Impact of Using Conventional Inlet/Outlet Boundary Conditions on Haemodynamic Metrics in a Subject-Specific Rabbit Aorta. (February 2018)

• Record Type: Journal Article
• Authors: McElroy, Michael; Keshmiri, Amir
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 2(2018:Feb.)
• Page Start: 103
• Page End: 113
• Publication Date: 2018-02
• Subjects: Aortic lesion -- boundary conditions -- cardiovascular disease -- computational fluid dynamics -- haemodynamic metrics -- rabbit aorta -- wall shear stress; Biomedical engineering -- Periodicals; Medical instruments and apparatus -- Periodicals; 610.28
• DOI: 10.1177/0954411917699237
• Languages: English
• ISSNs: 0954-4119

Abstract

Computational fluid dynamics is a tool capable of accurately measuring metrics currently used to predict the behaviour of cardiovascular diseases. This study quantifies the impact various commonly used inlet and outlet boundary conditions have on various shear rate–based haemodynamic metrics currently used for predicting the localisation of cardiovascular diseases. Simulations are conducted on an accurately represented rabbit aorta configuration and comparison has been made against available in vivo data. The boundary conditions studied include two different inlet profiles, three outlet boundary conditions, and steady-state versus pulsatile flow cases. Large variations were found in the results, particularly when using different outlet boundary conditions, and the discrepancies were evaluated both quantitatively and qualitatively. The results clearly highlight the importance of the type of boundary condition used when simulating complex cardiovascular models. By restricting the attention to the flow within the aorta and the intercostal branches, the results suggest that prescribing transient simulation and fully developed flow at the inlet are not required. Furthermore, assuming the widely accepted low wall shear stress theory of Caro, it was found that Murray's law–based outlet boundary condition returns the most physiologically accurate results when compared to in vivo data.