Multidisciplinary Design Optimization Method Applied to a HOV Design

Transcription

1 Multidisciplinary Design Optimization Method Applied to a HOV Design Cao Anxi and Cui Weicheng Oct. 8-10, 2008 Inmartech08, Toulon 1

2 Table of Contents Introduction Multi-Objective Collaborative Optimization (MOCO) HOV Design Application Summary 2

3 1 Introduction Conceptual design is the most critical stage; HOV design is a complex and multidisciplinary task which requires analyses of hydrodynamics, structure, propulsion, weight, control, operations, cost and the others; Traditional sequential design may lead to non-optimal system designs; 3

4 1 Introduction The field of multidisciplinary design optimization (MDO) has emerged to develop approaches for optimizing the design of large coupled systems. MDO has been widely used not only in aerospace and aeronautical industries, but also in other complex engineering systems such as automobile, underwater vehicles, ships etc. and resulted in a more reliable and better design. 4

5 1 Introduction For the purpose of attaining the overall performance optimization of a HOV and improving procedure of a HOV conceptual design, MDO technique has been employed. The purpose of this paper is to explore how Collaborative Optimization (CO), one of the MDO methods, can be applied in the conceptual design of a HOV. 5

6 2 Multi-Objective Collaborative Optimization Collaborative Optimization (CO) has been developed to promote autonomy while providing a coordinating mechanism that guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. It basically consists of a two-level optimization structure. 6

7 2 MOCO System-Level optimizer Goal: Design objective s.t.: Interdisciplinary compatibility constraints Subspace optimizer 1 Goal: Interdisciplinary compatibility S.t.: Analysis 1 constraints Subspace optimizer 2 Goal: Interdisciplinary compatibility S.t.: Analysis 2 constraints Subspace optimizer N Goal: Interdisciplinary compatibility S.t.: Analysis N constraints Analysis 1 Analysis 2 Analysis N 7

8 2 MOCO CO has been widely discussed and applied in practical engineering problems. CO has been judged highly advantageous in its applications to practical engineering design problems. At the same time, many researchers have focused on extension or modifications to CO aimed at improving overall efficiency, permitting their use on problems with high dimensionality coupling and simplifying their implementation. 8

9 2 MOCO In this study, a MOCO has been selected to handle multiobjective systems. In the MOCO, the goal of the system level optimizer is to minimize a system level multiobjective function of target variables while satisfying compatibility constraints using a Pareto Genetic Algorithm (PGA). PGA solves system level optimization problem with respect to system design variables. For each generation at the system level, the disciplines are optimized for each candidate design from the population. 9

10 3 HOV Design Application A MOCO method has been completed for the deep sea HOV which is shown conceptually in Figure 3. The shape, type of propulsion, ascent depth, pressure hull structure and material have been identified. The design space of this vehicle has been described in Table 1. The cruise and operation time and the Ratio are the objective attributes. 10

11 3 HOV Design Application Firstly, we decompose the design problem into a system module and four disciplinary optimization modules: geometry & hydro, structure, propulsion, weight & volume. Figure 4 describes the MOCO architecture of the HOV design. Equations described the formulations of system-level optimization problem, and four subsystem optimization problems are given in the text. 11

12 3 HOV Design Application A Multi-Objective Collaborative Optimization has been run for 328 generation with a population of 50 HOVs. In system-level optimization problem, the relaxation factor of compatible constraints is set to , the crossover probability, the mutation probability and the maximum generation are set to 0.9, 0.1 and 350. For the sub-space optimization problems, the sequence quadratic programming (SQP) is used to attain the discipline optimization solution. The different sub-space optimizations are solved in-parallel. 12

13 3 HOV Design Application Parato front HOV1 HOV2 6.0 T co (h) HOV3 4.5 HOV R w 13

14 3 HOV Design Application HOV1 and HOV4 are located at the ends of the Pareto front, and HOV2 and HOV3 are located at the middle of the Pareto front. HOV1 has the longest cruise and operation time which is up to 7 hours, but the alternative has the minimum ratio which is just In contrast, HOV4 has the maximum ratio and the shortest cruise and operation time. If the time is the most important performance for a HOV, the HOV between HOV1 and HOV2 are excellent choices. 14

15 4 Summary and Conclusions An application of Multidisciplinary Design Optimization to a HOV conceptual design has been presented. In this application, the MOCO architecture was used. The method integrates the multi-objective optimization methods within the collaborative optimization framework, which remains the main metrics of CO architecture and ability of PGA to seeking non-inferior solution set. 15

16 4 Summary and Conclusions So the method is effective in that it makes a chance to execute in-parallel for disciplinary design, and it is more flexible in that it enables the designer to select the fittest solution among the Pareto optimal set in according with their preference and the nature of the design problem. These practical advantages make the architecture well-suited for the design of HOVs. 16

17 Thank you for your attention! 17