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Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Viscosity of Fluid01:19

Viscosity of Fluid

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Viscosity measures the resistance a fluid offers to flow and deformation. It results from internal friction between layers of fluid moving relative to one another. Dynamic viscosity, denoted by the Greek letter mu (μ), quantifies the force needed to move one fluid layer over another. For Newtonian fluids like water and air, the relationship between the shearing stress and the rate of shearing strain is linear, meaning their viscosity remains constant regardless of the applied stress.
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

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In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
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Dimensionless Groups in Fluid Mechanics01:15

Dimensionless Groups in Fluid Mechanics

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Dimensionless groups in fluid mechanics provide simplified ratios that help analyze fluid behavior without relying on specific units. The Reynolds number (Re), which represents the ratio of inertial to viscous forces, distinguishes between laminar and turbulent flows, making it essential in the design of pipelines and aerodynamic surfaces. The Froude number (Fr), the ratio of inertial to gravitational forces, is particularly useful in predicting wave formation and hydraulic jumps in...
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Autoregulation of Blood Flow01:17

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Autoregulation mechanisms are characterized by their inherent capacity for self-regulation without necessitating specific nervous stimulation or endocrine control. These mechanisms facilitate the adjustment of blood flow and, therefore, perfusion specific to each tissue region. This self-regulation encompasses chemical signals and myogenic controls.
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Continuum microhaemodynamics modelling using inverse rheology.

Joseph van Batenburg-Sherwood1, Stavroula Balabani2

  • 1Department of Bioengineering, Imperial College London, London, UK. jvbsherwood@imperial.ac.uk.

Biomechanics and Modeling in Mechanobiology
|December 15, 2021
PubMed
Summary

This study presents an efficient continuum model for simulating blood flow in microvessels. By assimilating red blood cell (RBC) data, the model accurately predicts velocities, improving upon traditional methods.

Keywords:
Blood flowComputational fluid dynamicsContinuum modellingData assimilationHaemorheologyInverse rheologyMicrofluidicsParticle image VelocimetryRed blood cells

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Area of Science:

  • Biophysics
  • Computational Fluid Dynamics
  • Biomedical Engineering

Background:

  • Modeling blood flow in microvascular networks is complex due to haemorheology.
  • Existing models (0D, 1D, individual RBC) have limitations in accuracy or computational cost.
  • Continuum models offer efficiency but require extensive parameterization and validation data.

Purpose of the Study:

  • To develop and validate a continuum numerical modeling framework for microvascular blood flow.
  • To assimilate experimental red blood cell (RBC) velocity and concentration data into the model.
  • To assess the impact of RBC distribution and rheology on flow prediction accuracy.

Main Methods:

  • Acquired RBC imaging data in microchannels under various flow conditions.
  • Mapped RBC concentration distributions into computational fluid dynamics (CFD) simulations.
  • Employed the Quemada model for rheology and Particle Image Velocimetry (PIV) for velocity validation.
  • Optimized model parameters using a subset of data and validated on a larger dataset.

Main Results:

  • The pre-optimized continuum model reduced velocity prediction errors by 60% compared to Newtonian fluid assumptions.
  • Further optimization reduced errors by an additional 40%.
  • Asymmetry in RBC velocity and concentration profiles was critical; excluding it doubled prediction error.

Conclusions:

  • A continuum model with optimized rheological parameters can accurately reproduce microvascular velocities when RBC concentration distributions are known.
  • This approach offers an efficient method for modeling network-scale hemodynamics.
  • Further development for diverse network configurations holds significant potential.