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Physics-informed machine learning for surrogate modeling of wind pressure and optimization of pressure sensor placement.
- Source :
-
Computational Mechanics . Mar2023, Vol. 71 Issue 3, p481-491. 11p. - Publication Year :
- 2023
-
Abstract
- This paper presents a predictive computational framework for surrogate modeling of pressure field and optimization of pressure sensor placement for wind engineering applications. Firstly, a machine learning-derived surrogate model, trained by high-fidelity simulation data using finite element-based CFD and informed by a turbulence model, is developed to construct the full-field pressure from scattered sensor measurements in near real-time. Then, the surrogate pressure model is embedded in another neural network (NN) for optimizing pressure sensor placement. The goal of the NN-based optimizer is to learn the best layout of a fixed number of pressure sensors over the structural surface to deliver the most accurate full-field pressure prediction for various inflow wind conditions. We deploy the model to a representative low-rise building subjected to different wind conditions. The performance of the proposed framework is assessed by comparing the predicted results with finite element-based CFD simulation results. The framework shows excellent accuracy and efficiency, which could be potentially integrated with structural health monitoring to enable digital twins of civil structures. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01787675
- Volume :
- 71
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Computational Mechanics
- Publication Type :
- Academic Journal
- Accession number :
- 161795238
- Full Text :
- https://doi.org/10.1007/s00466-022-02251-1