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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

INCORPORATING PRIOR KNOWLEDGE FOR QUANTIFYING AND REDUCING MODEL-FORM UNCERTAINTY IN RANS SIMULATIONS

Volumen 6, Edición 2, 2016, pp. 109-126
DOI: 10.1615/Int.J.UncertaintyQuantification.2016015984
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SINOPSIS

Simulations based on Reynolds-averaged Navier-Stokes (RANS) models have been used to support high-consequence decisions related to turbulent flows. Apart from the deterministic model predictions, the decision makers are often equally concerned about the prediction confidence. Among the uncertainties in RANS simulations, the model-form uncertainty is an important or even a dominant source. Therefore, quantifying and reducing the model-form uncertainties in RANS simulations are of critical importance to make risk-informed decisions. Researchers in statistics communities have made efforts on this issue by considering numerical models as black boxes. However, this physics-neutral approach is not a most efficient use of data, and is not practical for most engineering problems. Recently, we proposed an open-box, Bayesian framework for quantifying and reducing model-form uncertainties in RANS simulations based on observation data and physics-prior knowledge. It can incorporate the information from the vast body of existing empirical knowledge with mathematical rigor, which enables a more efficient usage of data. In this work, we examine the merits of incorporating various types of prior knowledge in the uncertainties quantification and reduction in RANS simulations. The result demonstrates that informative physics-based prior knowledge plays an important role in improving the performance of model-form uncertainty reduction, particularly when the observation data are limited. Moreover, it suggests that the proposed Bayesian framework is an effective way to incorporate empirical knowledge from various sources of turbulence modeling.

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