Contribution of the computational tools in understanding the blood rheology

Document Type : Original Article

Authors

1 Foothill College, Biological & Health Science Department, California, USA

2 Shiraz University of Medical Sciences, Shiraz, Iran

3 School of Paramedical Sciences, Bushehr University of Medical Science, Bushehr, Iran

4 Kazerun Islamic Azad University, Medical School, Kazerun, Iran

10.22034/jbr.2021.294370.1041

Abstract

This article presents a literature review to evaluate the capabilities and limitations of available computational tools in studying the behavioral properties of blood flow in capillaries. PubMed, Embase, and Web of Science databases are explored for articles published on topics such as red blood cells, blood flow, non-Newtonian fluid field, and finite element analysis of biomedical devices. Recent advancements in the modeling of complex fluid fields help researchers better understand RBCs' different mechanisms and interactions in micro-capillaries. Hence, the characteristics of a single red blood cell in micro-capillaries is reviewed and discussed in this article. Such understanding could help us predict, manipulate and control the blood flow by changing the viscosity or interactions between different components. Moreover, computational tools provide a quantitative assessment of interactions between various components. While more research is required to fully understand the blood flow in veins & arteries, the presence of experimental studies is of paramount importance to verify and validate the current models.

Graphical Abstract

Contribution of the computational tools in understanding the blood rheology

Keywords

References

1.            Fedosov DA, Noguchi H, Gompper G. Multiscale modeling of blood flow: from single cells to blood rheology. Biomechanics and modeling in mechanobiology. 2014;13(2):239-58.
2.            McCulloch A, Guccione J, Waldman L, Rogers J. Large-scale finite element analysis of the beating heart. High-performance computing in biomedical research. 2020:27-49.
3.            Popescu R, Haritinian EG, Cristea S. Relevance of Finite Element in Total Knee Arthroplasty-Literature Review. Chirurgia (Bucharest, Romania: 1990). 2019;114(4):437-42.
4.            Shekouhi N, Dick D, Baechle MW, Kaeley DK, Goel VK, Serhan H, et al. Clinically relevant finite element technique based protocol to evaluate growing rods for early onset scoliosis correction. Jor Spine. 2020;3(3):e1119.
5.            Shekouhi N, DD BM, Kaeley D, Goel V, editors. Finite element based test protocol to evaluate the effect of distraction on growth rods spanning over multiple spinal segments for pediatric scoliosis patients. ORS Annual Meeting; 2020.
6.            Shekouhi N. Towards A Standard Clinically Relevant Testing Protocol For the Assessment of Growing Rods: The University of Toledo; 2020.
7.            Kaeley D, BMW SN, Dick D, Serhan H, Goel V, editors. Finite element based test protocol to evaluate growth rods used in pediatric scoliosis patients. Biomedical Engineering Society (BMES) Annual Meeting; 2019.
8.            Malevanets A, Kapral R. Mesoscopic model for solvent dynamics. The Journal of chemical physics. 1999;110(17):8605-13.
9.            Kapral R. Multiparticle collision dynamics: Simulation of complex systems on mesoscales. Advances in Chemical Physics. 2008;140:89.
10.          Succi S. The lattice Boltzmann equation: for fluid dynamics and beyond: Oxford university press; 2001.
11.          Hoogerbrugge P, Koelman J. Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics. EPL (Europhysics Letters). 1992;19(3):155.
12.          Lei Y, Chen M, Xiong G, Chen J. Influence of virtual intervention and blood rheology on mass transfer through thoracic aortic aneurysm. Journal of biomechanics. 2015;48(12):3312-22.
13.          Gardner D, Li Y, Small B, Geddes JB, Carr RT. Multiple equilibrium states in a micro-vascular network. Mathematical biosciences. 2010;227(2):117-24.
14.          Pozrikidis C. Axisymmetric motion of a file of red blood cells through capillaries. Physics of fluids. 2005;17(3):031503.
15.          McWhirter JL, Noguchi H, Gompper G. Flow-induced clustering and alignment of vesicles and red blood cells in microcapillaries. Proceedings of the National Academy of Sciences. 2009;106(15):6039-43.
16.          Reasor Jr DA, Clausen JR, Aidun CK. Coupling the lattice‐Boltzmann and spectrin‐link methods for the direct numerical simulation of cellular blood flow. International Journal for Numerical Methods in Fluids. 2012;68(6):767-81.
17.          Dupin MM, Halliday I, Care CM, Alboul L, Munn LL. Modeling the flow of dense suspensions of deformable particles in three dimensions. Physical Review E. 2007;75(6):066707.
18.          Freund JB. Leukocyte margination in a model microvessel. Physics of fluids. 2007;19(2):023301.
19.          Fedosov DA, Caswell B, Karniadakis GE. A multiscale red blood cell model with accurate mechanics, rheology, and dynamics. Biophysical journal. 2010;98(10):2215-25.
20.          Pan W, Caswell B, Karniadakis GE. A low-dimensional model for the red blood cell. Soft matter. 2010;6(18):4366-76.
21.          Janoschek F, Toschi F, Harting J. Simplified particulate model for coarse-grained hemodynamics simulations. Physical Review E. 2010;82(5):056710.
22.          Melchionna S. A Model for Red Blood Cells in Simulations of Large‐scale Blood Flows. Macromolecular theory and simulations. 2011;20(7):548-61.
23.          Zhao H, Shaqfeh ES. The dynamics of a vesicle in simple shear flow. Journal of Fluid Mechanics. 2011;674:578.
24.          Chien S, Sung LA, Kim S, Burke AM, Usami S. Determination of aggregation force in rouleaux by fluid mechanical technique. Microvascular research. 1977;13(3):327-33.
25.          Tran-Son-Tay R, Sutera S, Rao P. Determination of red blood cell membrane viscosity from rheoscopic observations of tank-treading motion. Biophysical journal. 1984;46(1):65-72.
26.          Abkarian M, Faivre M, Viallat A. Swinging of red blood cells under shear flow. Physical review letters. 2007;98(18):188302.
27.          Bessonov N, Sequeira A, Simakov S, Vassilevskii Y, Volpert V. Methods of blood flow modelling. Mathematical modelling of natural phenomena. 2016;11(1):1-25.
28.          Astarita G, Marrucci G. Principles of non-Newtonian fluid mechanics: McGraw-Hill Companies; 1974.
29.          Perktold K, Peter R, Resch M. Pulsatile non-Newtonian blood flow simulation through a bifurcation with an aneurysm. Biorheology. 1989;26(6):1011-30.
30.          Moosaie A, Shekouhi N, Ahmadi A. Large-eddy simulation of fiber-induced turbulent drag reduction in a channel flow at Reτ= 180.
31.          Moosaie A, Shekouhi N, Nouri N, Manhart M. An algebraic closure model for the DNS of turbulent drag reduction by Brownian microfiber additives in a channel flow. Journal of Non-Newtonian Fluid Mechanics. 2015;226:60-6.

Files

Share

How to cite

Statistics