Many visual applications display fluid systems. Most of these systems have been generated artificially using Fluid Simulation, a technique whereby many miniature fluid particles interact and behave according to Newtonian Physics to create realistic simulations. Calculating thousands of interactions for thousands of millions of particles is a very compute intensive task , and requires massive parallelism to solve efficiently. With the rise in use of GPUs as parallel computer which have thousands of cores, parallel computations like fluid simulation have become feasible to compute in real time. To build upon this Idea is the physical model of SPH. SPH or Smoothed Particle Hydrodynamics is massively parrelisable algorithm designed for simulating fluid motion in real time by making approximations that allow it to outperform other models at the cost of some accuracy while still performing good enough to produce realistic simulations.
Smoothed-particle hydrodynamics (SPH) is a computational method used for simulating the mechanics of continuum media, such as solid mechanics and fluid flows. It was developed by Gingold and Monaghan and Lucy in 1977, initially for astrophysical problems. It has been used in many fields of research, including astrophysics, ballistics, volcanology, and oceanography. It is a meshfree Lagrangian method (where the coordinates move with the fluid), and the resolution of the method can easily be adjusted with respect to variables such as density. Smoothed-particle hydrodynamics is being increasingly used to model fluid motion as well. This is due to several benefits over traditional grid-based techniques. First, SPH guarantees conservation of mass without extra computation since the particles themselves represent mass. Second, SPH computes pressure from weighted contributions of neighboring particles rather than by solving linear systems of equations
!python3 src/Simulate.py
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