Pith. sign in

REVIEW 4 cited by

AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.20801 v1 pith:CQHZ4EJ3 submitted 2024-07-30 physics.flu-dyn cs.CEcs.LG

AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics

Neil Ashton , Danielle C. Maddix , Samuel Gundry , Parisa M. Shabestari This is my paper
classification physics.flu-dyn cs.CEcs.LG
keywords datasetbodyopen-sourceahmedflowhigh-fidelityavailablebluff
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

The development of Machine Learning (ML) methods for Computational Fluid Dynamics (CFD) is currently limited by the lack of openly available training data. This paper presents a new open-source dataset comprising of high fidelity, scale-resolving CFD simulations of 500 geometric variations of the Ahmed Car Body - a simplified car-like shape that exhibits many of the flow topologies that are present on bluff bodies such as road vehicles. The dataset contains simulation results that exhibit a broad set of fundamental flow physics such as geometry and pressure-induced flow separation as well as 3D vortical structures. Each variation of the Ahmed car body were run using a high-fidelity, time-accurate, hybrid Reynolds-Averaged Navier-Stokes (RANS) - Large-Eddy Simulation (LES) turbulence modelling approach using the open-source CFD code OpenFOAM. The dataset contains boundary, volume, geometry, and time-averaged forces/moments in widely used open-source formats. In addition, the OpenFOAM case setup is provided so that others can reproduce or extend the dataset. This represents to the authors knowledge, the first open-source large-scale dataset using high-fidelity CFD methods for the widely used Ahmed car body that is available to freely download with a permissive license (CC-BY-SA).

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. M$^3$: Reframing Training Measures for Discretized Physical Simulations

    cs.AI 2026-05 unverdicted novelty 7.0

    M³ partitions space by physical variation using multi-scale Morton ordering to balance training measures, yielding up to 4.7× lower error on industrial volumetric datasets and outperforming higher-resolution training ...

  2. M$^3$: Reframing Training Measures for Discretized Physical Simulations

    cs.AI 2026-05 conditional novelty 6.5

    M³ constructs multi-scale variation-aware training measures via Morton octrees, cutting physics-weighted errors up to 4.7× and beating denser random data under aggressive subsampling.

  3. NeuroForge: A Self-Correcting, Geometry-Native Neural CFD Engine with Calibrated Physics-Residual Trust

    physics.flu-dyn 2026-07 conditional novelty 6.0

    The steady-RANS residual of a neural CFD prediction is a backbone-robust case-level trust signal but a poor correction objective; a supervised DEQ corrector cuts field MSE on a SOTA backbone without needing residual c...

  4. Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries

    cs.LG 2026-02 conditional novelty 5.0

    Transolver-3 scales transformer-based PDE surrogates to 160-million-cell meshes and reports the best accuracy on three aerodynamics benchmarks using memory-efficient tiling, subset training, and cached physical states.