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A mathematical model for blood flow in magnetic field

Tzirtzilakis, E. E.·2005·Physics of Fluids

Abstract

In the present study a mathematical model of biomagnetic fluid dynamics (BFD), suitable for the description of the Newtonian blood flow under the action of an applied magnetic field, is proposed. This model is consistent with the principles of ferrohydrodynamics and magnetohydrodynamics and takes into account both magnetization and electrical conductivity of blood. As a representative application the laminar, incompressible, three-dimensional, fully developed viscous flow of a Newtonian biomagnetic fluid (blood) in a straight rectangular duct is numerically studied under the action of a uniform or a spatially varying magnetic field. The numerical results are obtained using a finite differences numerical technique based on a pressure-linked pseudotransient method on a collocated grid. The flow is appreciably influenced by the application of the magnetic field and in particularly by the strength and the magnetic field gradient. A comparison of the derived results is also made with those obtained using the existing BFD model indicating the necessity of taking into account the electrical conductivity of blood.

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Metadata

Authors
Tzirtzilakis, E. E.
Venue
Physics of Fluids
Published
2005-07-01
Volume
17
Issue
7
ISSN
  • 1070-6631 (ISSN-L)
  • 1089-7666
OpenAlex
W2168734274
Cited by
371
References
16
FWCI
2.89
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A mathematical model for blood flow in magnetic field. https://app.cochat.ai/papers/10.1063/1.1978807
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Tzirtzilakis, E. E. (2005). A mathematical model for blood flow in magnetic field. Physics of Fluids. https://doi.org/10.1063/1.1978807

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.