  This article presents the application of machine learning (ML) algorithms in
modeling of the heat transfer correlations (e.g. Nusselt number and friction
factor) for a heat exchanger with twisted tape inserts. The experimental data
for the heat exchanger at different Reynolds numbers and twist ratios were used
for the correlation modeling. Three machine learning algorithms: Polynomial
Regression (PR), Random Forest (RF), and Artificial Neural Network (ANN) were
used in the data-driven surrogate modeling. The hyperparameters of the ML
models are carefully optimized to ensure generalizability. The performance
parameters (e. g. $R^2$ and $MSE$) of different ML algorithms are analyzed. It
was observed that the ANN predictions of heat transfer coefficients outperform
the predictions of PR and RF across different test datasets. Based on our
analysis we make recommendations for future data-driven modeling efforts of
heat transfer correlations and similar studies.
