Efficient Quantification of Uncertainties in Complex Computer Code Results, Phase I

Metadata Updated: February 28, 2019

This proposal addresses methods for efficient quantification of margins and uncertainties (QMU) for models that couple multiple, large-scale commercial or proprietary simulation codes, effective methods for treating epistemic uncertainty in large scale simulations, scalability to models with hundreds or thousands of uncertain parameters, and competition with traditional Monte Carlo Based methods. The Reduced-Order Clustering Uncertainty Quantification (ROCUQ) methodology described in this proposal has been under development over the past several years at the University of Illinois, and is being commercialized by IllinoisRocstar LLC. ROCUQ uses a combination of common stratified Monte Carlo techniques, coupled with well-chosen reduced order models, statistical clustering, and a few (less than tens) high-fidelity simulation runs to provide estimates of the uncertainty distributions for the System Response Quantities (SRQs) of interest to the modelers. The goal of the ROCUQ methodology is to minimize the number of high-fidelity, computationally-intensive simulation runs that are needed in order to provide estimates of output uncertainties of interest, especially when it is not possible to run the high-fidelity model more than a few (e.g., 5 to 10) times. ROCUQ has been, or is currently being applied to solid propellant rocket internal ballistics uncertainties, coupled fluid-structure interaction modeling of stresses in an Air Force Training Fighter wing, and structural dynamics/vibration of a specially-designed experimental apparatus for studying simulation validation under uncertainty.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_10331
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 1dec3c6b-2330-4a5e-8908-36d517492820
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2011-01-01
Homepage URL https://techport.nasa.gov/view/10331
License http://www.usa.gov/publicdomain/label/1.0/
Data Last Modified 2018-07-19
Program Code 026:027
Source Datajson Identifier True
Source Hash 6814bc5c4e5834c877fa37698031484121fb14f2
Source Schema Version 1.1

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