Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Theses

Utilisation de l’apprentissage automatique en mécanique des fluides pour la réduction, la reconstruction et la prédiction orientée données du champ de vitesse fluctuante d’un écoulement

Abstract : Fluid mechanics is an important part in industrial questions. The modelisation, reduction and control of fluid flows require to solve a nonlinear and multiscale optimisation problem. In the age of artificial intelligence, there is a craze to solve these optimisation problems using the wealth of experimental and numerical data. In this context, the objective of the manuscript is to present how machine learning tools can be used for the data-driven estimation of fluid flow velocity fields. In particular, we aim at reducing, reconstructing and predicting four increasing complexity flows: the laminar wake of a cylinder, a spatial mixing layer, the turbulent wake of a square cylinder and the flow around an isolated tower. To do so, we start by reformulating the reduction, the reconstruction and the prediction problems. For the reduction, we use autoencoders to find a latent space and rewrite the flow with with a low rank approximation. Different methods are used: proper orthogonal decomposition, linear autoencoder, linear variational autoencoder and variational autoencoder. For the reconstruction, we use supervised learning tools to estimate the latent state from limited measurements of the fluctuating velocity field. The methods include the linear regression (in its multitask and single task formulation), the support vector regression (linear regression in higher dimensional space), the shallow neural network and gradient boosted trees. For the prediction, we search for a finite approximation of the Koopman operator to linearly advance in time observations of the latent state. Different refinements of the core dynamical mode decomposition algorithm are used: direct models, higher order DMD (use of delayed coordinates), extended DMD and DMD with dictionnary learning. The results show that machine learning is a promising direction to establish fluid flow estimation models. However, the deployment of models for highly turbulent flows remains questionable mainly because of robustness issues.
Complete list of metadata

https://tel.archives-ouvertes.fr/tel-03525301
Contributor : ABES STAR :  Contact
Submitted on : Thursday, January 13, 2022 - 5:25:08 PM
Last modification on : Friday, June 10, 2022 - 10:53:44 AM
Long-term archiving on: : Thursday, April 14, 2022 - 11:37:25 PM

File

These_DUBOIS_Pierre.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-03525301, version 1

Citation

Pierre Dubois. Utilisation de l’apprentissage automatique en mécanique des fluides pour la réduction, la reconstruction et la prédiction orientée données du champ de vitesse fluctuante d’un écoulement. Mécanique des fluides [physics.class-ph]. Université de Lille, 2021. Français. ⟨NNT : 2021LILUN004⟩. ⟨tel-03525301⟩

Share

Metrics

Record views

120

Files downloads

25