Andrea Brambilla 1 Øyvind Andreassen 2,3 Helwig Hauser 1 Integrated Multi-aspect Visualization of 3D Fluid Flows 1 University of Bergen, Norway 2 Norwegian Defence Research Establishment, Norway 3 University Graduate Center at Kjeller, Norway

CFD Simulations Brambilla et al. 2 / 27

Velocity Brambilla et al. 3 / 27

Flow aspects Brambilla et al. 4 / 27 Velocity (motion) Rate of strain (deformation) Vorticity (rotation)

Related work Brambilla et al. 5 / 27 De Leew and van Wick ‘93 Schafhitzel et al. ‘11 Helgeland et al. ‘07 Bürger et al. ‘08 Kirby et al. ‘99

Integrated Multi-aspect Vis Brambilla et al. 6 / 27

Requirements Brambilla et al. 7 / 27 Visual representation Convey local information Handle vector and tensor data Manage visibility issues Focus + Context visualization Exploit data coherency Glyph Color -> magnitude Geometry -> direction Velocity magnitude Vorticity magnitude Rate of Strain magnitude min max

Multiple Focus + Context Brambilla et al. 8 / 27 Different flow aspects can be more or less relevant Swirling motion in a constant laminar flow? The relevance of an attribute can vary over the domain Multiple relevance measures

Relevance measures Brambilla et al. 9 / 27 For each attribute a Define a set of potential locations P a Define relevance a : P a -> [0, 1] uxux min max 1010 relevance uyuy 0 1 Relevance measures are user defined

Relevance measures Brambilla et al. 10 / 27 For each attribute a Define a set of potential locations P a Define relevance a : P a -> [0, 1] uxux min max 1010 relevance uyuy 0 1 Relevance measures are user defined Flow feature detectors can capture physical aspects Hunt’s Q / λ 2 / Haimes and Kenwright 1010 relevance λ2λ2 min max 1010 relevance Q min max

Coherency and Visual Redundancy Brambilla et al. 11 / 27 Information can be replicated over many samples Visualize that information only once!

Coherency measures Brambilla et al. 12 / 27 A coherency measure encodes the degree of redundancy of a set of data samples For each attribute a, coherency a : P (P a ) -> R + Specified by the user 4 measures implemented in our system More measures can be easily added Similar overall behaviour, but small differences test dataset 2 nd moment entropy c_diff v c_dot v

for each attribute a.. γ a = coherency threshold.. build(P a ).. sort(P a, relevance a ).. for p in P a.... A p = sphere around p.... while coherency a (A p ) < γ a increase radius of A p.... place a glyph in p.... P a = P a - A p Visualization strategy Brambilla et al. 13 / 27 Glyph placement algorithm:

for each attribute a.. γ a = coherency threshold.. build(P a ).. sort(P a, relevance a ).. for p in P a.... A p = sphere around p.... while coherency a (A p ) < γ a increase radius of A p.... place a glyph in p.... P a = P a - A p Visualization strategy Brambilla et al. 14 / 27 Glyph placement algorithm: PaPa

for each attribute a.. γ a = coherency threshold.. build(P a ).. sort(P a, relevance a ).. for p in P a.... A p = sphere around p.... while coherency a (A p ) < γ a increase radius of A p.... place a glyph in p.... P a = P a - A p Visualization strategy Brambilla et al. 15 / 27 Glyph placement algorithm: PaPa

for each attribute a.. γ a = coherency threshold.. build(P a ).. sort(P a, relevance a ).. for p in P a.... A p = sphere around p.... while coherency a (A p ) < γ a increase radius of A p.... place a glyph in p.... P a = P a - A p Visualization strategy Brambilla et al. 16 / 27 Glyph placement algorithm: PaPa ApAp

for each attribute a.. γ a = coherency threshold.. build(P a ).. sort(P a, relevance a ).. for p in P a.... A p = sphere around p.... while coherency a (A p ) < γ a increase radius of A p.... place a glyph in p.... P a = P a - A p Visualization strategy Brambilla et al. 17 / 27 Glyph placement algorithm: PaPa ApAp

