A discrete-particle model of blood dynamics in capillary vessels

Dzwinel, Witold; Boryczko, Krzysztof; Yuen, David A.

doi:10.1016/s0021-9797(02)00075-9

Cited by 117 publications

(69 citation statements)

References 22 publications

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“…The model can be supplemented also by such important process like blood circulation. In [14] we show that blood flow can be modeled by using dissipative particle dynamics in a very short spatio-temporal scale [14] (see Fig. 8).…”

Section: Discussionmentioning

confidence: 85%

“…8. The snapshot from simulation of red blood cells flowing in the vain with the chocking point modeled using fluid particle model [14] Currently, we are working on using smoothed particle hydrodynamics (SPH) employing viscoelastic rheological models (like the Casson fluid) for simulating blood flows in larger scales. These two models of the processes of tumor growth and blood flow are planned to be combined together (see Fig.…”

Section: Discussionmentioning

confidence: 99%

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Particle Based Model of Tumor Progression Stimulated by the Process of Angiogenesis

Wcisło

Dzwinel

2008

Computational Science – ICCS 2008

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Abstract. We discuss a novel metaphor of tumor progression stimulated by the process of angiogenesis. The realistic 3-D dynamics of the entire system consisting of the tumor, tissue cells, blood vessels and blood flow can be reproduced by using interacting particles. The particles mimic the clusters of tumor cells. They interact with their closest neighbors via semi-harmonic forces simulating mechanical resistance of the cell walls and the external pressure. The particle dynamics is governed by both the Newtonian laws of motion and the rules of cell life-cycle. The particles replicate by a simple mechanism of division, similar to that of a single cell reproduction and die due to necrosis or apoptosis. We conclude that this concept can serve as a general framework for designing advanced multi-scale models of tumor dynamics. In respect to spatio-temporal scale, the interactions between particles can define e.g., cluster-to-cluster, cellto-cell, red blood cells and fluid particles interactions, cytokines motion, etc. Consequently, they influence the macroscopic dynamics of the particle ensembles in various sub-scales ranging from diffusion of cytokines, blood flow up to growth of tumor and vascular network expansion.

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Particle Based Model of Tumor Progression Stimulated by the Process of Angiogenesis

Wcisło

Dzwinel

2008

Computational Science – ICCS 2008

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“…We now calculate the kinetic energy E k of particles within one finite element as (22) Using (15) and (5), we find that the last two terms are equal to zero, since we have that (23) This orthogonality relation results from the definition of the projection operators Q and P according to (16) and (17), and the relations (20) and (21). The last two terms are equal…”

Section: Decomposition Of Kinetic Energymentioning

confidence: 99%

“…The Lagrangian description of motion employed in the DP methods assumes appropriate quantification of interaction forces, which include conservative, dissipative and random forces (and moments). One of the most advanced methods in this field is the dissipative particle dynamics (DPD) method for fluids, introduced by Hoogerbrugge and Koelman [6], further generalized theoretically, particularly by Espanol and co-authors [7][8][9][10][11][12][13][14][15][16], Flekkoy and co-authors [2,3], and in [17][18][19], and applied to various problems [20][21][22][23][24]. The DPD method will be described here in some detail and used subsequently.…”

Section: Introductionmentioning

confidence: 99%

A mesoscopic bridging scale method for fluids and coupling dissipative particle dynamics with continuum finite element method

Kojić

Filipović

Tsuda

2008

Computer Methods in Applied Mechanics and Engineering

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A multiscale procedure to couple a mesoscale discrete particle model and a macroscale continuum model of incompressible fluid flow is proposed in this study. We call this procedure the mesoscopic bridging scale (MBS) method since it is developed on the basis of the bridging scale method for coupling molecular dynamics and finite element models [G.J. Wagner, W.K. Liu, Coupling of atomistic and continuum simulations using a bridging scale decomposition, J. Comput. Phys. 190 (2003) 249-274]. We derive the governing equations of the MBS method and show that the differential equations of motion of the mesoscale discrete particle model and finite element (FE) model are only coupled through the force terms. Based on this coupling, we express the finite element equations which rely on the Navier-Stokes and continuity equations, in a way that the internal nodal FE forces are evaluated using viscous stresses from the mesoscale model. The dissipative particle dynamics (DPD) method for the discrete particle mesoscale model is employed. The entire fluid domain is divided into a local domain and a global domain. Fluid flow in the local domain is modeled with both DPD and FE method, while fluid flow in the global domain is modeled by the FE method only.The MBS method is suitable for modeling complex (colloidal) fluid flows, where continuum methods are sufficiently accurate only in the large fluid domain, while small, local regions of particular interest require detailed modeling by mesoscopic discrete particles. Solved examplessimple Poiseuille and driven cavity flows illustrate the applicability of the proposed MBS method. KeywordsMultiscale modeling of fluid flow; Mesoscopic bridging scale method; Dissipative particle dynamics method; Finite element method; Coupling Navier; Stokes and dissipative particle dynamics equations

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“…One is the discrete method, in which the individual cells or a collection (PSI cell) are modelled by the immersed boundary method (IBM) or the immersed finite element method (IFEM) and the interactions between cells and the bulk flow are explicitly represented [14][15]. The other approach is the representation of the bulk flow field as a single-phase fluid with bulk properties to represent the affects of the cells [5,12].…”

Section: Introductionmentioning

confidence: 99%

Effect of fluid dynamics and device mechanism on biofluid behaviour in microchannel systems: Modelling biofluids in a microchannel biochip separator

Xue

Patel

Kersaudy-Kerhoas

et al. 2009

2009 International Conference on Electronic Packaging Technology &Amp; High Density Packaging

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Biofluid behaviour in microchannel systems is investigated in this paper through the modelling of a microfluidic biochip developed for the separation of blood plasma. Based on particular assumptions, the effects of some mechanical features of the microchannels on behaviour of the biofluid are explored. These include microchannel, constriction, bending channel, bifurcation as well as channel length ratio between the main and side channels. The key characteristics and effects of the microfluidic dynamics are discussed in terms of separation efficiency of the red blood cells with respect to the rest of the medium. The effects include the Fahraeus and Fahraeus-Lindqvist effects, the Zweifach-Fung bifurcation law, the cell-free layer phenomenon. The characteristics of the microfluid dynamics include the properties of the laminar flow as well as particle lateral or spinning trajectories. In this paper the fluid is modelled as a single-phase flow assuming either Newtonian or Non-Newtonian behaviours to investigate the effect of the viscosity on flow and separation efficiency. It is found that, for a flow rate controlled Newtonian flow system, viscosity and outlet pressure have little effect on velocity distribution. When the fluid is assumed to be Non-Newtonian more fluid is separated than observed in the Newtonian case, leading to reduction of the flow rate ratio between the main and side channels as well as the system pressure as a whole.

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A discrete-particle model of blood dynamics in capillary vessels

Cited by 117 publications

References 22 publications

Particle Based Model of Tumor Progression Stimulated by the Process of Angiogenesis

Particle Based Model of Tumor Progression Stimulated by the Process of Angiogenesis

A mesoscopic bridging scale method for fluids and coupling dissipative particle dynamics with continuum finite element method

Effect of fluid dynamics and device mechanism on biofluid behaviour in microchannel systems: Modelling biofluids in a microchannel biochip separator

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