Multiscale wavelet analysis of 3D Lagrangian trajectories in a mechanically agitated vessel

Flow turbulence properties are of utmost importance for the optimization of mixing in stirred vessels. A new experimental-theoretical framework is developed to study such turbulence properties. A discrete wavelet transform is used to decompose Lagrangian flow trajectories measured by positron emission particle tracking into their mean and stochastic components representing different scales. The mean component represents low frequency scale motion or non-diffusive/background motion, while the stochastic component accounts for high frequency scale motion or diffusive motion of small eddies. Decomposed Lagrangian trajectories are used to construct maps of local mean and fluctuation flow velocity, turbulent kinetic energy and its dissipation rate, and, for the first time, turbulent diffusion coefficients. Particle image velocimetry measurements are utilised to independently validate results and tune the input parameters of the analysis. Such detailed information on local mixing scales is invaluable to aid equipment design and operation and facilitate heat/mass transfer and enhance reaction kinetics.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This article develops a new experimental-theoretical framework to study flow turbulence properties in mixing processes. It uses a discrete wavelet transform to decompose flow trajectories into mean and stochastic components representing different scales. By constructing maps of local flow velocity, turbulent kinetic energy, turbulence dissipation rate, and turbulent diffusion coefficients, it provides valuable detailed information for equipment design and operation.