SHIFT-Truck：面向皮卡的高保真空气动力学数据集与基准
SHIFT-Truck: A High-Fidelity Aerodynamics Dataset and Benchmark for Pickup Trucks
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- Luminary（Luminary公司）
- Stanford University（斯坦福大学）
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中文总结 AI 辅助
SHIFT-Truck是首个面向皮卡的高保真空气动力学数据集与基准，包含1000次SA-DDES模拟，用于评估神经代理模型的准确性及其在物理和数值分布偏移下的泛化能力。
中文摘要 AI 辅助
皮卡占美国新生产轻型车辆的14%，但却是空气动力学性能最差的车型之一。其开放式货厢引入了一种现有汽车空气动力学数据集（如DrivAerML和SHIFT-SUV）中不存在的流动：从驾驶室顶部离开的剪切层在尾门处再次分离之前，会经过货厢内的回流区。由此产生的阻力降低了燃油效率，增加了排放，并限制了电动皮卡的续航里程。尺度分辨计算流体动力学（CFD）对于广泛的设计探索而言成本过高；神经代理模型可以以该成本的一小部分预测流动特征，前提是它们在大规模、高保真、特定领域的数据上进行训练。我们推出了SHIFT-Truck，这是首个针对皮卡的此类数据集。它包含对参考皮卡几何形状进行17个形状参数变形后的1000次Spalart-Allmaras延迟分离涡模拟（SA-DDES）。每个案例在约1亿个单元的网格上以雷诺数$1.4 \ imes 10^7$运行，并发布时均表面压力、壁面剪切应力、体积压力和速度。该设置通过网格细化和重复运行进行验证，并与风洞测量结果进行核对。我们定义了按几何分组的划分，并在表面和体积轨道上对四个神经代理模型DoMINO、GeoTransolver、AB-UPT和SMART进行了基准测试。SHIFT-Truck还引入了运行点、输入表面离散化和车辆原型方面的受控分布偏移。在分布内性能良好的模型在这些偏移下可能会大幅退化：运行条件的变化暴露了推断速度依赖性的失败，而网格划分和跨车辆偏移则揭示了不同架构之间显著不同的鲁棒性。因此，SHIFT-Truck不仅是代理模型准确性的基准，也是跨物理和数值分布泛化能力的基准。
英文摘要
Pickup trucks account for 14% of new light-duty vehicles produced in the United States, yet are among the least aerodynamic. Their open cargo bed adds a flow absent from existing automotive aerodynamics datasets such as DrivAerML and SHIFT-SUV: the shear layer leaving the cab roof passes over a recirculating bed flow before separating again at the tailgate. The resulting drag lowers fuel efficiency, raises emissions and limits the range of electric trucks. Scale-resolved Computational Fluid Dynamics (CFD) is too costly for broad design exploration; neural surrogates can predict flow features at a fraction of that cost, provided they are trained on large-scale, high-fidelity, domain-specific data. We introduce SHIFT-Truck, the first such dataset for pickup trucks. It comprises 1,000 Spalart-Allmaras delayed detached-eddy simulations (SA-DDES) of a reference pickup geometry morphed across 17 shape parameters. Each case is run 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 setup is verified by grid refinement and repeated runs, and checked against wind-tunnel measurements. We define geometry-grouped splits and benchmark four neural surrogates, DoMINO, GeoTransolver, AB-UPT and SMART, on surface and volume tracks. SHIFT-Truck also introduces controlled distribution shifts in the operating point, the input surface discretization and the vehicle archetype. Models with strong in-distribution performance can degrade substantially under these shifts: operating-condition changes expose failures to infer speed dependence, while tessellation and cross-vehicle shifts reveal markedly different robustness across architectures. SHIFT-Truck is thus a benchmark not only for surrogate accuracy but also for generalization across physical and numerical distributions.
