Bapuji Institute of Engineering and Technology, Davangere (BIET) is an Engineering and Technology institute located in the city of Davangere, Karnataka, India. The College houses around 18 different departments. It offers Bachelor of Engineering (B.E) in 12 Engineering Disciplines, Master of Technology in 5 specializations, Master of Computer Applications and Master of Business Administration degrees. Thirteen of its departments have been recognized as research centers and 12 departments have been accredited by National Board of Accreditation, New Delhi. It began offering courses from the academic year 1979-1980 and is affiliated to Visvesvaraya Technological University (VTU).
The composite insulators are being utilized extensively in the transmission and distribution system because of their better mechanical and electrical properties. They suffer from internal damage/fracture of the Fiber Reinforced Plastic (FRP) rod resulting in degradation of the insulator due to the partial discharges and corona activity. However, degradation of the Fiber Reinforced Plastic (FRP) rod due to electrical and environmental stresses is a critical reliability concern. Hence this research offers an empirical and theoretical exploration of leakage current (LC) signals acquired from FRP rods under accelerated aging conditions through the Rotating Wheel Dip Test (RWDT).To handle this issue, a multi-domain analysis structure has been developed to process LC signals in the time, frequency, and time-frequency domains. The scope of this work is to analysis degradation in the time domain analysis using several statistical parameters such as peak current, RMS, mean, variance, skewness, kurtosis, and crest factor. The harmonic components are related to surface degradation using FFT and PSD methods in the frequency domain. CWT has been adopted to analyze the time-frequency behavior of LC signals.Degradation progression is measured by means of energy and entropy indicators as well as harmonic analysis. Comparative analysis of samples of FRP rods shows unique degradation trends, allowing detection of the critical state of insulation. The results demonstrate that the proposed approach provides improved diagnostic capability for early detection and condition monitoring of insulator degradation.This work establishes an efficient framework for LC-based condition assessment, contributing to improved reliability and preventive maintenance of composite insulators.
The effect of wall speed ratios on magnetohydrodynamic (MHD) mixed convective flow in a square cavity filled with CuO-water nanofluid, incorporating a diagonally moving heated/cooled wall, is examined. The governing equations, which account for magnetic field effects, buoyancy forces, and nanoparticle concentration, are solved numerically using the finite volume method with the SIMPLE algorithm. The study investigates the effects of wall speed ratios ($\gamma$ = 0, 1, 2), Richardson numbers (Ri = 0.1, 1, 10), Hartmann numbers (Ha = 0, 10, 25, 50), and nanoparticle volume fractions ($\phi$ = 0.0, 0.05) on flow behavior and convective heat transfer within the cavity. The results demonstrate that the wall speed ratio strongly influences streamline patterns and heat transfer. Increasing the wall speed ratio ($\gamma$ = 0--2) enhances heat transfer by up to 476% at low Ri, while nanoparticle addition improves it by 11–18%. In contrast, increasing the Hartmann number (Ha = 0–50) suppresses convection and reduces heat transfer by 8–16% due to magnetic damping. These findings identify optimal wall speed ratios for maximizing thermal performance, highlighting the importance of tailored flow control strategies in MHD nanofluid engineering applications. These findings have significant implications for thermal management design in applications such as heat exchangers, electronic cooling systems, and energy storage devices.
The next-generation multiple-input Multiple-Output Non-Orthogonal Medium Access (MIMO-NOMA) system requires seamless mobility, enhanced spectral efficiency, and higher sum rates with minimal interference. Selecting the optimal network and optimizing resources to meet user quality-of-service (QoS) requirements is challenging in highly crowded, fast-fading MIMO-NOMA networks with high mobility, resource fluctuations, and interference. Various network selection and resource optimization models have been designed using predictive machine learning (ML) and deep learning (DL) techniques with good results. However, in the rapidly fading MIMO-NOMA system, existing methods fail to optimize both network selection and resource allocation. This study introduces the Optimal Spectral Interference Aware Network Resource Optimization (OSIANRO) strategy for the MIMO-NOMA system. The OSIANRO strategy introduces effective network selection optimization using an enhanced Extreme Gradient Boosting (XGB) model with an ideal feature identification mechanism to reduce network selection failures. Then, the OSIANRO strategy leverages effective resource optimization to improve spectral efficiency by increasing the sum rate while minimizing interference. Finally, optimal performance is achieved by leveraging a deep reinforcement learning (DRL) model to optimize network resources. The simulation study shows the proposed model reduces collisions by 53.9%, increases the sum rate by 18.76%, and enhances spectral efficiency by 25.55% compared to baseline models under urban and expressway propagation models.
Efficient utilization of low-speed wind resources remains a significant challenge for small- and medium-scale wind turbines because of low starting torque, premature flow separation, and aerodynamic losses. This study presents the design and aerodynamic evaluation of a bio-inspired dragonfly–owl wind turbine blade developed to improve energy extraction under low wind speed conditions. The blade combines dragonfly-inspired corrugated surface geometry with owl-inspired leading-edge serrations and trailing-edge fringes, integrating complementary passive flow-control mechanisms within a single blade configuration. A three-dimensional blade model was developed using computer-aided design (CAD) software and investigated using computational fluid dynamics (CFD) under steady-state operating conditions representative of low-speed wind environments. The aerodynamic characteristics of the hybrid blade were compared with those of a conventional blade based on velocity distribution, pressure contours, turbulence intensity, lift coefficient, drag coefficient, lift-to-drag ratio, and power coefficient. The numerical analysis indicated improved airflow attachment and modified vortex development over the biomimetic blade surface, together with improved pressure distribution and reduced wake disturbances. The combined corrugation and serration features enhanced the aerodynamic behavior of the blade by promoting more stable flow structures and delaying flow separation. These effects contributed to improved lift generation and aerodynamic efficiency, thereby indicating enhanced starting and low-speed operating characteristics compared with the conventional configuration. The owl-inspired serrated leading edge also demonstrated potential for mitigating unsteady wake structures and aerodynamic noise. The results indicate that combining dragonfly and owl morphological characteristics can provide an effective flow-control strategy for low-speed wind energy applications. The biomimetic blade offers a promising approach for developing compact, efficient, and environmentally compatible wind turbines for distributed renewable energy generation.
