Multi-Scale Flow Field Mapping Method Based on Real-Time Feature Streamlines

Fang, Yuheng; Ai, Bo; Fang, Jing; Xin, Wenpeng; Zhao, Binru; Lv, Guannan

doi:10.3390/ijgi8080335

Cited by 3 publications

(2 citation statements)

References 5 publications

(8 reference statements)

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“…When particles are tracking, the solution of this differential equation needs to be obtained by numerical integration to plot the trajectory [30]. Here we use the Runge-Kutta 4 method to solve the problem:…”

Section: Streamlets Generation Based On Visual Perceptionmentioning

confidence: 99%

Particle System-Based Multi-Hierarchy Dynamic Visualization of Ocean Current Data

Shi

Wen³

et al. 2021

IJGI

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In three-dimensional (3D) digital Earth environment, there are many problems when using the existing methods to express the ocean current, such as uneven distribution of seed points, density leap in scale change and messy visualization. In this paper, a new dynamic visualization method of multi-hierarchy flow field based on particle system is proposed; Specifically, three typical spherical uniform algorithms are studied and compared, and the streamline becoming denser from the equator to the poles on globe is eliminated by placing seed points using Marsaglia polar method as the most efficient. In addition, a viewport-adaptive adjustment algorithm is proposed, which realizes that the density of particles is always suitable to any viewing distance during continuous zooming. To solve the visual representation deficiency, we design a new dynamic pattern to enhance the expression and perception of current, which makes up for the shortcoming of the arrow glyph and streamline methods. Finally, a prototype of GPU parallel and viewport coherence is achieved, whose feasibility and effectiveness are verified by a series of experiments. The results show that our method can not only represent ocean current data clearly and efficiently, but also has outstanding uniformity and hierarchy effect.

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Section: Streamlets Generation Based On Visual Perceptionmentioning

confidence: 99%

Particle System-Based Multi-Hierarchy Dynamic Visualization of Ocean Current Data

Shi

Wen³

et al. 2021

IJGI

Self Cite

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“…Sampling data based on the features of flow fields involves the selection of subsets, structure analysis, feature detection, and accounting for the user's region of interest within the original dataset [4]. This process can significantly reduce the amount of data points by filtering out uninteresting data.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Representation of Ocean Flow Fields Based on Feature Analysis

Sun

Liu

et al. 2020

IJGI

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When it comes to feature retention in multi-scale representations of ocean flow fields, not all data points are equal. Therefore, this paper proposes a method of selecting data points based on their importance. First, an autocorrelation analysis is performed on flow speed and the rate of change in flow direction. Then, the magnitude of speed and variation in the rate of change in flow direction are classified. Feature regions are determined according to autocorrelation aggregation and classification analysis. Then, rough set theory and evidence theory are applied, using these results to determine the weights of different points. Finally, these weights are used to construct multi-scale representations of ocean flow fields, which effectively retain flow-field characteristics.

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A Spatio-Temporal Dynamic Visualization Method of Time-Varying Wind Fields Based on Particle System

Chu

Wen³

et al. 2023

IJGI

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The particle system is widely used in vector field feature visualization due to its dynamics and simulation. However, there are some defects of the vector field visualization method based on the Euler fields, such as unclear feature expression and discontinuous temporal expression, so the method cannot effectively express the characteristics of wind field on the temporal scale. We propose a Lagrangian visualization method based on spatio-temporal interpolation to solve these problems, which realizes the fusion and expression of the particle system and the time-varying wind data based on the WebGL shader. Firstly, the linear interpolation algorithm is used to interpolate to obtain continuous and dense wind field data according to the wind field data at adjacent moments. Then, we introduce the Lagrangian analysis method to study the structure of the wind field and optimize the visualization effect of the particle system based on Runge–Kutta algorithms. Finally, we adopt the nonlinear color mapping method with double standard deviation (2SD) to improve the expression effect of wind field features. The experimental results indicate that the wind visualization achieves a comprehensive visual effect and the rendering frame rate is greater than 45. The methods can render the particles smoothly with stable and outstanding uniformity when expressing continuous spatio-temporal dynamic visualization characteristics of the wind field.

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Multi-Scale Flow Field Mapping Method Based on Real-Time Feature Streamlines

Cited by 3 publications

References 5 publications

Particle System-Based Multi-Hierarchy Dynamic Visualization of Ocean Current Data

Particle System-Based Multi-Hierarchy Dynamic Visualization of Ocean Current Data

Multi-Scale Representation of Ocean Flow Fields Based on Feature Analysis

A Spatio-Temporal Dynamic Visualization Method of Time-Varying Wind Fields Based on Particle System

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