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Simple analytic model for peristaltic flow and mixing.

Ruy Ibanez1, Mohammad Shokrian1, Jong-Hoon Nam1

  • 1Department of Mechanical Engineering, University of Rochester, Rochester, New York 14620, USA.

Physical Review Fluids
|October 17, 2022
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Summary
This summary is machine-generated.

A new analytic model simplifies the study of peristaltic flows, crucial for understanding fluid transport in biological systems and pumps. This model accurately predicts flow and mixing, overcoming the computational expense of simulations.

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Area of Science:

  • Fluid Dynamics
  • Biophysics
  • Applied Mathematics

Background:

  • Peristaltic flows, driven by traveling wall deformations, are vital in biological systems (e.g., stomachs, cochleae) and industrial pumps.
  • These flows cause net solute transport and mixing via Lagrangian drift, despite periodic spatial variations.
  • Predicting peristaltic flow dynamics is computationally intensive using direct numerical simulations.

Purpose of the Study:

  • To develop a computationally efficient analytic model for peristaltic flows.
  • To express flow and drift velocities as functions of deformation parameters (speed, amplitude).
  • To accommodate arbitrary waveform shapes using Fourier series expansion.

Main Methods:

  • Development of a simplified analytic model for peristaltic flow.
  • Validation through direct numerical simulations and experimental data.
  • Analysis of model's predictive capabilities across varying deformation speeds and amplitudes.

Main Results:

  • The analytic model accurately predicts flow velocity and drift velocity.
  • Model validation shows close agreement with simulations and experiments.
  • The model successfully quantifies reflux region thickness and models cochlear mixing.

Conclusions:

  • The developed analytic model offers a computationally efficient alternative to simulations for peristaltic flows.
  • The model provides a versatile tool for analyzing transport and mixing phenomena in systems exhibiting peristaltic motion.
  • This approach enhances understanding of biological fluid dynamics and optimizes designs for peristaltic pumps.