Machine learning approach to predict the thermal performance of closed-loop thermosyphon

This paper presents the machine learning (ML) algorithm to predict the thermal performance of closed-loop thermosyphon (CLT). The experimentation is carried out on the acetone-charged CLT at different test conditions such as heat inputs, filling ratios, and adiabatic lengths. The test data is used to calculate the performance parameters such as thermal resistance, heat transfer coefficient, and effectiveness of the system. Based on the experimental dataset, the ML algorithms are developed to predict the performance parameters of the CLT system. The ML algorithms such as linear regression, decision tree (DT), random forest (RF), and lasso regression are used for the development of the prediction model. The hyperparameters are well-tuned and optimized. The prediction measuring parameters (mean absolute error, R²) are analyzed carefully. It is noticed that the DT model outperformed the prediction of the other used models. The R² score of the DT model was 98.504; whereas, the R² scores of the RF model and linear regression model were about 94.76 and 92.17, respectively. This study will become a roadmap to the ML approach in the thermosyphon system.