Article title: Developing simplified numerical calculation and BP neural network modeling for the cooling capacity in a radiant floor cooling system
Authors: Jiying Liu, Meng Su, Moon Keun Kim & Shoujie Song
Journal: Journal of Asian Architecture and Building Engineering
DOI: https://doi.org/10.1080/13467581.2023.2244730
This article was published with four mistakes in the original, when increases were misrepresented as decreases This has now been updated and the article has now been republished.
The below amendment has been made to the article since it was first published online:
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.
Conclusion
(3) The prediction results showed that with the increase of the thickness of the floor structural layers, Tf,min increase and Qt increase gradually. However, with the increase of the heat transfer coefficient of the floor structural layers, the cooling effect is enhanced. With the increase of δcover and δfilling, Tf,min respectively increases by 10.97% and 11.01%. With the increase of λcover and λfilling, Qt respectively increases by 30.56% and 23.46%.