用物理神经网络加速F1前翼气动设计，预测精度高且省时。
Computational Fluid Dynamics Optimization of F1 Front Wing using Physics Informed Neural Networks
- 结合流体方程与仿真数据，用物理约束神经网络快速预测气动性能。
- 预测阻力系数R²达0.968，升力系数R²达0.981，精度高。
- 适合受风洞时间与预算限制的F1车队快速迭代设计。
面对国际汽联新规下风洞测试时间缩减（末位车队从320小时降至200小时）及每年1.35亿美元预算上限，赛车队亟需更高效的空气动力学开发工具。传统计算流体力学（CFD）虽精度高，但单次配置分析耗时8至24小时，计算资源消耗大。本文提出一种物理信息神经网络（PINN），用于快速预测一级方程式前翼气动系数。该方法融合SimScale平台的CFD数据与流体动力学基本原理，通过混合损失函数同时保证数据拟合度与物理一致性，基于12种气动构型的力与力矩数据进行训练。模型在阻力系数预测上R²达0.968，升力系数达0.981，显著降低计算时间。物理约束机制确保预测结果符合纳维-斯托克斯方程，为车队在法规约束下高效探索设计空间提供可靠工具。
原文摘要 · Abstract (English)
In response to recent FIA regulations reducing Formula 1 team wind tunnel hours (from 320 hours for last-place teams to 200 hours for championship leaders) and strict budget caps of 135 million USD per year, more efficient aerodynamic development tools are needed by teams. Conventional computational fluid dynamics (CFD) simulations, though offering high fidelity results, require large computational resources with typical simulation durations of 8-24 hours per configuration analysis. This article proposes a Physics-Informed Neural Network (PINN) for the fast prediction of Formula 1 front wing aerodynamic coefficients. The suggested methodology combines CFD simulation data from SimScale with first principles of fluid dynamics through a hybrid loss function that constrains both data fidelity and physical adherence based on Navier-Stokes equations. Training on force and moment data from 12 aerodynamic features, the PINN model records coefficient of determination (R-squared) values of 0.968 for drag coefficient and 0.981 for lift coefficient prediction while lowering computational time. The physics-informed framework guarantees that predictions remain adherent to fundamental aerodynamic principles, offering F1 teams an efficient tool for the fast exploration of design space within regulatory constraints.
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