空气动力学学报2026，Vol.44Issue(8)：20-28,9.DOI:10.7638/kqdlxxb-2025.0255
梯度引导的高速列车表面传感器布局优化和稀疏压力场重建策略
G-PROSNet:Gradient-guided pressure-field reconstruction with optimal sensor placement for high-speed trains
摘要
Abstract
Accurate knowledge of the surface pressure distribution is essential for the aerodynamic design and optimization of high-speed trains.However,due to the physical constraints imposed by the train structure,only sparse pressure measurements can be obtained in practical tests.To address this limitation,this study proposes a gradient-guided framework for sensor layout optimization and sparse pressure-field reconstruction.The method integrates a Laplacian regularization based on point-cloud neighborhoods within a deep learning model to enhance spatial smoothness and physical consistency of the reconstructed pressure field.A two-stage training strategy is employed to achieve collaborative optimization of data-driven learning and physical constraints.In the first stage,the network is pretrained to learn the mapping from sparse sensor measurements to dense pressure fields.In the second stage,a differentiable node-selection module is combined with gradient-based constraints to jointly optimize the sensor configuration and reconstruction performance.Experiments conducted on the CRH380A high-speed train dataset demonstrate that the proposed approach achieves RMSE and L2RE values of 1.18×10-4 and 0.73×10-3,representing improvements of approximately 4.8％and 3.9％over the PROSNet baseline.The results indicate that the method maintains high reconstruction accuracy and physical fidelity under complex operating conditions,effectively bridging the gap between sparse aerodynamic testing and dense pressure-field acquisition,and providing a reliable tool for aerodynamic analysis and sensor deployment planning.关键词
压力场重建/传感器布局优化/深度学习/梯度引导/物理约束/高速列车Key words
pressure field reconstruction/sensor placement optimization/deep learning/gradient-guided/physics-based constraint/high-speed train分类
交通工程引用本文复制引用
李桐,邹俊泓,孙振旭,雷震,朱翔昱..梯度引导的高速列车表面传感器布局优化和稀疏压力场重建策略[J].空气动力学学报,2026,44(8):20-28,9.基金项目
新一代人工智能国家科技重大专项(2025ZD0122000) （2025ZD0122000）
