Plus d'info sur IFP Energies nouvelles - Sciences et Technologies du Numérique
Stage Data / Mathématiques Appliquées Hauts-de-Seine entre février et mars 2024 5 mois
IFP Energies nouvelles (IFPEN) est un acteur majeur de la recherche et de la formation dans les domaines de l’énergie, du transport et de l’environnement. De la recherche à l'industrie, l'innovation technologique est au cœur de toutes ses activités.
Dans le cadre de la mission d'intérêt général qui lui a été confiée par les pouvoirs publics, IFP Energies nouvelles (IFPEN) s'attache à :
In fluid dynamics simulations, the effects of wall turbulent boundary layers are generally taken into account through the use of wall laws. However, the latter suffer from a restricted range of validity that limits their predictivity, particularly in the presence of out-of-equilibrium phenomena - pressure gradients, flow separation or high transverse velocities - which are often of crucial importance in many industrial applications. This is one of the key factors limiting the use of simulation for design and optimization.
Recently, a number of research studies have explored the potential of machine learning (ML) techniques to develop approaches for overcoming the limitations of wall laws by learning from high-fidelity data of canonical parietal flows. While these studies have confirmed the potential of these algorithms to improve the prediction of wall friction, they have also revealed limitations linked to their non-interpretability and generalizability.
In this context, the aim of the internship is to implement a GEP (Gene Expression Programming) ML technique, as an alternative to the neural networks (NN) commonly used in the literature, to formulate analytical relationships that can replace wall laws based on training on high-fidelity data from DNS or WR-LES CFD simulations.
The aim is thus to explore whether the expected advantages of GEP in terms of interpretability, regularity and generalizability (Weatheritt and Sandberg 2016; Weatheritt and Sandberg 2017; Akolekar et al. 2018) over NN are indeed proven, and what the prerequisites are in terms of methodology. This will be done by exploiting high-fidelity data available at IFPEN concerning canonical cases for out-of-equilibrium turbulent boundary layers: flow over a hill, abrupt widening and impacting jet. In addition, wall laws based on NNs trained on these data are already available (E. Rondeaux’s PhD thesis) and will be used to critically analyze the advantages and disadvantages of the method developed during the internship.
The main stages of work proposed within the framework of this internship are:
At the end of the internship, this work could be continued in a PhD thesis between IFPEN and the LISN laboratory at the University of Paris-Saclay, aimed at formulating analytical relationships to improve prediction of parietal heat fluxes during liquid cooling of electric machine components.
University master’s degree or last year engineering school.
Duration of the internship: 5 months, from February 2024
Location: IFP Energies nouvelles in Rueil-Malmaison (near Paris in France). Possibility of regular teleworking.
IFP Energies nouvelles - Sciences et Technologies du Numérique