Jove
Visualize
Contact Us

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

35
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
35

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Investigation of pressure balance in proximity of sidewalls in deterministic lateral displacement.

Biomicrofluidics·2025
Same author

A universal framework for design and manufacture of deterministic lateral displacement chips.

Lab on a chip·2025
Same author

Structural coloration with hourglass-shaped vertical silicon nanopillar arrays.

Optics express·2018
Same author

Anorexic effect of (R)-sibutramine: comparison with (RS)-sibutramine [corrected] and (S)-sibutramine.

Indian journal of physiology and pharmacology·2008
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 21, 2025

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

14.8K

A tracking algorithm for finite-size particles.

Aryan Mehboudi1, Shrawan Singhal1, S V Sreenivasan

  • 1NASCENT Engineering Research Center, The University of Texas at Austin, Austin, Texas 78758, USA.

Biomicrofluidics
|May 19, 2025
PubMed
Summary

This study introduces a new particle tracking algorithm for finite-sized particles, improving accuracy in microfluidic devices. The method enhances simulations for applications like cell sorting and particle separation.

More Related Videos

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence
12:34

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence

Published on: June 24, 2016

10.0K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

6.9K

Related Experiment Videos

Last Updated: May 21, 2025

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

14.8K
Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence
12:34

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence

Published on: June 24, 2016

10.0K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

6.9K

Area of Science:

  • Fluid dynamics
  • Microfluidics
  • Computational physics

Background:

  • Accurate simulation of particle-wall interactions is crucial for microfluidic applications like cell sorting and separation.
  • Existing particle tracking algorithms often treat particles as point masses, neglecting finite-size effects and wall interactions.
  • Implementing finite-size particle-wall interactions is complex but essential for precise microfluidic device design.

Purpose of the Study:

  • To develop and validate a novel particle tracking algorithm capable of accurately simulating interactions between finite-sized particles and solid walls.
  • To enhance the fidelity of microfluidic simulations by accounting for particle geometry and boundary conditions.
  • To provide a computational framework for the precise and convenient design of microfluidic chips.

Main Methods:

  • A particle tracking algorithm models particles as a set of circumferential points.
  • Fluid-particle interactions are tracked via the particle center, while particle-wall interactions are explicitly modeled using circumferential points and a reflection scheme.
  • A modified auxiliary structured grid method and boundary condition scheme are employed to capture particle-solid object interactions.

Main Results:

  • The algorithm successfully reproduces experimental observations of particle motion in deterministic lateral displacement microfluidic devices, including zigzag and bump modes.
  • Numerical simulations of particle behavior in pinched flow microfluidic devices align with existing experimental data.
  • The algorithm demonstrates significant parallel processing capabilities, achieving an 8x speedup on an eight-thread system.

Conclusions:

  • The developed particle tracking algorithm accurately accounts for finite-size particle-wall interactions in microfluidic systems.
  • The computational framework offers potential for efficient and precise design of microfluidic devices.
  • The algorithm's parallel processing efficiency suggests scalability for complex simulations on multi-thread systems.