Paper
31 March 2009 Predicting magnetorheological fluid flow behavior using a multiscale kinetic theory-based model
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Abstract
Magnetorheological (MR) fluids have rheological properties, such as the viscosity and yield stress that can be altered by an external magnetic field. The design of novel devices utilizing the MR fluid behavior in multi-degree of freedom applications require three dimensional models characterizing the coupling of magnetic behavior to mechanical behavior in MR fluids. A 3-D MR fluid model based on multiscale kinetic theory is presented. The kinetic theory-based model relates macroscale MR fluid behavior to a first-principle description of magnetomechanical coupling at the microscale. A constitutive relation is also proposed that accounts for the various forces transmitted through the fluid. This model accounts for the viscous drag on the spherical particles as well as Brownian forces. Interparticle forces due to magnetization and external magnetic forces applied to ferrous particles are considered. The tunable rheological properties of the MR fluids are studied using a MR rheological instrument. High and low viscosity carrier fluids along with small and large carbonyl iron particles are used to make and study the behavior of four different MR fluids. Experiments measuring steady, and dynamic oscillatory shear response under a range of magnetic field strengths are performed. The rheological properties of the MR fluid samples are investigated and compared to the proposed kinetic theory-based model. The storage (G') and loss (G") moduli of the MR fluids are studied as well.
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Monon Mahboob, Farzad Ahmadkhanlou, Christopher Kagarise, Gregory Washington, Stephen Bechtel, and Kurt Koelling "Predicting magnetorheological fluid flow behavior using a multiscale kinetic theory-based model", Proc. SPIE 7289, Behavior and Mechanics of Multifunctional Materials and Composites 2009, 72891W (31 March 2009); https://doi.org/10.1117/12.816133
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KEYWORDS
Particles

Magnetism

Microfluidics

Fluid dynamics

3D modeling

Data modeling

Iron

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