Physically based fluid simulation requires much time in numerical calculation to solve Navier-Stokes equations. Especially in grid-based fluid simulation, because of iterative computation, the projection step is much more time-consuming than other steps. In this paper, we propose a novel data-driven projection method using an artificial neural network to avoid iterative computation. Once the grid resolution is decided, our data-driven method could obtain projection results in relatively constant time per grid cell, which is independent of scene complexity. Experimental results demonstrated that our data-driven method drastically speeded up the computation in the projection step. With the growth of grid resolution, the speed-up would increase strikingly. In addition, our method is still applicable in different fluid scenes with some alterations, when computational cost is more important than physical accuracy.
We present a new method to create and preserve the turbulent details generated around moving objects in SPH fluid. In our approach, a high-resolution overlapping grid is bounded to each object and translates with the object. The turbulence formation is modeled by resolving the local flow around objects using a hybrid SPH-FLIP method. Then these vortical details are carried on SPH particles flowing through the local region and preserved in the global field in a synthetic way. Our method provides a physically plausible way to model the turbulent details around both rigid and deformable objects in SPH fluid, and can efficiently produce animations of complex gaseous phenomena with rich visual details.
Figure 1: An animated dancer made of fluid using our position-based fluid control method.
AbstractWe present a novel fluid control method that is capable of driving particle-based fluid simulation to match a rapidly changing target while keeping natural fluid-like motion. To achieve the desired behavior, we first generate control particles by sampling the target shape and then apply a non-linear constraint to each control particle, with its neighboring fluid particles keeping a constant fluid density within its influence region. This density constraint is highly in line with the incompressible nature of the fluid, which can drive the fluid to match the target shape in a natural way. In addition, to match a fast moving or deforming target, we add an adaptive spring for each fluid particle in the control region, connecting with its nearest control particle. The spring constraint takes effect only when the fluid particle is far from its corresponding control particle to avoid introducing artificial viscosity. Therefore, the fluid particles are well controlled even if the target shape changes rapidly. Furthermore, we integrate a velocity constraint to adjust the stiffness of the controlled fluid. All these three constraints are solved under position-based framework which enables our simulation fast, robust and well-suitable for interactive applications. We demonstrate the efficiency and effectiveness of our method in various scenarios in real time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.