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.

Particle-based simulations have become increasingly popular in real-time applications due to their efficiency and adaptability, especially for generating highly dynamic fluid effects. However, the swift and stable simulation of interactions among distinct fluids continues to pose challenges for current mixture model techniques. When using a single-mixture flow field to represent all fluid phases, numerical discontinuities in phase fields can result in significant losses of dynamic effects and unstable conservation of mass and momentum. To tackle these issues, we present an advanced implicit mixture model for smoothed particle hydrodynamics. Instead of relying on an explicit mixture field for all dynamic computations and phase transfers between particles, our approach calculates phase momentum sources from the mixture model to derive explicit and continuous velocity phase fields. We then implicitly obtain the mixture field using a phase-mixture momentum-mapping mechanism that ensures conservation of incompressibility, mass, and momentum. In addition, we propose a mixture viscosity model and establish viscous effects between the mixture and individual fluid phases to avoid instability under extreme inertia conditions. Through a series of experiments, we show that, compared to existing mixture models, our method effectively improves dynamic effects while reducing critical instability factors. This makes our approach especially well-suited for long-duration, efficiency-oriented virtual reality scenarios.

Abstract Fluid simulation is well‐known for being visually stunning while computationally expensive. Spatial adaptivity can effectively ease the computational cost by discretizing the simulation space with varying resolutions. Adaptive methods nowadays mainly focus on the mechanism of refining the fluid surfaces to obtain more vivid splashes and wave effects. But such techniques hinder further performance gain under the condition where most of the vast fluid surface is tranquil. Moreover, energetic flow beneath the surface cannot be adequately captured with the interior of the fluid still being simulated under coarse discretization. This article proposes a novel boundary‐distance based adaptive method for smoothed particle hydrodynamics fluid simulation. The signed‐distance field constructed with respect to the coupling boundary is introduced to determine particle resolution in different spatial positions. The resolution is maximal within a specific distance to the boundary and decreases smoothly as the distance increases until a threshold is reached. The sizes of the particles are then adjusted towards the resolution via splitting and merging. Additionally, a wake flow preservation mechanism is introduced to keep the particle resolution at a high level for a period of time after a particle flows through the boundary object to prevent the loss of flow details. Experiments show that our method can refine fluid–solid coupling details more efficiently and effectively capture dynamic effects beneath the surface.

Vitrectomy combined with silicone oil tamponade is an effective treatment for rhegmatogenous retinal detachment (RRD). The high viscosity and surface tension of the silicone oil make it suitable for treating large retinal tears by pressing against the retina. However, silicone oil becomes emulsified over time as it remains in the eye, which can cause serious complications. Clear visual acquisitions of silicone oil-water interaction and silicone oil emulsification progress are difficult during and after the surgery. To help doctors and patients perceive the two-phase interaction and emulsification progress intuitively, we propose a physically based simulation method for intraocular silicone oil visualization. For the visualization of immiscible silicone oil-water interaction, we introduce a volume-incompressible Smoothed Particle Hydrodynamics (SPH) approach to improve simulation precision of multiphase flow coupling. A diffusion model based on volume fraction is proposed to visualize emulsification progress. Additionally, we combine our method with cohesion and surface-minimization driven surface tension model to describe the high surface tension of silicone oil. Experiments show that our scheme can obtain a precise pressure gradient near phase boundary and perform noticeable mixing effect that evolves over time. Our method has the advantage of higher accuracy than other visualization methods, and has the potential to help doctors make decisions and estimate surgical outcomes.