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

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
Steady, Laminar Flow Between Parallel Plates01:17

Steady, Laminar Flow Between Parallel Plates

Understanding steady, laminar flow between parallel plates is essential for analyzing and designing flow in narrow rectangular channels, commonly found in various water conveyance and drainage systems. The Navier-Stokes equations govern fluid motion and are generally challenging to solve due to their nonlinearity. However, simplifications are possible in certain cases, like the steady laminar flow between parallel plates. For this scenario, we assume steady, incompressible, laminar flow.
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...
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...

You might also read

Related Articles

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

Sort by
Same author

FLUID: A Neural Operator-Based Framework for Learning Multi-Fidelity of Unstructured Data.

IEEE transactions on visualization and computer graphics·2026
Same author

VizGenie: Toward Self-Refining, Domain-Aware Workflows for Next-Generation Scientific Visualization.

IEEE transactions on visualization and computer graphics·2025
Same author

Navigating Uncertainty: Challenges in Visualizing Ensemble Data and Surrogate Models for Decision Systems.

IEEE computer graphics and applications·2025
Same author

Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Exploration.

IEEE transactions on visualization and computer graphics·2025
Same author

VADIS: A Visual Analytics Pipeline for Dynamic Document Representation and Information-Seeking.

IEEE transactions on visualization and computer graphics·2024
Same author

Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network.

IEEE transactions on visualization and computer graphics·2024
Same journal

Two-phase Impulse Fluid on Particle Flow Map.

IEEE transactions on visualization and computer graphics·2026
Same journal

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

IEEE transactions on visualization and computer graphics·2026
Same journal

Blue Noise Dithering for Reservoir-based Spatio-temporal Importance Resampling.

IEEE transactions on visualization and computer graphics·2026
Same journal

ROS-GS: Relightable Outdoor Scenes With Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

Load-balanced parallel streamline generation on large scale vector fields.

Boonthanome Nouanesengsy1, Teng-Yok Lee, Han-Wei Shen

  • 1The Ohio State University, USA. nouanese@cse.ohio-state.edu

IEEE Transactions on Visualization and Computer Graphics
|October 29, 2011
PubMed
Summary
This summary is machine-generated.

Supercomputers generate visualizations from large datasets. A new workload-aware partitioning algorithm balances computation for parallel streamline generation, improving supercomputer performance.

More Related Videos

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

High-speed Particle Image Velocimetry Near Surfaces
11:59

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

Related Experiment Videos

Last Updated: May 28, 2026

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

High-speed Particle Image Velocimetry Near Surfaces
11:59

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

Area of Science:

  • Scientific visualization
  • High-performance computing

Background:

  • Supercomputers are essential for visualizing large scientific simulation data.
  • Parallel processing of vector fields for streamline generation faces load imbalance issues due to varying computational complexity across regions.
  • This imbalance limits the performance and scalability of visualization tasks.

Purpose of the Study:

  • To develop a load-balanced streamline generation method for large-scale flow fields.
  • To address the performance limitations caused by computational load imbalance in parallel vector field processing.

Main Methods:

  • Proposed a workload-aware partitioning algorithm to decompose vector fields into partitions of equal computational workload.
  • Developed a workload estimation algorithm using a graph-based representation of the vector field to predict computational demands.
  • Implemented a hierarchical application of the partitioning algorithm to distribute workloads across processors.

Main Results:

  • Demonstrated improved scalability for streamline generation through workload estimation and aware partitioning.
  • Achieved near-equal workload distribution across partitions.
  • Showcased minimal overhead associated with the proposed methods.

Conclusions:

  • The workload estimation and partitioning algorithms effectively mitigate load imbalance in parallel streamline generation.
  • The proposed approach enhances the scalability and performance of scientific visualization on supercomputers.
  • This method offers a practical solution for efficiently processing large-scale vector fields.