Particle-based Fluid Simulation Research Articles

Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation

In this paper, we propose a learning model for tracking the isolines of fluid based on the physical properties of particles in particle-based fluid simulations. Our method involves analyzing which weights, closely related to surface tracking among the various physical properties of fluid particles, are significant. These weights are used as input values for the learning algorithm, enabling relatively accurate isoline tracking. In addition, compared to existing learning models such as linear regression, LSTM (long short-term memory), and learning representation (1-layer) models, our method obtained superior surface tracking results without accumulating errors. By using our proposed network structure to track the fluid surface, it learns and predicts values derived from existing fluid simulation algorithms, eliminating the need for computational processes for level-set values and enabling real-time surface tracking. As the scale of the simulation increases, our method significantly reduces the time and resources consumed compared to traditional methods and can track the fluid surface without additional resource consumption. Furthermore, due to our method’s simple network structure, the time consumed in the initial process of loading the model into memory is faster than models such as CNN and LSTM. Our proposed model occupies less than 30 kb of storage space, making it suitable for use in middleware. Lastly, to verify the generality of our method, we conducted tests in a total of five scenes, and in all test scenes, visually natural fluid isolines were represented.

Fast Fluid Simulation via Dynamic Multi-Scale Gridding

Recent works on learning-based frameworks for Lagrangian (i.e., particle-based) fluid simulation, though bypassing iterative pressure projection via efficient convolution operators, are still time-consuming due to excessive amount of particles. To address this challenge, we propose a dynamic multi-scale gridding method to reduce the magnitude of elements that have to be processed, by observing repeated particle motion patterns within certain consistent regions. Specifically, we hierarchically generate multi-scale micelles in Euclidean space by grouping particles that share similar motion patterns/characteristics based on super-light motion and scale estimation modules. With little internal motion variation, each micelle is modeled as a single rigid body with convolution only applied to a single representative particle. In addition, a distance-based interpolation is conducted to propagate relative motion message among micelles. With our efficient design, the network produces high visual fidelity fluid simulations with the inference time to be only 4.24 ms/frame (with 6K fluid particles), hence enables real-time human-computer interaction and animation. Experimental results on multiple datasets show that our work achieves great simulation acceleration with negligible prediction error increase.

Anisotropic screen space rendering for particle-based fluid simulation

This paper proposes a real-time fluid rendering method based on the screen space rendering scheme for particle-based fluid simulation. Our method applies anisotropic transformations to the point sprites to stretch the point sprites along appropriate axes, obtaining smooth fluid surfaces based on the weighted principal components analysis of the particle distribution. Then we combine the processed anisotropic point sprite information with popular screen space filters like curvature flow and narrow-range filters to process the depth information. Experiments show that the proposed method can efficiently resolve the issues of jagged edges and unevenness on the surface that existed in previous methods while preserving sharp high-frequency details.

Optimizations for predictive–corrective particle-based fluid simulation on GPU

Optimizations for predictive–corrective particle-based fluid simulation on GPU

Elastic Simulation of Joints with Particle-Based Fluid

Skinning, which is used in skeletal simulations to express the human body, has been weighted between bones to enable muscle-like motions. Weighting is not a form of calculating the pressure and density of muscle fibers in the human body. Therefore, it is not possible to express physical changes when external forces are applied. To express a similar behavior, an animator arbitrarily customizes the weight values. In this study, we apply the kernel and pressure-dependent density variations used in particle-based fluid simulations to skinning simulations. As a result, surface tension and elasticity between particles are applied to muscles, indicating realistic human motion. We also propose a tension yield condition that reflects Tresca’s yield condition, which can be easily approximated using the difference between the maximum and minimum values of the principal stress to simulate the tension limit of the muscle fiber. The density received by particles in the kernel is assumed to be the principal stress. The difference is calculated by approximating the moment of greatest force to the maximum principal stress and the moment of least force to the minimum principal stress. When the density of a particle increases beyond the yield condition, the object is no longer subjected to force. As a result, one can express realistic muscles.

