A Study on Real-Time Condition Monitoring Methods for Wind Tunnels Based on POD and BPNN

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.