期刊
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW
卷 142, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijmultiphaseflow.2021.103719
关键词
Nucleate pool boiling correlations; Pool boiling heat transfer; Heat transfer coefficient; Roughness fabrication methods; Artificial intelligence
类别
资金
- Ministry of Science Technology of Taiwan [MOST 1082221E009037MY3]
A high-fidelity deep learning approach has been developed to accurately predict the boiling heat transfer performance of roughened surfaces, taking into account surface characteristics, testing conditions, and liquid thermophysical properties. The model identifies heat flux, surface inclination, surface roughness, among others, as prime factors affecting the nucleate boiling heat transfer coefficient. The final selected model achieved an R-squared of 0.994 and MAE of 0.65, showing high accuracy in estimating the investigated parameter.
The existing nucleate pool boiling correlations have theoretical footings and their usefulness is restricted by the failure to effectively account for the surface effect. To tackle this problem, a high-fidelity approach based on deep learning has been developed to predict the nucleate boiling surfaces subject to various surface roughness. The proposed model accounts for the effect of surface roughness, roughness fabrication method, surface material, surface inclination, saturation temperature, and pressure on the pool boiling performance of dielectric liquids, water, and refrigerants. The proposed method can accurately predict the boiling heat transfer performance of roughened surfaces by including the most influential surface characteristics, testing conditions, and liquid thermophysical properties into the architecture of the developed model. Correlation matrix identifies that heat flux, surface inclination, surface roughness, thermal conductivity of surface material, liquid saturation temperature, and pressure are the prime factors to affect the nucleate boiling heat transfer coefficient. Different neural networks (DNNs) are built and tested in order to find an optimal model based on an experimental dataset of 13000 data points. The final selected model can estimate the investigated parameter with a coefficient of determination (R-2) = 0.994 and mean absolute error (MAE) = 0.65. The suggested method can be utilized to predict the boiling heat transfer performance of a variety of roughened surfaces subject to different working fluids and testing conditions. (C) 2021 Elsevier Ltd. All rights reserved.
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