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Title: Parameterization of Large Variability Using the Hyper-Dual Meta-Model

Abstract

One major problem in the design of aerospace components is the nonlinear changes in the response due to a change in the geometry and material properties. Many of these components have small nominal values and any change can lead to a large variability. In order to characterize this large variability, traditional methods require either many simulation runs or the calculations of many higher order derivatives. Each of these paths requires a large amount of computational power to evaluate the response curve. In order to perform uncertainty quantification analysis, even more simulation runs are required. The hyper-dual meta-model is introduced and used to characterize the response curve with the use of basis functions. The information of the response is generated with the utilization of the hyper-dual step to determine the sensitivities at a few number of simulation runs to greatly enrich the response space. This study shows the accuracy of this method for two different systems with parameterizations at different stages in the design analysis.

Authors:
 [1];  [1]
  1. Univ. of Wisconsin-Madison, Madison, WI (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1429755
Report Number(s):
SAND-2017-3216J
Journal ID: ISSN 2377-2158; 652047
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Verification, Validation and Uncertainty Quantification
Additional Journal Information:
Journal Volume: 3; Journal Issue: 1; Journal ID: ISSN 2377-2158
Publisher:
ASME
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Hyper-Dual; HDM; PROM; Parameter Uncertainty; Surrogate Mode

Citation Formats

Bonney, Matthew S., and Kammer, Daniel C. Parameterization of Large Variability Using the Hyper-Dual Meta-Model. United States: N. p., 2018. Web. doi:10.1115/1.4040476.
Bonney, Matthew S., & Kammer, Daniel C. Parameterization of Large Variability Using the Hyper-Dual Meta-Model. United States. doi:10.1115/1.4040476.
Bonney, Matthew S., and Kammer, Daniel C. Mon . "Parameterization of Large Variability Using the Hyper-Dual Meta-Model". United States. doi:10.1115/1.4040476. https://www.osti.gov/servlets/purl/1429755.
@article{osti_1429755,
title = {Parameterization of Large Variability Using the Hyper-Dual Meta-Model},
author = {Bonney, Matthew S. and Kammer, Daniel C.},
abstractNote = {One major problem in the design of aerospace components is the nonlinear changes in the response due to a change in the geometry and material properties. Many of these components have small nominal values and any change can lead to a large variability. In order to characterize this large variability, traditional methods require either many simulation runs or the calculations of many higher order derivatives. Each of these paths requires a large amount of computational power to evaluate the response curve. In order to perform uncertainty quantification analysis, even more simulation runs are required. The hyper-dual meta-model is introduced and used to characterize the response curve with the use of basis functions. The information of the response is generated with the utilization of the hyper-dual step to determine the sensitivities at a few number of simulation runs to greatly enrich the response space. This study shows the accuracy of this method for two different systems with parameterizations at different stages in the design analysis.},
doi = {10.1115/1.4040476},
journal = {Journal of Verification, Validation and Uncertainty Quantification},
number = 1,
volume = 3,
place = {United States},
year = {2018},
month = {6}
}

Journal Article:
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