TY - JOUR
T1 - A Bayesian approach to blood rheological uncertainties in aortic hemodynamics
AU - Ranftl, Sascha
AU - Müller, Thomas Stephan
AU - Windberger, Ursula
AU - Brenn, Günter
AU - von der Linden, Wolfgang
N1 - Funding Information:
This work was funded by Graz University of Technology (TUG) through the LEAD Project “Mechanics, Modeling, and Simulation of Aortic Dissection” ( https://www.tugraz.at/projekte/biomechaorta/home/ ), and supported by GCCE: Graz Center of Computational Engineering and HPC resources of TUG ZID. Open Access supported by TUG Library.
Publisher Copyright:
© 2022 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.
PY - 2022
Y1 - 2022
N2 - Computational hemodynamics has received increasing attention recently. Patient-specific simulations require questionable model assumptions, for example, for geometry, boundary conditions, and material parameters. Consequently, the credibility of these simulations is much doubted, and rightly so. Yet, the matter may be addressed by a rigorous uncertainty quantification. In this contribution, we investigated the impact of blood rheological models on wall shear stress uncertainties in aortic hemodynamics obtained in numerical simulations. Based on shear-rheometric experiments, we compare the non-Newtonian Carreau model to a simple Newtonian model and a Reynolds number-equivalent Newtonian model. Bayesian Probability Theory treats uncertainties consistently and allows to include elusive assumptions such as the comparability of flow regimes. We overcome the prohibitively high computational cost for the simulation with a surrogate model, and account for the uncertainties of the surrogate model itself, too. We have two main findings: (1) The Newtonian models mostly underestimate the uncertainties as compared to the non-Newtonian model. (2) The wall shear stresses of specific persons cannot be distinguished due to largely overlapping uncertainty bands, implying that a more precise determination of person-specific blood rheological properties is necessary for person-specific simulations. While we refrain from a general recommendation for one rheological model, we have quantified the error of the uncertainty quantification associated with these modeling choices.
AB - Computational hemodynamics has received increasing attention recently. Patient-specific simulations require questionable model assumptions, for example, for geometry, boundary conditions, and material parameters. Consequently, the credibility of these simulations is much doubted, and rightly so. Yet, the matter may be addressed by a rigorous uncertainty quantification. In this contribution, we investigated the impact of blood rheological models on wall shear stress uncertainties in aortic hemodynamics obtained in numerical simulations. Based on shear-rheometric experiments, we compare the non-Newtonian Carreau model to a simple Newtonian model and a Reynolds number-equivalent Newtonian model. Bayesian Probability Theory treats uncertainties consistently and allows to include elusive assumptions such as the comparability of flow regimes. We overcome the prohibitively high computational cost for the simulation with a surrogate model, and account for the uncertainties of the surrogate model itself, too. We have two main findings: (1) The Newtonian models mostly underestimate the uncertainties as compared to the non-Newtonian model. (2) The wall shear stresses of specific persons cannot be distinguished due to largely overlapping uncertainty bands, implying that a more precise determination of person-specific blood rheological properties is necessary for person-specific simulations. While we refrain from a general recommendation for one rheological model, we have quantified the error of the uncertainty quantification associated with these modeling choices.
KW - aortic hemodynamics
KW - Bayesian probability theory
KW - blood rheology
KW - computational fluid dynamics
KW - non-Newtonian fluids
KW - uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85124753730&partnerID=8YFLogxK
U2 - 10.1002/cnm.3576
DO - 10.1002/cnm.3576
M3 - Article
AN - SCOPUS:85124753730
SN - 2040-7939
JO - International Journal for Numerical Methods in Biomedical Engineering
JF - International Journal for Numerical Methods in Biomedical Engineering
M1 - e3576
ER -