Developing simplified numerical calculation and BP neural network modeling for the cooling capacity in a radiant floor cooling system

Abstract

To upgrade the computational efficiency and ensure the accuracy of calculated cooling capacity of radiant cooling floor, this study proposed a simplified three-dimensional modeling by combining with a user-defined function compilation. The cooling capacity and minimum floor temperature were taken as evaluation indices considering different radiant floor thicknesses, layers’ thermal conductivities, pipe diameters, pipe spacing, and floor surface sizes. Moreover, a backpropagation neural network model and a prediction program were developed to quickly predict minimum floor temperature and cooling capacity. The results demonstrate that the established backpropagation neural network model can predict the values of cooling capacity and minimum floor temperature well, and the coefficients of determination were 0.9117 and 0.9435, respectively. With the thickness increase of cover layer and filling layer, the minimum floor temperature respectively increases by 10.97% and 11.01%. With the heat transfer coefficient increase of cover layer and filling layer, cooling capacity respectively increases by 30.56% and 23.46%. This study proposes an artificial intelligence method for the rapid prediction of cooling capacity and minimum floor temperature, and provides their theoretical support for engineering application.

Keywords

• radiant cooling system
• cooling capacity
• bp neural network
• computational fluid dynamics
• floor temperature