SHIFT-Truck: A High-Fidelity Aerodynamics Dataset And Benchmark For Pickup Trucks

Pickup trucks account for 14% of new light-duty vehicles produced in the United States and yet, are among the least aerodynamic. Their open cargo bed adds a flow that existing automotive aerodynamics datasets such as DrivAerML and SHIFT-SUV do not contain, in which the shear layer leaving the cab roof passes over a recirculating bed flow before separating again at the tailgate. The resulting high aerodynamic drag on pickup trucks decreases fuel efficiency while increasing emissions in internal combustion engines, while also limiting highway range on electric counterparts. Drag reduction through design optimization is therefore crucial, however, the time and compute required to run scale-resolved Computational Fluid Dynamics (CFD) simulations serve as a bottleneck to expansive design exploration. Neural surrogates offer a way to predict flow features at a fraction of that cost, provided they are trained on large-scale, high-fidelity and domain specific data. To bridge this gap, we introduce SHIFT-Truck, the first such dataset for pickup trucks. It comprises 800 Spalart–Allmaras delayed detached-eddy simulations (SA-DDES) of a reference pickup geometry morphed across 17 shape parameters, such as cab geometry, bed dimensions and windshield rake. Every case is computed on a mesh of about 100 million cells at a Reynolds number of $1.4\times 10^7$ and released with time-averaged surface pressure, wall shear stress, volumetric pressure and velocity. The numerical setup is verified by grid refinement and repeated simulations, as well as checked against wind-tunnel measurements. We define geometry-grouped training, validation and test splits and benchmark four recent neural surrogate architectures, DoMINO, GeoTransolver, AB-UPT and SMART, on surface and volume tracks. Beyond in-distribution evaluation, SHIFT-Truck introduces controlled distribution shifts in the operating point, the discretization of the input surface and the vehicle archetype. We find that models with strong in-distribution performance can exhibit substantial degradation under distribution shifts: operating-condition changes expose failures to infer speed dependence, while tessellation and cross-vehicle shifts reveal markedly different robustness across architectures. These controlled evaluations make SHIFT-Truck a benchmark not only for aerodynamic surrogate accuracy, but also for studying generalization of neural surrogates across physical and numerical distributions.