Turbulence Generation from a stochastic wavelet model

TL;DR

This paper introduces a novel turbulence generation technique using stochastic wavelets, offering computational efficiency and accurate reproduction of turbulence properties in both homogeneous and inhomogeneous flows.

Contribution

The study presents a new wavelet-based turbulence generation method that reduces basis size and computational cost compared to traditional Fourier approaches.

Findings

Generated turbulence aligns well with input data and theory.

Method achieves adaptive inhomogeneous turbulence with lower computational cost.

Fewer basis functions needed than Fourier-based methods.

Abstract

This research presents a new turbulence generation method based on stochastic wavelets and tests its various properties in both homogeneous and inhomogeneous turbulence. Turbulence field can be generated with less basis compared to previous synthetic Fourier methods. Adaptive generation of inhomogeneous turbulence is achieved by scale reduction algorithm and lead to smaller computation cost. The generated turbulence shows good agreement with input data and theoretical results.