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Dynamic simulation of hydrodynamically interacting particles

Durlofsky, L., Brady, J. F., Bossis, G.·1987·Journal of Fluid Mechanics

Abstract

A general method for computing the hydrodynamic interactions among N suspended particles, under the condition of vanishingly small particle Reynolds number, is presented. The method accounts for both near-field lubrication effects and the dominant many-body interactions. The many-body hydrodynamic interactions reproduce the screening characteristic of porous media and the ‘effective viscosity’ of free suspensions. The method is accurate and computationally efficient, permitting the dynamic simulation of arbitrarily configured many-particle systems. The hydrodynamic interactions calculated are shown to agree well with available exact calculations for small numbers of particles and to reproduce slender-body theory for linear chains of particles. The method can be used to determine static (i.e. configuration specific) and dynamic properties of suspended particles that interact through both hydrodynamic and non-hydrodynamic forces, where the latter may be any type of Brownian. colloidal, interparticle or external force. The method is also readily extended to dynamically simulate both unbounded and bounded suspensions.

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Metadata

Authors
Durlofsky, L., Brady, J. F., & Bossis, G.
Venue
Journal of Fluid Mechanics
Published
1987-07
Volume
180
Pages
21-49
ISSN
  • 0022-1120 (ISSN-L)
  • 1469-7645
OpenAlex
W2104547497
Cited by
597
References
0
FWCI
56.23
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Dynamic simulation of hydrodynamically interacting particles. https://app.cochat.ai/papers/10.1017/s002211208700171x
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Durlofsky, L., Brady, J. F., & Bossis, G. (1987). Dynamic simulation of hydrodynamically interacting particles. Journal of Fluid Mechanics. https://doi.org/10.1017/s002211208700171x

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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.