An insight into the estimation of frost thermal conductivity on parallel surface channels using kernel based GPR strategy

Abstract In heat exchange applications, frost formation on the cold surface causes a decrease in the rate of heat transfer and growth in the pressure drop. Thus, the study on the frost thermal conductivity has a significant and vital place for the engineers and researchers dealing with the heat exch...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xuejun Zhou, Fangyuan Zhou, Maryam Naseri
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b5ba67d7209243beb1408772093b4074
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b5ba67d7209243beb1408772093b4074
record_format	dspace
spelling	oai:doaj.org-article:b5ba67d7209243beb1408772093b40742021-12-02T14:23:18ZAn insight into the estimation of frost thermal conductivity on parallel surface channels using kernel based GPR strategy10.1038/s41598-021-86607-22045-2322https://doaj.org/article/b5ba67d7209243beb1408772093b40742021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86607-2https://doaj.org/toc/2045-2322Abstract In heat exchange applications, frost formation on the cold surface causes a decrease in the rate of heat transfer and growth in the pressure drop. Thus, the study on the frost thermal conductivity has a significant and vital place for the engineers and researchers dealing with the heat exchangers. In the literature, there is a lack of accurate and applicable methods for determination of frost thermal conductivity. Additionally, the high cost and difficulties of experimental works clarify the importance of computational and mathematical methods. The errors in the determination of frost thermal conductivity on parallel surface channels can cause inaccuracy in estimations of frost density and thickness. The main aim of present work is suggesting Gaussian Process Regression (GPR) models based on four different kernel functions for the estimation of frost thermal conductivity in terms of time, air velocity, relative humidity, air temperature, wall temperature, and frost porosity. To achieve this purpose, a total number of 57 frost thermal conductivity values has been collected. Comparing the suggested GPR models and other available computational methods express the quality of the developed models. The best predictive tool has been selected as a GPR model, including Matern kernel function with R2 values of 0.997 and 0.994 in training and testing phases, respectively. In addition, the effectiveness of discussing variables on frost thermal conductivity has been investigated by sensitivity analysis and showed that air temperature is the most effective parameter. The present work gives engineers an insight into frost thermal conductivity and the effective parameters in its determination.The significant advantage of present work is the accurate prediction of thermal conductivity by a brief knownledge in artificial intelligence.Xuejun ZhouFangyuan ZhouMaryam NaseriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xuejun Zhou Fangyuan Zhou Maryam Naseri An insight into the estimation of frost thermal conductivity on parallel surface channels using kernel based GPR strategy
description	Abstract In heat exchange applications, frost formation on the cold surface causes a decrease in the rate of heat transfer and growth in the pressure drop. Thus, the study on the frost thermal conductivity has a significant and vital place for the engineers and researchers dealing with the heat exchangers. In the literature, there is a lack of accurate and applicable methods for determination of frost thermal conductivity. Additionally, the high cost and difficulties of experimental works clarify the importance of computational and mathematical methods. The errors in the determination of frost thermal conductivity on parallel surface channels can cause inaccuracy in estimations of frost density and thickness. The main aim of present work is suggesting Gaussian Process Regression (GPR) models based on four different kernel functions for the estimation of frost thermal conductivity in terms of time, air velocity, relative humidity, air temperature, wall temperature, and frost porosity. To achieve this purpose, a total number of 57 frost thermal conductivity values has been collected. Comparing the suggested GPR models and other available computational methods express the quality of the developed models. The best predictive tool has been selected as a GPR model, including Matern kernel function with R2 values of 0.997 and 0.994 in training and testing phases, respectively. In addition, the effectiveness of discussing variables on frost thermal conductivity has been investigated by sensitivity analysis and showed that air temperature is the most effective parameter. The present work gives engineers an insight into frost thermal conductivity and the effective parameters in its determination.The significant advantage of present work is the accurate prediction of thermal conductivity by a brief knownledge in artificial intelligence.
format	article
author	Xuejun Zhou Fangyuan Zhou Maryam Naseri
author_facet	Xuejun Zhou Fangyuan Zhou Maryam Naseri
author_sort	Xuejun Zhou
title	An insight into the estimation of frost thermal conductivity on parallel surface channels using kernel based GPR strategy
title_short	An insight into the estimation of frost thermal conductivity on parallel surface channels using kernel based GPR strategy
title_full	An insight into the estimation of frost thermal conductivity on parallel surface channels using kernel based GPR strategy
title_fullStr	An insight into the estimation of frost thermal conductivity on parallel surface channels using kernel based GPR strategy
title_full_unstemmed	An insight into the estimation of frost thermal conductivity on parallel surface channels using kernel based GPR strategy
title_sort	insight into the estimation of frost thermal conductivity on parallel surface channels using kernel based gpr strategy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b5ba67d7209243beb1408772093b4074
work_keys_str_mv	AT xuejunzhou aninsightintotheestimationoffrostthermalconductivityonparallelsurfacechannelsusingkernelbasedgprstrategy AT fangyuanzhou aninsightintotheestimationoffrostthermalconductivityonparallelsurfacechannelsusingkernelbasedgprstrategy AT maryamnaseri aninsightintotheestimationoffrostthermalconductivityonparallelsurfacechannelsusingkernelbasedgprstrategy AT xuejunzhou insightintotheestimationoffrostthermalconductivityonparallelsurfacechannelsusingkernelbasedgprstrategy AT fangyuanzhou insightintotheestimationoffrostthermalconductivityonparallelsurfacechannelsusingkernelbasedgprstrategy AT maryamnaseri insightintotheestimationoffrostthermalconductivityonparallelsurfacechannelsusingkernelbasedgprstrategy
_version_	1718391446644457472

An insight into the estimation of frost thermal conductivity on parallel surface channels using kernel based GPR strategy

Ejemplares similares