901 research outputs found
Fingerprints of Random Flows?
We consider the patterns formed by small rod-like objects advected by a
random flow in two dimensions. An exact solution indicates that their direction
field is non-singular. However, we find from simulations that the direction
field of the rods does appear to exhibit singularities. First, ` scar lines'
emerge where the rods abruptly change direction by . Later, these scar
lines become so narrow that they ` heal over' and disappear, but their ends
remain as point singularities, which are of the same type as those seen in
fingerprints. We give a theoretical explanation for these observations.Comment: 21 pages, 11 figure
Visualizing 2D Flows with Animated Arrow Plots
Flow fields are often represented by a set of static arrows to illustrate
scientific vulgarization, documentary film, meteorology, etc. This simple
schematic representation lets an observer intuitively interpret the main
properties of a flow: its orientation and velocity magnitude. We propose to
generate dynamic versions of such representations for 2D unsteady flow fields.
Our algorithm smoothly animates arrows along the flow while controlling their
density in the domain over time. Several strategies have been combined to lower
the unavoidable popping artifacts arising when arrows appear and disappear and
to achieve visually pleasing animations. Disturbing arrow rotations in low
velocity regions are also handled by continuously morphing arrow glyphs to
semi-transparent discs. To substantiate our method, we provide results for
synthetic and real velocity field datasets
Multiscale Image Based Flow Visualization
We present MIBFV, a method to produce real-time, multiscale animations of flow datasets. MIBFV extends the attractive features of the Image-Based Flow Visualization (IBFV) method, i.e. dense flow domain coverage with flow-aligned noise, real-time animation, implementation simplicity, and few (or no) user input requirements, to a multiscale dimension. We generate a multiscale of flow-aligned patterns using an algebraic multigrid method and use them to synthesize the noise textures required by IBFV. We demonstrate our approach with animations that combine multiple scale noise layers, in a global or level-of-detail manner
Transport-Based Neural Style Transfer for Smoke Simulations
Artistically controlling fluids has always been a challenging task.
Optimization techniques rely on approximating simulation states towards target
velocity or density field configurations, which are often handcrafted by
artists to indirectly control smoke dynamics. Patch synthesis techniques
transfer image textures or simulation features to a target flow field. However,
these are either limited to adding structural patterns or augmenting coarse
flows with turbulent structures, and hence cannot capture the full spectrum of
different styles and semantically complex structures. In this paper, we propose
the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric
smoke data. Our method is able to transfer features from natural images to
smoke simulations, enabling general content-aware manipulations ranging from
simple patterns to intricate motifs. The proposed algorithm is physically
inspired, since it computes the density transport from a source input smoke to
a desired target configuration. Our transport-based approach allows direct
control over the divergence of the stylization velocity field by optimizing
incompressible and irrotational potentials that transport smoke towards
stylization. Temporal consistency is ensured by transporting and aligning
subsequent stylized velocities, and 3D reconstructions are computed by
seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional
materials: http://www.byungsoo.me/project/neural-flow-styl
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