An artificial neural network model to predict mini/micro-channels saturated flow boiling heat transfer coefficient based on universal consolidated data

By Yue Qiu, Deepak Garg, Liwei Zhou, Chirag R. Kharangate, Sung-Min Kim, I. Mudawar. International Journal of Heat and Mass Transfer, 2020

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Abstract Flow boiling in mini/micro channels is a very effective technique for meeting the high dissipating requirements of thermal management systems. However, accurate prediction of heat transfer coefficients remains an elusive task because of the complex fluid and thermal behavior in these two-phase systems. In this study, a machine learning based approach for predictingheat transfer for saturated flow boiling in mini/micro channels is proposed. A consolidated database of 16953 data points for flow boiling heat transfer in mini/micro-channels is amassed from 50 sources that includes 16 working fluids, reduced pressures of 0.0046 – 0.77, hydraulic diameters of 0.15mm – 6.5mm, mass velocities of 19

Subfields: Computational Mechanics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/4565373c85dc19e09876b239a75ebdbf2ffe0f92