AI 中文总结
提出HGPTrans，一种结合图同构卷积、物理感知切片注意力和分层池化的网络，直接从车辆网格预测气动阻力系数，在DrivAerNet等数据集上达到最低误差，推理速度较CFD加速数个数量级。
AI 中文摘要
准确且快速地预测气动阻力系数（$C_D$）对于车辆设计至关重要，尤其是在早期造型迭代阶段，此时必须评估大量候选几何形状。尽管计算流体力学（CFD）能够提供可靠的气动估算，但其高昂的计算成本（通常单个配置需要数小时至数天）限制了其在大规模设计探索中的应用。本文提出了HGPTrans，一种基于Transolver注意力机制的分层图池化网络，可直接从车辆表面网格预测$C_D$。鉴于车辆空气动力学同时依赖于局部几何特征和空间上相距较远的表面区域之间的长程相互作用，HGPTrans集成了三个互补组件：图同构卷积编码判别性局部几何，物理感知切片注意力以线性计算复杂度捕获全局交互，以及信息冗余感知的分层池化逐步移除冗余节点，同时保留信息丰富的几何结构。该模型在大规模DrivAerNet和DrivAerNet++数据集上进行了训练和评估，在评估的基线中取得了最低的平均绝对误差和均方误差。其泛化能力通过在一个包含轿车和SUV的真实车辆数据集上进行迁移学习进一步评估，实现了1.56%（轿车）和2.12%（SUV）的相对$L_1$误差，每辆车的推理时间约为$0.293$秒。这相对于高保真CFD实现了数个数量级的加速，同时将预测的阻力系数保持在CFD参考值的百分之几以内。消融研究证实了每个组件的贡献，并揭示了深度和池化比率的影响。
英文摘要
Accurate and rapid prediction of the aerodynamic drag coefficient ($C_D$) is essential for vehicle design, particularly during early-stage design, where many candidate geometries must be evaluated. Although computational fluid dynamics (CFD) provides reliable aerodynamic estimates, its high computational cost limits large-scale design exploration. This paper proposes the hierarchical graph-pooling Transolver (HGPTrans), which combines hierarchical graph pooling with Transolver-based attention to directly predict $C_D$ from vehicle surface meshes. Motivated by the fact that vehicle aerodynamics depends on both local geometric features and long-range interactions among spatially distant surface regions, HGPTrans integrates three complementary components. Graph isomorphism convolutions encode discriminative local geometry, Transolver-style slice attention captures global interactions with linear computational complexity, and information-redundancy-aware hierarchical pooling progressively removes redundant nodes while preserving informative geometric structures. The model is trained and evaluated on the large-scale DrivAerNet and DrivAerNet++ datasets, where it achieves the lowest mean absolute error and mean squared error among the evaluated baselines. Its generalization capability is further assessed through transfer learning on a real-vehicle dataset containing both sedans and sport utility vehicles (SUVs), achieving relative $L_1$ errors of 1.56\% (sedans) and 2.12\% (SUVs) with an inference time of approximately $0.293$ s per vehicle. This corresponds to an acceleration of several orders of magnitude relative to high-fidelity CFD while keeping the predicted drag coefficients within a few percent of the CFD reference. Ablation studies confirm each component's contribution and reveal the effects of depth and pooling ratio.
