GitLab

Title: Large-scale modeling of liquid sheet atomization using Convolutional Neural Networks.
Date: 2021-11-23 08:14
Slug: job_fff7a474fba88501dfdb123d9ef902bc
Category: job
Authors: Pierre Trontin
Email: pierre.trontin@univ-lyon1.fr
Job_Type: Post-doctorat
Tags: postdoc
Template: job_offer
Job_Location: Université Claude Bernard Lyon 1, Villeurbanne, France
Job_Duration: 24 mois
Job_Website: https://labeximust.universite-lyon.fr/medias/fichier/sujet-postdoc-imust-trontin-en_1635944513174-pdf?ID_FICHE=72669
Job_Employer: Labex iMUST, université de Lyon
Expiration_Date: 2022-02-15
Attachment: job_fff7a474fba88501dfdb123d9ef902bc_attachment.pdf

In the aeronautical combustion chambers, fuel is injected and pulverized in a set of
mechanisms known as atomization. This phenomenon involves the break-up of the liquid
jet, sometimes in the form of a thin sheet, into a multitude of smaller structures, up to the
formation of a spray. The numerical simulation of atomization usually severely challenges
even the state-of-the-art numerical methods, mainly in reason of its intrinsic multi-scale
aspect.
This project aims at pursuing the development of a multi-scale numerical methodology
able to efficiently simulate both the liquid injection and the spray in the same unsteady
simulation. This is achieved by dynamically coupling two models on the same
simulation: a separated two-phase flow solver (dedicated to the description of the large
scales of the sheet and its stretching into ligaments) and a dispersed two-phase flow solver
(dedicated to the Lagrangian description of the droplets forming a spray and resulting from
ligaments breakup). This coupling allows an optimal resolution of the atomization process
at large scales (LES).
One drawback of this approach is that the space scale transition between the separated
and the dispersed two-phase flow solvers leads to a loss of information about the liquid
topology. This results in the failure to predict the particle size distribution of the generated
droplets during the coupling. This information is of paramount importance for the
simulation of the spray evolution. One possible solution is to use subgrid scale modeling
for under-resolved liquid structures such as ligaments and smaller droplets in the separated
two-phase flow solver. The postdoc project is part of this context and aims at using "deep
learning" techniques (artificial intelligence) to enrich the description of the thin liquid
structures computed in the separated two-phase flow solver in the framework of large eddy
simulation (LES).