Particle-Based Dynamic Water Drops with High Surface Tension in Real Time

Surface tension has a great influence on the shape of the fluid interface, and is an important physical characteristic in expressing not only liquids but also liquid metals such as mercury and gallium. In the field of physics-based particle fluid simulations, it is a challenging problem to express the high surface tension generated by fluid-air or fluid-solid interaction in real time. The main reasons for this are (1) The magnitude of the force that can be stably expressed in real-time fluid simulation is limited, so when the magnitude of the surface tension increases at a large time-step, the simulation stability decreases, and (2) If we use a small time-step, a stronger force can be expressed. However, it becomes difficult to operate in real time because the computational cost increases. Techniques were proposed to solve this problem for a few specific scenes, but there has not yet been a general approach that can reliably express high surface tension in various scenarios. In this paper, we propose a real-time particle-based fluid simulation framework that can efficiently and stably express high surface tension. Unlike the previous methods, we newly model the surface tension so that the strong surface tension force generated in the droplet area with a large curvature is applied evenly in the normal and tangent directions regardless of the size of the droplet. We also propose new pressure constraints that converge quickly and accurately using this force. Our method can be effectively used in various physics-based simulation scenarios because it can easily express and control surface tension effects that appear in materials such as liquid metal as well as water.

입자 기반 유체 시물레이션에서 디테일한 액체 시트를 표현하기 위한 보존과 분해 기법

In this paper, we propose a new method to improve the details of the fluid surface by removing liquid sheets that are overpreserved in particle-based water simulation. A variety of anisotropic approaches have been proposed to address the surface noise problem, one of the chronic problems in particle-based fluid simulation. However, a method of stably expressing the preservation and breakup of the liquid sheet has not been proposed. We propose a new framework that can dynamically add and remove the water particles based on anisotropic kernel and density to simultaneously represent two features of liquid sheet preservation and breakup in particle-based fluid simulations. The proposed technique well represented the characteristics of a fluid sheet that was breakup by removing the excessively preserved liquid sheet in a particle-based fluid simulation approach. As a result, the quality of the liquid sheet was improved without noise.

Realistic fluid representation by anisotropic particle

This paper presents a novel method for tracking multiphase fluid surfaces more accurately using the particle level-set (PLS) method with anisotropic instead of spherical particles. We create an anisotropic particle by considering the distribution of particles. To construct the anisotropic particles, we use the weighted version of principal component analysis and adopt the distribution of particles from the directional derivatives. This represents the fluid surface accurately, but the search for neighbor particles requires to calculate singular value decomposition with a high computational cost. Therefore, we devised a new method that uses directional derivatives to generate the anisotropic particles. This reduces the computational costs significantly but still allows the fluid interface to be tracked more accurately. Compared to the PLS method, our approach provides more detailed surfaces, corrects numerical dissipation, and preserves the volume of the fluid. Furthermore, we present particle-based fluid simulations with a surface reconstruction that uses anisotropic particles.

Ray Tracer based rendering solution for large scale fluid rendering

Fluid simulation using meshless methods has increasingly become a robust way to solve mechanics problems that require dealing with large deformations and has become very popular in many applications such as naval engineering, mechanical engineering, movies, and games. The current challenge is to simulate and render the fluid in real time. This works contributions are: a GPU implementation of the SPH method, which provided an average speedup of 11.55 in comparison to the reference multicore CPU implementation, being able to simulate 1M particles at, approximately, 3 fps; also, a Ray Tracing based rendering solution that can be used to render any particle based fluid simulation method is proposed, which is able to render a large number of particles in interactive rates, for instance, 2M particles at 2.7 fps for a resolution of 1920 x 1920 pixels.

Explosion simulation for viscoelastic objects

In particle-based fluid simulations, viscoelastic materials, which exist in intermediate forms between fluids and perfect elastic bodies, require yield stress for material deformation. Existing studies on particle-based viscoelastic materials can represent the deformation of viscoelastic materials using the von Mises yield criterion, but not the explosion. In this study, we propose an ideal viscoelastic material yield criterion developed by modifying the Tresca yield criterion that can be easily approximated using the difference between the maximum principal stress and the minimum principal stress. This is different from the von Mises yield criterion in which the forces in numerous directions applied to an object are calculated. Unlike existing particle-based simulations that represent forces applied to an object as deformed lengths in order to easily approximate the von Mises yield criterion, the proposed ideal viscoelastic material yield criterion assumes the area of the object deformed owing to the forces applied to it as the principal stress. The moment at which the object is subjected to the largest force is approximated as the maximum principal stress, and the moment at which the object is subjected to the smallest force is approximated as the minimum principal stress. Using this method, we can realistically represent the process through which a viscoelastic material explodes because it cannot endure the critical stress when its interface decreases beyond the ideal yield criterion.

