Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1. R. Fahraeus and T. Lindqvist, “ The viscosity of the blood in narrow capillary tubes,” Am. J. Physiol. 96, 562568 (1931).
2. R. M. Hochmuth, R. N. Marple, and S. P. Sutera, “ Capillary blood flow. 1. Erythrocyte deformation in glass capillaries,” Microvasc. Res. 2, 409419 (1970).
3. E. Evans and Y. C. Fung, “ Improved measurements of the erythrocyte geometry,” Microvasc. Res. 4, 335347 (1972).
4. T. M. Fischer, M. Stohr-Liesen, and H. Schmid-Schonbein, “ The red cell as a fluid droplet: Tank tread-like motion of the human erythrocyte membrane in shear flow,” Science 202, 894896 (1978).
5. C. Pozrikidis, “ Effect of membrane bending stiffness on the deformation of capsules in simple shear flow,” J. Fluid Mech. 440, 269291 (2001).
6. J. Li, M. Dao, C. T. Lim, and S. Suresh, “ Spectrin-level modeling of the cytoskeleton and optical tweezers stretching of the erythrocyte,” Biophys. J. 88, 37073719 (2005).
7. P. Bagchi, P. C. Johnson, and A. S. Popel, “ Computational fluid dynamic simulation of aggregation of deformable cells in a shear flow,” J. Biomech. Eng. 127, 10701080 (2005).
8. T. W. Secomb, B. Styp-Rekowska, and A. R. Pries, “ Two-dimensional simulation of red blood cell deformation and lateral migration in microvessels,” Ann. Biomed. Eng. 35, 755765 (2007).
9. Y. Sui, Y. T. Chew, P. Roy, Y. P. Cheng, and H. T. Low, “ Dynamic motion of red blood cells in simple shear flow,” Phys. Fluids 20, 112106 (2008).
10. B. Kaoui, G. Biros, and C. Misbah, “ Why do red blood cells have asymmetric shapes even in a symmetric flow?Phys. Rev. Lett. 103, 188101 (2009).
11. S. M. Hosseini and J. J. Feng, “ A particle-based model for the transport of erythrocytes in capillaries,” Chem. Eng. Sci. 64, 44884497 (2009).
12. T. Ye, H. Li, and K. Y. Lam, “ Modeling and simulation of microfluid effects on deformation behavior of a red blood cell in a capillary,” Microvasc. Res. 80, 453463 (2010).
13. N. Tahiri, T. Biben, H. Ez-Zahraouy, A. Benyoussef, and C. Misbah, “ On the problem of slipper shapes of red blood cells in the microvasculature,” Microvasc. Res. 85, 4045 (2013).
14. H. Noguchi and G. Gompper, “ Shape transitions of fluid vesicles and red blood cells in capillary flows,” PNAS 102, 1415914164 (2005).
15. J. Zhang, P. C. Johnson, and A. S. Popel, “ An immersed boundary lattice boltzmann approach to simulate deformable liquid capsules and its application to microscopic blood flows,” Phys. Biol. 4, 285295 (2007).
16. I. V. Pivkin and G. E. Karniadakis, “ Accurate coarse-grained modeling of red blood cells,” Phys. Rev. Lett. 101, 118105 (2008).
17. D. V. Le, J. White, J. Peraire, K. M. Lim, and B. C. Khoo, “ An implicit immersed boundary method for three-dimensional fluid-membrane interactions,” J. Comp. Phys. 228, 84278445 (2009).
18. D. A. Fedosov, B. Caswell, and G. E. Karniadakis, “ A multiscale red blood cell model with accurate mechanics, rheology, and dynamics,” Biophys. J. 98, 22152225 (2010).
19. A. Yazdani and P. Bagchi, “ Three-dimensional numerical simulation of vesicle dynamics using a front-tracking method,” Phys. Rev. E 85, 056308 (2012).
20. A. R. Pries and T. W. Secomb, “ Rheology of the microcirculation,” Clin. Hemorheol. Microcirc. 29, 1438 (2003).
21. H. Sakai, A. Sato, N. Okuda, S. Takeoka, N. Maeda, and E. Tsuchida, “ Peculiar flow patterns of rbcs suspended in viscous fluids and perfused through a narrow tube (25 microm),” Am. J. Physiol. Heart Circ. Physiol. 297, H583H589 (2009).
22. J. B. Freund and M. M. Orescanin, “ Cellular flow in a small blood vessel,” J. Fluid Mech. 671, 466490 (2011).
23. H. Zhao and E. S. G. Shaqfeh, “ Shear-induced platelet margination in a microchannel,” Phys. Rev. E 83, 061924 (2011).
24. J. B. Freund, “ Numerical simulation of flowing blood cells,” Annu. Rev. Fluid Mech. 46, 6795 (2014).
25. A. R. Pries, D. Neuhaus, and P. Gaehtgens, “ Blood viscosity in tube flow: dependence on diameter and hematocrit,” Am. J. Physiol. 263, H1770H1778 (1992).
26. P. Bagchi, “ Mesoscale simulation of blood flow in small vessels,” Biophys. J. 92, 18581877 (2007).
27. S. K. Doddi and P. Bagchi, “ Three-dimensional computational modeling of multiple deformable cells flowing in microvessels,” Phys. Rev. E 79, 046318 (2009).
28. J. Zhang, “ Effect of suspending viscosity on red blood cell dynamics and blood flows in microvessels,” Microcirculation 18, 562573 (2011).
29. C. Pozrikidis, “ Axisymmetric motion of a file of red blood cells through capillaries,” Phys. Fluids 17, 031503 (2005).
30. K. Tsubata, S. Wada, and T. Yamaguchi, “ Simulation study on effects of hematocrit on blood flow properties using particle method,” J. Biomech. Sci. Eng. 1, 159170 (2006).
