Fluid film bearings are well suited for high-speed industrial machineries like turbine generators and marine propulsion systems. Several numerical methods are employed to theoretically investigate the flow phenomenon and performance parameters of journal bearings, operating under various conditions. This paper reviews various Computational Fluid Dynamics (CFD) and Fluid–Structure Interaction (FSI) techniques that are adopted to solve the flow equations pertaining to fluid film bearings. This review attempts to highlight the importance of cavitation, temperature, and their effect on bearing performance using CFD and FSI techniques. A comparison of various performance parameters in the form of load-carrying capacity, oil flow rate, friction as well as deformation and stresses developed in the bearing element due to oil film pressure using CFD and FSI, are also presented. Observations from the reviewed literature are presented which aims to benefit the researchers working in the field of both CFD and FSI analysis on hydrodynamic journal bearing. Graphical Abstract This review article emphasized the significance of cavitation and temperature effects on journal bearing performance using CFD and FSI techniques.

Artificial neural networks (ANNs) have recently gained popularity as useful models for grouping, clustering, and analysis in a wide range of fields. An ANN is a kind of machine learning (ML) model that has become competitive with traditional regression and statistical models in terms of useability. Lightweight composite materials have been acknowledged to be the suitable materials, and they have been widely implemented in various industrial settings due to their adaptability. In this research exploration, hybrid composite materials using Al2618 reinforced with B₄C and Gr were prepared and then evaluated for hardness and wear behavior. Reinforced alloys have a higher (approximately 36%) amount of ceramic phases than unreinforced metals. With each B₄C and Gr increase, the wear resistance continued to improve. It was found that microscopic structures and an appearance of homogenous particle distribution were observed with an electron microscope, and they revealed a B₄C and Gr mixed insulation surface formed as a mechanically mixed layer, and this served as an effective insulation surface that protected the test sample surface from the steel disc. The ANN and Taguchi results confirm that load contributed more to the wear rate of the composites.

The adverse climate change has posed an emergency for the world to shift to green energy systems. The largest contributor for air pollution, which is the primary cause for the adverse climate change is the fossil fuel driven conventional vehicles. Therefore, electric vehicles are going to be the future of the automotive industry. Due to the abundancy of lithium mineral and high-power density characteristics, lithium-ion powered electric vehicles are prominent choice. Similar to the vehicles driven by internal combustion engines, the electric vehicles should be able to travel long distances while carrying significantly heavy loads. This requires the battery to operate continuously for long duration under heavy loads, which results in increase of battery temperatures beyond the normal operating range. High battery temperature will degrade its performance and subsequently deteriorate battery life. Therefore, it is important to ensure that the battery temperature lies well within normal range for all possible operating conditions of the electric vehicle. This can be achieved by adopting a suitable Battery Thermal Management System (BTMS). In the present study, computational analysis of a single Lithium-ion (LiCoO­­­2 26650) cell designed based on Newman, Tiedemann, Gu and Kim (NTGK) formulation has been carried out to evaluate the maximum temperature attained by the cell. Furthermore, a BTMS is designed for the same using a novel liquid cooling plate (containing three channels) which is in contact with the cell surface. The maximum temperature attained by the cell with BTMS at different discharge rates of 0.5 C, 1.0 C and 1.5 C is determined. To enhance cooling, two fins are attached on top of the liquid cooling plate. Water is used as coolant and analysis is carried out for 0.01 m3/s flow rate. The maximum temperatures attained by the cell with and without BTMS are found to be is 313.72 K, 307.37 K, 302.31 K and 298.55 K, 298.52 K, 297.41 K at discharge rates of 1.5 C, 1.0 C and 0.5 C. Therefore, with BTMS the maximum cell temperature attained is 39%, 25% and 16% less compared to the bare cell, which indicates that the BTMS adopted in the present work is significantly effective in controlling rise in maximum cell temperature.