Glyph‐Based Multi‐Scale Visualization of Spatial Turbulent Multi‐Physics Statistics

Abstract Many scientific and engineering problems involving multi‐physics span a wide range of scales. Understanding the interactions across these scales is essential for fully comprehending such complex problems. However, visualizing multivariate, multi‐scale data within an integrated view where correlations across space, scales and fields are easily perceived remains challenging. To address this, we introduce a novel local spatial statistical visualization of flow fields across multiple fields and turbulence scales. Our method leverages the curvelet transform for scale decomposition of fields of interest, a level‐set‐restricted centroidal Voronoi tessellation to partition the spatial domain into local regions for statistical aggregation, and a set of glyph designs that combines information across scales and fields into a single, or reduced set of perceivable visual representations. Each glyph represents data aggregated within a Voronoi region and is positioned at the Voronoi site for direct visualization in a 3D view centred around flow features of interest. We implement and integrate our method into an interactive visualization system where the glyph‐based technique operates in tandem with linked 3D spatial views and 2D statistical views, supporting a holistic analysis. We demonstrate with case studies visualizing turbulent combustion data—multi‐scalar compressible flows—and turbulent incompressible channel flow data. This new capability enables scientists to better understand the interactions between multiple fields and length scales in turbulent flows.

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Publication Details

Journal
Computer Graphics Forum
Published
2026-03-04
DOI
https://doi.org/10.1111/cgf.70399
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
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article

Glyph‐Based Multi‐Scale Visualization of Spatial Turbulent Multi‐Physics Statistics

J H Chen, Kwan‐Liu Ma, Myoungkyu Lee, Tyson Neuroth et al.
Computer Graphics Forum
Data Visualization and Analytics
article

Glyph‐Based Multi‐Scale Visualization of Spatial Turbulent Multi‐Physics Statistics

J H Chen, Kwan‐Liu Ma, Myoungkyu Lee, Tyson Neuroth, Arisa Charlize Cowe, Martin Rieth, Qi Wu
article en

Abstract

Abstract Many scientific and engineering problems involving multi‐physics span a wide range of scales. Understanding the interactions across these scales is essential for fully comprehending such complex problems. However, visualizing multivariate, multi‐scale data within an integrated view where correlations across space, scales and fields are easily perceived remains challenging. To address this, we introduce a novel local spatial statistical visualization of flow fields across multiple fields and turbulence scales. Our method leverages the curvelet transform for scale decomposition of fields of interest, a level‐set‐restricted centroidal Voronoi tessellation to partition the spatial domain into local regions for statistical aggregation, and a set of glyph designs that combines information across scales and fields into a single, or reduced set of perceivable visual representations. Each glyph represents data aggregated within a Voronoi region and is positioned at the Voronoi site for direct visualization in a 3D view centred around flow features of interest. We implement and integrate our method into an interactive visualization system where the glyph‐based technique operates in tandem with linked 3D spatial views and 2D statistical views, supporting a holistic analysis. We demonstrate with case studies visualizing turbulent combustion data—multi‐scalar compressible flows—and turbulent incompressible channel flow data. This new capability enables scientists to better understand the interactions between multiple fields and length scales in turbulent flows.

Computer Graphics Forum
Sandia National Laboratories California (US), Nvidia (United States) (US), University of Houston (US), University of California, Davis (US)
Openalex Percentile: Top 32%
Data Visualization and Analytics
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Glyph‐Based Multi‐Scale Visualization of Spatial Turbulent Multi‐Physics Statistics — J H Chen, Kwan‐Liu Ma, et al. · Computer Graphics Forum (2026) | TGRS Research Map | TGRS