26,455 research outputs found
Modelling pyro-convection phenomenon during a mega-fire event in Portugal
The present study contributes to an increased understanding of
pyro-convection phenomena by using a fire-atmosphere coupled simulation, and
investigates in detail the large-scale meteorological conditions affecting
Portugal during the occurrence of multiple mega-fires events on 15 October
2017. Two numerical simulations were performed using the MesoNH atmospheric
model. The first simulation, was run for a large single domain (300 x 250 grid
points) with a 15 km resolution. In the second one, the MesoNH was coupled to a
fire propagation model (ForeFire) to study in detail the Quiaios's fire. To
optimize both high resolution in the proximity of the fire region and
computational efficiency, the simulation is set up using 3 nested domains (300
x 300 grid points) with horizontal resolution of 2000 m, 400 m, and 80 m
respectively. The emission into the atmosphere of the heat and the water vapour
fluxes caused by the evolving fire is managed by the ForeFire code. The fire
spatio-temporal evolution is based on an assigned map, which follows what
reported by public authorities. At the large scale, the simulation shows the
evolution of the hurricane Ophelia, pointing out the influence of
south/southwest winds on the rapid spread of active fires, as well as the
subtropical moisture transport toward mainland Portugal in the early evening,
when violent pyro-convective activity was observed in Central Portugal. The
coupled simulation allowed to reproduce the formation of a PyroCu cloud inside
the smoke plume. The convective updraughts caused by the fire led to the
vertical transport of water vapour to higher levels and enhanced the
development of a high-based cloud over a dry atmospheric layer within the smoke
plume
Dense Motion Estimation for Smoke
Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. Our algorithm is robust, fast, and has better performance
over different types of smoke compared to other dense motion estimation
algorithms, including state of the art and neural network approaches. The key
to our contribution is to use skeletal flow, without explicit point matching,
to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this
paper we describe our algorithm in greater detail, and provide experimental
evidence to support our claims.Comment: ACCV201
Accelerating Eulerian Fluid Simulation With Convolutional Networks
Efficient simulation of the Navier-Stokes equations for fluid flow is a long
standing problem in applied mathematics, for which state-of-the-art methods
require large compute resources. In this work, we propose a data-driven
approach that leverages the approximation power of deep-learning with the
precision of standard solvers to obtain fast and highly realistic simulations.
Our method solves the incompressible Euler equations using the standard
operator splitting method, in which a large sparse linear system with many free
parameters must be solved. We use a Convolutional Network with a highly
tailored architecture, trained using a novel unsupervised learning framework to
solve the linear system. We present real-time 2D and 3D simulations that
outperform recently proposed data-driven methods; the obtained results are
realistic and show good generalization properties.Comment: Significant revisio
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
This paper presents a novel generative model to synthesize fluid simulations
from a set of reduced parameters. A convolutional neural network is trained on
a collection of discrete, parameterizable fluid simulation velocity fields. Due
to the capability of deep learning architectures to learn representative
features of the data, our generative model is able to accurately approximate
the training data set, while providing plausible interpolated in-betweens. The
proposed generative model is optimized for fluids by a novel loss function that
guarantees divergence-free velocity fields at all times. In addition, we
demonstrate that we can handle complex parameterizations in reduced spaces, and
advance simulations in time by integrating in the latent space with a second
network. Our method models a wide variety of fluid behaviors, thus enabling
applications such as fast construction of simulations, interpolation of fluids
with different parameters, time re-sampling, latent space simulations, and
compression of fluid simulation data. Reconstructed velocity fields are
generated up to 700x faster than re-simulating the data with the underlying CPU
solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019),
additional materials: http://www.byungsoo.me/project/deep-fluids
A Survey of Ocean Simulation and Rendering Techniques in Computer Graphics
This paper presents a survey of ocean simulation and rendering methods in
computer graphics. To model and animate the ocean's surface, these methods
mainly rely on two main approaches: on the one hand, those which approximate
ocean dynamics with parametric, spectral or hybrid models and use empirical
laws from oceanographic research. We will see that this type of methods
essentially allows the simulation of ocean scenes in the deep water domain,
without breaking waves. On the other hand, physically-based methods use
Navier-Stokes Equations (NSE) to represent breaking waves and more generally
ocean surface near the shore. We also describe ocean rendering methods in
computer graphics, with a special interest in the simulation of phenomena such
as foam and spray, and light's interaction with the ocean surface
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