Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces

The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influ...

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Autores principales:	Uzair Sajjad, Imtiyaz Hussain, Muhammad Sultan, Sadaf Mehdi, Chi-Chuan Wang, Kashif Rasool, Sayed M. Saleh, Ashraf Y. Elnaggar, Enas E. Hussein
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	pool boiling heat transfer coefficient sintered coated porous surfaces deep neural network Bayesian optimization gaussian process gradient boosting regression trees Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/f01d0689aa4e446a9ef61fe1e12c516e
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spelling	oai:doaj.org-article:f01d0689aa4e446a9ef61fe1e12c516e2021-11-25T19:02:47ZDetermining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces10.3390/su1322126312071-1050https://doaj.org/article/f01d0689aa4e446a9ef61fe1e12c516e2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12631https://doaj.org/toc/2071-1050The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R<sup>2</sup> = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R<sup>2</sup> (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.Uzair SajjadImtiyaz HussainMuhammad SultanSadaf MehdiChi-Chuan WangKashif RasoolSayed M. SalehAshraf Y. ElnaggarEnas E. HusseinMDPI AGarticlepool boiling heat transfer coefficientsintered coated porous surfacesdeep neural networkBayesian optimizationgaussian processgradient boosting regression treesEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12631, p 12631 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pool boiling heat transfer coefficient sintered coated porous surfaces deep neural network Bayesian optimization gaussian process gradient boosting regression trees Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	pool boiling heat transfer coefficient sintered coated porous surfaces deep neural network Bayesian optimization gaussian process gradient boosting regression trees Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Uzair Sajjad Imtiyaz Hussain Muhammad Sultan Sadaf Mehdi Chi-Chuan Wang Kashif Rasool Sayed M. Saleh Ashraf Y. Elnaggar Enas E. Hussein Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces
description	The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R<sup>2</sup> = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R<sup>2</sup> (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.
format	article
author	Uzair Sajjad Imtiyaz Hussain Muhammad Sultan Sadaf Mehdi Chi-Chuan Wang Kashif Rasool Sayed M. Saleh Ashraf Y. Elnaggar Enas E. Hussein
author_facet	Uzair Sajjad Imtiyaz Hussain Muhammad Sultan Sadaf Mehdi Chi-Chuan Wang Kashif Rasool Sayed M. Saleh Ashraf Y. Elnaggar Enas E. Hussein
author_sort	Uzair Sajjad
title	Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces
title_short	Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces
title_full	Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces
title_fullStr	Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces
title_full_unstemmed	Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces
title_sort	determining the factors affecting the boiling heat transfer coefficient of sintered coated porous surfaces
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f01d0689aa4e446a9ef61fe1e12c516e
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Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces

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