The need of a sustainable clean future has paved the way for environmental friendly electric vehicle technology. In electric vehicles, overloading is limited by the maximum temperature rise in the electric motor. Although an improved cooling jacket design is of vital importance in lowering the maximum temperature of the motor, there has not been as much study in the thermal analysis of motors compared to electromagnetic design studies. In this study, a three-dimensional steady state numerical method is used to investigate the performance of a cooling jacket using water as the primary coolant of a three-phase induction motor with special emphasis on the maximum temperature and the required pumping power. The effective thermal conductivity approach is employed to model the stator winding, stator yoke, rotor winding and rotor yoke. Heat transfer by induced air is treated as forced convection at the motor ends and effective conductivity is obtained for air in the stator-rotor gap. Motor power losses, i.e., copper and iron losses, are treated as heat generation sources. The effect of bearings and end windings is not considered in the current model. Pressure and temperature distributions under various coolant flow rates, number of flow passes and different cooling jacket configurations are obtained. The study is successful in identifying the hot spots and understanding the critical parameters that affect the temperature profile. The cooling jacket configuration affects the region of maximum temperature inside the motor. Increasing the number of flow passes and coolant flow rate decreases maximum motor temperature but results in an increase in the pumping power. Of the cooling jacket configurations and operating conditions investigated, a cooling jacket with six passes at a flow rate of 10 LPM with two-port configuration was found to be optimal for a 90-kW induction motor for safe operation at the maximum output.

Hybrid renewable energy systems with photovoltaic and energy storage systems have gained popularity due to their cost-effectiveness, reduced dependence on fossil fuels and lower CO₂ emissions. However, their techno-economic advantages are crucially dependent on the optimal sizing of the system. Most of the commercially available optimization programs adopt an algorithm that assumes repeated weather conditions, which is becoming more unrealistic considering the recent erratic behavior of weather patterns. To address this issue, a data-driven framework is proposed that combines machine learning and hybrid metaheuristics to predict weather patterns over the lifespan of a hybrid renewable energy system in optimizing its size. The framework uses machine learning tree ensemble methods such as the cat boost regressor, light gradient boosting machine and extreme gradient boosting to predict the hourly solar radiation and load demand. Nine different hybrid metaheuristics are used to optimize the hybrid renewable energy system using forecasted data over 15 years, and the optimal sizing results are compared with those obtained from 1-year data simulation. The proposed approach leads to a more realistic hybrid renewable energy system capacity that satisfies all system constraints while being more reliable and environmentally friendly. The proposed framework provides a robust approach to optimizing hybrid renewable energy system sizing and performance evaluation that accounts for changing weather conditions over the lifespan of the system.

Information on the rheological characteristics of clathrate hydrate slurry is vital due to its diverse applications including hydrate slurry transportation as in seawater desalination by gas hydrate process, gas delivery through slurry pipelines, cold thermal energy storage, and secondary refrigeration by hydrate slurries. The current study experimentally investigated the rheological behavior of Tetrafluoroethane (Freon) hydrate slurry formed from R-134a and water serving as a medium for sea water desalination. Experiments were performed in a flow loop with a volume of 5.68 L and an inner pipe diameter of 21.5 mm, which was immersed in a constant temperature bath to maintain hydrate stable condition. Experiments were conducted with two phases in the loop; solid hydrate particles and liquid water. The hydrate solid volume fraction ranged from 15.8 to 31.7 vol%. Pressure drops along the straight section of the pipe were monitored while temperature, solid volume fraction and flow rate were kept constant at desired values. The experimental results indicated that Freon slurry can be considered as a pseudo-plastic fluid. The shear-thinning characteristics of Freon slurry became more pronounced as the hydrate solid fraction increased. An empirical power law type equation that relates the apparent viscosity of the Freon slurry to the hydrate solid volume fraction and shear rate was developed and compared with experimental values. The experimental results well supported the values of the apparent viscosity calculated from the modeled equations.

Two-phase flow is encountered in various engineering areas, including the pharmaceutical, chemical, and food industries, desalination facilities, and thermal energy storage systems. Cost-effective and non-invasive monitoring of the solid volume fraction, which governs the thermos-physical properties of two-phase medium, is important for flow assurance. The flow loop having an inner diameter of 21.5 mm and length of about 12.2 m was equipped with square-edged orifice and slash plate pump. Tetrafluroethane (R134a) hydrate slurry of the specified solid volume fraction could be formed within the flow loop by removing an appropriate amount of water, and simultaneously injecting the pertinent amount of R134a while chilled at 275 K. The uncertainty in the thus-obtained solid volume fraction was smaller than 9%, with the largest contribution originating from the uncertain hydration number. The near power-law relationship between the orifice pressure loss coefficient and Metzner–Reed Reynolds number was recognized. However, the nonlinear nature of the Reynolds number with respect to the solid volume fraction inevitably makes the solution procedure iterative. The short span pressure differences across the orifice were regressed to yield empirical correlation, with which the solid volume fraction of R134a slurry could be determined from the measured pressure drop across the orifice and the flow rate. The uncertainty was less than 12% of the thus determined solid volume fraction.