Year: 2025
Type: article
Abstract: To address challenges in holistic real-time condition monitoring of conventional wind tunnels—caused by large structural dimensions and complex parameter monitoring—this study proposes a wind tunnel condition monitoring surrogate model (POD-BPNN) integrating Proper Orthogonal Decomposition (POD) for data dimensionality reduction with Back Propagation Neural Networks (BPNNs). By implementing POD-based order reduction, the computational load for neural network training is significantly reduced while maintaining predictive accuracy through reduced-order data utilization. When applied to reconstruct stress/displacement fields in a wind tunnel test section and the flow field in its fan section, the POD-BPNN model demonstrated prediction errors below 5% when validated against finite element and computational fluid dynamics simulations, with three orders of magnitude improvement in computational efficiency. This methodology satisfies precision and real-time requirements for structural/fluid field monitoring in wind tunnels. When deployed with an existing health management system, online monitoring and predictive maintenance of the digital twin for the wind tunnel will be achievable.
Source: Symmetry
DOI: 10.3390/sym17111923
Authors: Yisheng Yang, Cheng Zhang, Ming Li, Hanwei Wang, Xiqiang Yan, Miao Xian, Hongqiang Xiong, Sijie Yan
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416088942