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International Journal for Uncertainty Quantification

Publicado 6 números por año

ISSN Imprimir: 2152-5080

ISSN En Línea: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

REDUCED ORDER MODELING FOR NONLINEAR MULTI-COMPONENT MODELS

Volumen 2, Edición 4, 2012, pp. 341-361
DOI: 10.1615/Int.J.UncertaintyQuantification.2012003523
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SINOPSIS

Reduced order modeling plays an indispensible role in most real-world complex models. A hybrid application of order reduction methods, introduced previously, has been shown to effectively reduce the computational cost required to find a reduced order model with quantifiable bounds on the reduction errors, which is achieved by hybridizing the application of local variational and global sampling methods for order reduction. The method requires the evaluation of first-order derivatives of pseudo-responses with respect to input parameters and the ability to perturb input parameters within their user-specified ranges of variations. The derivatives are employed to find a subspace that captures all possible response variations resulting from all possible parameter variations with quantifiable accuracy. This paper extends the applicability of this methodology to multi-component models. This is achieved by employing a hybrid methodology to enable the transfer of sensitivity information between the various components in an efficient manner precluding the need for a global sensitivity analysis procedure, which is often envisaged to be computationally intractable. Finally, we introduce a new measure of conditioning for the subspace employed for order reduction. Although, the developments are general, they are applied here to smoothly behaving functions only. Extension to non-smooth functions will be addressed in a future article. In addition to introducing these new developments, this manuscript is intended to provide a pedagogical overview of our current developments in the area of reduced order modeling to real-world engineering models.

CITADO POR
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  6. Foad Basma, Yamamoto Akio, Endo Tomohiro, Uncertainty and regression analysis of the MSLB accident in PWR based on unscented transformation and low rank approximation, Annals of Nuclear Energy, 143, 2020. Crossref

  7. Mandelli Diego, Parisi Carlo, Anderson Nolan, Ma Zhegang, Zhang Hongbin, Dynamic PRA Methods to Evaluate the Impact on Accident Progression of Accident Tolerant Fuels, Nuclear Technology, 207, 3, 2021. Crossref

  8. Foad Basma, Novog David R., Implementation and testing of unscented transformation and low rank approximation to enhance SCALE code uncertainty calculations, Annals of Nuclear Energy, 167, 2022. Crossref

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