Multidisciplinary wing design optimization considering global sensitivity and uncertainty of approximation models

Park, Hyeong-Uk; Chung, Joon; Behdinan, Kamran; Lee, Jae-Woo

doi:10.1007/s12206-014-0127-1

Cited by 16 publications

(12 citation statements)

References 32 publications

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“…GSA method was performed to find the important design variables for the RBDO and PBDO were implemented to consider errors associated with low conceptual design [41][42][43][44][45][46]. Nuefeld et al (2011) and Jaeger et al (2013) implem from the fidelity of the analysis model [43,45].…”

Section: Aircraft Derivative Designmentioning

confidence: 99%

“…GSA method was performed to find the important design variables for the new requirements [42]. Moreover, RBDO and PBDO were implemented to consider errors associated with low fidelity analysis tools of aircraft conceptual design [41][42][43][44][45][46].…”

Section: Aircraft Derivative Designmentioning

confidence: 99%

“…GSA method was performed to find the important design variables for the new requirements [42]. Moreover, [43,45].…”

Section: Aircraft Derivative Designmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Aircraft Derivative Designmentioning

confidence: 99%

Section: Aircraft Derivative Designmentioning

confidence: 99%

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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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“…Jiang et al [8] proposed a spatial-random-process (SRP) based on multidisciplinary uncertainty analysis (MUA) method to address both aleatory and epistemic uncertainties. For more details on how to conduct mechanical design under uncertainties, including physics-based and reliability-based design, one can refer to [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Du and Chen [16] presented a modified concurrent subsystem uncertainty analysis (MCSSUA) method for uncertainty analysis in MDO. Park et al [17,18] combined reliability-based design optimization, possibility-based design optimization, and robust design optimization methods with the MDO method to obtain the reliable results. Lin and Gea [19] developed a probabilistic gradient-based transformation method (PGTM) to solve the complex design optimization problems under design uncertainties.…”

Section: Introductionmentioning

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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Surrogate- and possibility-based design optimization for convective polymerase chain reaction devices

et al. 2020

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Multidisciplinary wing design optimization considering global sensitivity and uncertainty of approximation models

Cited by 16 publications

References 32 publications

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

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

Reliability-Based Multidisciplinary Design Optimization under Correlated Uncertainties

Surrogate- and possibility-based design optimization for convective polymerase chain reaction devices

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