The Method of Combining ‘FCN’ Module with M-File for Modeling the Gas-Liquid Two-Phase State of Refrigerant

This paper constructs a simulation model of the mathematical equations for the two-phase and superheated zones of an air conditioner evaporator, simulates and calculates unknowns in the equations, such as the surface heat transfer coefficients on the refrigerant and chilled water sides in the two zones, the evaporation pressure, and the chilled water outlet temperature, using a method that combines Matlab's ‘FCN’ block and M-file. The process begins by declaring three output parameters-refrigerant outlet temperature, enthalpy in the superheat zone, and chilled water outlet temperature-as global variables in the ‘FCN’ module for the evaporator's superheat zone. Next, in the ‘M’ function file, the expressions for the specific enthalpy and density as functions of temperature in the superheated zone are obtained using the method of solving cubic equations with the ‘Cardano’ formula. Following this, the refrigerant outlet temperature, enthalpy, saturated gas density, coolant water outlet temperature, and pipe length are calculated as five global variables in the two-phase zone. Finally, by passing global variables in the Simulink simulation window, real-time parameter in the two areas are updated iteratively, and the steady value is determined. This study focuses on enthalpy change and heat transfer-dominated processes, which aligns with the phase-change heat transfer characteristics of evaporators in vapor compression cycles (VCC). By declaring interface parameters and global variables, it achieves mass/energy coupling between different zones, improving computational efficiency compared to traditional segmented modeling approaches. The structure is concise and intuitive, facilitating understanding of model principles and parameter relationships. Additionally, adding boundary constraints for unknown variables in M-files reduces simulation time, while setting breakpoints enables real-time step-by-step monitoring of simulation values. This approach allows for rapid and accurate fault diagnosis, as well as optimization of structural parameters and operating conditions.