STUDY ON INTEGRATED FRAMEWORK OF MULTIDISCIPLINARY DESIGN OPTIMIZATION FOR COMPLEX ENGINEERING SYSTEM

2004 ◽  
Vol 40 (09) ◽  
pp. 100 ◽  
Author(s):  
Minghong Han
Author(s):  
Zhao Liu ◽  
Zhouzhou Song ◽  
Ping Zhu ◽  
Can Xu

Abstract Uncertainty-based multidisciplinary design optimization (UMDO) is an effective methodology to deal with uncertainties in the engineering system design. In order to shorten the design cycle and improve the design efficiency, the time-consuming computer simulation models are often replaced by metamodels, which consequently introduces metamodeling uncertainty into the UMDO procedure. The optimal solutions may deviate from the true results or even become infeasible if the metamodeling uncertainty is neglected. However, it is difficult to quantify and propagate the metamodeling uncertainty, especially in the UMDO process with feedback-coupled systems since the interdisciplinary consistency needs to be satisfied. In this paper, a new approach is proposed to solve the UMDO problem for the feedback-coupled systems under both parametric and metamodeling uncertainties. This approach adopts the decoupled formulation and it applies the Kriging technique to quantify the metamodeling uncertainty. The polynomial chaos expansion (PCE) technique is applied to propagate the two types of uncertainties and represent the interdisciplinary consistency constraints. In the optimization approach, the proposed method uses the iterative construction of PCE models for response means and variances to satisfy the multidisciplinary consistency at the optimal solution. The proposed approach is verified by a mathematical example and applied to the fire satellite design. The results demonstrate the proposed approach can solve the UMDO problem for coupled systems accurately and efficiently.


2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Huanwei Xu ◽  
Xin Wang ◽  
Wei Li ◽  
Mufeng Li ◽  
Suichuan Zhang ◽  
...  

Complex mechanical system is usually composed of several subsystems, which are often coupled with each other. Reliability-based multidisciplinary design optimization (RBMDO) is an efficient method to design such complex system under uncertainties. However, the present RBMDO methods ignored the correlations between uncertainties. In this paper, through combining the ellipsoidal set theory and first-order reliability method (FORM) for multidisciplinary design optimization (MDO), characteristics of correlated uncertainties are investigated. Furthermore, to improve computational efficiency, the sequential optimization and reliability assessment (SORA) strategy is utilized to obtain the optimization result. Both a mathematical example and a case study of an engineering system are provided to illustrate the feasibility and validity of the proposed method.


2017 ◽  
Vol 25 (3) ◽  
pp. 262-275 ◽  
Author(s):  
Huanwei Xu ◽  
Wei Li ◽  
Liudong Xing ◽  
Shun-Peng Zhu

Uncertainty analysis is a hot research topic in multidisciplinary design optimization for complex mechanical systems. Existing multidisciplinary design optimization works typically assume that uncertainties are uncorrelated of each other. In real-world engineering systems, however, correlations do exist between different uncertainties. The multidisciplinary design optimization methods without considering correlations between uncertainties may cause inaccuracy and thus misleading optimization results. In this article, we make contributions by proposing a new multidisciplinary design optimization approach based on the ellipsoidal set theory to investigate the characteristics of correlated uncertainties and incorporate their effects in the multidisciplinary design optimization through an advanced collaborative optimization method, where the quantitative model of correlated uncertainties is transformed into constrains of subsystems. Both a mathematical example and a case study of an engineering system are provided to illustrate feasibility and validity of the proposed method.


2016 ◽  
Vol 24 (3) ◽  
pp. 251-265 ◽  
Author(s):  
Edris Safavi ◽  
Mehdi Tarkian ◽  
Johan Ölvander ◽  
Hossein Nadali Najafabadi ◽  
Raghu Chaitanya Munjulury

Sign in / Sign up

Export Citation Format

Share Document