Estimation of vegetation drag force on flow fields using physics-informed neural networks

Abstract

When it is necessary to improve the wind environment after the construction of a relatively large structure, measures such as the placement of windbreak plantings are required. In the evaluation of the wind-blocking effect of vegetation using Computational Fluid Dynamics (CFD), the drag force of the vegetation is given by a canopy model with a drag coefficient and leaf area density. However, there are difficulties in determining the drag coefficient and leaf area density. Physics-informed neural networks (PINNs) are effective for inverse estimation of parameters from flow field information, but the configuration of PINNs needs to be considered when the influence of the parameters to be estimated on space is small. In this study, PINNs that inversely estimate drag force using information on the flow field around a plant canopy model with drag force coefficient and leaf area density are constructed. The estimated values of the constructed PINNs are dependent on the initial values. There is room for improvement in convergence.