Efficient first-order reliability analysis of multidisciplinary systems

Mahadevan, Sankaran; Smith, Natasha

doi:10.1504/ijrs.2006.010694

Cited by 42 publications

(25 citation statements)

References 16 publications

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“…Past work has dealt with uncertainty analysis of feedback-coupled systems by using decoupling approaches such as collaborative reliability analysis, 16 the first order reliability method (FORM) 17 and a likelihood-based approach, 18 and polynomial chaos expansion based method. 19 FORM and the likelihood-based decoupling approach cast the feedback-coupled system as a feed-forward system, thus avoiding coupled system analyses and their associated FPI, but at the cost of neglecting the dependence between the inputs and the coupling variables.…”

Section: -15mentioning

confidence: 99%

Multifidelity Uncertainty Propagation in Coupled Multidisciplinary Systems

Chaudhuri

Willcox

2016

18th AIAA Non-Deterministic Approaches Conference

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Fixed point iteration is a common strategy to handle interdisciplinary coupling within a coupled multidisciplinary analysis. For each coupled analysis, this requires a large number of disciplinary high-fidelity simulations to resolve the interactions between different disciplines. When embedded within an uncertainty analysis loop (e.g., with Monte Carlo sampling over uncertain parameters) the number of high-fidelity disciplinary simulations quickly becomes prohibitive, since each sample requires a fixed point iteration and the uncertainty analysis typically involves thousands or even millions of samples. This paper develops a method for uncertainty analysis in feedback-coupled black-box systems that leverages adaptive surrogates to reduce the number of cases for which fixed point iteration is needed. The multifidelity coupled uncertainty propagation method is an iterative process that uses surrogates for approximating the coupling variables and adaptive sampling strategies to refine the surrogates. The adaptive sampling strategies explored in this work are residual error, information gain, and weighted information gain. The surrogate models are adapted in a way that does not compromise accuracy of the uncertainty analysis relative to the original coupled high-fidelity problem.

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Section: -15mentioning

confidence: 99%

Multifidelity Uncertainty Propagation in Coupled Multidisciplinary Systems

Chaudhuri

Willcox

2016

18th AIAA Non-Deterministic Approaches Conference

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“…Studies on uncertainty propagation through feedback-coupled MDA are even fewer [9,10,11,26,19], and have only addressed aleatory uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Mahadevan and Smith [10] developed a multi-constraint FORM approach for MPP estimation. Sankararaman and Mahadevan [11] proposed a likelihood-based approach for 4 MDA (known as the LAMDA approach).…”

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confidence: 99%

“…However, such analysis is computationally prohibitive. Therefore, efficient alternatives, in the presence of aleatory uncertainty alone, have been investigated by several researchers [9,10,11,22,23,24] (see Section 1.1).…”

mentioning

confidence: 99%

“…For example, in the decoupled approach adopted by Du and Chen [9] and Mahadevan and Smith [10], the probability density functions (PDF) of the coupling variables are calculated by Taylor series-based first-order second moment approximation. These approaches improve the efficiency by 12 trading off the accuracy since they ignore the dependence between the coupling variables.…”

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confidence: 99%

See 2 more Smart Citations

Multi-disciplinary analysis and optimization under uncertainty

Chen¹

2014

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures

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A decomposition‐based approach to uncertainty analysis of feed‐forward multicomponent systems

Amaral

Allaire

Willcox

2014

Numerical Meth Engineering

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SUMMARYTo support effective decision making, engineers should comprehend and manage various uncertainties throughout the design process. Unfortunately, in today's modern systems, uncertainty analysis can become cumbersome and computationally intractable for one individual or group to manage. This is particularly true for systems comprised of a large number of components. In many cases, these components may be developed by different groups and even run on different computational platforms. This paper proposes an approach for decomposing the uncertainty analysis task among the various components comprising a feed-forward system and synthesizing the local uncertainty analyses into a system uncertainty analysis. Our proposed decomposition-based multicomponent uncertainty analysis approach is shown to be provably convergent in distribution under certain conditions. The proposed method is illustrated on quantification of uncertainty for a multidisciplinary gas turbine system and is compared to a traditional system-level Monte Carlo uncertainty analysis approach. Copyright © 2014 John Wiley & Sons, Ltd.

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Efficient first-order reliability analysis of multidisciplinary systems

Cited by 42 publications

References 16 publications

Multifidelity Uncertainty Propagation in Coupled Multidisciplinary Systems

Multifidelity Uncertainty Propagation in Coupled Multidisciplinary Systems

Multi-disciplinary analysis and optimization under uncertainty

A decomposition‐based approach to uncertainty analysis of feed‐forward multicomponent systems

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