Insitu synthesis of Al- MgAl2O4 composites and parametric optimization of tribological characteristics.

In this research, multiobjective optimization of tribological characteristics of Al-4Mg/in-situ MgAl2O4 composites fabricated via ultrasonic cavitation treatment assisted stir casting technique was carried out. Al-4Mg alloy dispersed with 0.5, 1 and 2 wt% in-situ MgAl2O4 was prepared and the microstructural and mechanical characterisation of the same has been carried out. Reinforcement addition, load and sliding velocity at 3 different levels was considered to attain the responses wear rate and friction coefficient. To identify optimised process condition for the developed composites to attain reduced friction coefficient and wear rate condition, grey analysis is tried out. Experimental results analysed via Grey relation and analysis of variance (ANOVA) proved wt.% of MgAl2O4 particles as significant parameter trailed by load and speed. Based on grey relational grade, minimal wear loss at lowest frictional coefficient can be attained for the composite dispersed with 2 wt% of in-situ MgAl2O4 at 20 N load and 2 m/s sliding velocity. Mechanisms behind the wear loss was analysed from worn out surface micrographs.

A comprehensive review of critical analysis of biodegradable waste PCM for thermal energy storage systems using machine learning and deep learning to predict dynamic behavior.

This article explores the use of phase change materials (PCMs) derived from waste, in energy storage systems. It emphasizes the potential of these PCMs in addressing concerns related to fossil fuel usage and environmental impact. This article also highlights the aspects of these PCMs including reduced reliance on renewable resources minimized greenhouse gas emissions and waste reduction. The study also discusses approaches such as integrating nanotechnology to enhance thermal conductivity and utilizing machine learning and deep learning techniques for predicting dynamic behavior. The article provides an overall view of research on biodegradable waste-based PCMs and how they can play a promising role in achieving energy-efficient and sustainable thermal storage systems. However, specific conclusions drawn from the presented results are not explicitly outlined, leaving room, for investigation and exploration in this evolving field. Artificial neural network (ANN) predictive models for thermal energy storage devices perform differently. With a 4% adjusted mean absolute error, the Gaussian radial basis function kernel Support Vector Regression (SVR) model captured heat-related charging and discharging issues. The ANN model predicted finned tube heat and heat flux better than the numerical model. SVM models outperformed ANN and ANFIS in some datasets. Material property predictions favored gradient boosting, but Linear Regression and SVR models performed better, emphasizing application- and dataset-specific model selection. These predictive models provide insights into the complex thermal performance of building structures, aiding in the design and operation of energy-efficient systems. Biodegradable waste-based PCMs' sustainability includes carbon footprint, waste reduction, biodegradability, and circular economy alignment. Nanotechnology, machine learning, and deep learning improve thermal conductivity and prediction. Circular economy principles include waste reduction and carbon footprint reduction. Specific results-based conclusions are not stated. Presenting a comprehensive overview of current research highlights biodegradable waste-based PCMs' potential for energy-efficient and sustainable thermal storage systems.

A mathematical model for investigation of dry band location on 22 kV bushing with and without RTV coating: Experimental study.

As different pollutants are deposited on the high voltage bushings, a dry band forms, which causes a flashover. The bushing's contaminated layer will weaken its insulation and have an impact on its electrical characteristics. The performance of bushings in dry band conditions of various lengths was investigated in this proposed piece of work, and a dynamic arc model is presented for the arc process in polluted bushings. It shows satisfactory performance in modelling the arc variables for various dry band positions. The developed dynamic open model for contaminated bushings with and without RTV coating predicted the flashover voltage and dry band positions. Any type of contamination, such as sea salt, road salt, and industrial pollutants prevalent in several sites, can be studied using the established model. Ultimately, it was discovered that there was good agreement between the model's results and the outcomes of the experiments. •Mathematical modeling of 22 kV bushing is conceded out for diverse polluted dry band location at lead-in, lead-out and middle region of bushing surface.•Dynamic arc modeling involved in bushing flashover process for different dry band location is done and flashover voltage is predicted•Experimental work is carried out to find FOV for the bushing with different dry location and compared with predicted FOV.

The impact of 180° return bend inclination on pressure drop characteristics and phase distribution during oil-water flow.

The present work aims to capture the influence of the inclination of the return bend on flow patterns and pressure drop during oil-water flow. The experiments were carried out for different inclinations (0°, 15°, 30°, and 45°) of return bend for various superficial velocity combinations of oil (kerosene) and water ranging from 0.07 to 0.66 m/s. The experiments showed that pressure drop increases with the increase in inclination. However, the pressure drop at a fixed inclination (say 15°) decreases with the increase in the superficial velocity of the water. Distinct flow patterns observed in the return bend were droplet flow, film inversion, slug flow, plug flow and large slug flow. Droplet flow dominates at the lower range of kerosene (i.e., Usk = 0.07-0.2 m/s) and higher range of water superficial velocity (i.e., Usw = 0.40-0.66 m/s) at all the inclinations considered in this study. Additionally, comparisons between the experimental and numerical simulation results were made. The numerical solution utilized the Euler-Euler approach, considering the different phases as interpenetrating continua. The Volume of Fluid (VOF) model was used within this approach, monitoring the volume fraction of each phase over the domain while calculating one set of momentum equations for each phase. To capture the turbulent effects accurately, the k-ε turbulence model was incorporated. It happened to be found that the numerical findings showed remarkable agreement with the experimental data.

Effect of pressure profile of shock waves on lipid membrane deformation.

Use of shock waves to temporarily increase the permeability of the cell membrane is a promising approach in drug delivery and gene therapy to allow the translocation of macromolecules and small polar molecules into the cytoplasm. Our understanding of how the characteristics of the pressure profile of shock waves, such as peak pressure and pulse duration, influences membrane properties is limited. Here we study the response of lipid bilayer membranes to shock pulses with different pressure profiles using atomistic molecular dynamics simulations. From our simulation results, we find that the transient deformation/disordering of the membrane depends on both the magnitude and the pulse duration of the pressure profile of the shock pulse. For a low pressure impulse, peak pressure has a dominant effect on membrane structural changes, while for the high pressure impulse, we find that there exists an optimal pulse duration at which membrane deformation/disordering is maximized.