Volume 12, Issue 8, August 2024 Edition - IEEE-SEM Journal Publication
On a Class of Piecewise Continuous Lyapunov Functions and Asymptotic Practical Stability of Nonlinear Impulsive Caputo Fractional Differential Equations Via New Modelled Generalized Dini DerivativePDF
Jackson Efiong Ante, Jeremiah Ugeh Atsu, Etimbuk Emmanuel Abraham, Okoi Okoi, Enobong Joseph Oduobuk, Augustine Brendan Inyang
In this paper, the asymptotic practical stability of nonlinear impulsive Caputo fractional differential equations with fixed moments of impulse is examined using a class of piecewise continuous Lyapunov functions which generalizes the vector Lyapunov functions. Together with comparison results, sufficient conditions for the practical stability as well as asymptotic practical stability of the impulsive Caputo fractional order systems are presented. An illustrative example is given to confirm the suitability of the obtained results.
Solar Radiation Analysis & Prediction Using Machine Learning AlgorithmsPDF
Mohammad Hasan, Sabrina Sharmin, Mohammad Mizanur Rahman
The solar radiation analysis and prediction model aims to develop and implement machine learning algorithms for analyzing and predicting solar radiation, with the goal of enhancing the efficiency and integration of solar power systems. Accurate solar radiation prediction is crucial for agriculture, weather forecasting, health awareness, effective energy management, grid stability and optimizing the utilization of solar energy resources. This project begins with an extensive collection of solar radiation data from Kaggle.com, including weather stations, satellite imagery, and ground-based sensors. The dataset is preprocessed and feature engineering techniques are applied to extract relevant meteorological and environmental parameters. Several machine learning techniques including Linear Regression Models, Decision Trees, Support Vector Regressors, KNN, XGBoost Regressor, Random Forests, and Gradient Boosting Regressor, are trained and evaluated using the prepared dataset. Then performance of these algorithms is assimilated and compared. This project will showcase the effectiveness of machine learning algorithms by predicting accurate solar radiation.
Comparative Analysis of Phase Change Materials-Based Heat SinksPDF
Zain Ahmed, Shahmeer Khalid Chatha, Muhammad Sher Ali, Muhammad Maaz Imran, Saqlain Mushtaq
In response to the escalating demand for efficient cooling solutions in data centers, this paper explores the integration of Phase Change Materials (PCMs) within heat sinks for passive cooling of electronics, aiming to decrease the load on active cooling systems. Furthermore, PCMs offer the potential to flatten out peak loads, which often leads to the overdesigning of cooling systems. Computa-tional fluid dynamics (CFD) analysis was conducted on various models of HP ProLiant DL160 G6 1U servers created using Ansys Fluent. Each model employed a different PCM within the heat sink, allowing for comparisons with a model containing conventional heat sinks. Commercially available PCMs including RT-44, RT-54, RT-64, RT-69, and RT-80 were used in this study due to their non-toxic nature and higher heat storage capacities. While some models encountered unrealistic results, RT-80 emerged as a promising PCM, demonstrating a noteworthy 24% reduction in maximum processor temperature. RT-80's solid-state stability, coupled with its capability to absorb cyclic peak loads, positions it as a viable solution for steady-state cooling and thermal time-shifting applications.
