In the present work, a direct oil cooling strategy using a multi-nozzle configuration is proposed for the thermal management of high-power density electric machines. The stator and winding temperatures, heat transfer coefficient, injection pressure, and power consumption are investigated for different nozzle types, nozzle numbers, heights of nozzle combinations, and oil flow rates. In addition, an artificial neural network (ANN) model based on two algorithms is developed for predicting thermal performance under various operating conditions. The flat jet nozzle shows the lowest maximum winding temperature of 120.3 °C and a superior heat transfer coefficient of 3028.6 W/m2-K compared to both full cone nozzles. The power consumption for the flat jet nozzle is higher at 123.9 W compared to other nozzle types. The combination of four flat jet nozzles shows improved oil spray distribution and enhanced cooling compared to combinations of two and six flat jet nozzles. Further, the thermal performance of oil spray cooling with four flat jet nozzles improves when height and oil flow rate are increased. Oil spray cooling with the best configuration shows a winding temperature, heat transfer coefficient, and injection pressure of 98.9 °C, 3408.6 W/m2-K and 4.86 bar, respectively, at a flow rate of 20 LPM. The proposed neural network model with a Levenberg–Marquardt (LM) training variant and logarithmic–sigmoidal (Log) transfer function shows the lowest prediction error within ±2%.
Garud et al. (2026) studied this question.