The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Numerical Simulation of Flowing Blood Cells
Abstract
The cellular detail of blood is an essential factor in its flow, especially in vessels or devices with size comparable to that of its suspended cells. This article motivates and reviews numerical simulation techniques that provide a realistic description of cell-scale blood flow by explicitly representing its coupled fluid and solid mechanics. Red blood cells are the principal focus because of their importance and because of their remarkable deformability, which presents particular simulation challenges. Such simulations must couple discretizations of the large-deformation elasticity of the cells with the viscous flow mechanics of the suspension. The Reynolds numbers are low, so the effectively linear fluid mechanics is amenable to a wide range of simulation methods, although the constitutive models and geometric factors of the coupled system introduce challenging nonlinearity. Particular emphasis is given to the relative merits of several fundamentally different simulation methods. The detailed description provided by such simulations is invaluable for advancing our scientific understanding of blood flow, and their ultimate impact will be in the design of biomedical tools and interventions.
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Cites
Particle mesh Ewald: An N ⋅log( N ) method for Ewald sums in large systems
Darden, T., York, D., & Pedersen, L. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464397 · Cited by 30,546
Computer Simulation Using Particles
Hockney, R. W., & Eastwood, J. W. (1988) · Institute of Physics eBooks · doi:10.1887/0852743920 · Cited by 5,441
A fast algorithm for particle simulations
Greengard, L., & Rokhlin, V (1987) · Journal of Computational Physics · doi:10.1016/0021-9991(87)90140-9 · Cited by 4,868
Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics
Hoogerbrugge, P. J., & Koelman, J. M. V. A. (1992) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/19/3/001 · Cited by 3,811
Non-linear elastic deformations
Ogden, R. (1984) · Engineering Analysis with Boundary Elements · doi:10.1016/0955-7997(84)90049-3 · Cited by 3,489
Mittal, R., & Iaccarino, G. (2005) · Annual Review of Fluid Mechanics · doi:10.1146/annurev.fluid.37.061903.175743 · Cited by 3,472
Lattice-Boltzmann method for complex flows
Aidun, C. K., & Clausen, J. R. (2010) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-121108-145519 · Cited by 1,991
DIRECT NUMERICAL SIMULATION: A Tool in Turbulence Research
Moin, P., & Mahesh, K. (1998) · Annual Review of Fluid Mechanics · doi:10.1146/annurev.fluid.30.1.539 · Cited by 1,795
Low Reynolds Number Hydrodynamics
Pearson, J. R. A. (1966) · Chemical Engineering Science · doi:10.1016/0009-2509(66)85045-5 · Cited by 1,671
Vectors, Tensors, and the Basic Equations of Fluid Mechanics.
Christie, D. E., & Aris, R. (1965) · The American Mathematical Monthly · doi:10.2307/2313035 · Cited by 1,562
Cited by
Ye, T., Pan, D., Huang, C., & Liu, M. (2019) · Physics of Fluids · doi:10.1063/1.5068697 · Cited by 423
Metadata
- Authors
- Freund, J. B.
- Venue
- Annual Review of Fluid Mechanics
- Publisher
- Annual Reviews
- Published
- 2014-01-03
- Volume
- 46
- Issue
- 1
- Pages
- 67-95
- ISSN
- 0066-4189 (ISSN-L)
- 1545-4479
- OpenAlex
- W2155266039
- Cited by
- 323
- References
- 95
- FWCI
- 3.84
- Type
- journal article
- Language
- en
- Open access
- closed
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