Abstract:
Design optimization of an unmanned underwater vehicle (UUV) is a complex and a computationally expensive exercise that requires the identification of optimal vehicle dime...Show MoreMetadata
Abstract:
Design optimization of an unmanned underwater vehicle (UUV) is a complex and a computationally expensive exercise that requires the identification of optimal vehicle dimensions offering the best tradeoffs between the objectives, while satisfying the set of design constraints. Although hull form optimization of marine vessels has long been an active area of research, limited attempts in the past have focused on the design optimization of UUVs and there are even fewer reports on the use of high-fidelity analysis methods within the course of optimization. While it is understood that the high-fidelity analysis is more accurate, they also tend to be far more computationally expensive. Thus, it is important to identify when a high-fidelity analysis is required as opposed to a low-fidelity estimate. The work reported in this paper is an extension of the authors previous work of a design optimization framework, where the design problem was solved using a low-fidelity model based on empirical estimates of drag. In this paper, the framework is extended to deal with high-fidelity estimates derived through seamless integration of computer-aided design, meshing and computational fluid dynamics analysis tools i.e., computer aided 3-D interactive application, ICEM, and FLUENT. The effects of using low-fidelity and high-fidelity analyses are studied in depth using a small-scale (length nominally less than 400 mm) and light-weight (less than 450 g) toy submarine. Useful insights on possible means to identify appropriateness of fidelity models via correlation measures are proposed. The term optimality used in this paper refers to optimal hull form shapes that satisfy placement of a set of prescribed internal components.
Published in: IEEE Transactions on Systems, Man, and Cybernetics: Systems ( Volume: 47, Issue: 11, November 2017)