A hierarchical gray-box dynamic modeling methodology for direct-expansion cooling systems to support control stability analysis. (January 2022)

• Record Type: Journal Article
• Authors: Liu, Haopeng; Cai, Jie; Kim, Donghun
• Is Part Of: International journal of refrigeration. Volume 133(2022)
• Page Start: 191
• Page End: 200
• Publication Date: 2022-01
• Subjects: Gray-box model -- Direct-expansion systems -- Vapor-compression dynamic model -- Control stability; Modèle de type boîte grise -- Systèmes à détente directe -- Modèle dynamique de compression de vapeur -- Stabilité de la commande; Refrigeration and refrigerating machinery -- Periodicals; 621.56
• DOI: 10.1016/j.ijrefrig.2021.10.013
• Languages: English
• ISSNs: 0140-7007

Abstract

Highlights: A multi-stage dynamic modeling approach is proposed. The methodology achieves superb computational efficiency and prediction accuracy. Proper control execution time steps are critical for stable and efficient operation. Stable control execution time step is highly dependent on operating conditions. Abstract: In this paper, a gray-box dynamic modeling approach for direct-expansion cooling systems is presented. The overall approach incorporates a multi-stage training procedure that consists of 1) identification of component sub-models from quasi-steady-state performance data, 2) system model integration with estimation of refrigerant charge and 3) fine tuning of thermal capacitances of the evaporator and condenser to capture the system dynamic responses. Compared to traditional physics-based models, the proposed modeling approach has advantages including reduced engineering efforts in the model development phase, improved computational efficiency and enhanced prediction accuracy. The modeling method was validated using a 3-ton variable-speed heat pump and proved to be capable of accurately predicting the system transient behaviors over a wide range of operating conditions. The established dynamic model was then applied for control stability analysis, with a specific goal of determining a proper control execution time step. The case study results showed that the stable control execution time step could change significantly, from 3 sec to 19 sec, as the operating conditions and control settings vary, and a proper selection of the execution time step is critical to ensure stable and reliable operations.