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• W2894440114 endingPage "20180486" @default.
• W2894440114 startingPage "20180486" @default.
• W2894440114 abstract "Patient-specific computational fluid dynamics (CFD) is a promising tool that provides highly resolved haemodynamics information. The choice of blood rheology is an assumption in CFD models that has been subject to extensive debate. Blood is known to exhibit shear-thinning behaviour, and non-Newtonian modelling has been recommended for aneurysmal flows. Current non-Newtonian models ignore rouleaux formation, which is the key player in blood's shear-thinning behaviour. Experimental data suggest that red blood cell aggregation and rouleaux formation require notable red blood cell residence-time (RT) in a low shear rate regime. This study proposes a novel hybrid Newtonian and non-Newtonian rheology model where the shear-thinning behaviour is activated in high RT regions based on experimental data. Image-based abdominal aortic and cerebral aneurysm models are considered and highly resolved CFD simulations are performed using a minimally dissipative solver. Lagrangian particle tracking is used to define a backward particle RT measure and detect stagnant regions with increased rouleaux formation likelihood. Our novel RT-based non-Newtonian model shows a significant reduction in shear-thinning effects and provides haemodynamic results qualitatively identical and quantitatively close to the Newtonian model. Our results have important implications in patient-specific CFD modelling and suggest that non-Newtonian models should be revisited in large artery flows." @default.
• W2894440114 created "2018-10-05" @default.
• W2894440114 creator A5054293534 @default.
• W2894440114 date "2018-09-01" @default.
• W2894440114 modified "2023-09-29" @default.
• W2894440114 title "Accounting for residence-time in blood rheology models: do we really need non-Newtonian blood flow modelling in large arteries?" @default.
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• W2894440114 doi "https://doi.org/10.1098/rsif.2018.0486" @default.
• W2894440114 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6170779" @default.
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• W2894440114 hasPublicationYear "2018" @default.
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