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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

A NEW INVERSE METHOD FOR THE UNCERTAINTY QUANTIFICATION OF SPATIALLY VARYING RANDOM MATERIAL PROPERTIES

Volumen 6, Edición 6, 2016, pp. 515-531
DOI: 10.1615/Int.J.UncertaintyQuantification.2016018673
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SINOPSIS

In this paper, a new inverse uncertainty quantification method was proposed to identify statistical parameters associated with spatially varying material properties and to reconstruct their heterogeneous distributions from limited experimental measurements. The proposed method parameterizes statistical models of random fields with analytic co-variance functions and spectral decomposition into Karhunen-Loeve random variables. The statistical model parameters are identified by an experimental-numerical inverse analysis method, which is expected to significantly reduce time and cost required for quantification of material uncertainties.

CITADO POR
  1. Jeong SungWoo, Lim Hyung-Jun, Zhu Fei-Yan, Park Chanwook, Kim Yeonghwan, Yun Gunjin, Nano-Micro Multiscale Modeling for Graphene-Reinforced Nanocomposites, 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2018. Crossref

  2. Webbe Kerekes Tomas, Lim Hyoungjun, Joe Woong Yeol, Yun Gun Jin, Characterization of process–deformation/damage property relationship of fused deposition modeling (FDM) 3D-printed specimens, Additive Manufacturing, 25, 2019. Crossref

  3. Lim Hyoungjun, Choi Hoil, Lee Min Jung, Yun Gun Jin, Elasto-plastic damage modeling and characterization of 3D needle-punched Cf/SiCm composite materials, Ceramics International, 46, 10, 2020. Crossref

  4. Choi Ho-il, Park Chanwook, Lim Hyoung Jun, Yun Gun Jin, A Nano-Micro–Macro Multiscale Modeling for Carbon Fiber-Reinforced Graphene/Epoxy Nanocomposites, Multiscale Science and Engineering, 3, 1, 2021. Crossref

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