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This article presents the application of machine learning (ML) algorithms inmodeling of the heat transfer correlations (e.g. Nusselt number and frictionfactor) for a heat exchanger with twisted tape inserts. The experimental datafor the heat exchanger at different Reynolds numbers and twist ratios were usedfor the correlation modeling. Three machine learning algorithms: PolynomialRegression (PR), Random Forest (RF), and Artificial Neural Network (ANN) wereused in the data-driven surrogate modeling. The hyperparameters of the MLmodels are carefully optimized to ensure generalizability. The performanceparameters (e. g. $R^2$ and $MSE$) of different ML algorithms are analyzed. Itwas observed that the ANN predictions of heat transfer coefficients outperformthe predictions of PR and RF across different test datasets. Based on ouranalysis we make recommendations for future data-driven modeling efforts ofheat transfer correlations and similar studies.

Machine learning assisted modeling of thermohydraulic correlations for heat exchangers with twisted tape inserts