Incompressibility Enforcement for Multiple-Fluid SPH Using Deformation Gradient

Ren, Bo; He, Wei; Li, Chenfeng; Xu, Chuang

doi:10.1109/tvcg.2021.3062643

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Cuzol et al [19] proposed a new motion estimator for image sequences depicting fluid flows, relying on the Helmholtz decomposition of a motion field, which consists of decoupling the velocity field into a divergence free component and a vorticity free component. Ren et al [20] proposed a novel incompressible SPH solver, where the compressibility of fluid is directly measured by the deformation gradient. Although these motion estimators improve the accuracy of the fluid motion estimation, they still do not handle the feature distortions caused by the changes of fluid shapes and illuminations, and also do not capture some important small-scale motion structures.…”

Section: Variational Optical Flow Methodsmentioning

confidence: 99%

CNN-Based Fluid Motion Estimation Using Correlation Coefficient and Multiscale Cost Volume

et al. 2022

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Motion estimation for complex fluid flows via their image sequences is a challenging issue in computer vision. It plays a significant role in scientific research and engineering applications related to meteorology, oceanography, and fluid mechanics. In this paper, we introduce a novel convolutional neural network (CNN)-based motion estimator for complex fluid flows using multiscale cost volume. It uses correlation coefficients as the matching costs, which can improve the accuracy of motion estimation by enhancing the discrimination of the feature matching and overcoming the feature distortions caused by the changes of fluid shapes and illuminations. Specifically, it first generates sparse seeds by a feature extraction network. A correlation pyramid is then constructed for all pairs of sparse seeds, and the predicted matches are iteratively updated through a recurrent neural network, which lookups a multi-scale cost volume from a correlation pyramid via a multi-scale search scheme. Then it uses the searched multi-scale cost volume, the current matches, and the context features as the input features to correlate the predicted matches. Since the multi-scale cost volume contains motion information for both large and small displacements, it can recover small-scale motion structures. However, the predicted matches are sparse, so the final flow field is computed by performing a CNN-based interpolation for these sparse matches. The experimental results show that our method significantly outperforms the current motion estimators in capturing different motion patterns in complex fluid flows, especially in recovering some small-scale vortices. It also achieves state-of-the-art evaluation results on the public fluid datasets and successfully captures the storms in Jupiter’s White Ovals from the remote sensing images.

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Section: Variational Optical Flow Methodsmentioning

confidence: 99%

CNN-Based Fluid Motion Estimation Using Correlation Coefficient and Multiscale Cost Volume

et al. 2022

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“…Yang et al 19,20 proposed an energy-based model to deal with multiple fluid, which can also capture distillation phenomenon. Ren et al 21 used deformation gradient to enforce incompressibility in multiple fluid simulation, they also proposed method 22 to deal with the interaction between multiple fluids and porous materials. Huang et al 5 applied the mixture model 17 to describe the coupling between blood and the contrast media injected into the vessels by accurately calculating the pressure and velocity of each phase of "blood-contrast media" particles.…”

Section: Related Workmentioning

confidence: 99%

Efficient angiography simulation for complex vessels

Huang

Zhou

Zeng

et al. 2022

Computer Animation & Virtual

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Angiography simulation is a critical step in virtual interventional surgery. However, the blood vessels are always complex and irregular, which poses a significant challenge to the accuracy and efficiency of simulation. In this paper, we present a novel method to simulate contrast media propagation, which can efficiently and accurately deal with various complex vascular structures and the coupling between blood and contrast media. Our method represents the vascular structures by signed distance functions and to impose boundary conditions, and then we compute the boundary volume by Gauss-Kronrod quadrature, which yields more accurate results even in complex vessels. Furthermore, we improve the simulation's initialization efficiency and real-time force computational efficiency by precomputing the boundary volume values throughout the vascular structure and saving them to a volume map, which can be queried very efficiently during runtime. Moreover, we add term to the multiple-fluid model to handle the vascular viscosity, which achieves a more realistic effect. Experiments show that our method obtains more smooth and accurate particle states, and significantly improves initialization efficiency. Finally, we invited 12 clinicians to evaluate the algorithm and verify the clinical value of our algorithm.

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Physics-based fluid simulation in computer graphics: Survey, research trends, and challenges

Wang,

Xu,

Liu

et al. 2024

Comp. Visual Media

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Physics-based fluid simulation has played an increasingly important role in the computer graphics community. Recent methods in this area have greatly improved the generation of complex visual effects and its computational efficiency. Novel techniques have emerged to deal with complex boundaries, multiphase fluids, gas–liquid interfaces, and fine details. The parallel use of machine learning, image processing, and fluid control technologies has brought many interesting and novel research perspectives. In this survey, we provide an introduction to theoretical concepts underpinning physics-based fluid simulation and their practical implementation, with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fluid simulation, with a focus on developments in the last decade. Driven by the distribution of recent publications in the field, we structure our survey to cover physical background; discretization approaches; computational methods that address scalability; fluid interactions with other materials and interfaces; and methods for expressive aspects of surface detail and control. From a practical perspective, we give an overview of existing implementations available for the above methods.

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Incompressibility Enforcement for Multiple-Fluid SPH Using Deformation Gradient

Cited by 5 publications

References 25 publications

CNN-Based Fluid Motion Estimation Using Correlation Coefficient and Multiscale Cost Volume

CNN-Based Fluid Motion Estimation Using Correlation Coefficient and Multiscale Cost Volume

Efficient angiography simulation for complex vessels

Physics-based fluid simulation in computer graphics: Survey, research trends, and challenges

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