Advances in Multidisciplinary Analysis and Optimization Proceedings of the 2nd National Conference on Multidisciplinary Analysis and Optimization / [electronic resource] : edited by Raviprakash R. Salagame, Palaniappan Ramu, Indira Narayanaswamy, Dhish Kumar Saxena. - 1st ed. 2020. - XXII, 316 p. 216 illus., 175 illus. in color. online resource. - Lecture Notes in Mechanical Engineering, 2195-4364 . - Lecture Notes in Mechanical Engineering, .

Gerlach Shaping based Air Intake Duct Optimisation -- Multidisciplinary Analysis of a Re-entry Vehicle -- Optimization of multiple gravity assist trajectory with deep space maneuver using evolutionary algorithm -- Conceptual Design Optimization of High Altitude Airships having a Tri-lobed Envelope -- Multiobjective Aerodynamic Optimization of a Hypersonic Scramjet Inlet -- Study of Wing Flutter at Preliminary Stages of Design -- Coupled Aero-structural Simulation Techniques using High-fidelity Analysis and Design Tools -- Optimization Of B-Spline Launch Vehicle Payload Fairing -- Design of Vortex Flaps for Reducing Approach Speed of a Supersonic Naval Fighter Aircraft.-Performance comparison of discrete Kalman filter and dynamic programming methods for pavement roughness identification -- Multi-Disciplinary Optimization of Sensor Bracket.

This volume contains select papers presented during the 2nd National Conference on Multidisciplinary Analysis and Optimization. It discusses new developments at the core of optimization methods and its application in multiple applications. The papers showcase fundamental problems and applications which include domains such as aerospace, automotive and industrial sectors. The variety of topics and diversity of insights presented in the general field of optimization and its use in design for different applications will be of interest to researchers in academia or industry.

9789811554322

10.1007/978-981-15-5432-2 doi


Engineering mathematics.
Engineering—Data processing.
Mathematical optimization.
Machine learning.
Mathematical and Computational Engineering Applications.
Optimization.
Machine Learning.

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