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

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Correction

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article was originally published with errors, which have now been corrected in the online version. Please see Correction (https://doi.org/10.1080/13467581.2023.2251335).

Additional information

Funding

This work was funded by Natural Science Foundation of Shandong Province [ZR2021ME199, ZR2020ME211], and the Support Plan for Outstanding Youth Innovation Team in Colleges and Universities of Shandong Province [2019KJG005]. This work acknowledges the support of the Plan of Introduction and Cultivation for Young Innovative Talents in Colleges and Universities of Shandong Province.

Notes on contributors

Jiying Liu

Jiying Liu is a professor of Shandong Jianzhu University, Jinan, China. He is engaged in urban microclimate and human health, energy-saving and optimization of radiation air conditioning systems, and optimization of regional energy systems.

Meng Su

Meng Su is graduating from Shandong Jianzhu University, Jinan, China. She obtained her Master's degree in Engineering in 2023. Her research interest is radiant floor cooling system and computational fluid dynamics.

Moon Keun Kim

Moon Keun Kim is an associate professor of Oslo Metropolitan University, Oslo, Norway. He is interested in research of ventilation and air quality, building energy efficiency and radiant cooling system.

Shoujie Song

Shoujie Song is a senior engineer of Shandong Jianzhu University, Jinan, China. His major research field is the radiant floor cooling and heating system and building energy-saving technology.