Jove
Visualize
Contact Us

Related Concept Videos

Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

12.6K
The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
12.6K
Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

26.8K
An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
26.8K
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

1.5K
The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
1.5K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.9K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.9K
Assessment of blood pressure in brachial artery(one-step method)01:15

Assessment of blood pressure in brachial artery(one-step method)

1.2K
This procedural guide systematically measures blood pressure using an oscillometric digital sphygmomanometer, emphasizing accuracy, patient safety, and comfort.
Prepare for the Procedure:
1.2K
Assessment of blood pressure in brachial artery(two-step method)01:23

Assessment of blood pressure in brachial artery(two-step method)

1.7K
Measuring blood pressure is a fundamental skill in healthcare that aids in diagnosing and monitoring hypertension and other cardiovascular conditions. An aneroid sphygmomanometer, commonly used in clinical settings, offers a manual and precise method for blood pressure measurement. The technique for using this instrument involves specific steps that must be carefully executed to ensure accuracy. The following detailed description outlines a two-step technique for assessing blood pressure using...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Dynamic confinement controls the porous-to-free convection transition.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Quantum lattice Boltzmann method for several time steps: A local Carleman linearization algorithm.

Physical review. E·2026
Same author

Adaptive lattice-gas algorithm: Classical and quantum implementations.

Physical review. E·2025
Same author

Lattice gas automata with floating-point numbers: A connection between molecular dynamics and lattice Boltzmann method for quantum computers.

Physical review. E·2025
Same author

Cross-platform programming model for many-core lattice Boltzmann simulations.

PloS one·2021
Same author

Linear stability and isotropy properties of athermal regularized lattice Boltzmann methods.

Physical review. E·2020
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: Feb 15, 2026

Step By Step: Microsurgical training method combining two nonliving animal models
05:25

Step By Step: Microsurgical training method combining two nonliving animal models

Published on: May 9, 2015

16.0K

Recursive regularization step for high-order lattice Boltzmann methods.

Christophe Coreixas1, Gauthier Wissocq1, Guillaume Puigt1

  • 1CERFACS, 42 Avenue G. Coriolis, 31057 Toulouse Cedex, France.

Physical Review. E
|January 20, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an improved lattice Boltzmann method (LBM) using recursive Hermite polynomial calculations. This novel approach significantly enhances numerical stability and accuracy for fluid dynamics simulations, reducing computational costs.

More Related Videos

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
08:32

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

13.0K
Trapping of Micro Particles in Nanoplasmonic Optical Lattice
07:20

Trapping of Micro Particles in Nanoplasmonic Optical Lattice

Published on: September 5, 2017

7.0K

Related Experiment Videos

Last Updated: Feb 15, 2026

Step By Step: Microsurgical training method combining two nonliving animal models
05:25

Step By Step: Microsurgical training method combining two nonliving animal models

Published on: May 9, 2015

16.0K
Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
08:32

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

13.0K
Trapping of Micro Particles in Nanoplasmonic Optical Lattice
07:20

Trapping of Micro Particles in Nanoplasmonic Optical Lattice

Published on: September 5, 2017

7.0K

Area of Science:

  • Computational fluid dynamics
  • Numerical analysis
  • Mesoscopic physics

Background:

  • The lattice Boltzmann method (LBM) is a powerful numerical technique for simulating fluid flows.
  • Standard LBM implementations can face limitations in stability and accuracy, particularly in under-resolved conditions.
  • Hermite tensor-based lattice structures offer advanced capabilities for LBM.

Purpose of the Study:

  • To present a lattice Boltzmann method (LBM) with enhanced stability and accuracy for Hermite tensor-based lattice structures.
  • To improve the collision operator's regularization step through recursive computation of nonequilibrium Hermite polynomial coefficients.
  • To demonstrate the benefits of this recursive approach in terms of computational cost, stability, and accuracy.

Main Methods:

  • Implementation of a recursive computation for nonequilibrium Hermite polynomial coefficients in the LBM collision operator.
  • Application of the enhanced LBM to simulate isothermal doubly periodic shear layers across a range of Reynolds numbers (10^4 to 10^6).
  • Comparison of the proposed method against the Bhatnagar-Gross-Krook LBM using standard (D2Q9) and high-order (D2V17, D2V37) lattices.
  • Extension and application of the method to simulate thermal and fully compressible flows, including Sod shock tubes with the D2V37 lattice.

Main Results:

  • The recursive computation significantly reduces computational cost compared to standard methods.
  • The enhanced LBM demonstrates a considerable increase in stability and accuracy, especially in under-resolved conditions.
  • Simulations of shear layers show a tremendous increase in the stability range of the proposed approach.
  • Numerical simulations of Sod shock tubes confirm the enhanced stability for thermal and compressible flows.

Conclusions:

  • The recursive computation of Hermite polynomial coefficients offers a substantial improvement in LBM stability and accuracy.
  • This method effectively filters non-hydrodynamic contributions, enhancing performance in challenging flow regimes.
  • The proposed LBM approach provides a more robust and efficient tool for a wide range of fluid dynamics simulations.