TY - GEN
T1 - Reduced order model-based uncertainty modeling of structures with localized response
AU - Song, Pengchao
AU - Mignolet, Marc
N1 - Publisher Copyright:
© 2017 The Authors.
PY - 2017
Y1 - 2017
N2 - This paper focuses on the introduction of uncertainty in reduced order models of structures exhibiting a localized static response in the neighborhood of the excitation. A straightforward application of the maximum entropy framework is first considered to carry out the stochastic modeling of the uncertainty. Quite consistently with the maximization of the entropy, it is found that this modeling may lead to a "globalization" of the response and thus an extension of the nonparametric stochastic modeling approach is sought. To this end, the eigenvalues and eigenvectors of the stiffness matrix of structures exhibiting this localization property are first studied. It is found that their lowest eigenvalues are closely spaced when the corresponding eigenvectors are extended to the entire structure. On this basis, a novel version of the nonparametric stochastic modeling approach is introduced to randomize the entire stiffness matrix while distorting only slightly the closely spaced eigenvalue structure. The above concepts are demonstrated on a thin annulus clamped at its inner radius and a localization of the uncertain response is indeed observed using the proposed approach.
AB - This paper focuses on the introduction of uncertainty in reduced order models of structures exhibiting a localized static response in the neighborhood of the excitation. A straightforward application of the maximum entropy framework is first considered to carry out the stochastic modeling of the uncertainty. Quite consistently with the maximization of the entropy, it is found that this modeling may lead to a "globalization" of the response and thus an extension of the nonparametric stochastic modeling approach is sought. To this end, the eigenvalues and eigenvectors of the stiffness matrix of structures exhibiting this localization property are first studied. It is found that their lowest eigenvalues are closely spaced when the corresponding eigenvectors are extended to the entire structure. On this basis, a novel version of the nonparametric stochastic modeling approach is introduced to randomize the entire stiffness matrix while distorting only slightly the closely spaced eigenvalue structure. The above concepts are demonstrated on a thin annulus clamped at its inner radius and a localization of the uncertain response is indeed observed using the proposed approach.
KW - Localized Response.
KW - Maximum Entropy
KW - Reduced Order Modeling
KW - Structural Uncertainty
KW - Uncertainty Modeling
UR - http://www.scopus.com/inward/record.url?scp=85043459004&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043459004&partnerID=8YFLogxK
U2 - 10.7712/120217.5389.17224
DO - 10.7712/120217.5389.17224
M3 - Conference contribution
AN - SCOPUS:85043459004
T3 - UNCECOMP 2017 - Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering
SP - 531
EP - 542
BT - UNCECOMP 2017 - Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering
A2 - Stefanou, George
A2 - Papadrakakis, M.
A2 - Papadopoulos, Vissarion
PB - National Technical University of Athens
T2 - 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2017
Y2 - 15 June 2017 through 17 June 2017
ER -