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, an Artificial Neural Network (ANN) model is developed based on the universal consolidated database to predict the saturated flow boiling heat transfer coefficients. An optimization is conducted and different ANN model architectures are tested. The best model architecture is selected by investigating different combination of input parameters that included flow parameters, geometric parameters, fluid properties, and dimensionless numbers and a combination of ANN hidden layer networks. The optimized ANN model is superior to generalized correlations for saturated flow boiling heat transfer at predicting the test data, even predicting individual databases with high accuracy. The robustness of the ANN model was tested by excluding databases from the training datasets altogether and utilized to predict these excluded databases. The ANN model did extremely well when a working fluid data was included in the training dataset, and poorly when a working fluid data was excluded from training dataset. The use of a universal ANN model utilizing a consolidated database can become an extremely useful tool when it comes to predicting heat transfer coefficients for saturated flow boiling in mini/micro channels.


Artificial Neural Network Model for Predicting Mini/micro-Channel Flow Boiling Heat Transfer

Division / Sponsors: 
Heat Transfer Division (HTD), Fluids Engineering Division (FED)

Heat Transfer Summer Conference
Paper Proceedings: https://asmedigitalcollection.asme.org/HT/HT2020/volume/83709

18th International Conference on Nanochannels, Microchannels, and Minichannels
Paper Proceedings: https://asmedigitalcollection.asme.org/ICNMM/ICNMM2020/volume/83693

Fluids Engineering Division Summer Meeting
Paper Proceedings: https://asmedigitalcollection.asme.org/FEDSM/FEDSM2020/volume/83716

HT-ICNMM2020 - Virtual Conference, SHTC - Virtual Conference, FEDSM2020 - Virtual Conference

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