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Align task taxonomy and IDs with the updated paper; main evaluation 306
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{
"id": "single-software-execution--cli--openfast--task-12--ubuntu",
"snapshot": "openfast5",
"instruction": "`/home/user/Desktop/yaw_error.fst`, `/home/user/Desktop/NRELOffshrBsline5MW_ElastoDyn_Yaw.dat`, and `/home/user/Desktop/inflow_yaw_steady.dat` is provided. Only initialization inputs are preloaded; generate all result artifacts yourself.\n\nBefore starting the work, the OpenFAST executable is `/home/user/miniconda3/bin/openfast` when working on `yaw_error.fst`. Complete a fixed yaw-misalignment sensitivity analysis from the command line: 1. Confirm the shared InflowWind input uses `WindType=1`, `HWindSpeed=11.4 m/s`, and `RefHt=90 m`. 2. Generate `elasto_yaw_0.dat`, `elasto_yaw_5.dat`, `elasto_yaw_10.dat`, `elasto_yaw_15.dat`, and `elasto_yaw_20.dat` under `/home/user/Desktop`. In every file set `YawDOF=False` so `NacYaw` is fixed, and set `NacYaw` to the filename angle. Generate matching `case_yaw_ANGLE.fst` files under `/home/user/Desktop` that reference the shared steady wind and their own ElastoDyn file. 3. Run all five 60-second cases independently and retain their `.out` files and normal-termination logs under `/home/user/Desktop`. 4. Write `/home/user/Desktop/run_cases.py` and `/home/user/Desktop/postprocess.py`; for each output compute the arithmetic mean of `GenPwr` over `Time>=50 s`. Save `/home/user/Desktop/summary.txt` as `yaw_error_deg, genpwr_kw`. 5. Do not use GUI tools.",
"source": "https://github.com/openFAST/r-test",
"config": [
{
"type": "upload_file",
"parameters": {
"files": [
{
"local_path": "task-12/init_file/init_file.tar",
"path": "/home/user/Desktop/init_file.tar"
}
]
}
},
{
"type": "launch",
"parameters": {
"command": "bash -c \"mkdir -p '/home/user/Desktop/' && tar -xf '/home/user/Desktop/init_file.tar' -C '/home/user/Desktop/' && rm -f '/home/user/Desktop/init_file.tar'\"",
"shell": true
}
}
],
"trajectory": "trajectories/",
"related_apps": [
"openfast"
],
"artifact_paths": [
"/home/user/Desktop/elasto_yaw_0.dat",
"/home/user/Desktop/elasto_yaw_5.dat",
"/home/user/Desktop/elasto_yaw_10.dat",
"/home/user/Desktop/elasto_yaw_15.dat",
"/home/user/Desktop/elasto_yaw_20.dat",
"/home/user/Desktop/case_yaw_0.fst",
"/home/user/Desktop/case_yaw_0.out",
"/home/user/Desktop/case_yaw_0.log",
"/home/user/Desktop/case_yaw_5.fst",
"/home/user/Desktop/case_yaw_5.out",
"/home/user/Desktop/case_yaw_5.log",
"/home/user/Desktop/case_yaw_10.fst",
"/home/user/Desktop/case_yaw_10.out",
"/home/user/Desktop/case_yaw_10.log",
"/home/user/Desktop/case_yaw_15.fst",
"/home/user/Desktop/case_yaw_15.out",
"/home/user/Desktop/case_yaw_15.log",
"/home/user/Desktop/case_yaw_20.fst",
"/home/user/Desktop/case_yaw_20.out",
"/home/user/Desktop/case_yaw_20.log",
"/home/user/Desktop/inflow_yaw_steady.dat",
"/home/user/Desktop/run_cases.py",
"/home/user/Desktop/postprocess.py",
"/home/user/Desktop/summary.txt"
],
"evaluator": {
"postconfig": [
{
"type": "upload_file",
"parameters": {
"files": [
{
"local_path": "task-12/eval.py",
"path": "/home/user/Desktop/eval.py"
}
]
}
}
],
"func": "exact_match",
"result": {
"type": "vm_command_line",
"command": "python3 /home/user/Desktop/eval.py",
"shell": "true"
},
"expected": {
"type": "rule",
"rules": {
"expected": "True\n"
}
}
},
"proxy": false,
"fixed_ip": false,
"possibility_of_env_change": "low"
}