300000000000001, 'actual_initial_cells': 271220}\\n                      Cell Count                    \\n-------------------------------- -------------------\\n                           fluid              271220\\n{'adaptation_controls_before': {'adaption_method': 'puma', 'cell_zones': None, 'verbosity': 0, 'encapsulate_children': False, 'maximum_refinement_level': 2, 'minimum_edge_length': 0.0, 'minimum_cell_quality': 0.01, 'maximum_cell_count': 0, 'prismatic_adaption': False}}\\n\\n   cell_count_ad9ac9\\n -------------------\\n                      Cell Count                    \\n-------------------------------- -------------------\\n                           fluid              271220\\n{'region': 'frontwing', 'state': {'name': 'ref_frontwing_ad9ac9', 'python_name_1': 'ref_frontwing_ad9ac9', 'type': {'option': 'hexahedron', 'hexahedron': {'min_point': [-0.04, -0.85, 0.035], 'max_point': [0.36, 0.85, 0.29], 'inside': True, 'create_volume_surface': False}}, 'display_options': {'draw_mesh': False, 'filled': False, 'marker': True, 'marker_symbol': 'o', 'marker_size': 0.1, 'wireframe': True, 'color': 'black'}}, 'cell_budget': 335000}\\n\", \"stderr\": \"\\nTraceback (most recent call last):\\n  File \\\"ansys\\\\fluent\\\\mcp\\\\solve\\\\backends\\\\pyfluent.py\\\", line 2793, in _do\\n    exec(  # noqa: S102  # nosec B102\\n  File \\\"<mcp>\\\", line 128, in <module>\\nRuntimeError: Native cell-register refinement unavailable\\n\", \"return_value\": null, \"message\": \"Fluent gRPC channel was lost during the call. Reconnect with `connect` and re-load any case/data before retrying.\", \"error_code\": \"solver_disconnected\", \"warnings\": []}", "note": "可能有部分操作已生效，请先检查实时状态，修复后继续。"}]
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<question>执行反馈显示Fluent的gRPC连接已断开，尚未执行网格加密，原解仍停在0.23秒。请先通过FluentAGI重新连接原Fluent会话，并回传连接状态。若原解丢失，请加载任务目录中的before-8f0a12.cas.h5及配套.dat.h5，不要初始化。连接恢复后，我将改用v242的mesh.adapt加密入口继续。</question>