Experimental investigations of thermal solutions to increase heat transfer rate by utilizing the effects of pitch ratio and length

Lakshmaiya, Natrayan; Murugan, V. Sakthi

doi:10.1117/12.3011873

International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 20 2023

DOI: 10.1117/12.3011873

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Experimental investigations of thermal solutions to increase heat transfer rate by utilizing the effects of pitch ratio and length

Natrayan Lakshmaiya,

V. Sakthi Murugan

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2024

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Cited by 9 publications

References 43 publications

(36 reference statements)

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Optimizing AGC in Implantable Devices

Selvakanmani,

Kamal,

Saravanan

et al. 2024

Advances in Chemical and Materials Engineering

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This study introduces an advanced algorithm for optimizing automatic generation control (AGC) in implantable devices, addressing the challenges posed by the variability of renewable energy sources such as solar and wind power. The proposed approach utilizes a predictive proportional-integral-derivative (PID) controller enhanced with square root derivatives, specifically designed for power systems with high photovoltaic and wind penetration. This innovative method aims to mitigate the instability caused by the fluctuating nature of renewable energy sources. The research develops a framework that models the electricity network dynamics, incorporating high levels of solar (PV) and wind energy. The introduction of a 'Kindly Please' regulator is a novel aspect of this study, designed to augment system stability amidst increasing reliance on renewable resources. This regulator is integrated into the simulation environment, allowing for the prediction and optimization of AGC performance based on various output scenarios.

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Optimizing AGC in Implantable Devices

Selvakanmani,

Kamal,

Saravanan

et al. 2024

Advances in Chemical and Materials Engineering

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Analyzing Renewable Power Integration in Edible Devices on Variable Rate Pumped Storage

Seeniappan,

Krishnakumari,

Shanmugapriya

et al. 2024

Advances in Chemical and Materials Engineering

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This study embarks on a novel exploration of integrating renewable power sources, particularly wind and solar energy, into edible devices, focusing on the Indian power grid's context. As the prevalence of renewable energy sources grows, the dynamics of power systems are rapidly evolving, presenting unique challenges such as network stability and fluctuating energy outputs. This research specifically addresses the variability inherent in renewable energy sources and investigates the potential of minimum variable rate pumped storage systems to provide fundamental consistency in power grids. Key issues such as network inertia minimization and the challenges posed by nano-grids are examined, with an emphasis on their impact on edible devices. The study also explores the use of rapid dispatchable generation (DG) units, like the Kenneth and configurable speed hydel charger, as viable solutions to the energy fluctuation issues typical in renewable-rich power systems.

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A Novel Approach for Optimizing Wire Electric Discharge Machining of Mg-Cu-RE-Zr Alloy Using Machine Learning Algorithm

M. K.,

Prasad,

Banda

et al. 2024

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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This study focuses on the optimisation of the wire electric discharge machining (WEDM) process for WE43 alloy using machine learning methods. The alloy, made of magnesium (Mg), copper (Cu), rare earth (RE) elements, and zirconium (Zr), is extensively employed in aerospace and automotive sectors for its lightweight and high-strength features. The research applies three machine learning models—artificial neural networks (ANN), random forest (RF), and decision trees (DT)—to optimize the important process parameters, including current (A), pulse on (P On), and pulse off (P Off). A full experimental design based on the Taguchi L27 array is undertaken, methodically altering each parameter at three levels. Material removal rate (MRR) is chosen as the response variable for optimisation. The process parameters are adjusted by the use of machine learning techniques, with ANN emerging as the most accurate predictor, obtaining an accuracy of 96.7%.

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Experimental investigations of thermal solutions to increase heat transfer rate by utilizing the effects of pitch ratio and length

Cited by 9 publications

References 43 publications

Optimizing AGC in Implantable Devices

Optimizing AGC in Implantable Devices

Analyzing Renewable Power Integration in Edible Devices on Variable Rate Pumped Storage

A Novel Approach for Optimizing Wire Electric Discharge Machining of Mg-Cu-RE-Zr Alloy Using Machine Learning Algorithm

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