ON FORECASTING OF THE HEAT TRANSFER CRISIS AT FLOW BOILING IN CHANNELS BY USING MACHINE LEARNING
S. S. Abdurakipov1, N. V. Kiryukhina2, E. B. Butakov1
1Kutateladze Institute of Thermophysics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia
2Tsiolkovsky Kaluga State University, Kaluga, Russia
Keywords: machine learning, boiling heat transfer crisis, bubble boiling crisis, critical heat flux density
Abstract
The paper presents a comparative analysis of various machine learning algorithms for solving the problem of predicting the heat transfer crisis during boiling in two-phase flows inside channels of various geometries. Twelve classical regression models implemented in the Scikit-learn, LightGBM, XGBoost, and CatBoost libraries, as well as neural network methods are considered. The models are compared with each other, as well as with traditional forecasting methods based on the use of skeletal tables, approximate semi-empirical ratios, and correlation formulas. Possibilities of hybrid models that combine the approach based on domain knowledge with machine learning algorithms are discussed. The results of experiments with a model that combines the CatBoost regressor with one of the traditional methods in a hybrid scheme are presented. The advantage of machine learning models over the traditional approaches is revealed. It is shown that the best performance for all metrics among machine learning models can be achieved by using ensembles of algorithms based on gradient boosting.
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