A computationally efficient scroll compressor model for both single-phase and two-phase compression considering scroll wrap temperature distribution. (June 2022)

• Record Type: Journal Article
• Authors: Yang, Minghong; Wang, Baolong; Li, Xianting; Shi, Wenxing; Shao, Shuangquan
• Is Part Of: International journal of refrigeration. Volume 138(2022)
• Page Start: 159
• Page End: 168
• Publication Date: 2022-06
• Subjects: Scroll compressor -- Wrap temperature distribution -- Wet compression -- Liquid injection -- Vapor injection -- Refrigeration; Compresseur à spirale -- Distribution de la température de l'enveloppe -- Compression humide -- Injection de liquide -- Injection de vapeur -- Froid [artificiel]; Refrigeration and refrigerating machinery -- Periodicals; 621.56
• DOI: 10.1016/j.ijrefrig.2022.03.008
• Languages: English
• ISSNs: 0140-7007

Abstract

Highlights: A high fidelity scroll compressor model with numerically efficient equations is developed for performance prediction. The governing equations are generic for both single-phase compression and two-phase compression. A detailed scroll wrap temperature distribution calculation method is proposed. Validation shows predicted cooling capacity error within 3.5% and predicted power error within 2.8%. Simulation speed is 7.9–23.7 s per case and convergence rate is 100% for various conditions. Abstract: A general scroll compressor model with high accuracy, fast simulation speed and good numerical robustness is proposed in this study. The governing ordinary differential equations derived from mass and energy conservation use pressure and specific enthalpy as state variables, which can be applied to both single-phase compression and two-phase compression. Both the governing equations for inner compression and the nonlinear equations for internal leakages are explicit in pressure and specific enthalpy to avoid severe numerical stiff problems. In addition, the detailed scroll wrap temperature distribution is calculated based on the proposed second order differential equation considering heat conduction and periodic heat convection. Validation against experimental data shows that cooling capacity errors are less than 3.5% and power consumption errors are less than 2.8% in non-injection and liquid injection conditions. Evaluation of model speed and robustness shows that the model has a convergence rate of 100% and an average calculation speed of 12.4 s per case in various conditions.