The NASA Langley Multidisciplinary Uncertainty Quantification Challenge

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

doi:10.2514/6.2014-1347

Cited by 62 publications

(64 citation statements)

References 7 publications

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“…A multidisciplinary system that describes the dynamic of a remotely operated twin-jet aircraft has been developed by NASA Langley Research Center to provide an in-flight validation capability for high risk flight testing beyond the normal flight envelope. The scope of the analysis is to advance and validate control upset prevention and recovery technologies for transport aircraft, thereby reducing vehicle loss-of-control accidents resulting from adverse and upset conditions (Crespo et al 2013). …”

Section: Application To the Robust Design Of A Twin-jet Aircraft Contmentioning

confidence: 99%

OpenCossan: An Efficient Open Tool for Dealing with Epistemic and Aleatory Uncertainties

Patelli

Broggi

Angelis

et al. 2014

Vulnerability, Uncertainty, and Risk

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In this work, an integrated computational framework for uncertainty quantification and management is presented. Dealing with uncertainty might lead to impractical computational costs especially for detailed models. Hence, it is of paramount importance the availability of efficient numerical methods in order to reduce the computational costs of non-deterministic analyses by implementing the most efficient algorithms and taking advantages of the high performance computing.The computational framework OpenCossan is able to deal with different representation of uncertainty such as random variables, interval, distributional and free p-boxes. A wide range of engineering and scientific problems can be solved by the proposed computational framework thanks to its modular design. An application to a real case multidisciplinary optimization problem involving aleatory and epistemic variables is presented to show the flexibility and the power and the applicability of the software.

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Section: Application To the Robust Design Of A Twin-jet Aircraft Contmentioning

confidence: 99%

OpenCossan: An Efficient Open Tool for Dealing with Epistemic and Aleatory Uncertainties

Patelli

Broggi

Angelis

et al. 2014

Vulnerability, Uncertainty, and Risk

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show abstract

“…Notwithstanding that the observations provided are "perfect", in general they might very well be subject to an additional model-measurement discrepancy, 15 i.e. "imperfect.…”

Section: Iiid Bayesian Problem Statementmentioning

confidence: 99%

“…15 While the latter is comparably straightforward within the existing frame of Bayesian multilevel modeling, we will have to establish the slightly more measure-theoretical foundation of the former. Eventually Bayesian calibration of the derived Bayesian multilevel model will be accomplished by appropriately transforming, conditioning and marginalizing probability distributions.…”

Section: 13mentioning

confidence: 99%

“…Now we will interpret the uncertainty characterization subproblem A of the NASA Langley UQ challenge 14,15 in Bayesian terms. For that purpose we will compose an appropriate Bayesian multilevel model and formulate the main objective, i.e.…”

Section: The Nasa Langley Multidisciplinary Uq Challengementioning

confidence: 99%

“…Based on the established theory of Bayesian multilevel modeling we will initially formulate a framework for the solution of the NASA Langley UQ challenge 14 and for the inverse problem posed in the presence of additional measurement uncertainty. 15 While the latter is comparably straightforward within the existing frame of Bayesian multilevel modeling, we will have to establish the slightly more measure-theoretical foundation of the former.…”

Section: 13mentioning

confidence: 99%

See 2 more Smart Citations

A Bayesian Multilevel Framework for Uncertainty Characterization and the NASA Langley Multidisciplinary UQ Challenge

Nagel

Sudret

2014

16th AIAA Non-Deterministic Approaches Conference

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Achieving robust design through statistical effect screening

Buren

Hemez

2015

Numerical Meth Engineering

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Summary This work proposes a method for statistical effect screening to identify design parameters of a numerical simulation that are influential to performance while simultaneously being robust to epistemic uncertainty introduced by calibration variables. Design parameters are controlled by the analyst, but the optimal design is often uncertain, while calibration variables are introduced by modeling choices. We argue that uncertainty introduced by design parameters and calibration variables should be treated differently, despite potential interactions between the two sets. Herein, a robustness criterion is embedded in our effect screening to guarantee the influence of design parameters, irrespective of values used for calibration variables. The Morris screening method is utilized to explore the design space, while robustness to uncertainty is quantified in the context of info‐gap decision theory. The proposed method is applied to the National Aeronautics and Space Administration Multidisciplinary Uncertainty Quantification Challenge Problem, which is a black‐box code for aeronautic flight guidance that requires 35 input parameters. The application demonstrates that a large number of variables can be handled without formulating simplifying assumptions about the potential coupling between calibration variables and design parameters. Because of the computational efficiency of the Morris screening method, we conclude that the analysis can be applied to even larger‐dimensional problems. (Approved for unlimited, public release on October 9, 2013, LA‐UR‐13‐27839, Unclassified.) Copyright © 2015 John Wiley & Sons, Ltd.

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The NASA Langley Multidisciplinary Uncertainty Quantification Challenge

Abstract: This paper presents the formulation of an uncertainty quantification challenge problem consisting of five subproblems. These problems focus on key aspects of uncertainty characterization, sensitivity analysis, uncertainty propagation, extreme-case analysis, and robust design.

Cited by 62 publications

References 7 publications

OpenCossan: An Efficient Open Tool for Dealing with Epistemic and Aleatory Uncertainties

OpenCossan: An Efficient Open Tool for Dealing with Epistemic and Aleatory Uncertainties

A Bayesian Multilevel Framework for Uncertainty Characterization and the NASA Langley Multidisciplinary UQ Challenge

Achieving robust design through statistical effect screening

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