A Vortex Method for Bi-phasic Fluids Interacting with Rigid Bodies

We present an accurate Lagrangian method based on vortex particles, level-sets, and immersed boundary methods, for animating the interplay between two fluids and rigid solids. We show that a vortex method is a good choice for simulating bi-phase flow, such as liquid and gas, with a good level of realism. Vortex particles are localized at the interfaces between the two fluids and within the regions of high turbulence. We gain local precision and efficiency from the stable advection permitted by the vorticity formulation. Moreover, our numerical method straightforwardly solves the two-way coupling problem between the fluids and animated rigid solids. This new approach is validated through numerical comparisons with reference experiments from the computational fluid community. We also show that the visually appealing results obtained in the CG community can be reproduced with increased efficiency and an easier implementation

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

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

Fluid Simulation by the Smoothed Particle Hydrodynamics Method: A Survey.

This paper presents a survey of Smoothed Particle Hydrodynamics (SPH) and its use in computational fluid dynamics. As a truly mesh-free particle method based upon the Lagrangian formulation, SPH has been applied to a variety of different areas in science, computer graphics and engineering. It has been established as a popular technique for fluid based simulations, and has been extended to successfully simulate various phenomena such as multi-phase flows, rigid and elastic solids, and fluid features such as air bubbles and foam. Various aspects of the method will be discussed: Similarities, advantages and disadvantages in comparison to Eulerian methods; Fundamentals of the SPH method; The use of SPH in fluid simulation; The current trends in SPH. The paper ends with some concluding remarks about the use of SPH in fluid simulations, including some of the more apparent problems, and a discussion on prospects for future work

Real-time fluid simulations under smoothed particle hydrodynamics for coupled kinematic modelling in robotic applications

Although solids and fluids can be conceived as continuum media, applications of solid and fluid dynamics differ greatly from each other in their theoretical models and their physical behavior. That is why the computer simulators of each turn to be very disparate and case-oriented. The aim of this research work, captured in this thesis book, is to find a fluid dynamics model that can be implemented in near real-time with GPU processing and that can be adapted to typically large scales found in robotic devices in action with fluid media. More specifically, the objective is to develop these fast fluid simulations, comprising different solid body dynamics, to find a viable time kinematic solution for robotics. The tested cases are: i) the case of a fluid in a closed channel flowing across a cylinder, ii) the case of a fluid flowing across a controlled profile, and iii), the case of a free surface fluid control during pouring. The implementation of the former cases settles the formulations and constraints to the latter applications. The results will allow the reader not only to sustain the implemented models but also to break down the software implementation concepts for better comprehension. A fast GPU-based fluid dynamics simulation is detailed in the main implementation. The results show that it can be used in real-time to allow robotics to perform a blind pouring task with a conventional controller and no special sensing systems nor knowledge-driven prediction models would be necessary.Aunque los sólidos y los fluidos pueden concebirse como medios continuos, las aplicaciones de la dinámica de sólidos y fluidos difieren mucho entre sí en sus modelos teóricos y su comportamiento físico. Es por eso que los simuladores por computadora de cada uno son muy dispares y están orientados al caso de su aplicación. El objetivo de este trabajo de investigación, capturado en este libro de tesis, es encontrar un modelo de dinámica de fluidos que se pueda implementar cercano al tiempo real con procesamiento GPU y que se pueda adaptar a escalas típicamente grandes que se encuentran en dispositivos robóticos en acción con medios fluidos. Específicamente, el propósito es desarrollar estas simulaciones de fluidos rápidos, que comprenden diferentes dinámicas de cuerpos sólidos, para encontrar una solución cinemática viable para robótica. Los casos probados son: i) el caso de un fluido en canal cerrado que fluye a través de un cilindro, ii) el caso de un fluido que fluye a través de un alabe controlado, y iii), el caso del control de un fluido de superficie libre durante el vertido. La implementación de estos primeros casos establece las formulaciones y limitaciones de aplicaciones futuras. Los resultados permitirán al lector no solo sostener los modelos implementados sino también desglosar los conceptos de la implementación en software para una mejor comprensión. En la implementación principal se consigue una simulación rápida de dinámica de fluidos basada en GPU. Los resultados muestran que esta implementación se puede utilizar en tiempo real para permitir que la robótica realice una tarea de vertido ciego con un controlador convencional sin que sea necesario algún sistema de sensado especial ni algún modelo predictivo basados en el conocimiento.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carmen Martínez Arévalo.- Secretario: Luis Santiago Garrido Bullón.- Vocal: Benjamín Hernández Arreguí

A viscous paint model for interactive applications

We present a viscous paint model for use in an interactive painting system based on the well-known Stokes’ equations for viscous flow. Our method is, to our knowledge, the first unconditionally stable numerical method that treats viscous fluid with a free surface boundary. We have also developed a real-time implementation of the Kubelka-Munk reflectance model for pigment mixing, compositing and rendering entirely on graphics hardware, using programmable fragment shading capabilities. We have integrated our paint model with a prototype painting system, which demonstrates the model’s effectiveness in rendering viscous paint and capturing a thick, impasto-like style of painting. Several users have tested our prototype system and were able to start creating original art work in an intuitive manner not possible with the existing techniques in commercial systems

Modelling Shear Flows with SPH and Grid Based Methods

Given the importance of shear flows for astrophysical gas dynamics, we study the evolution of the Kelvin-Helmholtz instability (KHI) analytically and numerically. We derive the dispersion relation for the two-dimensional KHI including viscous dissipation. The resulting expression for the growth rate is then used to estimate the intrinsic viscosity of four numerical schemes depending on code-specific as well as on physical parameters. Our set of numerical schemes includes the Tree-SPH code VINE, an alternative SPH formulation developed by Price (2008), and the finite-volume grid codes FLASH and PLUTO. In the first part, we explicitly demonstrate the effect of dissipation-inhibiting mechanisms such as the Balsara viscosity on the evolution of the KHI. With VINE, increasing density contrasts lead to a continuously increasing suppression of the KHI (with complete suppression from a contrast of 6:1 or higher). The alternative SPH formulation including an artificial thermal conductivity reproduces the analytically expected growth rates up to a density contrast of 10:1. The second part addresses the shear flow evolution with FLASH and PLUTO. Both codes result in a consistent non-viscous evolution (in the equal as well as in the different density case) in agreement with the analytical prediction. The viscous evolution studied with FLASH shows minor deviations from the analytical prediction.Comment: 16 pages, 17 figure