A new multidisciplinary robust design optimization framework for an autonomous underwater vehicle in system and tactic design. (August 2019)

• Record Type: Journal Article
• Authors: Bidoki, Mohsen; Mortazavi, Mehdi; Sabzehparvar, Mehdi
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 3(2019:Aug.)
• Page Start: 918
• Page End: 936
• Publication Date: 2019-08
• Subjects: Multidisciplinary design optimization -- autonomous underwater vehicle -- uncertain multidisciplinary feasible -- system and tactic design; Marine engineering -- Periodicals; 623.805
• DOI: 10.1177/1475090218791081
• Languages: English
• ISSNs: 1475-0902

Abstract

The design process of an autonomous underwater vehicle requires mathematical model of subsystems or disciplines such as guidance and control, payload, hydrodynamic, propulsion, structure, trajectory and performance and their interactions. In early phases of design, an autonomous underwater vehicle is often encountered with a high degree of uncertainty in the design variables and parameters of system. These uncertainties present challenges to the design process and have a direct effect on the autonomous underwater vehicle performance. Multidisciplinary design optimization is an approach to find both optimum and feasible design, and robust design is an approach to make the system performance insensitive to variations of design variables and parameters. It is significant to integrate the robust design and the multidisciplinary design optimization for designing complex engineering systems in optimal, feasible and robust senses. In this article, we present an improved multidisciplinary design optimization methodology for conceptual design of an autonomous underwater vehicle in both engineering and tactic aspects under uncertainty. In this methodology, uncertain multidisciplinary feasible is introduced as uncertain multidisciplinary design optimization framework. The results of this research illustrate that the new proposed robust multidisciplinary design optimization framework can carefully set a robust design for an autonomous underwater vehicle with coupled uncertain disciplines.