Efficient Implementation of the Lattice Boltzmann Method on a GPU for Predicting Near-Field and Far-field Jet Noise

Abstract

The lattice Boltzmann method (LBM) is a powerful technique for the computational modelling of a wide variety of single and multiphase flows in complex geometries. It is a discrete computational approach based on solving the Boltzmann equation numerically. LBM has recently become one of the new and promising CFD methods to solve many computational fluid mechanics problems. LBM works particularly well when solving incompressible flow problems, but limitations arise when solving compressible flow, especially at high Mach numbers. Compressible LBM was applied for the simulation jet flow in order to demonstrate the ability of the fifth-order equilibrium distribution function to simulate compressible flows efficiently. The near-field flow physics and noise simulations were performed using the compressible LBM. The results from the LBM simulation were then employed in Kirchhoff’s surface integral approach to predict far-field jet noise. Because of the ability of the lattice Boltzmann technique to be used in parallel computing and to improve LBM computational efficiency with respect to the numerical simulations of turbulent flows in predicting far-field noise, the final step in this research was to use the compute unified device architecture (CUDA) for implementing the LBM in a graphics processing unit (GPU), thus creating a hybrid code, LBM-MRT-LES, through the utilization of the Kirchhoff integral method, a powerful tool for simulating aeroacoustics problems.