National Institute of Technical Teachers Training and Research

The growing demands of high-speed rotary systems require journal bearings with enhanced tribological performance. Conventional lubricants consistently fall short, so nanolubricants-upgraded with nanoparticles are a viable alternative due to high thermal conductivity and load-carrying capacity. This article presents an extensive experimental investigation on elliptical journal bearing performance utilizing a journal bearing test rig with a variety of operating oils, speed and load parameters. Two base lubricants and nanolubricants synthesized by dispersing titanium dioxide (TiO 2 ) and copper oxide (CuO) nanoparticles at three concentrations (0.5%, 1.0%, and 2.0% by weight) have been studied. Two important performance parameters, namely, hydrodynamic pressure distribution and oil film temperature have been evaluated under steady-state conditions. The results reveal that nanolubricants significantly enhance the static thermal bearing performance with all operating parameters. Specifically, nanolubricants prepared by blending TiO 2 gave better results in comparison to CuO based nanolubricants with marked increase in hydrodynamic pressure (up to 15.67%) with insignificant rise (up to 3.83%) in oil temperature. Bearing performance has been further improved with increase in nanoparticles concentration. In addition, elliptical bearings also had preferred pressure contours compared with conventional circular bearings, which reveals their possible suitability under high-load bearing applications. The findings reveal the potential of nanolubricants and elliptical bearing geometries for increasing the reliability and performance of modern tribosystems.

Artificial Intelligence (AI) is revolutionizing contemporary marketing by bringing strategic choice from intuition-driven campaigns to data-driven, constantly evolving systems. This document provides an overview of artificial intelligence applications that are recently being utilized. These involve predictive segmentation, real-time sentiment, conversational bots, and automated media buying. In addition, critical issues around data heterogeneity, compliance with privacy, and openness of models are highlighted. Tooffer the Predictive Personalisation Engine (PPE), a modular platform that combines deep sequential learning and reinforcement learning to offer real-time content suggestions which take into account the context in which they are presented. A multi-modal consumer dataset is fed through PPE, audiences segmented by K-Means++ clustering, future behavior is predicted by LSTM networks, and best offers are decided through an epsilon-greedy contextual bandit.

The widespread adoption of intermittent Renewable Energy Sources (RES) and the dense Electric Vehicle (EV) charging systems is a tremendous challenge to the stability and performance of modern power distribution systems. The paper discusses the critical issues of power quality (PQ), i.e. power losses and the voltage deviation and the harmonic distortions, which are exacerbated by the bi-directional and stochastic nature of the power flow in smart grids. This research paper will solve these problems by suggesting a new hybrid optimisation model: Intelligent Neural Herd-Based Water Drop Optimisation (INH-WDO) algorithm. The framework has been synergistically developed to combine the efficient pathfinding properties of Water Drop Optimisation (WDO), the efficient global exploration properties of herd behaviour and the nonlinear modelling properties of neural networks. The strength and performance of the INHWDO algorithm are strictly tested on one of the standard IEEE 33bus radial distribution system, an application methodology that was not used in the preceding iterations of this paper. The suggested scheme is considered in the realistic conditions with moderate (20%) and high (40%) RES penetration, and a dynamic 24-hour EV charger load profile. The INH-WDO framework exhibits a better performance when compared to various state-of-the-art optimisation algorithms; such as Dandelion Optimiser (DBO), Artificial Vultures Optimisation Algorithm (AVOA), Artificial Neural Network (ANN) and Fuzzy Logic controllers in a comprehensive comparative analysis. The findings show that INH -WDO has a minimum power loss of 0.2 MW, a voltage deviation of 3.3936%, controlled, and total harmonic distortion of 2.5%. What is more, this paper presents a formal analysis of computational complexity and scalability of the algorithm in question, thus placing the INH-WDO as the possible solutions of computational feasibility and robustness to improve the resilience and efficiency of the future power systems.