Visual Simulation of Multiple Fluids in Computer Graphics: A State-of-the-Art Report

Ren, Bo; Yang, Xuyun; Thuerey, Nils; Teschner, Matthias; Li, Chenfeng

doi:10.1007/s11390-018-1829-0

Cited by 9 publications

(5 citation statements)

References 119 publications

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“…In recent years, machine learning has been widely adopted in scientific applications, leading to an emerging field referred to as scientific machine learning. Example scientific applications of machine learning include augmenting or constructing data-driven turbulence models [6][7][8], generating realistic animations of flows [9][10][11][12] discovering or solving differential equations [13][14][15][16][17][18][19].…”

Section: Physical Applications Of Gans: Progress and Challengesmentioning

confidence: 99%

“…Generative models for fluid flows have a wide range of distinct applications scenarios, e.g., accelerating animation scene generation in computer graphics [10] and providing inflow boundary conditions for turbulent flow simulations with direct simulations [41,43], large eddy simulations (LES) or hybrid LES/RANS simulations [44,45]. The present work is mainly motivated by current applications of GANs to generate inflow for LES and hybrid LES/RANS simulations.…”

Section: Scope and Contributions Of Present Workmentioning

confidence: 99%

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Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems

Zeng¹

2021

CiCP

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Generative adversarial networks (GANs) were initially proposed to generate images by learning from a large number of samples. Recently, GANs have been used to emulate complex physical systems such as turbulent flows. However, a critical question must be answered before GANs can be considered trusted emulators for physical systems: do GANs-generated samples conform to the various physical constraints? These include both deterministic constraints (e.g., conservation laws) and statistical constraints (e.g., energy spectrum of turbulent flows). The latter have been studied in a companion paper (Wu et al., Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. Journal of Computational Physics. 406, 109209, 2020). In the present work, we enforce deterministic yet imprecise constraints on GANs by incorporating them into the loss function of the generator. We evaluate the performance of physics-constrained GANs on two representative tasks with geometrical constraints (generating points on circles) and differential constraints (generating divergence-free flow velocity fields), respectively. In both cases, the constrained GANs produced samples that conform to the underlying constraints rather accurately, even though the constraints are only enforced up to a specified interval. More importantly, the imposed constraints significantly accelerate the convergence and improve the robustness in the training, indicating that they serve as a physics-based regularization. These improvements are noteworthy, as the convergence and robustness are two well-known obstacles in the training of GANs.

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Section: Physical Applications Of Gans: Progress and Challengesmentioning

confidence: 99%

Section: Scope and Contributions Of Present Workmentioning

confidence: 99%

Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems

Zeng¹

2021

CiCP

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“…Multiphase fluids have been topical in computer graphics in recent years, and some most relevant studies are briefly recapped in this section to lay the proposed dynamic non‐equilibrium mixture model in perspective. For a detailed overview of multiple‐fluid simulation we refer to [RYL*18].…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Mixture Model for Non‐equilibrium Multiphase Fluids

Jiang

Lan

2021

Computer Graphics Forum

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We present a dynamic mixture model for simulating multiphase fluids with highly dynamic relative motions. The previous mixture models assume that the multiphase fluids are under a local equilibrium condition such that the drift velocity and the phase transport can be computed analytically. By doing so, it avoids solving multiple sets of Navier‐Stokes equations and improves the simulation efficiency and stability. However, due to the local equilibrium assumption, these approaches can only deal with tightly coupled multiphase systems, where the relative speed between phases are assumed stable. In this work we abandon the local equilibrium assumption, and redesign the computation workflow of the mixture model to explicitly track and decouple the velocities of all phases. The phases still share the same pressure, with which we enforce the incompressibility for the mixture. The phase transport is calculated with drift velocities, and we propose a novel correction scheme to handle the transport at fluid boundaries to ensure mass conservation. Compared with previous mixture models, the proposed approach enables the simulation of much more dynamic scenarios with negligible extra overheads. In addition, it allows fluid control techniques to be applied to individual phases to generate locally dynamic and visually interesting effects.

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“…However, this model does not handle the density difference between phases, and also ignores the kinetic effects from the relative motions of component phases. For a detailed overview of multiple‐fluid simulation we refer readers to [RYL*18].…”

Section: Related Workmentioning

confidence: 99%

A Divergence‐free Mixture Model for Multiphase Fluids

Jiang

Deng

et al. 2020

Computer Graphics Forum

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We present a novel divergence free mixture model for multiphase flows and the related fluid‐solid coupling. The new mixture model is built upon a volume‐weighted mixture velocity so that the divergence free condition is satisfied for miscible and immiscible multiphase fluids. The proposed mixture velocity can be solved efficiently by adapted single phase incompressible solvers, allowing for larger time steps and smaller volume deviations. Besides, the drift velocity formulation is corrected to ensure mass conservation during the simulation. The new approach increases the accuracy of multiphase fluid simulation by several orders. The capability of the new divergence‐free mixture model is demonstrated by simulating different multiphase flow phenomena including mixing and unmixing of multiple fluids, fluid‐solid coupling involving deformable solids and granular materials.

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Visual Simulation of Multiple Fluids in Computer Graphics: A State-of-the-Art Report

Cited by 9 publications

References 119 publications

Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems

Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems

A Dynamic Mixture Model for Non‐equilibrium Multiphase Fluids

A Divergence‐free Mixture Model for Multiphase Fluids

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