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International Journal of Energy and Thermal Applications Cover

International Journal of Energy and Thermal Applications

E-ISSN: 3108-1479 | Peer-Reviewed Journal (Refereed Journal) | Hybrid Open Access

About the Journal

International Journal of Energy and Thermal Applications The International Journal of Energy and Thermal Applications is a peer-reviewed hybrid open-access journal launched in 2023 that publishes original, high-quality research and review papers that are capable of triggering a domino effect in the field of mechanical engineering and also supports the practical application of the established research that forms the core of the subject. The journal covers all major topics in thermal energy applications, ranging from thermal and non-thermal processes to ocean thermal energy. The journal aims to unroll the scroll of recent advancements and applications that can prove to be an asset in building a strong backbone on the subject matter.

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Journal Information

Title: International Journal of Energy and Thermal Applications
Abbreviation: ijeta
Issues Per Year: 2 Issues
E-ISSN: 3108-1479
Publisher: STM Journals, An imprint of Consortium e-Learning Network Pvt. Ltd.
DOI: 10.37591/IJETA
Starting Year: 2023
Subject: Engineering
Publication Format: Hybrid Open Access
Language: English
Copyright Policy: CC BY-NC-ND
Type: Peer-reviewed Journal (Refereed Journal)

Address:

STM Journals, An imprint of Consortium e-Learning Network Pvt. Ltd. A-118, 1st Floor, Sector-63, Noida, U.P. India, Pin - 201301

Editorial Board

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ijeta maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

Editor in Chief

Editor

Dr. S.V.S.S.N.V.G. Krishna Murthy, Professor and Head

Defence Institute of Advanced Technology, Maharastra, India, 411025

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Latest Articles

Ahead of Print

Enhancing Maintenance Decision-Making in Thermal Power Plants Using Generative AI-Based Fault Diagnosis

The growing complexity of operation and power consumption of thermal power stations involve the need to have intelligent fault diagnosis systems that can be used to guarantee reliability and safety in operation.

Generative Artificial Intelligence, Fault Diagnosis, Predictive Maintenance, Thermal Power Plant, Machine Learning Models.

Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants

Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance.

Generative Artificial Intelligence; Predictive Maintenance; Fault Diagnosis; Thermal Power Plants; Deep Learning.

Optimized Machine Learning Framework for Battery State Prediction in Smart Charging Systems

Good estimation of battery states, including State-of-Charge (SoC), State-of-Health (SoH), and Remaining Useful Life (RUL), are important in managing energy wisely and controlling the adaptive charging.

Optimized Machine Learning, electric vehicles, Remaining Useful Life (RUL), deep learning (DL), State-of-Charge (SoC), State-of-Health (SoH).

Design and Simulation of Solar Powered Semi-Automatic Grass Cutter

The maintenance of large area of grass lawns has always been a tedious task considering continuous human labour and long hours of work. Inorder to reduce the fatigue human work a novel design of automated grass cutting machine

Simulation, Automated grass cutter, Solar powered, CREO, Lawn mover

Comparative Study on Thermal Performance of Mud Rammed and Concrete Residential Building Envelope in Paro - Bhutan

A key factor in assessing the total energy efficiency of a building is the thermal performance of the building envelope.

efficiency, energy, building, heating, cooling, demand, comfort.

Bayesian Optimization–Driven Operating Parameter Tuning for Maximizing Methane Yield in Anaerobic Digestion

To achieve maximum methane production in an anaerobic digestion (AD) process, a combination of various operational parameters must be tuned nonlinearly in the digestion ecosystem.

Bayesian optimization, anaerobic digestion, increase in methane yield, surrogate modeling, constrained parameter search