Reliability-based multidisciplinary design optimization of an underwater vehicle including cost analysis

Today, due to the complexity of systems and interactions among their subsystems, the design optimization of a system is highly difficult and costly. Multidisciplinary optimization is an approach, in which interactions among different disciplines are taken into account, and it attempts to optimize all disciplines, simultaneously. In the design process of a system, there is usually some uncertainty in parameters. This uncertainty creates some challenges in the design process and affects the systems performance. To cope with the uncertainty, robust design and reliability-based design approaches are developed. In this paper, a reliability-based multidisciplinary design optimization is presented, in which some of the problem parameters are uncertain. In this regard, it is assumed that some of the problem parameters are in the form of fuzzy numbers. Moreover, in this problem cost is considered as one of the design disciplines, due to its importance in engineering problems. To solve the proposed model, a solution method named the sequential optimization and reliability assessment is presented in which Genetic Algorithm and Particle Swarm Optimization are used to solve the deterministic problem in each iteration. Finally, the design of an autonomous underwater vehicle including cost analysis is investigated and two solution methods are applied. The obtained results from two methods are compared and some conclusion are made. The results show that improving the reliability between 0.5 and 0.85 is more cost-effective. However, some other factors besides the cost play a role in choosing the reliability level that must be considered.