基于多精度深度神经网络的汽车气动外形优化设计方法

Optimization design method of automobile aerodynamic shape based on multi-fidelity deep neural network

在汽车气动外形优化设计中,往往需要大量的高精度CFD数据作为支撑。然而,高精度CFD数据获取难度大、成本高。为了缓解汽车气动优化设计中气动特性评估精度和效率之间的矛盾,根据迁移学习与数据融合的思想,提出了一种基于多精度深度神经网络(multi-fidelity deep neural network, MFDNN)的汽车外形优化设计方法,以减少优化设计中所需的高精度数据个数,从而有效提升优化速度、降低优化成本。将所发展的优化方法应用于快背式MIRA标准模型减阻优化设计中,优化结果表明,该方法能够充分融合不同精度数据所蕴含的知识,加速气动外形优化进程,提升优化效率。以收敛用时作为评价指标,在取得相近或更优优化结果的前提下,基于多精度神经网络的优化框架的收敛速度是基于单精度神经网络的离线优化框架的5.85倍,是基于单精度神经网络的在线优化框架的2.81倍。

The optimization process of automobile aerodynamic design that requires a vast amount of CFD data,which are both expensive and time-consuming to acquire,is arduous and costly.To get out of this predicament—the conflict between accuracy and efficiency—we devise an innovative automobile shape optimization technique relying on the Multi-fidelity deep neural network(MFDNN)aerodynamic model based on transfer learning and data fusion.Applying the developed optimization method to the drag reduction optimization design of the fast-back MIRA standard model demonstrates that the method can fully integrate the knowledge contained in different fidelity data,accelerate the aerodynamic shape optimization process,and improve the optimization efficiency.Specifically,the convergence speed of the optimization framework based on multiprecision neural network is 5.85 times that of the offline optimization framework based on single-precision neural network and 2.81 times that of the online optimization framework based on single-precision neural network.