Uncertainty quantification in nuclear criticality modelling using a high dimensional model representation
File(s)
Accepted version
Author(s)
Ayres, D
Eaton, MD
Type
Journal Article
Abstract
An adaptive high dimensional model representation (HDMR) is used to decompose the response parameter into a superposition of lower dimensional subspaces which are in-turn projected on to a polynomial basis. These projections are evaluated using an adaptive quadrature scheme which is used to infer the polynomial orders of the basis. The combination of adaptive HDMR and adaptive quadrature techniques results in a sparse polynomial expansion which has been optimised to represent the variance of the response with the minimum number of polynomials. The combined application of these techniques is illustrated using UOX and MOX pin cell problems with evaluated nuclear covariance data. We show that this approach to calculating the variance in is an order of magnitude more efficient when compared to Latin Hypercube sampling with the same number of samples for problems involving up to 988 random dimensions.
Date Issued
2015-03-07
Date Acceptance
2015-02-14
Citation
Annals of Nuclear Energy, 2015, 80, pp.379-402
ISSN
0306-4549
Publisher
Elsevier Masson
Start Page
379
End Page
402
Journal / Book Title
Annals of Nuclear Energy
Volume
80
Copyright Statement
© 2015 The Authors. Published by Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000352041200043&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
EP/J002011/1
Subjects
Science & Technology
Technology
Nuclear Science & Technology
Polynomial chaos
High dimensional model representation
Covariance data nuclear criticality
STOCHASTIC COLLOCATION METHOD
POLYNOMIAL CHAOS
MULTIDIMENSIONAL INTEGRATION
DIFFERENTIAL-EQUATIONS
NEUTRON-TRANSPORT
REDUCTION METHOD
RANDOM-MEDIA
DECOMPOSITION
MECHANICS
OUTPUT
Publication Status
Published