Investigation on the heat transfer estimation of subcooled liquid hydrogen for transportation applications using intelligent technique

Abstract

Hydrogen, as a sustainable fuel for transportation sector as well as some other heavy industries, should be stored or transferred in liquid phase. To prevent the evaporation of hydrogen in the tanks and the consequent hazards, it is common to lower its temperature below the saturation point known as subcooling of the fluid. When the subcooled liquid hydrogen flows through pipes with higher wall temperatures, the hydrogen can evaporate and flow boiling occurs, resulting in a complex heat transfer behavior of the fluid. In this study, we propose an advanced intelligent model based on the Cascade Forward Neural Network (CFNN) to predict Heat Transfer Coefficient (HTC) of subcooled liquid hydrogen under various flow conditions. Our model leverages on a dataset that is nearly twice the size of the largest previously used in the literature, significantly enhancing its generalization, reliability, and accuracy. The proposed CFNN model incorporates 19 input features, including crucial dimensionless parameters, orientation and thermodynamic condition of the fluid, and characteristics of the pipe that hydrogen flow through to accurately estimate HTC in unseen fluid conditions. The results demonstrate that the proposed model achieves an R-squared value of 0.9978, substantially outperforming the best empirical methods, which have an R-squared value of only 0.8058 for the same testing data. This innovative approach provides a highly accurate and reliable tool for predicting HTC, offering significant improvements over conventional methods and contributing valuable insights for real-world applications in the energy and transportation sectors.