for each attribute a.. γ a = coherency threshold.. build(P a ).. sort(P a, relevance a ).. for p in P a.... A p = sphere around p.... while coherency a (A p ) < γ a increase radius of A p.... place a glyph in p.... P a = P a - A p Visualization strategy Brambilla et al. 18 / 27 Glyph placement algorithm: PaPa ApAp

for each attribute a.. γ a = coherency threshold.. build(P a ).. sort(P a, relevance a ).. for p in P a.... A p = sphere around p.... while coherency a (A p ) < γ a increase radius of A p.... place a glyph in p.... P a = P a - A p Visualization strategy Brambilla et al. 19 / 27 Glyph placement algorithm: PaPa ApAp

for each attribute a.. γ a = coherency threshold.. build(P a ).. sort(P a, relevance a ).. for p in P a.... A p = sphere around p.... while coherency a (A p ) < γ a increase radius of A p.... place a glyph in p.... P a = P a - A p Visualization strategy Brambilla et al. 20 / 27 Glyph placement algorithm: PaPa ApAp The radius of A p is mapped to size The relevance of p is mapped to opacity

for each attribute a.. γ a = coherency threshold.. build(P a ).. sort(P a, relevance a ).. for p in P a.... A p = sphere around p.... while coherency a (A p ) < γ a increase radius of A p.... place a glyph in p.... P a = P a - A p Visualization strategy Brambilla et al. 21 / 27 Glyph placement algorithm: PaPa The radius of A p is mapped to size The relevance of p is mapped to opacity

Visualization strategy Brambilla et al. 22 / 27 Glyph placement algorithm: for each attribute a.. γ a = coherency threshold.. build(P a ).. sort(P a, relevance a ).. for p in P a.... A p = sphere around p.... while coherency a (A p ) < γ a increase radius of A p.... place a glyph in p.... P a = P a - A p PaPa Repeat until all the points have been processed Repeat for every attribute of interest

Integrated Multi-aspect Vis Brambilla et al. 23 / 27

Integrated Multi-aspect Vis Brambilla et al. 24 / 27

Integrated Multi-aspect Vis Brambilla et al. 25 / 27

Parameter settings Brambilla et al. 26 / 27 Relevance corresponds to user’s interest Coherency threshold Initially set to 10% of maximal coherency value Can be interactively adjusted high thresh. mid thresh. low thresh.

Final remarks Brambilla et al. 27 / 27 Our visualization strategy Presents multiple flow aspects simultaneously Handles visibility issues through smart placement Can be easily extended Future work Streamlets as a new representation Coherency measure based on tensor invariants Adapt the strategy to integral curves Extension to time-dependent datasets

Thanks for your attention! Brambilla et al.

Square Cylinder Brambilla et al.

Flow in a Box Brambilla et al.

Flow in a Box (pruned) Brambilla et al.

Andrea Brambilla 1 Øyvind Andreassen 2,3 Helwig Hauser 1 Integrated Multi-aspect Visualization of 3D Fluid Flows 1 University of Bergen, Norway 2 Norwegian Defence Research Establishment, Norway 3 University Graduate Center at Kjeller, Norway

Exhaust Manifold Brambilla et al.

Exhaust Manifold (pruned) Brambilla et al.

Thanks for your attention! Brambilla et al.

Attributes of interest Brambilla et al. Velocity vector field Rate of strain tensor Vorticity tensor Vorticity vector Vorticity transport equation

Performance Brambilla et al. Performance Relevance is pre-cumputed Coherency computation depends on Dataset size and number of relevant points Actual data coherency Exhaust Manifold (133x81x31) -> sec (2.8GHz CPU) Bottleneck is the geometry generation Exhaust Manifold -> sec / sec (depth sort) But GPU implementation feasible!

Streamline-based Pruning Brambilla et al.

Streamline-based Pruning Brambilla et al. 39 / 27

Vorticity Brambilla et al.

Rate of Strain Brambilla et al.