skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Journal Article · · Journal of Verification, Validation and Uncertainty Quantification
DOI:https://doi.org/10.1115/1.4040476· OSTI ID:1429755
 [1];  [1]
  1. Univ. of Wisconsin-Madison, Madison, WI (United States)
+ Show Author Affiliations

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.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1429755
Report Number(s):
SAND-2017-3216J; 652047
Journal Information:
Journal of Verification, Validation and Uncertainty Quantification, Vol. 3, Issue 1; ISSN 2377-2158
Publisher:
ASMECopyright Statement
Country of Publication:
United States
Language:
English

References (15)

Selection of Component Modes for Craig-Bampton Substructure Representations journal April 1996
Multicomplex Taylor Series Expansion for Computing High-Order Derivatives journal October 2014
A family of embedded Runge-Kutta formulae journal March 1980
Generation of finite difference formulas on arbitrarily spaced grids journal January 1988
The Monte Carlo Method journal September 1949
Optimization with Gradient and Hessian Information Calculated Using Hyper-Dual Numbers conference August 2012
Large Sample Properties of Simulations Using Latin Hypercube Sampling journal May 1987
Parametric reduced-order models for predicting the vibration response of complex structures with component damage and uncertainties journal March 2011
Parameterized reduced order models from a single mesh using hyper-dual numbers journal June 2016
Robust signal processing for damaged vehicles with variability journal January 2013
Multicomplex Newmark-Beta Time Integration Method for Sensitivity Analysis in Structural Dynamics journal May 2015
Coupling of substructures for dynamic analyses. journal July 1968
The Homogeneous Chaos journal October 1938
Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems journal July 2003
Next-generation parametric reduced-order models journal May 2013

Figures / Tables (20)

Figure 1(p. 5)figure Figure 1
Figure 2(p. 6)figure Figure 2
Figure 3(p. 7)figure Figure 3
Figure 4(p. 9)figure Figure 4
Figure 5(p. 10)figure Figure 5
Figure 6(p. 11)figure Figure 6
Figure 7(p. 11)figure Figure 7
Figure 8(p. 12)figure Figure 8
Table 1(p. 12)table Table 1
Table 2(p. 13)table Table 2
Figure 10(p. 14)figure Figure 10
Figure 9(p. 14)figure Figure 9
Table 3(p. 15)table Table 3
Figure 11(p. 16)figure Figure 11
Figure 12(p. 16)figure Figure 12
Figure 13(p. 17)figure Figure 13
Figure 14(p. 18)figure Figure 14
Figure 15(p. 18)figure Figure 15
Figure 16(p. 19)figure Figure 16
Figure 17(p. 19)figure Figure 17

Similar Records

Related Subjects

97 MATHEMATICS AND COMPUTING
Hyper-Dual
HDM
PROM
Parameter Uncertainty
Surrogate Mode