--- license: cc-by-sa-4.0 size_categories: - n<1K task_categories: - graph-ml pretty_name: 2D external aero CFD RANS datasets, under geometrical variations tags: - physics learning - geometry learning configs: - config_name: default data_files: - split: all_samples path: data/all_samples-* dataset_info: description: legal: owner: Safran license: CC-by-SA 4.0 data_production: type: simulation physics: 2D stationary RANS simulator: elsA split: train: - 0 - 1 - 2 - 3 - 4 - 5 - 6 - 7 - 8 - 9 - 10 - 11 - 12 - 13 - 14 - 15 - 16 - 17 - 18 - 19 - 20 - 21 - 22 - 23 - 24 - 25 - 26 - 27 - 28 - 29 - 30 - 31 - 32 - 33 - 34 - 35 - 36 - 37 - 38 - 39 - 40 - 41 - 42 - 43 - 44 - 45 - 46 - 47 - 48 - 49 - 50 - 51 - 52 - 53 - 54 - 55 - 56 - 57 - 58 - 59 - 60 - 61 - 62 - 63 - 64 - 65 - 66 - 67 - 68 - 69 - 70 - 71 - 72 - 73 - 74 - 75 - 76 - 77 - 78 - 79 - 80 - 81 - 82 - 83 - 84 - 85 - 86 - 87 - 88 - 89 - 90 - 91 - 92 - 93 - 94 - 95 - 96 - 97 - 98 - 99 - 100 - 101 - 102 - 103 - 104 - 105 - 106 - 107 - 108 - 109 - 110 - 111 - 112 - 113 - 114 - 115 - 116 - 117 - 118 - 119 - 120 - 121 - 122 - 123 - 124 - 125 - 126 - 127 - 128 - 129 - 130 - 131 - 132 - 133 - 134 - 135 - 136 - 137 - 138 - 139 - 140 - 141 - 142 - 143 - 144 - 145 - 146 - 147 - 148 - 149 - 150 - 151 - 152 - 153 - 154 - 155 - 156 - 157 - 158 - 159 - 160 - 161 - 162 - 163 - 164 - 165 - 166 - 167 - 168 - 169 - 170 - 171 - 172 - 173 - 174 - 175 - 176 - 177 - 178 - 179 - 180 - 181 - 182 - 183 - 184 - 185 - 186 - 187 - 188 - 189 - 190 - 191 - 192 - 193 - 194 - 195 - 196 - 197 - 198 - 199 - 200 - 201 - 202 - 203 - 204 - 205 - 206 - 207 - 208 - 209 - 210 - 211 - 212 - 213 - 214 - 215 - 216 - 217 - 218 - 219 - 220 - 221 - 222 - 223 - 224 - 225 - 226 - 227 - 228 - 229 - 230 - 231 - 232 - 233 - 234 - 235 - 236 - 237 - 238 - 239 - 240 - 241 - 242 - 243 - 244 - 245 - 246 - 247 - 248 - 249 - 250 - 251 - 252 - 253 - 254 - 255 - 256 - 257 - 258 - 259 - 260 - 261 - 262 - 263 - 264 - 265 - 266 - 267 - 268 - 269 - 270 - 271 - 272 - 273 - 274 - 275 - 276 - 277 - 278 - 279 - 280 - 281 - 282 - 283 - 284 - 285 - 286 - 287 - 288 - 289 - 290 - 291 - 292 - 293 - 294 - 295 - 296 - 297 - 298 test: - 299 - 300 - 301 - 302 - 303 - 304 - 305 - 306 - 307 - 308 - 309 - 310 - 311 - 312 - 313 - 314 - 315 - 316 - 317 - 318 - 319 - 320 - 321 - 322 - 323 - 324 - 325 - 326 - 327 - 328 - 329 - 330 - 331 - 332 - 333 - 334 - 335 - 336 - 337 - 338 - 339 - 340 - 341 - 342 - 343 - 344 - 345 - 346 - 347 - 348 - 349 - 350 - 351 - 352 - 353 - 354 - 355 - 356 - 357 - 358 - 359 - 360 - 361 - 362 - 363 - 364 - 365 - 366 - 367 - 368 - 369 - 370 - 371 - 372 - 373 - 374 - 375 - 376 - 377 - 378 - 379 - 380 - 381 - 382 - 383 - 384 - 385 - 386 - 387 - 388 - 389 - 390 - 391 - 392 - 393 - 394 - 395 - 396 - 397 - 398 task: regression in_scalars_names: - camb - rot - t_le - t_mx - t_te - u_mx out_scalars_names: [] in_timeseries_names: [] out_timeseries_names: [] in_fields_names: [] out_fields_names: - Mach - Pressure - Velocity-x - Velocity-y in_meshes_names: - /Base_2_2/Zone out_meshes_names: [] features: - name: sample dtype: binary splits: - name: all_samples num_bytes: 328720977 num_examples: 399 download_size: 225335734 dataset_size: 328720977 --- # Dataset Card ![image/png](https://i.ibb.co/zWLTtv06/Logo-2-D-profile-large-2-consolas-100.png) ![image/png](https://i.ibb.co/B5jB4fBc/2-D-profile.png) This dataset contains a single huggingface split, named 'all_samples'. The samples contains a single huggingface feature, named called "sample". Samples are instances of [plaid.containers.sample.Sample](https://plaid-lib.readthedocs.io/en/latest/autoapi/plaid/containers/sample/index.html#plaid.containers.sample.Sample). Mesh objects included in samples follow the [CGNS](https://cgns.github.io/) standard, and can be converted in [Muscat.Containers.Mesh.Mesh](https://muscat.readthedocs.io/en/latest/_source/Muscat.Containers.Mesh.html#Muscat.Containers.Mesh.Mesh). Example of commands: ```python import pickle from datasets import load_dataset from plaid.containers.sample import Sample # Load the dataset dataset = load_dataset("chanel/dataset", split="all_samples") # Get the first sample of the first split split_names = list(dataset.description["split"].keys()) ids_split_0 = dataset.description["split"][split_names[0]] sample_0_split_0 = dataset[ids_split_0[0]]["sample"] plaid_sample = Sample.model_validate(pickle.loads(sample_0_split_0)) print("type(plaid_sample) =", type(plaid_sample)) print("plaid_sample =", plaid_sample) # Get a field from the sample field_names = plaid_sample.get_field_names() field = plaid_sample.get_field(field_names[0]) print("field_names[0] =", field_names[0]) print("field.shape =", field.shape) # Get the mesh and convert it to Muscat from Muscat.Bridges import CGNSBridge CGNS_tree = plaid_sample.get_mesh() mesh = CGNSBridge.CGNSToMesh(CGNS_tree) print(mesh) ``` ## Dataset Details ### Dataset Description This dataset contains 2D external aero CFD RANS solutions, under geometrical variations (correspond to "large" in the Zenodo repository). The variablity in the samples are 6 input scalars and the geometry (mesh). Outputs of interest are 4 fields. Samples have been computed on large refined meshes, which have been cut close to the profil, and adapted (remeshed) using an anisotropic metric based on the output fields of interest. Dataset created using the [PLAID](https://plaid-lib.readthedocs.io/) library and datamodel, version 0.1. - **Language:** [PLAID](https://plaid-lib.readthedocs.io/) - **License:** cc-by-sa-4.0 - **Owner:** Safran ### Dataset Sources - **Repository:** [Zenodo](https://zenodo.org/records/14840426)