Manikandan M.
Also known as: MM
Ramaiah Institute of Science and Management, Manipal Institute of Technology, Indian Institute of Technology Bombay, MLR Institute of Technology
About
Dr. Manikandan Murugaiah is an Associate Professor in the School of Aerospace Engineering at Ramaiah Institute of Science and Management (RISM), Bengaluru, India. He received his Ph.D. in Aerospace Engineering from the Indian Institute of Technology Bombay (IIT Bombay). His research interests include aircraft design, lighter-than-air (LTA) systems, unmanned aerial vehicles (UAVs), multidisciplinary design optimization, computational fluid dynamics (CFD), surrogate modelling, and artificial intelligence and machine learning (AI/ML) for aerospace applications.
His research focuses on the design, modelling, optimization, and experimental investigation of sustainable and next-generation aerospace systems. He has authored research publications in international journals and conferences and has contributed to books, patents, and competitively funded research projects. His work emphasizes interdisciplinary research and the integration of computational methods, optimization, and emerging AI/ML techniques to address complex aerospace engineering problems. He is also actively engaged in industry collaboration, research mentorship, and engineering education.
Employment
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Ramaiah Institute of Science and Management Associate Professor2026 - Present
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Manipal Institute of Technology SENIOR ASSISTANT PROFESSOR2009 - 2026
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Manipal Institute of Technology Assistant Professor2009 - 2026
Education
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Indian Institute of Technology Bombay Ph.D.2018 - 2022
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Manipal Institute of Technology M.Tech2010 - 2013
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MLR Institute of Technology B.Tech2005 - 2009
Projects & Funding
Projects & funding information is unavailable.
Publications (33)
- STRATOS: Software-based Tool for Rapid Airship Translation & Optimized Shaping Save
- On the Role of Artificial Intelligence in Aerospace Engineering: Current State of the Art and Future Trajectories Save
- Machine Learning Driven Optimisation of Low-Reynolds Number Uav Airfoils Using Genetic Algorithm Save
- A comparative study of aerodynamic characteristics of conventional and multi-lobed airships Save
- A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain Save
- A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain Save
- A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain Save
- A Comprehensive Survey of Open-Source Tools for Computational Fluid Dynamics Analyses Save
- A parametric design approach for multi-lobed hybrid airships Save
- Application of Machine Learning and Deep Learning in High Performance Computing Save