A Novel Method for Inverse Uncertainty Propagation

Chen, Xin; Molina-Cristóbal, Arturo; Guenov, Marin D.; Datta, Varun; Riaz, Atif

doi:10.1007/978-3-319-89988-6_21

Cited by 4 publications

(11 citation statements)

References 17 publications

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“…x , the skewness γ x and the kurtosis Γ x . Adopting the URQ method, the output statistical quantities can be determined by evaluating the objective function at 2n + 1 points (n is the number of uncertain input factors) that are dened following a specic expression [17,31].…”

Section: Reliable and Robust Evolutionary Algorithmmentioning

confidence: 99%

Enhanced Evolutionary-Based Optimization Techniques Applied to a Nonlinear Landing Gear Design Problem

Tartaruga

Cooper

Löwenberg

et al. 2019

Journal of Aircraft

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Engineering design processes often use optimization strategies, which aim to minimize multi-objective functions. The analysis should consider the uncertainty in a system, which may cause signicant changes in its behaviour. The inclusion of the uncertainty in the design process makes the identication of an optimum design more challenging. In this paper, two novel optimization methods (Iterative Dierential Evolutionary Algorithm-I.D.E.A. and Reliable & Robust Evolutionary Algorithm-R.R.E.A.) are presented. These optimization strategies aim to solve problems that are very time demanding and for which it is dicult (and expensive) to determine derivatives and to identify and dene the optimum set of parameters. The approaches are validated considering as a test case the optimization of a landing gear system in order to avoid the onset of shimmy, assuring a reliable design.

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Section: Reliable and Robust Evolutionary Algorithmmentioning

confidence: 99%

Enhanced Evolutionary-Based Optimization Techniques Applied to a Nonlinear Landing Gear Design Problem

Tartaruga

Cooper

Löwenberg

et al. 2019

Journal of Aircraft

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“…Reversal refers to the capability of 'swapping' the input and output variables of a computational workflow. In Chen et al 8 , it was demonstrated that the standard deviation of an input variable of a default workflow (original sequencing) can be swapped with one of the standard deviations of the original output variables. This technique is based on a computational workflow management (CWM) method, developed by Balachandran et al 17 and Guenov et al 18 , where the user is able to specify the variables he or she wants to swap, after which the reversed workflow is created.…”

Section: Workflow Reversalmentioning

confidence: 99%

Interactive Uncertainty Allocation and Trade-off at Early-stage Aircraft Computational Design

Molina-Cristóbal

Guenov

Riaz

et al. 2018

2018 AIAA Non-Deterministic Approaches Conference

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A common probabilistic approach to uncertainty allocation is to assign acceptable variability in the sources of uncertainty, such that pre-specified probabilities of meeting performance constraints are satisfied. However, the computational cost of obtaining the associated trade-offs increases significantly when more sources of uncertainty and more outputs are considered. Consequently, visualizing and exploring the trade-off space becomes increasingly difficult, which, in turn, makes the decision-making process cumbersome for practicing designers. To tackle this problem, proposed is a parameterization of the input probability distribution functions, to account for several statistical moments. This, combined with efficient uncertainty propagation and inverse computation techniques, results in a computational system which performs order(s) of magnitude faster, compared with a combination of Monte Carlo Simulation and optimization techniques. Also, to aid decisionmaking regarding the potential combinations of uncertainty allocation, enablers for visualizing the trade space are proposed. The combined approach is demonstrated by means of a representative aircraft thermal system integration example.

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“…While these approaches are effective in finding robust solutions, they are not suited to address the uncertainty allocation problem. Chen et al [8] reported that inverse uncertainty propagation methods can be used as an enabler to solve the uncertainty allocation problem. Although several methods have been developed for inverse uncertainty propagation (e.g., the Gaussian process [9], Karhunen-Loève Expansion [10], [11], Polynomial Chaos [12], and maximum likelihood [13]) these may lead to relatively high computational cost.…”

Section: Introductionmentioning

confidence: 99%

“…The 'inverse uncertainty propagation' method introduced by Chen et al [8] demonstrated that a reduction in variability can be achieved by reversing (swapping) the standard deviation of an input variable with one of the standard deviations of the original output variables. It cannot, however, deal with manyto-many reversals (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The aim is to enable the systematic investigation of tradeoff strategies regarding input-variability. The approach builds upon the 'inverse uncertainty propagation' method, introduced by Chen et al [8]. The specific objectives of the work presented in this paper are to: a) Extend functionality, by introducing the ability to consider multiple outputs with multiple input reductions of uncertainty, by means of exploring the shapes of the parameterized distributions functions; b) Enable an interactive trade off process of multiple combinations of uncertainty allocation, by introducing visualization of the decision (trade) space; and c) combine (a) and (b) with an efficient uncertainty propagation method [15], to enable faster interactive uncertainty allocation.…”

Section: Introductionmentioning

confidence: 99%

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Interactive Uncertainty Allocation and Tradeoff for Early Stage Design of Complex Systems

et al. 2020

Self Cite

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A common probabilistic approach to perform uncertainty allocation is to assign acceptable variability in the sources of uncertainty, such that pre-specified probabilities of meeting performance constraints are satisfied. However, the computational cost of obtaining the associated tradeoffs increases significantly when more sources of uncertainty and more outputs are considered. Consequently, visualizing and exploring the decision (trade) space becomes increasingly difficult, which, in turn, makes the decisionmaking process cumbersome for practicing designers. To address this problem, proposed is a parameterization of the input probability distribution functions, to account for several statistical moments. This, combined with efficient uncertainty propagation and inverse computation techniques, results in a computational system that performs order(s) of magnitude faster than a state-of-the-art optimization technique. The approach is demonstrated by means of an illustrative example and a representative aircraft thermal system integration example.

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A Novel Method for Inverse Uncertainty Propagation

Cited by 4 publications

References 17 publications

Enhanced Evolutionary-Based Optimization Techniques Applied to a Nonlinear Landing Gear Design Problem

Enhanced Evolutionary-Based Optimization Techniques Applied to a Nonlinear Landing Gear Design Problem

Interactive Uncertainty Allocation and Trade-off at Early-stage Aircraft Computational Design

Interactive Uncertainty Allocation and Tradeoff for Early Stage Design of Complex Systems

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