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

Abstract

This article presents the application of machine learning (ML) algorithms in modeling 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 (Re), twist ratio (t), percentage of perforation (p), and a varied number of twisted tapes (n) were used for the correlation modeling. Three ML 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 were carefully optimized to ensure generalizability. The performance parameters (e.g., R2 and mean absolute error (MAE)) of different ML algorithms were 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.