Development and Validation of a Mechanistic Vapor-Compression Cycle Model

Detailed models are crucial tools for engineers in designing and optimizing systems. In particular, mechanistic modeling of vapor compression systems for accurate performance predictions at both full- and part-load conditions have been improved significantly in the past decades. Yet, fully deterministic models present still challenges in estimating charge inventory in order to optimize the performance. In this work, a generalized framework for simulating vapor compression cycles (VCC) has been develvoped with emphasis on a charge-sensitive model. In order to illustrate the capabilities of the tool, a direct–expansion (DX) cycle has been considered. In the cycle model, the compressor was mapped by employing the ANSI/AHRI 540 10-coefficient correlation, the evaporator and the condenser were constructed based on the ACHP models (Bell, 2010). Furthermore, a TXV model was implemented based on Li and Braun (2008) formulation. With respect to the charge inventory estimation, the two-point regression model proposed by Shen et al. (2009) was used to account for inaccurate estimation of refrigerant volumes, ambiguous flow patterns for two-phase flow, and amount of refrigerant dissolved in the oil. The solution scheme required manufacturer input data for each component as well as the amount of refrigerant charge. Hence, the degree of superheating at the evaporator outlet, the subcooling at the condenser outlet and the perfromance parameters of the VCC system can be predicted. The model was validated with available experimental and numerical data available in literature. The simulation results demonstrated that the proposed model is more accurate and more generic than other methods presented in the literature