Direct and inverse simulations of hydrodynamic and thermal characteristics in a room with random boundary conditions by feedforward neural network modelling

Abstract

Artificial Intelligence (AI) or Neural Network (NN) approaches are starting to be used in built environment areas. This study demonstrates that feedforward neural network (FNN) modelling is capable of simulating the hydrodynamic and thermal characteristics of a room with a radiant heating and cooling system. The FNN model was trained and tested to simulate air velocity and temperature profiles based on boundary conditions as direct simulation and to restore boundary conditions as inverse simulation, using a scaled and normalised database generated by a Computational Fluid Dynamics (CFD) model. In the CFD model, the non-dimensional form of the governing equations, including the radiative transfer equation (RTE) and vorticity equation, were solved using the discrete ordinate method (DOM) and finite difference method (FDM) respectively. The impacts of training database size, hidden layers, and the number of neurons in each layer on the accuracy of the FNN results in the direct and inverse simulations were evaluated in terms of the average root mean square error (RMSE) over the testing data. In the direct simulation, using a smaller CFD database with more hidden layers and neurons achieved comparable accuracy to an FNN trained with a larger CFD database and a less complex FNN. Furthermore, in the inverse simulation, where the FNN model was trained to use a subdomain of temperature profiles to simulate or restore the boundary conditions, the effect of the size of the subdomain on the accuracy in terms of RMSE was studied. It demonstrated that the FNN approach could conduct inverse simulations that are typically beyond the capability of conventional CFD modelling approaches. The results show that the boundary conditions could be accurately restored using subdomains of the temperature profile, covering approximately 60% of the main domain.