Heat transfer enhnacment in a circular tube fitted with new twisted tape insert with rings
This study investigates the impact of using new twisted tape (TT) inserts with rings on the Nusselt number (Nu) and friction factor (f) for heat exchangers, air intercoolers and for other thermofluidics applications. The analysis compares the effects of air and water as cooling fluids. Experimental setup is used to obtain experimental data needed for a Computational Fluid Dynamics (CFD) validation. Then the CFD model is used to predict the impact of using the new TT insert with rings compared with the conventional TT without rings under various conditions which can’t be attained by the experimental facilities at the lab scale. The obtained results showed that the new proposed TT insert with rings significantly enhances heat transfer efficiency while maintaining acceptable pressure drops. The findings suggest incorporating TT with rings can optimize heat transfer performance in various heating and cooling applications. The experimental results showed that adding the rings to the TT enhances the heat transfer characteristics compared with the TT without rings, especially at larger TT pitch distances. Further, the experimental results showed that the Nu values in the case of using the TT inserts with rings significantly increased by up to 25% more than smooth pipes at low air velocities with larger pitch distance and by about 43% at higher velocities with smaller pitch distance. However, this increase in heat transfer also led to a rise in the friction factor, which went up to four times higher at low velocities and larger pitch distances and up to seven times higher at greater velocities and smaller pitch distances.
Heuristic Optimization-based Fuzzy Logic and Pitch Control of Grid-tied Wind Farms for Enhanced Wind Power Distribution
The increasing demand for renewable energy sources has driven significant advancements in solar photovoltaic (PV) technology. Stand-alone PV systems, which operate independently of the grid, are especially vital for remote areas where grid access is infeasible. This paper presents the design and implementation of a stand-alone solar PV system with battery backup, leveraging Simulink for real-time monitoring and control. The system, integrating a solar PV array and a battery storage unit connected to a constant voltage single-phase AC supply, was implemented and rigorously evaluated using MATLAB SIMULINK across seven distinct operating modes. A bidirectional DC-DC converter, functioning in buck mode for charging and boost mode for discharging, is controlled by a comprehensive Battery Management System (BMS) to optimize performance and extend battery life. Notably, the system maintained a stable DC bus voltage around 375V, with minor initial fluctuations quickly stabilized, ensuring efficient power management with an overall efficiency exceeding 90% under varying environmental conditions. The integration of multiple Maximum Power Point Tracking (MPPT) techniques further enhanced system efficiency by up to 25% during fluctuating irradiance levels. The system’s real-time response, with mode transitions occurring in under 200 milliseconds, highlights its capability for continuous and stable power delivery. The PV monitoring Dashboard feature provides real-time parameter visualization and interactive control, allowing dynamic observation of mode transitions and demonstrates the system’s capability to maintain stable operation and efficient power management under varying conditions. This study demonstrates a robust solution for stand-alone renewable energy applications, ensuring efficient energy management and prolonged battery life.
Sentiment Analysis On YouTube Comments Using Machine Learning Techniques Based On Video Games Content
The rapid evolution of the gaming industry, driven
by technological advancements and a burgeoning community,
necessitates a deeper understanding of user sentiments, especially
as expressed on popular social media platforms like YouTube.
This study presents a sentiment analysis on video games based
on YouTube comments, aiming to understand user sentiments
within the gaming community. Utilizing YouTube API, comments
related to various video games were collected and analyzed
using the TextBlob sentiment analysis tool. The pre-processed
data underwent classification using machine learning algorithms,
including Na¨ıve Bayes, Logistic Regression, and Support Vector
Machine (SVM). Among these, SVM demonstrated superior
performance, achieving the highest classification accuracy across
different datasets. The analysis spanned multiple popular gaming
videos, revealing trends and insights into user preferences and
critiques. The findings underscore the importance of advanced
sentiment analysis in capturing the nuanced emotions expressed
in user comments, providing valuable feedback for game developers to enhance game design and user experience. Future research
will focus on integrating more sophisticated natural language
processing techniques and exploring additional data sources to
further refine sentiment analysis in the gaming domain.
Community Battery System Sizing To Maximize Financial Returns to the Prosumers in PV-Rich Neighborhood
The paper extends the knowledge on Community battery systems and sustainable energy.
A Channel Selection Strategy for Energy Harvesting in Cognitive Radio IoT Networks
formulated an optimal channel selection strategy based on the combination of reliable reputation model and multiarmed bandit (MAB) problem to determine an optimal channel selection policy for the SU’s. With the main goal to maximize the SUs harvested RF energy from the PUs channels during transmission.
Automated Leak Detection in Drip Irrigation Systems using RGB and Thermal Sensor Fusion
Water leaks are a common issue in surface drip irrigation systems. Visual inspection of irrigation pipelines by humans is the most prevalent method for leak detection. However, this approach is costly and labour-intensive due to the need for frequent on-site visits. This paper describes an AI based sensor fusion algorithm to automatically detect leaks along the drip lines using RGB and thermal images collected from a low-cost ground vision system. The proposed algorithm was tested using images collected from vineyard under various light conditions. Results indicated that proposed sensor fusion detection algorithm is accurate and efficient.
ActJOLO: Action Recognition Guided by Actionlets Using Joint Lightweight Optical Flow Information
In this study, we propose a novel method, named ActJOLO, which builds upon the existing JOLO model by incorporating an advanced self-supervised learning technique as an upstream guide for posture recognition. Our approach emphasizes the analysis of high-intensity motion features within the human body, thereby enhancing the efficiency of action modeling.
Experimental results on the NTU RGB+D dataset demonstrate that our framework improves processing speed compared to the original model, while maintaining high ccuracy. This work offers a new perspective on skeleton-based human action recognition and highlights its potential for deployment on low-performance processors.
Review of Mathematical Modelling and Interference Minimization Schemes for the Coexistence of 5G and Satellite Radio Access Networks
The aim of the study is to develop a suitable algorithm for interference minimizing in 5G and satellite communication networks coexistence employing Nakagami-m and Shadowed Rician models. Based on this aim, the following research contributes to:
1-Develop a suitable theoretical strategy that evaluates interference scenarios for co-existence between5G and satellite communication networks.
2-Develop an algorithm based on Nakagami-m and Shadowed Rician models for interference minimization in the co-existence between 5G and satellite communication networks.
Single-Stage PV-Grid Integrated Multilevel Inverter Driven Induction Motor Drive for Water Pumping
This paper presents an innovative single-stage grid-integrated solar photovoltaic (PV) system for water-pumping applications using an induction motor drive (IMD). The proposed system employs a seven-level diode-clamped multilevel inverter (MLI) to convert DC power from the PV array to AC power for the motor, eliminating the need for an intermediate DC-DC converter. A perturb and observe (P&O) algorithm is utilized for the PV array’s maximum power point tracking (MPPT). Direct torque control (DTC) with space vector modulation (SVM) provides precise speed regulation of the induction motor. The system enables bidirectional power flow between the PV array, motor load, and utility grid, optimizing energy utilization under varying irradiance and demand conditions. The proposed configuration exhibits enhanced efficiency and improved power quality compared to conventional two-stage topologies, offering a promising solution for grid-connected solar-powered water pumping systems.