Investigation of Flow Behavior in Loop Heat Pipe with Multiple Evaporator using Neutron Radiography and Deep Learning

This study investigates the flow dynamics in a loop heat pipe (LHP) with multiple evaporators, focusing on enhancing its performance for battery thermal management systems (BTMS) in electric vehicles. Neutron radiography will be employed to visualize two-phase fluid behavior, addressing challenges such as vapor pulse, liquid carryover, and heat leakage identified in prior experiments. The impact of varying capillary wick mesh numbers and working fluid filling ratios will be explored. Data collected will serve as training input for deep learning models to predict flow patterns and optimize LHP performance. This novel integration of neutron imaging with machine learning aims to advance the design of LHP-based BTMS, supporting sustainable transportation efforts in the UK and beyond.