A novel characterization methodology for vapor-injected compressors: A comparative analysis with existing black-box models

Abstract

In regions characterized by high temperature gradients, vapor compression systems often necessitate operation at very high pressure ratios resulting in a reduction in system capacity. Economized vapor injection compressors are used to avoid these issues, yet a precise predictive map for various compressor technologies with minimal data and relatively better performance remains unclear. This paper establishes a black-box compressor model to accurately predict compressor power, injection mass ratio, and evaporator mass flow rate in compressors with a single vapor injection port. This model is compared against three legacy models from literature and the ANN model, for reference. All five models are evaluated based on their ability to predict the aforementioned metrics. The proposed black-box model can predict the relevant metrics all within 5 % Mean Absolute Percentage Error (MAPE). Additionally, a refrigerant sensitivity analysis is performed with the black-box models. The model is trained using data from R410A and then used the coefficients to predict the performance of the same compressor when using R454B, and vice versa. It can estimate the evaporator mass flow rate with an accuracy within 3 %, the power within 2 %, and the injection mass ratio with MAPE less than 3 %.