Multidisciplinary Design Optimization Method And Its Application In HOV Design

Human Occupied Vehicle (HOV) is a complex system involving many different disciplines such as Hydrodynamics, structure, propulsion, weight/volume and cruise control, etc. HOV design is characterized by multidisciplinary interactions in which participating disciplines are intrinsically linked to one another. HOV design is also a complicated multistage process. In conceptual design phase and preliminary design phase, multidisciplinary optimization is especially significant for improving integration performance of HOV. However, for HOV design, it is really difficult to realize multidisciplinary optimization by conventional optimization. The difficulties are that such an integrated implementation is but dealing with the complex couple relationship among the disciplines, also subjected to complexities introduces as a result of a large number of design variables and constraints. The conventional optimization methods for general design of HOV are not capable of solving these problems.Under this circumstance, it is necessary to find new way for optimization design of HOV. Multidisciplinary design optimization (MDO) method has been emerged from aeronautics and astronautics fields, especially for such complicated engineering integrated optimization problems.The objective of this work is to explore MDO method and its application in HOV design. The main contents and contributions of this thesis may be summarized as follows:(1) Exiting MDO methods are reviewed and analyzed. This thesis reviews some of MDO approaches and focuses mainly on solution strategies, characters and recent advances of distributed MDO approaches. The advantages and disadvantages of these methods are discussed and analyzed.(2) Collaborative Optimization (CO) is a potential MDO method. In our work, CO is systematically investigated. Firstly, the motivation, architecture, mathematical description of CO is introduced in detail. Secondly, several varieties of CO are investigated and analyzed. Through numerical examples, the CO based on GA (GA-CO) method is proven to have better convergence performance and higher robust.(3) In order to deal with complicated multi-objective optimization problem in HOV design, multi-objective CO is investigated and developed in our study. We describe the novel integration of Pareto Genetic Algorithm (PGA), one of multi-objective optimization methods within the collaborative optimization framework, which remain the main metrics of CO architecture and ability of PGA to seeking non-inferior solution set. Introduction of PGA which is a direct search algorithm to CO can relieve the convergence difficulties in system-level. At the same time, the PGA 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. We have used some strategies such as regularization of objectives, graded penalized function technique to remove constraints, float code, Pareto rank of population and Pareto set filter of objective in the integration of PGA within CO. Through a numerical examp1e, our developed method is proven to be correct and effective.(4) Establishing the disciplinary analysis model According to the vehicle's characteristics, HOV system is decomposed into five disciplines such as shape/hydrodynamics, structure, propulsion, energy and weight/volume. And then, each of these disciplines is analyzed, the mathematical models for all disciplines are established, and the inputs and outputs of disciplinary models are defined. These models are proven to be correct and efficient by system analysis, and can be used in the optimization design.(5) The developed PGA-CO in our study is applied to solve the HOV design problem. The PGA-CO is successfully used in the conceptual design of the HOV. A robust and well-distributed noninferior set is obtained, which can help the designers to understand the project and make decisions. Through this application, the presented methods are proven to be applicable and have the potential for multidisciplinary design optimization of HOV.(6) Approximation is one of the most important critical techniques in MDO. In this study, the structure multi-objective optimal design of the pressure spherical hull in the HOV is completed with a combined optimal method and this method is based on Response Surface Method (RSM) and Pareto Genetic Algorithm (PGA). The FEM model of the Pressure Spherical Hull is built firstly by ABAQUS. With the Design of Experiment (DOE), the response property of design objects can be obtained. The response surface model is fitted with these samples. PGA is used in subsequent optimal design. Finally, the optimal design of the pressure spherical hull is obtained. Optimization design of structure based on the response surface model is proven to be efficient and effective.