Kernelized Stable Fluids for simulating physically accurate solutions

Abstract

The Stable Fluids algorithm is a fractional step method for solving the incompressible Navier-Stokes (NS) equations in an unconditionally stable way. It was initially developed for visual effects and allows real-time simulations because of its low computational complexity. The method solves the advection step by using the method of characteristics which is implemented via linear interpolation. This results in excessive diffusion of vorticity and is one of the main reasons for the physical inaccuracy of the algorithm. We propose Kernelized Stable Fluids with Sparse Cholesky Preconditioning (KSF-Chol) in order to address this shortcoming and make the algorithm thereby suitable for engineering problems and scientific computing. The algorithm is modified by replacing the linear interpolation approach with kernel interpolation in the advection step. We accelerate the computationally expensive kernel interpolation by using sparse Cholesky preconditioning and a near-/far-field approach and optimize both methods for computations on a uniform Cartesian grid. KSF-Chol achieves per time step a near-linear computational complexity of $\mathcal{O}(N\log^d(N/\varepsilon_{\mathrm{chol}}))$ in space and time. We test KSF-Chol against reference solvers and numerical reference data on multiple 2D and 3D benchmarks and demonstrate a significantly reduced numerical diffusion as well as a competitive accuracy concerning the computation of relevant flow quantities.