Determining the factors affecting the boiling heat transfer coefficient of sintered coated porous surfaces

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Authors
Sajjad, Uzair
Hussain, Imtiyaz
Sultan, Muhammad
Mehdi, Sadaf
Wang, Chichuan
Rasool, Kashif
Rasool, Kashif
Elnaggar, Ashraf Y.
Hussein, Enas E
Issue Date
2021-11-16
Type
Article
Language
en_US
Keywords
Pool boiling heat transfer coefficient , Sintered coated porous surfaces , Deep neural network , Bayesian optimization , Gaussian process , Gradient boosting regression trees
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Abstract

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, 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 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.

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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Citation
Sajjad, U.; Hussain, I.; Sultan, M.; Mehdi, S.; Wang, C.-C.; Rasool, K.; Saleh, S.M.; Elnaggar, A.Y.; Hussein, E.E. Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces. Sustainability 2021, 13, 12631. https://doi.org/10.3390/ su132212631
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MDPI
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ISSN
2071-1050
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