A universal correlation for predicting two-phase frictional pressure drop in horizontal tubes based on machine learning

Accurate prediction of the two-phase frictional pressure drop (FPD) facilitates designing compact phase-change heat exchangers. This study implements ML methods and develops a new universal correlation for predicting FPDs in adiabatic and diabatic flow. A consolidated database with 8663 experimental samples from 64 published literature covering 25 fluids and broad operating conditions is amassed to serve this purpose. The designed ML models, based on artificial neural network (ANN) and extreme gradient boosting (XGBoost) theory, can predict the unknown data with the best mean absolute relative deviate (MARD) of 8.59% and the coefficient of deter-mination (R2) of 0.988 or higher. With the aid of parametric importance analysis conducted by the trained ML models, the obtained key parameters, including vapor quality, dimensionless vapor velocity, Froude number, Bond number, and convection number, are adopted to formulate a new correlation. The new correlations can provide more reliable predictive performance than existing correlations for the consolidated database with a MARD of 24.84%. An excluded database is collected to verify the generality of the correlation, and a lower MARD of 27.47% than existing models is obtained. It demonstrates that ML methods can help develop the universal correlation with improved accuracy and universality.

Automated summary

This study develops a new universal correlation for predicting the frictional pressure drop (FPD) in two-phase flow. Machine learning models based on artificial neural network (ANN) and extreme gradient boosting (XGBoost) theory are used to predict the unknown data with high accuracy. The obtained key parameters are used to formulate the new correlation, which shows improved predictive performance compared to existing correlations.