Turbulence generation from a stochastic wavelet model.

This research presents a new turbulence generation method based on stochastic wavelets and tests various properties of the generated turbulence field in both the homogeneous and inhomogeneous cases. Numerical results indicate that turbulence fields can be generated with much smaller bases in comparison to synthetic Fourier methods while maintaining comparable accuracy. Adaptive generation of inhomogeneous turbulence is achieved by a scale reduction algorithm, which greatly reduces the computation cost and practically introduces no error. The generating formula issued in this research could be adjusted to generate fully inhomogeneous and anisotropic turbulence with given RANS data under divergence-free constraint, which was not achieved previously in similar research. Numerical examples shows that the generated homogeneous and inhomogeneous turbulence are in good agreement with the input data and theoretical results.