A Narrow-Range Filter for Screen-Space Fluid Rendering

We introduce a simple screen-space filtering technique for real-time rendering of particle-based fluid simulations. Starting with a depth-map generated directly from the particle data, our new filter formulation smooths the depth-map by considering the depth values in a narrow range. The depth values outside of this range are carefully handled to achieve the desired surface shape near discontinuities. The simplicity of our formulation leads to a computationally efficient filter. We present examples with complex particle-based fluid simulations and provide comparisons, clearly showing that our filter provides improved surface quality in terms of surface smoothness and preserving boundaries near discontinuities, as compared to prior filtering methods.

Smoothed particle hydrodynamics and modal reduction for efficient fluid–structure interaction

ABSTRACTFrom wind turbines to capillary blood flow, problems of fluid–structure interaction occur across different scales of length and time. Owed to the multiple scales involved, the inherent complexity of coupled fluid and structural dynamics requires advanced numerical methods for the computational analysis. The proposed method relies on the coupling of modally reduced flexible multibody systems with fluids represented by the smoothed particle hydrodynamics method. A reduced-order basis is employed to describe small flexible deformation of a structural component relative to its large rigid body motion that is represented by a body-fixed frame. An efficient evaluation of the surface deformation of the bodies involved is a vital ingredient for the coupling. Conventional approaches suffer from the drawback of flexible deformation being represented in a body-fixed local frame. The generalized component mode synthesis, in which the total displacement is interpolated, mitigates this computational limitation. The linear relation between generalized coordinates and the total displacement allows the coupling forces to be evaluated within the parallel fluid framework. Only the reduced set of equations of motion is solved on the solid side using an implicit time integration scheme with possibly large time-steps, whereas the particle-based fluid simulation relies on a fast explicit scheme.

Lightweight particle-based real-time fluid simulation for mobile environment

This paper presents a real-time lightweight fluid simulation based on a particle fluid technique developed for mobile environment . The Bullet physics engine and smoothed particle hydrodynamic (SPH) fluid algorithm will be used for our lightweight fluid simulation. First, we describe an advanced collision detection mechanism that will be used. By using this method, less computational resources are required. Secondly, we present a simplified SPH algorithm where nearby particles are grouped together to minimize the number of calculations. By decreasing the number of particles, an improved computational performance is expected. Finally, the ARM NEON based parallel computing technique was enabled to reduce execution time by lowering the number of arithmetic instructions. Several experiments are carried out where the experimental results indicate the first technique led to a 50% improvement in performance. The second technique provided a 17% overall improvement. The third technique delivered a performance improvement within the range of 26%–40%. Overall, the experimental results show that the proposed techniques provided an accumulative performance improvement of approximately 120% for all applied methodologies.

Position Based Triangulation for High Performance Particle Based Fluid Simulation

Position Based Triangulation for High Performance Particle Based Fluid Simulation

Coherent spherical range-search for dynamic points on GPUs

We present an approach to accelerate spherical range-search (SRS) for dynamic points that employs the computational power of many-core GPUs. Unlike finding kapproximate nearest neighbours (ANNs), exact SRS is needed in geometry processing and physical simulation to avoid missing small features. The spatial coherence of query points and the temporal coherence of dynamic points are exploited in our approach to achieve very efficient range-search on AABB-trees. We test our coherent SRS in several applications including point-point-set geometry processing, distance-field generation and particle-based simulation, which are best scenarios to present the spatial and the temporal coherence of spherical queries on dynamic points. On a PC with NVIDIA GTX 660 Ti GPUs, our approach can take 1M queries on 1M dynamic points at a rate of 1600 queries/ms, where 49 neighbours are found on average within the range of 1/100 of the bounding-box’s diagonal length. We observe an increase of up to 4x compared with conventional voxel-based GPU searching approaches in the benchmark of particle-based fluid simulation. Moreover, the speedup can be scaled up to 150x when being applied to highly non-uniform distribution of particles in the simulation.

Rigid Body Sampling and Individual Time Stepping for Rigid-Fluid Coupling of Fluid Simulation

In this paper, we propose an efficient and simple rigid-fluid coupling scheme with scientific programming algorithms for particle-based fluid simulation and three-dimensional visualization. Our approach samples the surface of rigid bodies with boundary particles that interact with fluids. It contains two procedures, that is, surface sampling and sampling relaxation, which insures uniform distribution of particles with less iterations. Furthermore, we present a rigid-fluid coupling scheme integrating individual time stepping to rigid-fluid coupling, which gains an obvious speedup compared to previous method. The experimental results demonstrate the effectiveness of our approach.

