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

Magnetic Field due to Moving Charges01:23

Magnetic Field due to Moving Charges

11.7K
A stationary charge creates and interacts with the electric field, while a moving charge creates a magnetic field.
Consider a point charge moving with a constant velocity. Like the electric field, the magnetic field at any point is directly proportional to the magnitude of the charge and inversely proportional to the square of the distance between the source point and the field point. However, unlike the electric field, the magnetic field is always perpendicular to the plane containing the line...
11.7K
Electric Potential and Potential Difference01:16

Electric Potential and Potential Difference

5.7K
Suppose a positive test charge moves away from a positive static charge, then the Coulomb force does positive work, and its electric potential energy decreases. The potential energy per unit charge is defined as the electric potential. The electric potential is independent of the test charge.
When a test charge moves from the initial to the final position, the electric potential difference between those positions is defined as the ratio of the change in the potential energy to the charge on the...
5.7K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.4K
Colors and Magnetism03:02

Colors and Magnetism

14.1K
Color in Coordination Complexes
When atoms or molecules absorb light at the proper frequency, their electrons are excited to higher-energy orbitals. For many main group atoms and molecules, the absorbed photons are in the ultraviolet range of the electromagnetic spectrum, which cannot be detected by the human eye. For coordination compounds, the energy difference between the d orbitals often allows photons in the visible range to be absorbed and emitted, which is seen as colors by the human...
14.1K
Conservation of Mass in Finite Cotrol Volume01:16

Conservation of Mass in Finite Cotrol Volume

1.8K
The principle of conservation of mass is a fundamental law in fluid mechanics and is applied using the continuity equation. We apply the concept to a finite control volume to derive the continuity equation.
A system is defined as a collection of unchanging contents, and the conservation of mass states that a system's mass is constant.
1.8K
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

3.9K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
3.9K

You might also read

Related Articles

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

Sort by
Same author

Height distribution of elastic interfaces in quenched random media.

Physical review. E·2026
Same author

Criticality of Interface Depinning and Origin of "Bump" in the Avalanche Distribution.

Physical review letters·2024
Same author

Magnetic domain wall dynamics studied by in-situ Lorentz microscopy with aid of custom-made Hall-effect sensor holder.

Ultramicroscopy·2024
Same author

Barkhausen noise in disordered striplike ferromagnets: Experiment versus simulations.

Physical review. E·2024
Same author

Reversible-to-irreversible transition of colloidal polycrystals under cyclic athermal quasistatic deformation.

Physical review. E·2024
Same author

Predicting elastic and plastic properties of small iron polycrystals by machine learning.

Scientific reports·2023

Related Experiment Video

Updated: Feb 8, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

49.2K

Moving magnets in a micromagnetic finite-difference framework.

Ilari Rissanen1, Lasse Laurson1

  • 1COMP Centre of Excellence and Helsinki Institute of Physics, Department of Applied Physics, Aalto University, P.O. Box 11100, FI-00076 Aalto, Espoo, Finland.

Physical Review. E
|June 17, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a fast GPU-accelerated method for simulating micromagnetic motion, enabling accurate analysis of magnetic friction and magnet dynamics with eddy current effects.

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.7K
A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth
10:50

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

Published on: April 8, 2020

10.2K

Related Experiment Videos

Last Updated: Feb 8, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

49.2K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.7K
A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth
10:50

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

Published on: April 8, 2020

10.2K

Area of Science:

  • Computational physics
  • Materials science
  • Magnetism

Background:

  • Accurate simulation of micromagnetic systems is crucial for understanding phenomena like magnetic friction.
  • Existing methods may lack computational efficiency when simulating moving magnets.
  • Incorporating effects like eddy currents in simulations adds complexity.

Purpose of the Study:

  • To develop and implement an efficient method for smooth linear motion in micromagnetic simulations.
  • To accurately model magnetization dynamics and relative motion of microscale magnets.
  • To enable the simulation of magnetic friction and eddy current effects in conducting magnets.

Main Methods:

  • Developed a finite-difference-based micromagnetic simulation code.
  • Combined fast scalar potential calculation with cubic b-spline interpolation.
  • Parallelized computations on a graphics processing unit (GPU).
  • Included explicit simulation of eddy currents for conducting magnets.

Main Results:

  • Successfully implemented smooth linear motion for micromagnetic simulations.
  • Achieved high computational speed while maintaining simulation accuracy.
  • Demonstrated the method's capability through simulations of stick-slip motion and eddy current effects on switching time.

Conclusions:

  • The developed GPU-accelerated method provides an efficient and accurate approach for simulating micromagnetic systems with moving magnets.
  • The implementation effectively handles complex phenomena such as magnetic friction and eddy currents.
  • This method advances the simulation of dynamic magnetic interactions at the microscale.