Flow control consists of introducing controlled disturbances to improve the operating performance of a dynamical system. In fluid mechanics, we can seek to improve aerodynamic performance (reduction of drag or instabilities, for example) while minimizing the energy consumed to introduce the control. For reasons of energy efficiency and robustness, we would like to develop closed-loop control strategies, i.e. taking into account the state of the system to determine at each time instant the optimal forcing to introduce. Historically, much work has been carried out using model-based approaches, developed from first-principles equations (Navier Stokes equations, for example) or simplified dynamical models based on different reduced basis (POD, DMD, stability modes, etc.). However, these models are known of being fragile in terms of parametric modeling, and are therefore often poorly suited to flow control applications. The aim of this thesis is to develop simplified dynamical models of separated flows using data-driven modeling approaches, and to use these models to develop closed-loop control strategies.
BD Pierre et Marie Curie
SP2MI
86360 Futuroscope Chasseneuil
Date limite candidature : juin 2024
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CDD Technicien-ne électronicien-ne - 12 Mois - Catégorie B
POST-DOC (M/F) - Control by Machine Learning of bluff body wakes
At the CNRS-Laboratory PPRIME, based at the Futuroscope, this post-doctorate position is part of the French ANR COWAVE program between the laboratories PRISME in Orleans, Pprime in Poitiers, LHEEA in Nantes and the PSA automotive industry. This Post-Doc position concerns the Pprime contribution to the COWAVE project which aims the experimental exploration of closed-loop wake control strategies with mobile flaps in a water tunnel facility. Three-dimensional bluff-body wakes generate pressure drag and side forces and thus contribute significantly to the fuel consumption and pollutant emission of road vehicles. Despite this crucial impact and the numerous attempts to reduce harmful environmental effect of bluff body wakes by flow control it is still unclear what is the most efficient control strategy! In this context, the ANR project COWAVE addresses two fundamental aspects of wake control: - First, what kind of actuators are most efficient? While most closed-loop control strategies use viscous entrainment effects to actuate the shear layers in the wake, the exploitation of pressure forces produced by mobile deflectors could be an interesting alternative to be tested. - Second, for the implementation of closed-loop control, we want to test if control strategies obtained by machine learning techniques allow to obtain better efficiency and robustness than the more classical model-based approaches? The proposed Post-Doc position is part of the French ANR COWAVE program between the laboratories PRISME in Orleans, Pprime in Poitiers, LHEEA in Nantes and the PSA automotive industry. This Post-Doc position concerns the Pprime contribution to the COWAVE project which aims the experimental exploration of closed-loop wake control strategies with mobile flaps in a water tunnel facility. APPLY Follow link / Application Deadline : 12 March 2021 https://bit.ly/3qDG6Ml