The automotive industry's pursuit of enhanced fuel economy and performance necessitates efficient aerodynamic design. However, traditional evaluation methods such as computational fluid dynamics (CFD) and wind tunnel testing are resource intensive, hindering rapid iteration in the early design stages. Machine learning-based surrogate models offer a promising alternative, yet many existing approaches suffer from high computational complexity, limited interpretability, or insufficient accuracy for detailed geometric inputs. This paper introduces a novel lightweight surrogate model for the prediction of the aerodynamic drag coefficient (Cd) based on a sequential slice-wise processing of the geometry of the 3D vehicle. Inspired by medical imaging, 3D point clouds of vehicles are decomposed into an ordered sequence of 2D cross-sectional slices along the stream-wise axis. Each slice is encoded by a lightweight PointNet2D module, and the sequence of slice embeddings is processed by a bidirectional LSTM to capture longitudinal geometric evolution. The model, trained and evaluated on the DrivAerNet++ dataset, achieves a high coefficient of determination (R^2 > 0.9528) and a low mean absolute error (MAE approx 6.046 x 10^{-3}) in Cd prediction. With an inference time of approximately 0.025 seconds per sample on a consumer-grade GPU, our approach provides fast, accurate, and interpretable aerodynamic feedback, facilitating more agile and informed automotive design exploration.

翻译：汽车工业对提升燃油经济性与性能的追求，使得高效空气动力学设计成为必需。然而，传统评估方法如计算流体动力学（CFD）与风洞测试资源消耗大，阻碍了早期设计阶段的快速迭代。基于机器学习的代理模型提供了一种有前景的替代方案，但现有许多方法存在计算复杂度高、可解释性有限，或对精细几何输入预测精度不足的问题。本文提出一种新颖的轻量级代理模型，用于预测空气动力学风阻系数（Cd），该模型基于对三维车辆几何形状的顺序切片式处理。受医学影像启发，车辆的三维点云被沿流向轴分解为一个有序的二维横截面切片序列。每个切片由一个轻量级PointNet2D模块编码，切片嵌入序列则由一个双向LSTM处理，以捕捉纵向几何演变。该模型在DrivAerNet++数据集上训练和评估，在Cd预测中实现了较高的决定系数（R^2 > 0.9528）和较低的平均绝对误差（MAE 约 6.046 x 10^{-3}）。在消费级GPU上，每个样本的推理时间约为0.025秒，我们的方法提供了快速、准确且可解释的空气动力学反馈，有助于实现更敏捷、更明智的汽车设计探索。