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International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 2152-5080

ISSN Online: 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

INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL

Volume 10, Issue 4, 2020, pp. 333-349
DOI: 10.1615/Int.J.UncertaintyQuantification.2020033186
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ABSTRACT

In order to accurately describe the mechanics of red blood cells (RBCs) and resulting fluid dynamics, a cell-resolved blood flow fluid solver is required. The parameters of the material model for the RBC membranes are carefully tuned to reproduce the behavior of real cells under various experimental conditions. In this work, uncertainty in the parameters of the material model for RBCs used in a model for RBC suspensions was estimated with Inverse Uncertainty Quantification (IUQ) using Bayesian Annealed Sequential Importance Sampling (BASIS). Due to the relatively high computational cost of the model, a Gaussian Process regression metamodel was trained in order to feasibly draw the large number of samples required to obtain an accurate posterior distribution estimate. Additionally, the identifiability of the model parameters was estimated using Sobol sensitivity indices. The elongation index of simulated RBCs in a perfect sheared environment was the model prediction used to calibrate model parameters. The results show good identifiability of the parameter defining the tensile properties of the cell membrane and viscosity ratio, and poor identifiability of the parameter defining the response of the cell surface while undergoing bending. This suggests that the latter should be identified using a different quantity of interest. Overall, the model outputs with the optimal values of the parameters obtained using the Gaussian Process metamodel match better or close to the measurements than the results with the parameters' values obtained with the original model. Therefore, we can conclude that it is a valid method to decrease the computational cost of IUQ of the model.

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CITED BY
  1. Czaja Benjamin, de Bouter Jonathan, Heisler Morgan, Závodszky Gábor, Karst Sonja, Sarunic Marinko, Maberley David, Hoekstra Alfons, The effect of stiffened diabetic red blood cells on wall shear stress in a reconstructed 3D microaneurysm, Computer Methods in Biomechanics and Biomedical Engineering, 2022. Crossref

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