Prediction technique for flow boiling heat transfer and critical heat flux in both microgravity and Earth gravity via artificial neural networks (ANNs)

• Published in: International journal of heat and mass transfer 2024-03, Vol.220, p.124998, Article 124998
• Main Authors: Mudawar, Issam, Darges, Steven J., Devahdhanush, V.S.
• Format: Article
• Language: English
• Subjects: Critical heat flux; Flow boiling; Heat transfer; Machine learning; Microgravity; Neural network
• ISSN: 0017-9310; 1879-2189
• DOI: 10.1016/j.ijheatmasstransfer.2023.124998

•Artificial neural network models developed for flow boiling heat transfer and CHF.•Databases consolidated from experiments in microgravity (ISS) and earth gravity.•For 29,226 heat transfer datapoints, the final ANN has an overall MAE of 7.99%.•For 641 CHF datapoints, the final CHF ANN has an overall MAE of 12.05%.•The ANNs outperform seminal correlations and capture physical parametric trends. This study is part of the Flow Boiling and Condensation Experiment (FBCE) and utilizes flow boiling data collected in both microgravity onboard the International Space Station (ISS) and Earth gravity at different channel orientations. The goal is to develop a prediction technique for heat transfer and critical heat flux (CHF) for flow boiling in both microgravity and Earth gravity using artificial neural networks (ANNs). The working fluid, n-perfluorohexane or FC-72, flows through a rectangular channel of 114.6 mm heated length, 2.5 mm heated width, and 5.0 mm unheated height with either one or two walls heated. The consolidated FBCE database for heat transfer coefficient comprises 29,226 datapoints spanning a mass velocity of 173 – 3200 kg/m2s, pressure of 102 – 238 kPa, subcooling of 0 – 44°C, and thermodynamic equilibrium quality of -0.60 – 0.95 (spanning from highly subcooled to high-quality saturated boiling). Following a statistical analysis of various input parameters relevant to flow boiling and optimization of key model parameters, a fully connected feed-forward ANN is developed to predict Nutp. It predicts the entire test database with an overall mean absolute error (MAE) of just 7.99% with consistent and accurate predictions in each subset. Similarly, 641 CHF datapoints from FBCE were consolidated into a database spanning a mass velocity of 99 – 3212 kg/m2s, inlet pressure of 97 – 239 kPa, inlet subcooling of 0 – 46°C, inlet thermodynamic equilibrium quality of -0.61 – 0.86, and CHF values of 4 – 54 W/cm2. A separate ANN is developed by following the same methodology as heat transfer, and it predicts dimensionless CHF, BoCHF, with an overall MAE of just 12.05%. Existing seminal correlations are assessed for subsets of the two consolidated FBCE databases, and the ANNs are shown to have better accuracies in each subset of the database. The ANNs’ high prediction accuracy, in conjunction with their ability to predict physical parametric trends in previously unseen data, shows their potential as prediction tools for both heat transfer and CHF for flow boiling