SHIFT-Truck: A High-Fidelity Aerodynamics Dataset and Benchmark for Pickup Trucks
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2512. 07847v2 Announce Type: replace Abstract: Benchmarking has been the cornerstone of progress in computer vision, natural language processing, and the broader deep learning domain, driving algorithmic innovation through standardized datasets and reproducible evaluation protocols.
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HiLiftAeroML is the first open high‑fidelity CFD dataset focused on high‑lift aircraft aerodynamics, comprising 1,800 simulations across 180 variants of the NASA Common Research Model and ten angles of attack from 4° to 22°. Each case uses a GPU‑accelerated explicit wall‑modeled LES on grids of 300–500 million cells, covering attached, separated, and post‑stall flow conditions. The dataset, released under CC‑BY‑4.0, includes geometries, time‑averaged fields, integrated loads, validation data, and benchmark splits, and is accompanied by baseline models that perform well on interpolation and held‑out geometry tests but still struggle with high‑angle separated flow and out‑of‑distribution regimes.
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ChannelFlow-Tools is an open‑source, configuration‑driven pipeline that generates machine‑learning‑ready datasets for three‑dimensional obstructed channel flows. It combines procedural obstacle geometry generation across six shape families, signed‑distance‑field voxelisation, lattice‑Boltzmann simulation, and packaging into ML‑ready tensors, all driven by reproducible configuration files. The pipeline is validated through mesh‑integrity audits, SDF representation checks, solver benchmarks, and data‑integrity audits, and it has been used to train surrogate models (3D U‑Net, FNO, U‑FNO) that learn geometry‑to‑flow mappings and exhibit physically interpretable behaviour on out‑of‑distribution splits.
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