Enhancing the prediction of flow characteristics in an inventive plate heat exchanger using deep learning techniques

Abstract In modern energy research, minimizing fuel usage and harmful gas emissions are critical priorities. The application of advanced deep learning (DL) models to predict thermohydraulic characteristics of an innovative plate heat exchanger (IPHE) is investigated in this study. Building upon our prior work utilizing machine learning (ML) models, the focus is placed on predicting the Nusselt Number (Nu), friction factor (f), and performance (P) within a Reynolds number range of 500 to 5000. Advanced DL architectures-GRU, LSTM, and CNN-are utilized, resulting in substantial improvements in prediction accuracy and robustness. The LSTM model demonstrates superior performance, achieving R 2 scores of 0.9986, 0.9985, and 0.9968 for Nu, f, and P, respectively, significantly surpassing prior ML model results of 0.98, 0.979, and 0.9628. The findings highlight the capacity of DL models to capture complex, nonlinear relationships in thermohydraulic data, offering an enhanced approach to optimizing plate heat exchanger (PHE) performance. This work contributes to energy-efficient technological advancements, supporting global efforts to reduce environmental impacts while addressing rising energy demands.