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README.md

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+---
+license: cc-by-sa-4.0
+pretty_name: DrivAerML (processed)
+tags: [cfd, aerodynamics, point-cloud, physics, surrogate-modeling]
+size_categories: [10B<n<100B]
+---
+
+# DrivAerML (processed)
+
+> **This is a processed, downsampled derivative of DrivAerML, not the original dataset.** Fields were converted to a common frame and non-dimensionalization, rows were randomly subsampled and some columns were dropped (details below). For the original data, see the upstream source.
+
+A preprocessed, downsampled release of DrivAerML. Total size is 0.75 TB. Every sample is a float32 point cloud `[n_points, n_cols]` stored as `.npy`, in a common frame and non-dimensionalization shared across the campaigns of this release.
+
+## Layout
+
+```
+collated/                 surface
+  manifest.json           [{stem, n_points, n_points_source, ...}]
+  splits/{train,val}.json
+  norm_stats*.npz         per-column normalization statistics
+  samples/<stem>.npy
+volume_collated/          volume
+  manifest.json
+  norm_stats_volume*.npz
+  samples/<stem>.npy
+RELEASE.json              downsampling factors, columns and row totals
+```
+
+## Columns
+
+**Surface** (`collated`, 7 columns):
+
+| col | name | meaning |
+|---|---|---|
+| 0 | `x` | position |
+| 1 | `y` | position |
+| 2 | `z` | position |
+| 3 | `p` | static pressure (raw solver units; Cp = (p - p_inf) / q) |
+| 4 | `tau_x` | wall shear stress (raw) |
+| 5 | `tau_y` | wall shear stress (raw) |
+| 6 | `tau_z` | wall shear stress (raw) |
+
+Dropped from this release: Cp, pPrime2.
+
+**Volume** (`volume_collated`, 7 columns):
+
+| col | name | meaning |
+|---|---|---|
+| 0 | `x` | position |
+| 1 | `y` | position |
+| 2 | `z` | position |
+| 3 | `ux` | velocity |
+| 4 | `uy` | velocity |
+| 5 | `uz` | velocity |
+| 6 | `p` | static pressure (raw solver units; Cp = (p - p_inf) / q) |
+
+## Downsampling
+
+| tree | samples | row factor | rows (release) | rows (collated source) |
+|---|---|---|---|---|
+| `collated` | 483 | 1/3 | 1.384e+09 | 4.151e+09 |
+| `volume_collated` | 483 | 1/3 | 2.545e+10 | 7.635e+10 |
+
+**Rows within each file are in random order**: they were permuted when the files were written. The release keeps the first `n // factor` rows of each collated sample, which is a uniform random subsample of the mesh points. For the same reason, **any contiguous window `arr[s:s+k]` is a uniform random crop**, so `np.load(..., mmap_mode='r')` plus a slice is the intended access pattern. The shipped `norm_stats*.npz` were computed on the full collated trees and remain valid for the subsample.
+
+## Splits
+
+The volume tree uses the surface splits (keyed by stem).
+
+- `train`: 386 samples
+- `val`: 97 samples
+
+## Source dataset and preprocessing
+
+### DrivAerML
+
+**What it is.** 500 morphs of the DrivAer notchback body, released as *time-averaged*
+hybrid-RANS/LES statistics — the highest-fidelity car campaign here.
+
+**Generation.** SA-based **σ-DDES**: a Spalart-Allmaras RANS branch with the Nicoud σ-model as
+the SGS operator, plus Enhanced Protection shielding (after Deck & Renard's ZDES) to keep LES
+content out of attached boundary layers; Spalding all-y⁺ wall functions. Run on a custom
+`pimpleFoam` derivative (Upstream CFD, OpenFOAM v2212) with modified Rhie-Chow interpolation
+and second-order implicit Euler time integration. ~160 M cells, first boundary-layer height
+0.75 mm, 7 layers to 12 mm total. Averaging continues until the drag estimate settles to
+±1.5 drag counts, generally 40–60 convective time units (CTU based on the 2.786 m wheelbase).
+
+**Parameters.** U∞ = 38.889 m/s (140 km/h), Re = 7.2e6 on the wheelbase (1.2e7 if
+length-based), air ν = 1.507e-5 m²/s, ρ = 1.2041 kg/m³, zero incidence.
+
+**Cost.** ~40 h on 1536 cores (AWS `hpc6a.48xlarge`) per case ≈ 6.1e4 core-hours, so ≈ 3.1e7
+core-hours for the campaign — the most expensive dataset in the set by a wide margin.
+
+**Processing.** Surface is cell-based (`boundary_<N>.vtp`, cell data already aligned with
+centroids, so no cross-file matching):
+`[x, y, z, p, τx, τy, τz, Cp, pPrime2]` (9 cols, kinematic p, `CF_SIGN = −1`). 483 of the 500
+runs are present (runs 8, 9, 80–99 and 167 are missing upstream). The volume is delivered as
+fixed-size 25 GiB split parts that must be concatenated byte-for-byte into one ~46 GB
+polyhedral VTU (~166 M points / 147 M cells); the builder reconstructs one file at a time on
+local NVMe and deletes it after reading, since holding all 483 at once would be ~8 TB. Fields
+are read from point data to avoid `cell_centers()` on 147 M polyhedra. Axes match
+DrivAerNet++, except that **the floor is at z ≈ −0.318, not 0** — so the ground-plane guard
+used for the other car campaigns is deliberately disabled here. The raw domain is a full (not
+half) wind tunnel, x ∈ [−40, 80], y ∈ [−22, 22], z ∈ [−0.318, 19.68]; the crop keeps ~92% of
+points. Splits: random 80/20 (386/97).
+
+## Citation
+
+TODO: this work, and the upstream dataset's citation.

RELEASE.json

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+{
+  "dataset": "drivaerml",
+  "row_order": "shuffled; any contiguous window is a uniform subsample",
+  "collated": {
+    "factor": 3,
+    "columns": [
+      "x",
+      "y",
+      "z",
+      "p",
+      "tau_x",
+      "tau_y",
+      "tau_z"
+    ],
+    "columns_dropped": [
+      "Cp",
+      "pPrime2"
+    ],
+    "n_samples": 483,
+    "rows_source": 4150939803,
+    "rows_release": 1383646440,
+    "bytes_release": 38742162144,
+    "source": "/mnt/ceph/users/azhou/data/drivaerml/collated"
+  },
+  "volume_collated": {
+    "factor": 3,
+    "columns": [
+      "x",
+      "y",
+      "z",
+      "ux",
+      "uy",
+      "uz",
+      "p"
+    ],
+    "columns_dropped": [],
+    "n_samples": 483,
+    "rows_source": 76349896661,
+    "rows_release": 25449965394,
+    "bytes_release": 712599092856,
+    "source": "/mnt/ceph/users/azhou/data/drivaerml/volume_collated"
+  }
+}

collated/manifest.json

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