Back to Search Start Over

A stochastic method to account for the ambient turbulence in Lagrangian Vortex computations

Authors :
Choma Bex, Camille
Carlier, Clement
Fur, Arnaud
Pinon, Gregory
Germain, Gregory
Rivoalen, Elie
Choma Bex, Camille
Carlier, Clement
Fur, Arnaud
Pinon, Gregory
Germain, Gregory
Rivoalen, Elie
Source :
Applied Mathematical Modelling (0307-904X) (Elsevier Science Inc), 2020-12 , Vol. 88 , P. 38-54
Publication Year :
2020

Abstract

This paper describes a detailed implementation of the Synthetic Eddy Method (SEM) initially presented in Jarrin et al. (2006) applied to the Lagrangian Vortex simulation. While the treatment of turbulent diffusion is already extensively covered in scientific literature, this is one of the first attempts to represent ambient turbulence in a fully Lagrangian framework. This implementation is well suited to the integration of PSE (Particle Strength Exchange) or DVM (Diffusion Velocity Method), often used to account for molecular and turbulent diffusion in Lagrangian simulations. The adaptation and implementation of the SEM into a Lagrangian method using the PSE diffusion model is presented, and the turbulent velocity fields produced by this method are then analysed. In this adaptation, SEM turbulent structures are simply advected, without stretching or diffusion of their own, over the flow domain. This implementation proves its ability to produce turbulent velocity fields in accordance with any desired turbulent flow parameters. As the SEM is a purely mathematical and stochastic model, turbulent spectra and turbulent length scales are also investigated. With the addition of variation in the turbulent structures sizes, a satisfying representation of turbulent spectra is recovered, and a linear relation is obtained between the turbulent structures sizes and the Taylor macroscale. Lastly, the model is applied to the computation of a tidal turbine wake for different ambient turbulence levels, demonstrating the ability of this new implementation to emulate experimentally observed tendencies.

Details

Database :
OAIster
Journal :
Applied Mathematical Modelling (0307-904X) (Elsevier Science Inc), 2020-12 , Vol. 88 , P. 38-54
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1409521544
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1016.j.apm.2020.05.025