Skip to main content
Log in

Numerical Simulation of Unsteady Blood Flow through Capillary Networks

  • Original Article
  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript

Abstract

A numerical method is implemented for computing unsteady blood flow through a branching capillary network. The evolution of the discharge hematocrit along each capillary segment is computed by integrating in time a one-dimensional convection equation using a finite-difference method. The convection velocity is determined by the local and instantaneous effective capillary blood viscosity, while the tube to discharge hematocrit ratio is deduced from available correlations. Boundary conditions for the discharge hematocrit at divergent bifurcations arise from the partitioning law proposed by Klitzman and Johnson involving a dimensionless exponent, q≥1. When q=1, the cells are partitioned in proportion to the flow rate; as q tends to infinity, the cells are channeled into the branch with the highest flow rate. Simulations are performed for a tree-like, perfectly symmetric or randomly perturbed capillary network with m generations. When the tree involves more than a few generations, a supercritical Hopf bifurcation occurs at a critical value of q, yielding spontaneous self-sustained oscillations in the absence of external forcing. A phase diagram in the mq plane is presented to establish conditions for unsteady flow, and the effect of various geometrical and physical parameters is examined. For a given network tree order, m, oscillations can be induced for a sufficiently high value of q by increasing the apparent intrinsic viscosity, decreasing the ratio of the vessel diameter from one generation to the next, or by decreasing the diameter of the terminal vessels. With other parameters fixed, oscillations are inhibited by increasing m. The results of the continuum model are in excellent agreement with the predictions of a discrete model where the motion of individual cells is followed from inlet to outlet.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bird, R. B., Stewart, W. E., & Lightfoot, E. N. (2001). Transport phenomena. New York: Wiley.

    Google Scholar 

  • Carr, R. T., & Lacoin, M. (2000). Nonlinear dynamics of microvascular blood flow. Ann. Biomed. Eng., 28, 641–652.

    Article  Google Scholar 

  • Carr, R. T., Geddes, J. B., & Wu, F. (2005). Oscillations in a simple microvascular network. Ann. Biomed. Eng., 33, 764–771.

    Article  Google Scholar 

  • Fenton, B. M., Wilson, D. W., & Cokelet, G. R. (1985). Analysis of the effects of measured white blood cell entrance times on hemodynamics in a computer model of a microvascular bed. Pflügers Arch., 403, 396–400.

    Article  Google Scholar 

  • Geddes, J. B., Carr, R. T., Karst, N. J., & Wu, F. (2007). The onset of oscillations in microvascular blood flow. SIAM J. Appl. Dyn. Syst., 6, 694–727.

    Article  MATH  MathSciNet  Google Scholar 

  • Iooss, G., & Joseph, D. D. (1990). Elementary stability and bifurcation theory (2nd ed.). New York: Springer.

    MATH  Google Scholar 

  • Karshafian, R., Burns, P. N., & Henkelman, M. R. (2003). Transit time kinetics in ordered and disordered vascular trees. Phys. Med. Biol., 48, 3225–3237.

    Article  Google Scholar 

  • Kiani, M. F., Pries, A. R., Hsu, L. L., Sarelius, I. H., & Cokelet, G. R. (1994). Fluctuations in microvascular blood flow parameters caused by hemodynamic mechanisms. Am. J. Physiol., 266, H1822–H1828.

    Google Scholar 

  • Klitzman, B., & Johnson, P. C. (1982). Capillary network geometry and red cell distribution in the hamster cremaster muscle. Am. J. Physiol., 242, H211–H219.

    Google Scholar 

  • Lipowsky, H. H., & Zweifach, B. W. (1974). Network analysis of microcirculation of cat mesentery. Microvasc. Res., 7, 73–83.

    Article  Google Scholar 

  • Obrist, D., Weber, B., Buck, A., & Jenny, P. (2010). Red blood cell distribution in simplified capillary networks. Philos. Trans. R. Soc. A, 368, 2897–2918.

    MATH  MathSciNet  Google Scholar 

  • Price, R. J., & Skalak, T. C. (1995). A circumferential stress-growth rule predicts arcade arteriole formation in a network model. Microcirculation, 2, 41–51.

    Article  Google Scholar 

  • Pozrikidis, C. (2009). Numerical simulation of blood flow through microvascular capillary networks. Bull. Math. Biol., 71, 1520–1541.

    Article  MATH  MathSciNet  Google Scholar 

  • Pries, A. R., Secomb, T. W., Gaehtgens, P., & Gross, J. F. (1990). Blood flow in microvascular networks. Experiments and simulation. Circ. Res., 67, 826–834.

    Google Scholar 

  • Pries, A. R., Neuhaus, D., & Gaetgens, P. (1992). Blood viscosity in tube flow: dependence on diameter and hematocrit. Am. J. Physiol., 263, H1770–H1778.

    Google Scholar 

  • Pries, A. R., Secomb, T. W., & Gaehtgens, P. (1996). Biophysical aspects of blood flow in the microvasculature. Cardiovasc. Res., 32, 654–667.

    Google Scholar 

  • Secomb, T. W. (2003). Mechanics of red blood cells and blood flow in narrow tubes. In C. Pozrikidis (Ed.), Modeling and simulation of liquid capsules and biological cells. Boca Raton: Chapman & Hall/CRC.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. M. Davis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Davis, J.M., Pozrikidis, C. Numerical Simulation of Unsteady Blood Flow through Capillary Networks. Bull Math Biol 73, 1857–1880 (2011). https://doi.org/10.1007/s11538-010-9595-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11538-010-9595-3

Keywords

Navigation