A data-driven artificial neural network model for predicting wind load of buildings using GSM-CFD solver
DOI10.1016/j.euromechflu.2021.01.007zbMath1494.76067OpenAlexW3124024140MaRDI QIDQ2055490
Publication date: 1 December 2021
Published in: European Journal of Mechanics. B. Fluids (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.euromechflu.2021.01.007
large eddy simulationdrag coefficientlift coefficientartificial neural networkrectangular cylinderbuilding wind loadsoftware package GSM-CFD
Neural networks for/in biological studies, artificial life and related topics (92B20) Fluid-solid interactions (including aero- and hydro-elasticity, porosity, etc.) (74F10) Direct numerical and large eddy simulation of turbulence (76F65) Basic methods in fluid mechanics (76M99)
Cites Work
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- A face-based smoothed finite element method (FS-FEM) for visco-elastoplastic analyses of 3D solids using tetrahedral mesh
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- A paradigm for data-driven predictive modeling using field inversion and machine learning
- Development of Three-Dimensional GSM-CFD Solver for Compressible Flows
- A MOVING-MESH GRADIENT SMOOTHING METHOD FOR COMPRESSIBLE CFD PROBLEMS
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