31. T. Wang and Z. Xing, “ Characterization of blood flow in capillaries by numerical simulation,” J. Mod. Phys. 1, 349356 (2010).
32. Y. Imai, H. Kondo, T. Ishikawa, C. Teck Lim, and T. Yamaguchi, “ Modeling of hemodynamics arising from malaria infection,” J. Biomech. 43, 13861393 (2010).
33. R. D. Groot and P. B. Warren, “ Dissipative particle dynamics: Bridging the gap between atomistic and mesoscopic simulation,” J. Chem. Phys. 107, 44234435 (1997).
34. P. Español, “ Fluid particle model,” Phys. Rev. E 57, 29302948 (1998).
35. A. Vázquez-Quesada, M. Ellero, and P. Español, “ Consistent scaling of thermal fluctuations in smoothed dissipative particle dynamics,” J. Chem. Phys. 130, 034901 (2009).
36. D. A. Fedosov, B. Caswell, and G. E. Karniadakis, “ Systematic coarse-graining of spectrin-level red blood cell models,” Comput. Meth. Appl. Mech. Eng. 199, 19371948 (2010).
37. T. Ye, N. Phan-Thien, B. C. Khoo, and C. T. Lim, “ Numerical modelling of a healthy/malaria-infected erythrocyte in shear flow using dissipative particle dynamics method,” J. Appl. Phys. 115, 224701 (2014).
38. Y. L. Liu, L. Zhang, X. Wang, and W. K. Liu, “ Coupling of navier-stokes equations with protein molecular dynamics and its application to hemodynamics,” Int. J. Numer. Methods Fluids 46, 12371252 (2004).
39. Y. L. Liu and W. K. Liu, “ Rheology of red blood cell aggregation by computer simulation,” J. Comput. Phys. 220, 139154 (2006).
40. J. Zhang, P. C. Johnson, and A. S. Popel, “ Red blood cell aggregation and dissociation in shear flows simulated by lattice boltzmann method,” J. Biomech. 41, 4755 (2008).
41. H. Li, T. Ye, and K. Y. Lam, “ Computational analysis of dynamic interaction of two red blood cells in a capillary,” Cell Biochem. Biophys. 69, 673680 (2014).
42. S. Bhattacharjee, M. Elimelech, and M. Borkovec, “ Dlvo interaction between colloidal particles: beyond derjaguin's approximation,” Croat. Chem. Acta 71, 883903 (1998).
43. B. Chung, P. C. Johnson, and A. S. Popel, “ Application of chimera grid to modelling cell motion and aggregation in a narrow tube,” Int. J. Num. Methods Fluids 53, 105128 (2007).
44. M. Revenga, I. Zúñiga, and P. Español, “ Boundary conditions in dissipative particle dynamics,” Comput. Phys. Commun. 121–122, 309311 (1999).
45. I. V. Pivkin and G. E. Karniadakis, “ A new method to impose no-slip boundary conditions in dissipative particle dynamics,” J. Comput. Phys. 207, 114128 (2005).
46. R. M. Hochmuth, “ Erythrocyte membrane elasticity and viscosity,” Ann. Rev. Physiol. 49, 209219 (1987).
47. G. Agresar, J. J. Linderman, G. Tryggvason, and K. G. Powell, “ An adaptive, cartesian, front-tracking method for the motion, deformation and adhesion of circulating cells,” J. Comput. Phys. 143, 346380 (1998).
48. J. J. Bishop, P. R. Nance, A. S. Popel, M. Intaglietta, and P. C. Johnson, “ Effect of erythrocyte aggregation on velocity profiles in venules,” Am. J. Physiol. Heart Circ. Physiol. 280, H222H236 (2001).
49. J. J. Bishop, A. S. Popel, M. Intaglietta, and P. C. Johnson, “ Effect of aggregation and shear rate on the dispersion of red blood cells flowing in venules,” Am. J. Physiol. Heart Circ. Physiol. 283, H1985H1996 (2002).

Data & Media loading...


Article metrics loading...



The rheology of a file of red blood cells (RBCs) in a tube flow is investigated based on a three-dimensional (3D) computational model using the dissipative particle dynamics (DPD) method. The 3D model consists of a discrete RBC model to describe the RBC deformation, a Morse potential model to characterize the cell–cell interaction, and a DPD model to provide all the relevant information on the suspension flow. Three important features of the suspension flow are simulated and analyzed, (i) the effect of the tube hematocrit, (ii) the effect of the cell spacing, and (iii) the effect of the flow velocity. We first study the cell deformation and the rheology of suspension at different tube hematocrit. The results show that the cell deformation decreases with increasing tube hematocrit, and a good agreement between the simulation and available experiments is found for the discharge hematocrit and relative apparent viscosity of RBC suspension. We then analyze the effect of non-uniform cell spacing, where the cell–cell interaction goes into effect, showing that a non-uniform cell spacing has a slight effect on the cell deformation, and almost has no effect on the rheology of suspension. We finally study the effect of the flow velocity and show that a typical plug-flow velocity profile is observed. The results also show that the cell deformation increases with increasing flow velocity, as expected. The discharge hematocrit also increases, but the relative apparent viscosity decreases, with increasing flow velocity.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd