A fast framework construction and visualization method for particle-based fluid

© 2017, The Author(s). Fast and vivid fluid simulation and visualization is a challenge topic of study in recent years. Particle-based simulation method has been widely used in the art animation modeling and multimedia field. However, the requirements of huge numerical calculation and high quality of visualization usually result in a poor computing efficiency. In this work, in order to improve those issues, we present a fast framework for 3D fluid fast constructing and visualization which parallelizes the fluid algorithm based on the GPU computing framework and designs a direct surface visualization method for particle-based fluid data such as WCSPH, IISPH, and PCISPH. Considering on conventional polygonization or adaptive mesh methods may incur high computing costs and detail losses, an improved particle-based method is provided for real-time fluid surface rendering with the screen-space technology and the utilities of the modern graphics hardware to achieve the high performance rendering; meanwhile, it effectively protects fluid details. Furthermore, to realize the fast construction of scenes, an optimized design of parallel framework and interface is also discussed in our paper. Our method is convenient to enforce, and the results demonstrate a significant improvement in the performance and efficiency by being compared with several examples

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

Lagrangian Neural Style Transfer for Fluids

Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer approach from images to 3D fluids formulated in a Lagrangian viewpoint. Using particles for style transfer has unique benefits compared to grid-based techniques. Attributes are stored on the particles and hence are trivially transported by the particle motion. This intrinsically ensures temporal consistency of the optimized stylized structure and notably improves the resulting quality. Simultaneously, the expensive, recursive alignment of stylization velocity fields of grid approaches is unnecessary, reducing the computation time to less than an hour and rendering neural flow stylization practical in production settings. Moreover, the Lagrangian representation improves artistic control as it allows for multi-fluid stylization and consistent color transfer from images, and the generality of the method enables stylization of smoke and liquids likewise.Comment: ACM Transaction on Graphics (SIGGRAPH 2020), additional materials: http://www.byungsoo.me/project/lnst/index.htm

EFFICIENT PARTICLE-BASED VISCOUS FLUID SIMULATION WITH VIDEO-GUIDED REAL-TO-VIRTUAL PARAMETER TRANSFER

Viscous fluids, such as honey and molten chocolate, are common materials frequently seen in our daily life. These viscous fluids exhibit characteristic behaviors. Capturing and understanding such dynamics have been required for various applications. Although recent research made advances in simulating the viscous fluid dynamics, still many challenges are left to be addressed. In this dissertation, I present novel techniques to more efficiently and accurately simulate viscous fluid dynamics and propose a parameter identification framework to facilitate the tedious parameter tuning steps for viscous materials. In fluid simulation, enforcing the incompressibility robustly and efficiently is essential. One known challenge is how to set appropriate boundary conditions for free surfaces and solid boundaries. I propose a new boundary handling approach for an incompressible particle-based solver based on the connectivity analysis for simulation particles. Another challenge is that previously proposed techniques do not scale well. To address this, I propose a new multilevel particle-based solver which constructs the hierarchy of simulation particles. These techniques improve the robustness and efficiency achieving the nearly linear scaling unlike previous approaches. To simulate characteristic behaviors of viscous fluids, such as coiling and buckling phenomena and adhesion to other materials, it is necessary to develop a specialized solver. I propose a stable and efficient particle-based solver for simulating highly viscous fluids by using implicit integration with the full form of viscosity. To simulate more accurate interactions with solid objects, I propose a new two-way fluid-solid coupling method for viscous fluids via the unified minimization. These approaches also improve the robustness and efficiency while generating rotational and sticky behaviors of viscous fluids. One important challenge for the physically-based simulation is that it is not obvious how to choose appropriate material parameters to generate our desirable behaviors of simulated materials. I propose a parameter identification framework that helps to tune material parameters for viscous fluids with example video data captured from real world fluid phenomena. This framework identifies viscosity parameters for the real viscous fluids while estimating the hidden variables for the fluids, and enables the parameter transfer from the real world to virtual environment.Doctor of Philosoph

Deep Multiphysics and Particle–Neuron Duality: A Computational Framework Coupling (Discrete) Multiphysics and Deep Learning

There are two common ways of coupling first-principles modelling and machine learning. In one case, data are transferred from the machine-learning algorithm to the first-principles model; in the other, from the first-principles model to the machine-learning algorithm. In both cases, the coupling is in series: the two components remain distinct, and data generated by one model are subsequently fed into the other. Several modelling problems, however, require in-parallel coupling, where the first-principle model and the machine-learning algorithm work together at the same time rather than one after the other. This study introduces deep multiphysics; a computational framework that couples first-principles modelling and machine learning in parallel rather than in series. Deep multiphysics works with particle-based first-principles modelling techniques. It is shown that the mathematical algorithms behind several particle methods and artificial neural networks are similar to the point that can be unified under the notion of particle–neuron duality. This study explains in detail the particle–neuron duality and how deep multiphysics works both theoretically and in practice. A case study, the design of a microfluidic device for separating cell populations with different levels of stiffness, is discussed to achieve this aim

