Sequential optimization and reliability assessment for multidisciplinary systems design

Du, Xiaoping; Guo, Jia; Beeram, Harish

doi:10.1007/s00158-007-0121-7

Cited by 171 publications

(90 citation statements)

References 33 publications

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“…A classical MDO problem is introduced to illustrate the proposed MDO method [35]. The problem has two subsystems.…”

Section: A Mathematical Examplementioning

confidence: 99%

Reliability-Based Multidisciplinary Design Optimization under Correlated Uncertainties

Wang

et al. 2017

Mathematical Problems in Engineering

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Complex mechanical system is usually composed of several subsystems, which are often coupled with each other. Reliabilitybased 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.

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“…A classical MDO problem is introduced to illustrate the proposed MDO method [35]. The problem has two subsystems.…”

Section: A Mathematical Examplementioning

confidence: 99%

Reliability-Based Multidisciplinary Design Optimization under Correlated Uncertainties

Wang

et al. 2017

Mathematical Problems in Engineering

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“…Many research of reliability based MDO methods and various RBDO techniques was studied [34][35][36][37][38][39][40]. In this research, the CO method was implemented and combined with RBDO and PBDO methods [41].…”

Section: Rbdo/pbdo With Mdomentioning

confidence: 99%

Aircraft derivative design optimization considering global sensitivity and uncertainty of analysis models

Park¹,

Chung²,

Lee³

2016

International Journal of Aeronautical and Space Sciences

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Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet new market demands while keeping the development time and cost to a minimum. Many researchers have studied the derivative design process, but these research efforts consider baseline and derivative designs together, while using the whole set of design variables. Therefore, an efficient process that can reduce cost and time for aircraft derivative design is needed. In this research, a more efficient design process is proposed which obtains global changes from local changes in aircraft design in order to develop aircraft derivatives efficiently. Sensitivity analysis was introduced to remove unnecessary design variables that have a low impact on the objective function. This prevented wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, uncertainty from the fidelity of analysis tools was considered in design optimization to increase the probability of optimization results. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle the uncertainty in aircraft conceptual design optimization. In this paper, Collaborative Optimization (CO) based framework with RBDO and PBDO was implemented to consider uncertainty. The proposed method was applied for civil jet aircraft derivative design that increases cruise range and the number of passengers. The proposed process provided deterministic design optimization, RBDO, and PBDO results for given requirements.

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“…In certain cases the methods based on the gradient are not available or are computationally cumbersome, for example, the cases when the limit-state function is complicated or it is very difficult to obtain the derivative. Recently, there are many SA techniques that are available for multidisciplinary analysis, in chemical engineering, environmental sciences, and risk analysis (Du et al, 2008;Xu and Gertner, 2008;Chakraborty et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A structural reliability-based sensitivity analysis method using particles swarm optimization: relative convergence rate

Lan

Peng

et al. 2016

J. Zhejiang Univ. Sci. A

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This paper proposes a novel reliability-based sensitivity analysis (SA) method, namely relative convergence rate of random variables using particles swarm optimization (PSO). The convergence rate of a random variable during the optimum evolution process reflects the sensitivity of the objective function with respect to the random variables. An optimized group strategy is proposed to consider the fluctuation of the convergence rate of a variable during the optimum process. The coefficient of variation (COV) for candidate particles and the relative convergence rate of a random variable can be calculated using the particles in the optimized group. The smaller the COV for candidate particles, i.e., the larger the relative convergence rate, the more sensitive the objective function with respect to the variable. Three examples are available for the application of this method, and the results indicate that the sensitivity of the reliability index with respect to the variable obtained using the PSO technique and gradient of limit-state function is the same in the quantitative sense.

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Sequential optimization and reliability assessment for multidisciplinary systems design

Cited by 171 publications

References 33 publications

Reliability-Based Multidisciplinary Design Optimization under Correlated Uncertainties

Reliability-Based Multidisciplinary Design Optimization under Correlated Uncertainties

Aircraft derivative design optimization considering global sensitivity and uncertainty of analysis models

A structural reliability-based sensitivity analysis method using particles swarm optimization: relative convergence rate

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