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Computer Research and Modeling, 2018, Volume 10, Issue 5, Pages 581–604 (Mi crm673)  

This article is cited in 3 scientific papers (total in 3 papers)


Modern methods of mathematical modeling of blood flow using reduced order methods

S. S. Simakovabc

a Moscow Institute of Physics and Technology, 9 Institutskiy per., Dolgoprudny, 141701, Russia
b Institute of Numerical Mathematics RAS, 8 Gukina st., Moscow, 119333, Russia
c Sechenov University, 8-2 Trubetskaya st., Moscow, 119991, Russia

Abstract: The study of the physiological and pathophysiological processes in the cardiovascular system is one of the important contemporary issues, which is addressed in many works. In this work, several approaches to the mathematical modelling of the blood flow are considered. They are based on the spatial order reduction and/or use a steady-state approach. Attention is paid to the discussion of the assumptions and suggestions, which are limiting the scope of such models. Some typical mathematical formulations are considered together with the brief review of their numerical implementation. In the first part, we discuss the models, which are based on the full spatial order reduction and/or use a steady-state approach. One of the most popular approaches exploits the analogy between the flow of the viscous fluid in the elastic tubes and the current in the electrical circuit. Such models can be used as an individual tool. They also used for the formulation of the boundary conditions in the models using one dimensional (1D) and three dimensional (3D) spatial coordinates. The use of the dynamical compartment models allows describing haemodynamics over an extended period (by order of tens of cardiaccycles and more). Then, the steady-state models are considered. They may use either total spatial reduction or two dimensional (2D) spatial coordinates. This approach is used for simulation the blood flow in the region of microcirculation. In the second part, we discuss the models, which are based on the spatial order reduction to the 1D coordinate. The models of this type require relatively small computational power relative to the 3D models.Within the scope of this approach, it is also possible to include all large vessels of the organism. The 1D models allow simulation of the haemodynamic parameters in every vessel, which is included in the model network.The structure and the parameters of such a network can be set according to the literature data. It also exists methods of medical data segmentation. The 1D models may be derived from the 3D Navier–Stokes equations either by asymptotic analysis or by integrating them over a volume. The major assumptions are symmetric flow and constant shape of the velocity profile over a cross-section. These assumptions are somewhat restrictive and arguable. Some of the current works paying attention to the 1D model's validation, to the comparing different1D models and the comparing 1D models with clinical data. The obtained results reveal acceptable accuracy.It allows concluding, that the 1D approach can be used in medical applications. 1D models allow describing several dynamical processes, such as pulse wave propagation, Korotkov’s tones. Some physiological conditions may be included in the 1D models: gravity force, muscles contraction force, regulation and autoregulation.

Keywords: mathematical modeling, haemodynamics, blood flow, reduced order models.

Funding Agency Grant Number
Russian Science Foundation 14-31-00024
This work was supported by the Russian Scientific Foundation, project No. 14-31-00024.


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UDC: 519.8
Received: 26.07.2018
Revised: 07.08.2018

Citation: S. S. Simakov, “Modern methods of mathematical modeling of blood flow using reduced order methods”, Computer Research and Modeling, 10:5 (2018), 581–604

Citation in format AMSBIB
\by S.~S.~Simakov
\paper Modern methods of mathematical modeling of blood flow using reduced order methods
\jour Computer Research and Modeling
\yr 2018
\vol 10
\issue 5
\pages 581--604

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    This publication is cited in the following articles:
    1. O. V. Ilin, “Modelirovanie odnomernykh nelineinykh pulsovykh voln v elastichnykh sosudakh na osnove reshetochnykh uravnenii Boltsmana”, Kompyuternye issledovaniya i modelirovanie, 11:4 (2019), 707–722  mathnet  crossref
    2. K. K. Loginov, N. V. Pertsev, V. A. Topchii, “Stokhasticheskoe modelirovanie kompartmentnykh sistem s trubkami”, Matem. biologiya i bioinform., 14:1 (2019), 188–203  mathnet  crossref
    3. O. V. Ilin, “Granichnye usloviya dlya reshetochnykh uravnenii Boltsmana v prilozheniyakh k zadacham gemodinamiki”, Kompyuternye issledovaniya i modelirovanie, 12:4 (2020), 865–882  mathnet  crossref
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