Study For Multidisciplinary Design Optimization Modeling And Solving Considered Uncertainty Factors

With the development of modern science and technology,engineering design issues have become very complicated.Both the product performance and the use environment have put forward higher requirements for design.Multidisciplinary Design Optimization(MDO)is one of the modern design methods to effectively improve the overall performance of a product.It is also a research hotspot in the world.The research content of multidisciplinary design optimization can be divided into system modeling,model analysis simulation,MDO strategy,approximate calculation,sensitivity analysis,etc.The most important issue is the MDO strategy.With the deepening of research,scholars have found that there are a lot of uncertainties in the actual engineering problems.These uncertainties have a greater impact on the final results of multidisciplinary design optimization.The content of this paper is as follows:(1)First,we summarize and summarize the development status of MDO and analyze the problems existing in MDO.Then,through typical mathematical examples,we study typical MDO strategies and deeply analyze the characteristics of these typical MDO strategies.,Application characteristics,and then laid a solid foundation for the subsequent algorithm research,uncertainty coupling research.(2)Due to the complexity of practical engineering problems,most of the MDO problems are relatively complex.The problems such as large computational volume and local optimality make multidisciplinary algorithms become the focus of scholars' research.In recent years,with the development of biomimetic biology,modern intelligent algorithm algorithms have emerged in spring.More and more scholars have also tried to apply modern intelligent algorithms to multidisciplinary design optimization.The traditional algorithm has its inherent defects.Due to the coupling problem of the complex multidisciplinary optimization design,and depending on the actual problem,there will be a situation in which the optimization space is discontinuous.It is difficult for the traditional algorithm to converge to the optimal solution.This paper constructs an augmented lag The Lange multiplier method improves the crow algorithm,and the improved crow algorithm is used to solve the MDO problem.Through concrete mathematics and engineering examples,the effectiveness of the improved crow algorithm was verified.(3)With the in-depth study of MDO issues,researchers have discovered that the impact of uncertainty on the optimization process becomes more pronounced.Although deterministic MDO can find feasible solutions that meet all constraints on the surface,due to the existence of uncertainties,the deterministic MDO optimization results are often infeasible.At present,many researchers assume that they are irrelevant when considering the uncertainty.As a result,there is a certain deviation between the design scheme and the actual application.In this paper,firstly,the non-probabilistic hyperellipsoid model is used to describe the correlation of uncertainties;the orthogonal transformation is used to transform the relevant uncertainties;the reliability of the first-order reliability method is used to examine the reliability of the uncertainties.Finally,the uncertainties related conditions are established.Under the multidisciplinary reliability design optimization model.Finally,an engineering example is used to verify the effectiveness of the proposed method.