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README.md

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+---
+license: cc-by-sa-4.0
+pretty_name: AhmedML (processed)
+tags: [cfd, aerodynamics, point-cloud, physics, surrogate-modeling]
+size_categories: [10B<n<100B]
+---
+
+# AhmedML (processed)
+
+> **This is a processed, downsampled derivative of AhmedML, 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 AhmedML. Total size is 0.28 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`, 9 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) |
+| 7 | `Cp` | pressure coefficient (as shipped upstream) |
+| 8 | `yPlus` | wall y+ |
+
+**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` | 500 | 1/1 | 5.345e+08 | 5.345e+08 |
+| `volume_collated` | 500 | 1/1 | 9.193e+09 | 9.193e+09 |
+
+**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`: 400 samples
+- `val`: 100 samples
+
+## Source dataset and preprocessing
+
+### AhmedML
+
+**What it is.** 500 variants of the Ahmed body — the canonical simplified automotive bluff
+body — spanning slant angle, body length/width/height and ground clearance. It models base
+separation and the slant-angle drag-crisis regime.
+
+**Generation.** Plain **SA-DDES** with the standard f_d shielding function (no σ-model, no EP
+shielding), same `pimpleFoam` / OpenFOAM v2212 lineage as DrivAerML, second-order in time.
+~21.3 M prismatic/hex-dominant cells, deliberately high-y⁺: 3 prism layers giving y⁺ ≈ 50 with
+Spalding wall functions. 40 convective transit times, with averaging started after the first
+10. Δt = 6e-4.
+
+**Parameters.** U∞ = **1 m/s** with Re = 7.68e5 set through the viscosity instead
+(ν = 3.75e-7 m²/s), Re based on body height H = 0.288 m (2.78e6 length-based). Verified in the
+data rather than taken from the paper: regressing the stored `Cp` column on the stored
+kinematic `p` returns q = 0.500000 to 12 digits.
+
+**Cost.** 288 cores on 4 AWS `hpc6a.48xlarge` nodes, ~30 min to mesh and ~48 h to solve
+(≈ 184 node-hours, ≈ 1.4e4 core-hours per case), so ≈ 6.9e6 core-hours for the campaign.
+
+**Processing.** Cell-based surface, `[x, y, z, p, τx, τy, τz, Cp, yPlus]` (9 cols, kinematic p,
+`CF_SIGN = −1`). Axes as DrivAerNet++/DrivAerML, full domain, no mirroring. One
+campaign-specific subtlety: the tunnel floor is at z = 0 but the body sits on stilts, so the
+*surface* z-minimum is ≈ 0.05, above the floor. The volume crop therefore clamps its lower
+vertical bound down to the ground plane z = 0 rather than to the surface bbox, which keeps the
+under-body gap flow and raises retention from ~62% to ~74%. Crop margins otherwise
+`[0.5, 0.75, 0.0]/[2.0, 0.75, 0.5] L`. Volume output `[x, y, z, ux, uy, uz, p]`. Splits: random
+80/20 (400/100).
+
+## Citation
+
+TODO: this work, and the upstream dataset's citation.

RELEASE.json

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+{
+  "dataset": "ahmedml",
+  "row_order": "shuffled; any contiguous window is a uniform subsample",
+  "collated": {
+    "factor": 1,
+    "columns": [
+      "x",
+      "y",
+      "z",
+      "p",
+      "tau_x",
+      "tau_y",
+      "tau_z",
+      "Cp",
+      "yPlus"
+    ],
+    "columns_dropped": [],
+    "n_samples": 500,
+    "rows_source": 534502194,
+    "rows_release": 534502194,
+    "bytes_release": 19242142984,
+    "source": "/mnt/ceph/users/azhou/data/ahmedml/collated"
+  },
+  "volume_collated": {
+    "factor": 1,
+    "columns": [
+      "x",
+      "y",
+      "z",
+      "ux",
+      "uy",
+      "uz",
+      "p"
+    ],
+    "columns_dropped": [],
+    "n_samples": 500,
+    "rows_source": 9192951736,
+    "rows_release": 9192951736,
+    "bytes_release": 257402712608,
+    "source": "/mnt/ceph/users/azhou/data/ahmedml/volume_collated"
+  }
+}

collated/manifest.json

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