Data‐driven optimization of wind pressure sensor placement on low‐rise buildings using computational fluid dynamics and multi‐resolution dynamic mode decomposition

This study presents a novel hybrid framework for optimal sensor placement to evaluate wind loads on low‐rise buildings. Recognizing the challenges of deploying dense sensor arrays in turbulent atmospheric boundary layer wind tunnel tests, the proposed method integrates large eddy simulation with multi‐resolution dynamic mode decomposition (mrDMD) to isolate spatiotemporally dominant flow features. Unlike traditional DMD‐based approaches that capture global modes, the use of mrDMD enables scale‐separated modal analysis, enhancing sensitivity to transient and localized flow dynamics. These modes guide a QR pivoting algorithm, which efficiently selects sensor locations that maximize information content. The framework demonstrates a sensor count reduction of over 80%, from 1426 candidates to just 182 sensors, while preserving high reconstruction accuracy ( R > 90%) for both mean and fluctuating pressure fields. This distinction enables robust and cost‐effective wind load assessment without compromising fidelity. The methodology is validated using wind tunnel experiments and is shown to be applicable for generalized wind scenarios through an angle‐of‐attack‐unified sensor configuration. By combining modal decomposition with informed optimization, this framework advances state‐of‐the‐art techniques in structural monitoring, offering practical utility in experimental and real‐world applications.