Aircraft Multidisciplinary Design Collaborative Optimization And Approximation Research

Multidisciplinary Design Optimization (MDO) is a new subject expanded very quickly from 90s 20th. Since it can effectively solve the problem of large-scale and complex engineering system design, it has widely potential application in the field of aerospace engineering. So, at present, MDO has been the newest and the best active field in aircraft design.The paper summarizes the MDO methods, and the emphasis are on the analysis and improvement of a distributed concurrent approach—Collaborative Optimization (CO), and applies the improved methods to a wing design. The main parts of this paper is as follows:1. The aircraft MDO mathematical model is constructed. Compared with common optimization problems, the aircraft MDO has many special concepts. The difficulty of aircraft MDO is pointed out, which gives the direction of this study. Describing the components of MDO and building up the framework of MDO.2. Several approximation methods are summarized, and they play an important role in MDO. These methods are parts of a complete MDO algorithm, and have much value in engineering. This part provides effective analysis and calculation tools for later study.3. Collaborative Optimization (CO) is an effective MDO algorithm, which divides the original problem into two parts: one system-level optimization and several disciplinary level optimizations. The mathematical property of CO is analyzed, which pointed out that the main difficulty in CO is how to construct the system-level constraints. According to this, and summarizing three approximate methods: dynamic slack variables, response surface and subspace approximate. With classic examples, validating these methods' practicality and availability.4. Collaborative Optimization based on approximate technology is applied to plane wing design. The data is from structure finite element (FE) analysis software-ANSYS and aerodynamic analysis software-MGAERO. In order to improve optimization efficiency and decrease calculation cost, applying response surface technology to approximate to the target function and condition constraints function.