Heat transfer performance prediction for heat pipe using deep learning based on wick type

Abstract

Heat pipes are highly efficient heat transfer devices that have found widespread use in various fields. However, accurately predicting their heat transfer performance under wide range conditions remains a challenge. In this study, we propose a deep learning-based approach for predicting the heat transfer performance of heat pipes, considering various input parameters such as wick type, nanoparticle type, and operating conditions. A comprehensive dataset was obtained from the literature to increase the range of prediction. The deep learning architectures including Artificial Neural Networks (ANNs), Deep Neural Networks (DNNs), and Convolutional Neural Networks (CNNs) were utilized. The CNN architecture with skip connections demonstrated the best prediction performance, outperforming other architectures considered in this study. The prediction performance of a skip connection-based CNN architecture shows a maximum percentage error of 7 % and a mean absolute percentage error of 0.39 % in the training dataset range. Experimental validation was conducted to assess the model's performance beyond the range of the training dataset. Experimental validation confirmed the accuracy and versatility of the proposed approach with approximately 20 % of the maximum percentage error. The proposed deep learning-based approach shows promise in efficiently predicting the heat transfer performance of heat pipes, offering potential cost and time savings in various industrial applications.