Animated surfaces in physically-based simulation

Physics-based animation has become a ubiquitous element in all application areas of computer animation, especially in the entertainment sector. Animation and feature films, video games, and advertisement contain visual effects using physically-based simulation that blend in seamlessly with animated or live-action productions. When simulating deformable materials and fluids, especially liquids, objects are usually represented by animated surfaces. The visual quality of these surfaces not only depends on the actual properties of the surface itself but also on its generation and relation to the underlying simulation. This thesis focuses on surfaces of cloth simulations and fluid simulations based on Smoothed Particle Hydrodynamics (SPH), and contributes to improving the creation of animations by specifying surface shapes, modeling contact of surfaces, and evaluating surface effects of fluids. In many applications, there is a reference given for a surface animation in terms of its shape. Matching a given reference with a simulation is a challenging task and similarity is often determined by visual inspection. The first part of this thesis presents a signature for cloth animations that captures characteristic shapes and their temporal evolution. It combines geometric features with physical properties to represent accurately the typical deformation behavior. The signature enables calculating similarities between animations and is applied to retrieve cloth animations from collections by example. Interactions between particle-based fluids and deformable objects are usually modeled by sampling the deformable objects with particles. When interacting with cloth, however, this would require resampling the surface at large planar deformations and the thickness of cloth would be bound to the particle size. This problem is addressed in this thesis by presenting a two-way coupling technique for cloth and fluids based on the simulation mesh of the textile. It allows robust contact handling and intuitive control of boundary conditions. Further, a solution for intersection-free fluid surface reconstruction at contact with thin flexible objects is presented. The visual quality of particle-based fluid animation highly depends on the properties of the reconstructed surface. An important aspect of the reconstruction method is that it accurately represents the underlying simulation. This thesis presents an evaluation of surfaces at interfaces of SPH simulations incorporating the connection to the simulation model. A typical approach in computer graphics is compared to surface reconstruction used in material sciences. The behavior of free surfaces in fluid animations is highly influenced by surface tension. This thesis presents an evaluation of three types of surface tension models in combination with different pressure force models for SPH to identify the individual characteristics of these models. Systematic tests using a set of benchmark scenes are performed to reveal strengths and weaknesses, and possible areas of applications.Physikalisch basierte Animationen sind ein allgegenwärtiger Teil in jeglichen Anwendungsbereichen der Computeranimation, insbesondere dem Unterhaltungssektor. Animations- und Spielfilme, Videospiele und Werbung enthalten visuelle Effekte unter Verwendung von physikalisch basierter Simulation, die sich nahtlos in Animations- oder Realfilme einfügen. Bei der Simulation von deformierbaren Materialien und Fluiden, insbesondere Flüssigkeiten, werden die Objekte gewöhnlich durch animierte Oberflächen dargestellt. Die visuelle Qualität dieser Oberflächen hängt nicht nur von den Eigenschaften der Fläche selbst ab, sondern auch von deren Erstellung und der Verbindung zu der zugrundeliegenden Simulation. Diese Dissertation widmet sich Oberflächen von Textil- und Fluidsimulationen mit der Methode der Smoothed Particle Hydrodynamics (SPH) und leistet einen Beitrag zur Verbesserung der Erstellung von Animationen durch die Beschreibung von Oberflächenformen, der Modellierung von Kontakt von Oberflächen und der Evaluierung von Oberflächeneffekten von Fluiden. In vielen Anwendungen gibt es eine Referenz für eine Oberflächenanimation, die ihre Form beschreibt. Das Abgleichen einer Referenz mit einer Simulation ist eine große Herausforderung und die Ähnlichkeit wird häufig visuell überprüft. Im ersten Teil der Dissertation wird eine Signatur für Textilanimationen vorgestellt, die charakteristische Formen und ihre zeitliche Veränderung erfasst. Sie ist eine Kombination aus geometrischen Merkmalen und physikalischen Eigenschaften, um das typische Deformationsverhalten genau zu repräsentieren. Die Signatur erlaubt es, Ähnlichkeiten zwischen Animationen zu berechnen, und wird angewendet, um Textilanimationen aus Kollektionen anhand eines Beispiels aufzufinden. Interaktionen zwischen partikelbasierten Fluiden und deformierbaren Objekten werden gewöhnlich durch das Abtasten des deformierbaren Objekts mit Partikeln modelliert. Bei der Interaktion mit Textilien würde dies jedoch ein neues Abtasten bei großen planaren Deformation erfordern und die Stärke des Textils wäre an die Partikelgröße gebunden. Mit diesem Problem befasst sich diese Dissertation und stellt eine Technik für die wechselseitige Kopplung zwischen Textilien und Fluiden vor, die auf dem Simulationsnetz des Textils beruht. Diese erlaubt eine robuste Kontaktbehandlung und intuitive Kontrolle von Randbedingungen. Des Weiteren wird ein Lösungsansatz für eine durchdringungsfreie Oberflächenrekonstruktion beim Kontakt mit dünnen flexiblen Objekten präsentiert. Die visuelle Qualität von partikelbasierten Fluidanimationen hängt stark von den Eigenschaften der rekonstruierten Oberfläche ab. Wichtig bei Rekonstruktionsmethoden ist, dass sie die zugrundeliegende Simulation genau repräsentieren. Die Dissertation präsentiert eine Evaluierung von Oberflächen an Grenzflächen, die den Zusammenhang zum Simulationsmodell miteinbezieht. Ein typischer Ansatz aus der Computergrafik wird mit der Oberflächenrekonstruktion in der Werkstoffkunde verglichen. Das Verhalten von freien Oberflächen in Fluidanimationen wird stark von der Oberflächenspannung beeinflusst. In dieser Dissertation wird eine Evaluierung von drei Oberflächenspannungsmodellen in Kombination mit verschiedenen Druckmodellen für SPH präsentiert, um die Charakteristika der jeweiligen Modelle zu identifizieren. Es werden systematische Tests mit Hilfe von Benchmark-Tests durchgeführt, um Stärken, Schwächen und mögliche Anwendungsbereiche deutlich zu machen