About the Journal
The Scientific Journal of Research Studies in Future Mechanical Engineering is a peer-reviewed academic journal that publishes original research articles, review papers, and scholarly studies in the field of mechanical engineering with a future-oriented perspective. The journal aims to promote interdisciplinary research, advanced engineering analysis, and applied studies addressing emerging technologies, energy systems, manufacturing processes, and future challenges in mechanical and industrial engineering.
Scientific Journal of Research Studies in Future Mechanical Engineering is published as a yearbook; and it works in the field of reviewing and publishing scientific research articles in the field of Mechanical Engineering in both Persian and English languages. This Journal is ready to receive and review valuable articles from the qualitative and quantitative researches of researchers and professors in this field, and the articles are published after collaborative reviews and Acceptance prints. Respecting the rules of ethics in publications, this publication is subject to the rules of the Committee on Ethics in Publication (COPE) and follows the executive regulations of the Law on Prevention and Combating Fraud in Scientific Works.
This Journal is published based on the license number 94033 of the Ministry of Culture and Islamic Guidance and with ISSN number 3041-9514.
Current Issue
This issue presents advanced research in renewable energy systems, advanced materials engineering, intelligent optimization, smart building energy management, data-driven supply chain control, and water infrastructure resilience. The published studies evaluate the operational performance of flat-plate solar collectors enhanced with nanofluids to improve thermal efficiency and analyze the influence of grain-orientation-induced anisotropy on cyclic fatigue behavior in nickel-based alloys produced via selective laser melting, compared with conventionally machined specimens.
The issue further investigates multi-objective optimization of energy consumption and thermal comfort in smart HVAC systems using metaheuristic algorithms, and assesses adaptive energy management algorithms in smart buildings through data-driven sustainability and operational efficiency metrics.
Additional contributions examine data-driven prediction and control of the bullwhip effect in multi-echelon supply chains by integrating machine learning with operational transactional data, and evaluate spillway performance and downstream flood risk based on long-term hydrological and structural records.
All contributions have undergone a rigorous peer-review process and aim to advance energy efficiency, material durability, intelligent system optimization, and infrastructure resilience through interdisciplinary and technology-oriented research.
