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• W2049554061 abstract "Simulations of blood flow in both healthy and diseased vascular models can be used to compute a range of hemodynamic parameters including velocities, time varying wall shear stress, pressure drops, and energy losses. The confidence in the data output from cardiovascular simulations depends directly on our level of certainty in simulation input parameters. In this work, we develop a general set of tools to evaluate the sensitivity of output parameters to input uncertainties in cardiovascular simulations. Uncertainties can arise from boundary conditions, geometrical parameters, or clinical data. These uncertainties result in a range of possible outputs which are quantified using probability density functions (PDFs). The objective is to systemically model the input uncertainties and quantify the confidence in the output of hemodynamic simulations. Input uncertainties are quantified and mapped to the stochastic space using the stochastic collocation technique. We develop an adaptive collocation algorithm for Gauss–Lobatto–Chebyshev grid points that significantly reduces computational cost. This analysis is performed on two idealized problems – an abdominal aortic aneurysm and a carotid artery bifurcation, and one patient specific problem – a Fontan procedure for congenital heart defects. In each case, relevant hemodynamic features are extracted and their uncertainty is quantified. Uncertainty quantification of the hemodynamic simulations is done using (a) stochastic space representations, (b) PDFs, and (c) the confidence intervals for a specified level of confidence in each problem." @default.
• W2049554061 created "2016-06-24" @default.
• W2049554061 creator A5081078400 @default.
• W2049554061 creator A5087790536 @default.
• W2049554061 date "2011-02-04" @default.
• W2049554061 modified "2023-10-03" @default.
• W2049554061 title "A Stochastic Collocation Method for Uncertainty Quantification and Propagation in Cardiovascular Simulations" @default.
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• W2049554061 doi "https://doi.org/10.1115/1.4003259" @default.
• W2049554061 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21303177" @default.
• W2049554061 hasPublicationYear "2011" @default.
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