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Abstract This study aims at modelling various performance parameters of an automotive air conditioning (AAC) system using support vector regression (SVR), a novel soft modelling technique. For this purpose, a bench-top AAC system was set up, charged with alternative refrigerant R1234yf, and tested in a wide range of operating conditions. Next, the cooling capacity and coefficient of performance of the AAC system were evaluated. Then, the proposed SVR was trained by using some of the input-output data pairs, and the performance of model predictions was tested using the remaining data. It was determined that the SVR model yielded very accurate predictions. [ABSTRACT FROM AUTHOR]

A laboratory automotive air conditioning (AAC) system was set up and tested for the cases of employing refrigerants R134a and R1234yf. Two different empirical correlations were developed to predict the steady state performance parameters of the AAC system based on some experimental data. The air stream temperatures entering the evaporator and condenser and the relative humidity of the air stream at the evaporator inlet were selected as the input parameters of the proposed correlations, while the output parameters were the cooling capacity, power absorbed by the refrigerant in the compressor, condenser heat rejection rate, coefficient of performance, conditioned air temperature, compressor discharge temperature and the rate of total exergy destruction in the AAC system. Then, the predictions of these correlations were compared with the experimental results which were not used in developing the correlations. The findings suggest that both correlations can be used for accurately predicting the performance parameters of the AAC systems. [ABSTRACT FROM AUTHOR]