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

Uzair Sajjad, Imtiyaz Hussain, Muhammad Sultan, Sadaf Mehdi, Chi-Chuan Wang, Kashif Rasool, Sayed M. Saleh, Ashraf Y. Elnaggar and Enas E. Hussein
Additional contact information
Uzair Sajjad: Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan
Imtiyaz Hussain: Department of Power Mechanical Engineering, National Tsing Hua University, No. 101, Section 2, Guangfu Road, East District, Hsinchu 300, Taiwan
Muhammad Sultan: Department of Agricultural Engineering, Bahauddin Zakariya University, Bosan Road, Multan 60800, Pakistan
Sadaf Mehdi: Department of Mechanical Engineering, Wichita State University, Wichita, KS 67260, USA
Chi-Chuan Wang: Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan
Kashif Rasool: Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha P.O. Box 5825, Qatar
Sayed M. Saleh: Department of Chemistry, College of Science, Qassim University, Buraidah 51452, Saudi Arabia
Ashraf Y. Elnaggar: Department of Food Nutrition Science, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Enas E. Hussein: National Water Research Center, P.O. Box 74, Shubra El-Kheima 13411, Egypt

Sustainability, 2021, vol. 13, issue 22, 1-19

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, R 2 = 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 2 (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.

Keywords: pool boiling heat transfer coefficient; sintered coated porous surfaces; deep neural network; Bayesian optimization; gaussian process; gradient boosting regression trees (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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