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A companion paper (Bach et al., 2015) introduced a mapping for a dual port vapor injected compressor, based on a non-dimensional, Π-type approach. The parameters in that model were chosen based on best fit to the data, and the accuracy of the mapping was reported based on the model predictions for the taken data. The vapor injection flowrates were limited for the heat pump dataset due to the coupling to the three-staged refrigerant expansion process from condenser to evaporator. It is likely that researchers and engineers will apply the obtained mapping to their applications, with higher or lower injection mass flowrates, which poses the question of accuracy. This paper investigates inter- and extra-polation accuracy of the mappings for the prediction of the overall isentropic efficiency of the compressor in a rigorous fashion. This includes the different sources of uncertainty (inputs, training data, model random error, and output) and their effects onto the prediction results. Actual test data from a different experimental setups was employed to investigate the behavior of the method. It was found that the main sources of uncertainty for predicting data outside of the training data range are model random error and uncertainty from training data (Maps 1 and 2). A reduction of the number of coefficiencts in the model lead to a reduction of the uncertainty from training data, with an increase in model random error and in maximum deviation between measured and predicted value. Uncertainty from input was much smaller than all other contributions to the uncertainty. Increasing the training data range to include the points that are mapped decreased these uncertainties significantly, while the uncertainty from the outputs remains approximately constant (Map 3, companion paper). This also led to a significant reduction in deviation between measured and predicted value, despite using fewer coefficients than for Map 1.

Citation: 2016 Winter Conference, Orlando, FL, Conference Papers