Component quantification of aortic blood flow energy loss using computational fluid-structure interaction hemodynamics. (June 2022)

• Record Type: Journal Article
• Authors: Qiao, Yonghui; Luo, Kun; Fan, Jianren
• Is Part Of: Computer methods and programs in biomedicine. Volume 221(2022)
• Publication Date: 2022-06
• Subjects: Computational hemodynamics -- Energy loss -- Viscosity friction -- Turbulence dissipation -- Fluid-structure interaction; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106826
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A fluid-structure interaction computational framework is proposed to acquire aortic hemodynamics. Four components of energy loss: viscous friction, turbulence dissipation, wall deformation, and local lesion are quantitatively revealed for the first time. Viscous friction contributes most to the total energy loss of aortic blood flow. The proportions of turbulence dissipation and local lesion are individualized. The mutual work process between the aortic wall and blood flow is captured. The buffering efficiency of the healthy and diseased aorta is about 80%. Abstract: Background and objectives: The aorta serves as the main tube of the human blood circulation system. Energy loss (EL) occurs when blood flows through the aorta and there may be a potential correlation between EL and aortic diseases. However, the components of blood flow EL are still not fully understood. This study aims to quantitatively reveal the EL components in healthy and diseased aortas. Methods: We construct an idealized healthy aorta and three idealized representative diseased aortas: aortic aneurysm, coarctation of the aorta, and aortic dissection. Computational hemodynamic studies are carried out by using the fluid-structure interaction simulation framework. Results: Four kinds of EL components: viscous friction, turbulence dissipation, wall deformation, and local lesion are firstly acquired in healthy and diseased aortas based on the high-resolution blood flow information. Viscous friction contributes most to the EL (45.69%-57.22%). EL caused by the deformation of the aortic wall ranks second (15.18%-33.12%). The proportions of turbulence dissipation and local lesion depend on individual geometric characteristics. Besides, the buffering efficiency of the healthy and diseased aorta is about 80%. Conclusions: This study quantitatively reports the components of blood flow EL in healthy and diseased aortas, the finding may provide novel insights into the pathogenesis of aortic diseases.