Experimental study for artificial neural network modeling on thermal and flow performances of electric traction motor with oil spray cooling

The oil spray cooling is emerging as advanced thermal management technique for next generation electric traction motor. The performance of oil spray cooling needs to be mapped and predicted based on various influential factors to develop efficient thermal management system for electric traction motors. In the present work, the thermal and flow performances are experimentally investigated and predicted using an artificial neural network (ANN) for electric traction motor with oil spray cooling. The thermal and flow performances are extracted in terms of maximum temperature, heat transfer coefficient, Nusselt number, spray pressure and spray power consumption. The effects of nozzle types, spray heights, volume flow rates and chiller temperatures are analyzed as operating factors. The ANNs are modeled considering Levenberg-Marquardt (LM) training variant, 10 hidden neurons and Tangential-sigmoidal (Tan) and Logarithmic-sigmoidal (Log) as transfer functions. The thermal and flow performances are superior for full cone followed by hollow cone and spiral nozzles in decreasing order. The higher spray height, higher volume flow rate and lower chiller temperature show optimum values of maximum temperature and heat transfer coefficient. The spray power consumption is maximum in case of higher volume flow rate, reported less variation with change in spray height and chiller temperature. The ANN model comprising of LM-Tan algorithm is recommended as the best model with prediction error within +/- 1% for all thermal and flow performances. The predicted results from the optimum ANN model are validated with experiments with maximum coefficient of determination (R2) and coefficient of variance (COV) as 0.99 and 3.20, respectively. The correlations for thermal and flow performances in terms of all influential factors are proposed using the optimum ANN model.