Editors:
- Discusses applications of multidisciplinary optimization in aerospace, automotive, manufacturing and biomedical industries
- Focuses on design and analysis as applicable to optimization of systems
- Discusses both established techniques and emerging technologies
Part of the book series: Lecture Notes in Mechanical Engineering (LNME)
Conference series link(s): NCMDAO: National Conference on Multidisciplinary Analysis and Optimization
Conference proceedings info: NCMDAO 2021.
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Table of contents (27 papers)
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Front Matter
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Optimization Applications: Aerospace
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Front Matter
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Optimization Applications: Automotive
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Front Matter
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General Applications of Optimization
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Front Matter
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About this book
Editors and Affiliations
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APTIV, Bangalore, India
Raviprakash R. Salagame
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Department of Engineering Design, IIT Madras, Chennai, India
Palaniappan Ramu
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Bangalore, India
Indira Narayanaswamy
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Dept. of Mechanical & Industrial Enginee, Indian Institute of Technology Roorkee, Roorkee, India
Dhish Kumar Saxena
About the editors
Dr. Palaniappan Ramu is anAssociate Professor in the Department of Engineering Design, IIT Madras. He heads the Advanced Design Optimization and Probabilistic Techniques (ADOPT) Laboratory where work revolves around using probability, statistics and optimization for design decisions. He investigates problems of varied interests in different areas. It includes optimal designs of computer models built in FEM/CFD towards light weighting, understanding performance of machine learning algorithms as surrogates, digital twins and visual exploration of design spaces. He holds a B.Eng. in Mechanical from Madurai Kamaraj University, India and a Ph.D. in Aerospace Engineering from University of Florida, USA. He has 10+ years’ experience working on optimization for engineering applications. He teaches courses on optimization, reliability, surrogates and DfMA.
Dr. Indira Narayanaswamy is a research Professor in Ramaiah University of Applied Sciences, Bangalore, India. She has previouslyserved as a scientist and technology Director in the Aeronautical Development Agency, Bangalore. Dr. Narayanaswamy's areas of specialization are CFD applications for Aerodynamics Shape Optimization and Multidisciplinary Design Optimization (MDO), MDO for MAV design, Numerical Investigations on Supersonic Retro Propulsion (SRP). She has authored more than 150 research papers and technical reports.
Dr. Dhish Kumar Saxena is an Associate Professor in the Department of Mechanical & Industrial Engineering, IIT Roorkee. His main research interests lie in multi-objective, multi-disciplinary optimization, and multi-criterion decision making. He is currently the principal investigator/co-investigator of different international projects in these domains, focusing on large scale industrial problems. He is also on the Reviewer Board and Project Monitoring Committee member for different national/international projects launched by the Ministry of Electronics & Information Technology, and Department of Science & Technology. Dr. Saxena brings on board his work experience in the United Kingdom where he worked with universities like Cranfield and Bath, in collaboration with companies like British Aerospace Systems, Hewlett Packard, and Airbus.
Bibliographic Information
Book Title: Advances in Multidisciplinary Analysis and Optimization
Book Subtitle: Proceedings of the 2nd National Conference on Multidisciplinary Analysis and Optimization
Editors: Raviprakash R. Salagame, Palaniappan Ramu, Indira Narayanaswamy, Dhish Kumar Saxena
Series Title: Lecture Notes in Mechanical Engineering
DOI: https://doi.org/10.1007/978-981-15-5432-2
Publisher: Springer Singapore
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2020
Hardcover ISBN: 978-981-15-5431-5Published: 11 August 2020
Softcover ISBN: 978-981-15-5434-6Published: 12 August 2021
eBook ISBN: 978-981-15-5432-2Published: 10 August 2020
Series ISSN: 2195-4356
Series E-ISSN: 2195-4364
Edition Number: 1
Number of Pages: XXII, 316
Number of Illustrations: 41 b/w illustrations, 175 illustrations in colour
Topics: Mathematical and Computational Engineering, Optimization, Machine Learning