27/03/2020
The Human Resources Strategy for Researchers

PhD thesis (M/F) - TURBULENT FLOW CONTROL BY MACHINE LEARNING : IMPOSITION OF SYMMETRY AND INVARIANCE CONDITIONS

This job offer has expired


  • ORGANISATION/COMPANY
    CNRS
  • RESEARCH FIELD
    Chemistry
    Engineering
    Physics
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    23/05/2020 23:59 - Europe/Brussels
  • LOCATION
    France › FUTUROSCOPE
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35
  • OFFER STARTING DATE
    01/10/2020

The doctoral student will depend of the SIMME Doctoral School (https://www.u-ldevinci.fr/simme/fr/page-daccueil-ed-simme/).
The Pprime laboratory is a « Proper Unit » of the CNRS.
Its scientific activity covers a broad spectrum ranging from materials physics to mechanical engineering, including fluid mechanics, thermal transfers and combustion.
The PhD student will be attached to the Turbulence Incompressible and Control team led by L. Cordier.

CNRS, at the Pprime laboratory (CNRS, UP, ISAE-ENSMA) in Poitiers-Futuroscope, recruits a PhD student as part of the 80|Prime 2020 interdisciplinary project.

This thesis will contribute to the development of realistic closed-loop control strategies for unsteady turbulent flows. Applications include the drag reduction around profiles (by attaching the boundary layer or delaying its separation), the reduction of radiated noise, the flow vectorization to improve the maneuverability or to remove some of the moving air spoilers, the decrease of vibrations induced by dynamic stall.

* Framework :
In recent years, continuous progress has been made on the performance of both civilian and military aircraft and helicopters, particularly in terms of flight envelope, radiated noise, maneuverability, vibration, etc. However, further improvements can be achieved by using closed-loop fluid flow control around the machine. This strategy consists of using measurements from sensors placed on the system, to adapt, if possible in real time, the control command to impose. From a control point of view, the main interest of closed loop is to improve the robustness of the control law. In practice, the development of closed-loop control strategies is largely complicated by the highly non-linear and multi-scale nature of the turbulent flows encountered in the targeted configurations.

In order to develop efficient control strategies, various issues must be addressed. In the classical approach followed in flow control, it is indeed necessary:
- to model the flow dynamics;
- to estimate the state of the system from scattered and/or indirect measurements;
- to place optimally actuators (used to introduce control) and sensors (used to reconstruct the state);
- to determine optimally a control law.

* Work program, methodologies and means :
We propose to develop a widely bio-inspired approach. In the living world, insects and birds develop very efficient flight control strategies by having at their disposal a minimum of sensors/actuators and without knowing a priori dynamical models. On the other hand, by interacting with their environment, they acquire information that is used as they evolve to optimize their performance. We will therefore develop an approach based solely on measurements (Data Driven), and not on a priori knowledge of physical models, and will exploit recently developed Machine Learning methods.
We will first focus our efforts on the dynamical modeling of turbulent flows based on data. For this purpose, we will make extensive use of neural networks, either deep (Deep Neural Network, DNN) or recurrent (Recurrent Neural Network, RNN). We are particularly interested in the properties of symmetry and invariance verified by the flow. A first approach will consist in imposing in the architecture of the neural models the properties of symmetry and invariance which are supposed to be verified by the system. A second approach will consist in studying the capacity of these neural models to autonomously derive these symmetry and invariance properties.
In a second step, we will revisit the data assimilation methods classically used in the literature (variational or stochastic approaches) under the prism of machine learning methods.
Finally, we will couple previously developed neural models to a Deep Reinforcement Learning (DRL) algorithm in order to determine a control strategy. Our strategies will be developed and tested on simple dynamical systems (Lorenz, Ginzburg-Landau, ...) to facilitate the development and, subsequently, on a case of turbulent flow.

This topic is at the heart of the CNRS Research Group "Flow Control Separations", whose Director is Laurent Cordier (Pprime Institute).

Additional comments

* Skills required :
- Master in Fluid Mechanics / Applied Mathematics / Machine Learning,
- Appetite for interdisciplinary approaches and machine learning,
- Desire to go beyond the borders.

Web site for additional job details

Required Research Experiences

  • RESEARCH FIELD
    Engineering
  • YEARS OF RESEARCH EXPERIENCE
    None
  • RESEARCH FIELD
    Chemistry
  • YEARS OF RESEARCH EXPERIENCE
    None
  • RESEARCH FIELD
    Physics
  • YEARS OF RESEARCH EXPERIENCE
    None

Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Engineering: Master Degree or equivalent
    Chemistry: Master Degree or equivalent
    Physics: Master Degree or equivalent
  • REQUIRED LANGUAGES
    FRENCH: Basic
Work location(s)
1 position(s) available at
Institut P' : Recherche et Ingénierie en Matériaux, Mécanique et Energétique
France
FUTUROSCOPE

EURAXESS offer ID: 508851
Posting organisation offer ID: 14893

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