Offer DescriptionA key component of the EU climate action strategy is the development of wind energy, which relies on scientific and technological advances aiming to increase its capacity and reliability. Most energy converters, including wind turbines but also both air and ground vehicles, are located in the turbulent atmospheric boundary layer. Their operating conditions are therefore characterized by unsteady fluctuations over a wide range of temporal and spatial scales, which affect their performances in terms of lift and drag. In particular, large load variations on wind turbines and airfoils are observed around maximum lift conditions at the inception of stall. Monitoring and if possible anticipating these effects is essential to ensure optimal production and minimize structural fatigue on the turbineThe goal is to develop data-driven reduced-order models that reproduce the key dynamics of the flow over the airfoil from limited information. A central question will be to estimate the flow separation on the airfoil. The first step of the project will be to develop flow estimation strategies for a numerical database obtained from RANS simulations (Pr E. Guilmineau, EC Nantes). A wide variety of data-driven approaches of increasing complexity will be implemented and compared with each other. In the second step of the project, new hybrid machine learning methods incorporating physics-based constraints will be co.nstructed and evaluated. The methods will then be applied and adapted to experimental dataThe position is supported by the ANR/SNF MISTERY project Modelling and estimation of unsteady aerodynamic flow at high Reynolds number, an interdisciplinary effort gathering aerodynamicists, MEMS specialists and data modelling experts from Ost University, ETH Zurich, EM2C and CSTB Nantes. .RequirementsResearch Field Physics Education Level PhD or equivalentLanguages FRENCH Level BasicResearch Field Physics Years of Research Experience 1 - 4Additional InformationEligibility criteriaThe project will require developing data-driven tools that incorporate physics-based constraints. The candidate will hold a PhD degree in Mechanics/Physics or Applied Mathematics /Statistics/Computer Science and will have an interest in these disciplines. Programming experience (python, fortran, C) is expected.Additional comments. Website for additional job detailsWork Location(s)Number of offers available 1 Company/Institute Laboratoire d'énergétique moléculaire et macroscopique, combustion Country France City GIF SUR YVETTE GeofieldWhere to apply WebsiteContact CityGIF SUR YVETTE WebsiteSTATUS: EXPIRED