Numerical modeling of heat transfer mechanism in remote sensing bulb of thermal expansion valves

Thermal management of automotive systems has garnered a lot of focus in recent times to improve the energy efficiency of vehicles and address the challenge of global warming. In combustible fuel vehicles, the heat pump compressor is driven by the engine power which leads to changes in operating condition of the cycle while the vehicle is in motion. In electric vehicles, a combined thermal management strategy handles both cabin and battery pack cooling/heating depending on the ambient conditions and power requirements by adjusting refrigerant flow rates and flow directions with sophisticated valves. Irrespective of the vehicle type, effective variable refrigerant flow control is prerequisite to ensure optimum thermal comfort with minimum energy. Thermal expansion valves (TXV) are the most widely used method for regulating mass flow rate of refrigerant during the expansion step of the vapor compression cycle based on the principle of trying to maintain a fixed degree of superheat at the evaporator outlet. Conventional models of the thermal expansion valve ignore the time lag between fluctuation of temperature at the evaporator outlet and the corresponding change in pressure of the TXV remote sensing bulb, treating the valve as an instantaneous element during transient operation. This paper presents a novel method for determining the temperature profile and time constant of the remote sensing bulb response using numerical methods, in an attempt to capture the dynamics of thermal expansion valve in active heat pump cycle. Finite difference method was used to solve for the conduction heat transfer between the bulb and the heat exchanger outlet tube in perfect thermal contact applying the necessary boundary conditions. This improved modeling approach will be helpful in better prediction of the dynamic behavior of thermal expansion valves and how it influences the evaporative heat exchanger performance during transient operations to determine control algorithm and strategies.