A Data-Driven Approach for the Prediction of Subcooled Boiling Heat Transfer

Jerol Soibam
Mälardalen University, Västerås, Sweden

Ioanna Aslanidou
Mälardalen University, Västerås, Sweden

Konstantinos Kyprianidis
Mälardalen University, Västerås, Sweden

Rebei Bel Fdhila
Mälardalen University, Västerås, Sweden and Hitachi ABB Power Grids, Västerås, Sweden

Ladda ner artikelhttps://doi.org/10.3384/ecp20176435

Ingår i: Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020, September 22-24, Virtual Conference, Finland

Linköping Electronic Conference Proceedings 176:62, s. 435-442

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Publicerad: 2021-03-03

ISBN: 978-91-7929-731-2

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


In subcooled flow boiling, heat transfer mechanism involves phase change between liquid phase to the vapour phase. During this phase change, a large amount of energy is transferred, and it is one of the most effective heat transfer methods. Subcooled boiling heat transfer is an attractive trend for industrial applications such as cooling electronic components, supercomputers, nuclear industry, etc. Due to its wide variety of applications for thermal management, there is an increasing demand for a faster and more accurate way of modelling. In this work, a supervised deep neural network has been implemented to study the boiling heat transfer in subcooled flow boiling heat transfer. The proposed method considers the near local flow behaviour to predict wall temperature and void fraction of a sub-cooled mini-channel. The input of the network consists of pressure gradients, momentum convection, energy convection, turbulent viscosity, liquid and gas velocities, and surface information. The output of the model is based on the quantities of interest in a boiling system i.e. wall temperature and void fraction. The network is trained from the results obtained from numerical simulations, and the model is used to reproduce the quantities of interest for interpolation and extrapolation datasets. To create an agile and robust deep neural network model, state-of-the-art methods have been implemented in the network to avoid the overfitting issue of the model. The results obtained from the deep neural network model shows a good agreement with the numerical data, the model has a maximum relative error of 0.5 % while predicting the temperature field, and for void fraction, it has approximately 5 % relative error in interpolation data and a maximum 10 % relative error for the extrapolation datasets.


deep neural network (DNN), CFD, machine learning (ML), sub-cooled boiling, heat transfer


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