Simulation-based Serious Games for Science Education and teacher assessment

This paper presents serious games developed for the science subject in elementary and middle schools, specifically on the three topics of “Force and Motion,” “State Change of Water,” and “Earth and Moon.” The PC game “Force and Motion” implemented frictional/gravitational/magnetic force simulations, in the mobile game “State Change of Water,” particle-based fluid simulations were implemented, and in the PC- and mobile-based multi-platform game “Earth and Moon,” a solar system simulation was implemented. In order to find out the essential components for the science educational games, the components of each topic were thoroughly analyzed, and then a game-based curriculum was developed for the components classified as having high- or mid-level difficulties in both teaching and learning. Based on the curriculum, the three games were created. The games were evaluated by elementary and middle school teachers, and the evaluation results showed that simulation-based serious games are promising tools for improving learning effects in science-related subjects.

DYVERSO: A Versatile Multi‐Phase Position‐Based Fluids Solution for VFX

AbstractMany impressive fluid simulation methods have been presented in research papers before. These papers typically focus on demonstrating particular innovative features, but they do not meet in a comprehensive manner the production demands of actual VFX pipelines. VFX artists seek methods that are flexible, efficient, robust and scalable, and these goals often conflict with each other. In this paper, we present a multi‐phase particle‐based fluid simulation framework, based on the well‐known Position‐Based Fluids (PBF) method, designed to address VFX production demands. Our simulation framework handles multi‐phase interactions robustly thanks to a modified constraint formulation for density contrast PBF. And, it also supports the interaction of fluids sampled at different resolutions. We put special care on data structure design and implementation details. Our framework highlights cache‐efficient GPU‐friendly data structures, an improved spatial voxelization technique based on Z‐index sorting, tuned‐up simulation algorithms and two‐way‐coupled collision handling based on VDB fields. Altogether, our fluid simulation framework empowers artists with the efficiency, scalability and versatility needed for simulating very diverse scenes and effects.

An evaluation of noise reduction algorithms for particle-based fluid simulations in multi-scale applications

Filtering of particle-based simulation data can lead to reduced computational costs and enable more efficient information transfer in multi-scale modelling. This paper compares the effectiveness of various signal processing methods to reduce numerical noise and capture the structures of nano-flow systems. In addition, a novel combination of these algorithms is introduced, showing the potential of hybrid strategies to improve further the de-noising performance for time-dependent measurements. The methods were tested on velocity and density fields, obtained from simulations performed with molecular dynamics and dissipative particle dynamics. Comparisons between the algorithms are given in terms of performance, quality of the results and sensitivity to the choice of input parameters. The results provide useful insights on strategies for the analysis of particle-based data and the reduction of computational costs in obtaining ensemble solutions.

A novel coupling of noise reduction algorithms for particle flow simulations

Proper orthogonal decomposition (POD) and its extension based on time-windows have been shown to greatly improve the effectiveness of recovering smooth ensemble solutions from noisy particle data. However, to successfully de-noise any molecular system, a large number of measurements still need to be provided. In order to achieve a better efficiency in processing time-dependent fields, we have combined POD with a well-established signal processing technique, wavelet-based thresholding. In this novel hybrid procedure, the wavelet filtering is applied within the POD domain and referred to as WAVinPOD. The algorithm exhibits promising results when applied to both synthetically generated signals and particle data. In this work, the simulations compare the performance of our new approach with standard POD or wavelet analysis in extracting smooth profiles from noisy velocity and density fields. Numerical examples include molecular dynamics and dissipative particle dynamics simulations of unsteady force- and shear-driven liquid flows, as well as phase separation phenomenon. Simulation results confirm that WAVinPOD preserves the dimensionality reduction obtained using POD, while improving its filtering properties through the sparse representation of data in wavelet basis. This paper shows that WAVinPOD outperforms the other estimators for both synthetically generated signals and particle-based measurements, achieving a higher signal-to-noise ratio from a smaller number of samples. The new filtering methodology offers significant computational savings, particularly for multi-scale applications seeking to couple continuum informations with atomistic models. It is the first time that a rigorous analysis has compared de-noising techniques for particle-based fluid simulations.