The focus of this work is on uncertainty characterization, sensitivity analysis, uncertainty propagation, and extreme-case analysis. To deal with the computationally expensive and complex NASA problem, a simpler toy problem is devised to mimic the NASA problem for which the true results were known. The toy problem helped in thoroughly testing the current methods and their repeatability. For uncertainty characterization, a novel cumulative density function matching method is proposed, which gave similar results as a standard Markov chain–Monte Carlo-based Bayesian approach. An efficient reliability reanalysis-based probability-box sensitivity analysis method is employed to identify the most sensitive parameters to the risk analysis metrics. Uncertainty propagation to find extreme values for the risk analysis metrics is done using a single-loop efficient reliability reanalysis-based method. A modified version of the efficient reliability reanalysis is proposed that uses self-normalizing weights and caps on the weights; this is referred to as a capped self-normalizing efficient reliability reanalysis. This method showed considerably better performance at estimating risk analysis metrics for this application as compared to the generic efficient reliability reanalysis. The use of efficient reliability reanalysis was dictated by the cost of the black-box functions provided by NASA.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    NASA Uncertainty Quantification Challenge: An Optimization-Based Methodology and Validation



    Published in:

    Publication date :

    2015-01-01




    Type of media :

    Conference paper , Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    NASA Langley Multidisciplinary Uncertainty Quantification Challenge

    L. G. Crespo / S. P. Kenny / D. P. Giesy | NTIS | 2014


    The NASA Langley Multidisciplinary Uncertainty Quantification Challenge

    Crespo, Luis G. / Kenny, Sean P. / Giesy, Daniel P. | NTRS | 2014


    NASA Langley Challenge on Optimization Under Uncertainty

    L. G. Crespo / S. P. Kenny | NTIS | 2020