空气动力学学报2025，Vol.43Issue(4)：91-102,12.DOI:10.7638/kqdlxxb-2024.0191
基于物理约束深度学习的大跨柔性光伏阵列绕流场重构
Reconstruction of flow fields around large-span flexible PV array based on physics-informed deep learning
摘要
Abstract
The complex three-dimensional inter-row flow interference induced by bidirectional series of large-span flexible PV arrays is the primary reason for the wind-induced damage.Wind tunnel tests are difficult to capture the flow fields subjected to inter-row interference.However,deep learning methods offer promising solutions for accurate reconstruction and prediction of complex flow fields.The present study focuses on the 5-row by 3-span PV array with a span of 40 meters,located at the State Power Investment Group Flexible PV Demonstration Base in Yancheng,Jiangsu.Large-eddy simulations of wind fields were performed to provide training data for a fully connected neural network deep learning method that incorporated the loss function into Navier-Stokes equations.Based on this innovative method,a data-driven model and a data-physics dual driven model was established for accurately reconstructing the velocity and pressure fields around an array of large-span flexible PV.Compared to the data-driven model,the data-physics dual driven model demonstrates superior efficacy in capturing the flow characteristics.Specifically,the streamwise velocity reconstruction errors upstream of the first two rows and the fourth row,at the upper and lower edges of the PV panels and in the wake region,are reduced by 60.2%and 36.6%,respectively.The reconstruction errors of the lateral velocity at the upper and lower edges of the PV panels are reduced by 53.7%.Moreover,the model has full-field reconstruction errors of 16.6%and 18.5%for the streamwise and lateral velocities,respectively.Meanwhile,when the pressure term is not considered in the loss function,the data-driven model cannot learn pressure information from the training data,while the data-physics dual driven model can obtain the pressure field through the velocity field,yielding an average reconstruction error of the inter-row pressure fields being only 16.1%.This study provides a reference for a new intelligent reconstruction method for the flow fields around complex structures under wind loads.关键词
大跨柔性光伏阵列/大涡模拟/物理约束神经网络/损失函数/流场重构/深度学习Key words
large-span flexible PV array/large vortex simulation/physics-informed neural network/loss function/flow field reconstruction/deep learning分类
土木建筑引用本文复制引用
张春伟,柯世堂,王伯洋,余玮,梁珂,马文勇..基于物理约束深度学习的大跨柔性光伏阵列绕流场重构[J].空气动力学学报,2025,43(4):91-102,12.基金项目
国家自然科学基金NSFC-RGC合作研究重点项目(52321165649) （52321165649）
江苏省杰出青年科学基金(BK20211518) （BK20211518）
江苏省研究生科研与实践创新计划项目(KYCX22_0374) （KYCX22_0374）
