Jove
Visualize
Contact Us
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 Concept Videos

Viscosity of Fluid01:19

Viscosity of Fluid

1.3K
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.
1.3K
Van der Waals Equation01:10

Van der Waals Equation

6.6K
The ideal gas law is an approximation that works well at high temperatures and low pressures. The van der Waals equation of state (named after the Dutch physicist Johannes van der Waals, 1837−1923) improves it by considering two factors.
First, the attractive forces between molecules, which are stronger at higher densities and reduce the pressure, are considered by adding to the pressure a term equal to the square of the molar density multiplied by a positive coefficient a. Second, the volume...
6.6K
Dimensionless Groups in Fluid Mechanics01:15

Dimensionless Groups in Fluid Mechanics

832
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...
832
The Fluid Mosaic Model01:34

The Fluid Mosaic Model

180.7K
The fluid mosaic model was first proposed as a visual representation of research observations. The model comprises the composition and dynamics of membranes and serves as a foundation for future membrane-related studies. The model depicts the structure of the plasma membrane with a variety of components, which include phospholipids, proteins, and carbohydrates. These integral molecules are loosely bound, defining the cell’s border and providing fluidity for optimal function.
180.7K
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

60.0K
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
60.0K
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

827
Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
827

You might also read

Related Articles

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

Sort by
Same author

Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N<sub>2</sub>O) fluxes from US croplands.

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

Quantum Ornstein-Zernike theory for two-temperature two-component plasmas.

Physical review. E·2025
Same author

Conservative dielectric functions and electrical conductivities from the multicomponent Bhatnagar-Gross-Krook equation.

Physical review. E·2025
Same author

Analyzing the effects of reflections on optical diagnostics in the main chamber and divertor of WEST (invited).

The Review of scientific instruments·2024
Same author

Preliminary study of plasma modes and electron-ion collisions in partially magnetized strongly coupled plasmas.

Physical review. E·2024
Same author

Mathematical modeling of disinformation and effectiveness of mitigation policies.

Scientific reports·2023

Related Experiment Video

Updated: Feb 19, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

9.1K

A viscous quantum hydrodynamics model based on dynamic density functional theory.

Abdourahmane Diaw1, Michael S Murillo2

  • 1Department of Computational Mathematics, Science and Engineering, Michigan State University East Lansing, Michigan, 48823, USA. rahmane@melix.org.

Scientific Reports
|November 12, 2017
PubMed
Summary
This summary is machine-generated.

Dynamic density functional theory (DDFT) is extended to quantum systems using a quantum hydrodynamics (QHD) approach. This method models dense plasmas and enables measurement of hydrodynamic properties via the electronic dynamic structure factor.

More Related Videos

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.4K
High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water
08:48

High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water

Published on: April 28, 2022

2.2K

Related Experiment Videos

Last Updated: Feb 19, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

9.1K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.4K
High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water
08:48

High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water

Published on: April 28, 2022

2.2K

Area of Science:

  • Plasma Physics
  • Quantum Many-Body Theory
  • Computational Physics

Background:

  • Dynamic density functional theory (DDFT) is a powerful tool for modeling correlated systems.
  • Quantum hydrodynamics (QHD) offers a framework for describing non-equilibrium, heterogeneous plasmas.
  • Accurate modeling of dense plasmas requires incorporating quantum effects and correlations.

Purpose of the Study:

  • Extend DDFT to quantum systems for dense plasma applications.
  • Develop a DDFT-based QHD approach that self-consistently includes correlations.
  • Generate a model for the electronic dynamic structure factor to probe hydrodynamic properties.

Main Methods:

  • Formulate a quantum hydrodynamics (QHD) approach by extending dynamic density functional theory (DDFT).
  • Incorporate correlations into the equation of state self-consistently.
  • Ensure the model satisfies sum rules and includes collisional irreversibility.

Main Results:

  • A DDFT-based QHD framework for modeling quantum systems, specifically dense plasmas.
  • A self-consistent inclusion of correlations and irreversibility in the plasma model.
  • A model for the electronic dynamic structure factor derived from the DDFT-QHD approach.

Conclusions:

  • The DDFT-QHD framework provides a robust method for simulating dense plasma dynamics.
  • The electronic dynamic structure factor serves as a measurable quantity for hydrodynamic properties.
  • This approach facilitates the experimental determination of transport coefficients using techniques like x-ray Thomson scattering.