Conference Agenda

The concept of effective flow and force areas is commonly used to evaluate the effectiveness
of the suction and discharge processes in reciprocating compressors, which is strongly
dependent on the performance of the orifice-valve pair. The typical approach considers
uniform and homogeneous flow at the port inlet. However, for the suction process, the suction
muffler disturbs the flow patterns towards the orifice generating, for instance, recirculation
structures. This work studies the effect of the suction muffler in the effective flow and force
areas during the suction process of a reciprocating compressor. A CFD model is applied for this
purpose. The results show that the suction muffler must be taken into consideration when
evaluating both effective areas. The analysis also indicates that the optimization of the suction
effective areas can be obtained by redesigning the suction muffler rather than the suction port
and the valve.

Rotary compressors are categorized into vertical and horizontal types based on the installation direction. Unlike vertical rotary compressors in which the compression part is submerged in oil, in horizontal rotary compressors, only a part of the compression unit is submerged, which may result in reduced oil supply performance and potential issues. For this reason, it is necessary to accurately predict oil level variations inside horizontal compressors. However, the hermetic design of rotary compressors makes it challenging to observe the internal oil level during operation. This study developed a numerical analysis approach for predicting oil level variation in horizontal rotary compressors. A multi-phase flow analysis technique for two-phase flow of refrigerant and oil was chosen and the influences of critical parameters were assessed. Using the numerical analysis technique, the oil level variations were predicted under various oil amounts and operation speed conditions, and the results were validated through the actual visualization tests using a bolted type compressor equipped with a sight glass. The predicted results such as refrigerant inflow through the oil flow path agreed well with the observation from the tests and the errors between the numerical predictions and the measurements obtained from the tests were within 6.8%. The outcomes of the validation tests clearly demonstrate the numerical methodology developed in this study successfully predicts the characteristics of oil level variations inside horizontal rotary compressors.

Positive displacement compressors have recently begun to include vapor injection more frequently to adapt to energy efficiency and decarbonization goals. High-accuracy models are crucial to predict the compressor performance for rapid integration into HVAC&R systems. Most existing empirical models use more than 10 experimental data points for accurate performance prediction, which can prove burdensome. This study aims to address the need for more universal and versatile compressor mapping methodologies that do not require such intensive and expensive experimental testing. An artificial neural network (ANN) based vapor-injected compressor performance mapping approach is proposed. The proposed ANN model architecture comprises of one input layer, one output layer, and one hidden layer. Input layer includes input parameters such as compressor speed, and suction, injection, and discharge pressures while output layer includes output parameters such as evaporator mass flow rate, injection mass flow rate, compressor power, and discharge temperature. In addition, this study qualifies the feasibility and reliability of the proposed ANN model by varying the number of training data points from 5 to 10 using Mean Absolute Percentage Error (MAPE) as error metric. Data is collected on vapor injected scroll and rotary compressors with R410A and R454B to train and test the model. The model can predict the evaporator mass flow rate, injection mass flow rate, compressor input power, and discharge temperature within 5% MAPE.

In regions characterized by high temperature gradients, vapor compression systems often necessitate operation at very high pressure ratios, resulting in higher discharge temperatures, a reduction in system capacity, and potentially accelerated degradation of compressor oil. Economized vapor injection compressors are used to avoid these issues, yet a precise predictive map for various compressor technologies with minimal data remains unclear. This paper establishes a black-box compressor model to accurately predict compressor evaporator mass flow rate, injection flow rate, and power in compressors with a single vapor injection port. This model is compared against three legacy models from the 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 model. The model is trained with data from R410A and used to predict the performance of the same compressor with R454B, and vice versa. The model can predict evaporator mass flow within 3%, power within 2%, and injection mass flow within 3%.