Wavelet-spectral Analysis and Large-eddy Simulation Using Neural Networks of Droplet-laden Decaying Isotropic Turbulence
Author | : Andreas Freund |
Publisher | : |
Total Pages | : 94 |
Release | : 2020 |
ISBN-10 | : OCLC:1261649080 |
ISBN-13 | : |
Rating | : 4/5 (80 Downloads) |
Book excerpt: In this work, we propose new methods for both the analysis and simulation of droplet-laden isotropic turbulence. First, we suggest using the wavelet energy spectrum to study multiphase turbulent flows to overcome the challenges of applying the Fourier energy spectrum to velocity fields with sharp velocity gradients. Also, we propose a new decomposition of the wavelet energy spectrum into three contributions corresponding to the carrier phase, droplets, and interaction between the two. We apply these new wavelet-decomposition tools in analyzing the direct numerical simulation (DNS) data of droplet-laden decaying isotropic turbulence of Dodd & Ferrante (2016, J. Fluid Mech. 806:3560́3412). Our results show that, in comparison to the spectrum of the single-phase case, the droplets (i) do not affect the carrier-phase energy spectrum at high wavenumbers (km/kmin>̲128), (ii) increase the energy spectrum at high wavenumbers (km/kmin>̲256) by increasing the interaction energy spectrum at these wavenumbers, and (iii) decrease the energy at low wavenumbers (km/kmin