Data-Driven Method of Modeling Sparse Flow Field Data

被引：0

作者：

Wang, Hongxin ^{[1
,2
]}

Xu, Degang ^{[1
]}

Zhou, Kaiwen ^{[3
]}

Li, Linwen ^{[2
]}

Wen, Xin ^{[3
]}

机构：

[1] School of Automation, Central South University, Changsha

[2] Shanghai Aircralt Design and Research Institute, Shanghai

[3] School of Mechanical and Power Engineering, Shanghai Jiao Tong University, Shanghai

来源：

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2025年 / 59卷 / 05期

关键词：

data-driven; dynamic mode decomposition; flow field reconstruction; location optimization; reduced Order model; sparse data;

D O I：

10.16183/j.cnki.jsjtu.2023.213

中图分类号：

学科分类号：

摘要：

Real-time perception and prediction of flow field havc vcry important application valuc in aviation and navigation, and posc challenges such as high flow field dimension and less real-time measurement information. To solvc such problem, a data-driven flow field modeling method framework is proposed, which realizes real-time reconstruction of online flow field by establishing sparse data and high-dimensional flow field mapping offline. In offline modeling, aimed at the high-dimensional challenge of the flow field, the eigenortho dccomposition and other methods arc uscd to reduce the dimensionality of the data and extract the spatial mode of the main flow field. The QR dccomposition method is used to mine the modal sensitivity characteristics of the flow field and optimize the measurement point position. Dynamic modal decomposition with time delay significantly reduces the number of measurement points. In the online reconstruction, based on real-time sparse measurement data and data-driven models, the prediction of the current and future full-field flow field is realized. In the test of cylinder wake flow, using this method and using 20 sparse measurement points, the full-field reconstruction error obtained can reach less than 10%. © 2025 Shanghai Jiaotong University. All rights reserved.

引用

页码：684 / 690

页数：6

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