Jove
Visualize
Contact Us

Related Concept Videos

Elastic Curve from the Load Distribution01:16

Elastic Curve from the Load Distribution

The structural behavior of beams under distributed loads is critical for engineering analysis, which focuses on predicting how beams bend and react under such conditions. Different types of beams (e.g., cantilever, supported, or overhanging) behave differently under distributed load conditions.
For all beams, the analysis of the beam's reaction to distributed loads begins by understanding the relationship between a beam's load and the resulting shear forces and bending moments. Initially, this...
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Distributed Loads01:19

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
Load-frequency control01:28

Load-frequency control

Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...

You might also read

Related Articles

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

Sort by
Same author

Insight into the Charge Density Wave Gap from Contrast Inversion in Topographic STM Images.

Physical review letters·2021
Same author

Local Real-Space View of the Achiral 1T-TiSe_{2} 2×2×2 Charge Density Wave.

Physical review letters·2018
Same author

Alane adsorption and dissociation on the Si(0 0 1) surface.

Journal of physics. Condensed matter : an Institute of Physics journal·2017
Same author

Stripe and Short Range Order in the Charge Density Wave of 1T-Cu_{x}TiSe_{2}.

Physical review letters·2017
Same author

Density functional theory: a tale of success in three codes.

Journal of physics. Condensed matter : an Institute of Physics journal·2016
Same author

Δ Self-Consistent Field Method for Natural Anthocyanidin Dyes.

Journal of chemical theory and computation·2015
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 Videos

Automatic data distribution and load balancing with space-filling curves: implementation in CONQUEST.

V Brázdová1, D R Bowler

  • 1Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK. London Centre for Nanotechnology, 17-19 Gordon Street, London WC1H 0AH, UK. Virtual Materials Laboratory, University College London, Gower Street, London WC1E 6BT, UK.

Journal of Physics. Condensed Matter : an Institute of Physics Journal
|June 23, 2011
PubMed
Summary

This study introduces an automatic method for distributing data and balancing loads in parallel computing for many-body simulations. The novel Hilbert curve approach ensures efficient, processor-independent performance across diverse systems.

Related Experiment Videos

Area of Science:

  • Computational Physics
  • Materials Science
  • High-Performance Computing

Background:

  • Many-body simulations require efficient data distribution and load balancing on parallel architectures.
  • Existing methods may be limited by system dimensionality, shape, or processor count.
  • Scalable algorithms are crucial for handling increasingly complex scientific problems.

Purpose of the Study:

  • To develop an automatic, spatially local data distribution and load balancing scheme.
  • To create a method applicable to various many-body problems on parallel systems.
  • To demonstrate the scheme's independence from processor number and system characteristics.

Main Methods:

  • Spatial decomposition of the simulation cell.
  • Mapping a one-dimensional Hilbert curve onto the 3D real space cell.
  • Assigning spatially local cell parts to individual processors.
  • Implementation within the CONQUEST linear-scaling density functional code.

Main Results:

  • The Hilbert curve mapping effectively reduces problem dimensionality.
  • The scheme provides efficient data distribution and load balancing.
  • Demonstrated applicability to systems with up to 55,755 particles.
  • Successful implementation in a real-world computational code.

Conclusions:

  • The presented scheme offers a versatile and processor-independent solution for parallel many-body simulations.
  • It is effective for both ordered and disordered structures, regardless of system dimensionality or shape.
  • This approach enhances the scalability and efficiency of large-scale scientific computations.