Evaluation and quantification of compressor model predictive capabilities under modulation and extrapolation scenarios

Testing and evaluation of select semi-empirical and black-box compressor models is carried out to quantify performance in modulation (variable speed), extrapolation, and additionally, variable superheat scenarios. Three representative literature models and an artificial neural network (ANN) model are benchmarked against the industry standard AHRI model. A methodology quantifying model performance, compared against experimental data, in said scenarios is presented. High-fidelity test data taken from either a hot-gas bypass load stand or compressor calorimeter. Scroll, screw, reciprocating, and spool compressor technologies were collected with R410A, R1234ze(E), R134a, and R32 refrigerants totaling 434 experimental points. Data is divided into training, extrapolation, variable speed, and variable superheat data splits to examine model performance. Mean Absolute Percentage Error (MAPE) is computed for mass flow rate and power after training models with training data and evaluating them against the other data splits. Two literature models are true semi-empirical formulations while the other, the ANN, and AHRI model are more empirical in nature. Neither semi-empirical model predicted all compressors. When the compressor type is predicted, the semi-empirical models yield MAPE's less than 8%, 5%, and 4% for mass flow rate and power prediction in extrapolation, modulation, and variable superheat scenarios, respectively. The exception is the Popovic and Shapiro model performing at 21% MAPE in variable superheat power prediction for the spool compressor with R1234ze(E). The ANN showed highest errors of 9.3%, 12%, and 17% in extrapolation, modulation, and variable superheat scenarios, respectively. All models outperformed the AHRI model by several orders of magnitude in these scenarios.