The activities of the department are grouped around Fluid Mechanics, Mass and Heat Transfer and Energetics. We conduct research of a generally fundamental nature, with applications in the energy, transport, health and environment sectors.
Approach and perspective
The approach of the Mechanics-Energetics department is at the interface between computer science, physics and applied mathematics.
We wish to maintain a balance between interdependent activities:
understanding the fundamental phenomena of turbulent fluid mechanics,
tackling complex multiphysics problems coupled at large scales,
leveraging our physical knowledge while considering data as an inherent part of modeling, experiments and simulations.
In this context, we are very open to recent developments in machine learning, which offer a powerful information processing framework that can augment our current lines of research with broad-spectrum applications in the energy, transportation, health, and environmental sectors.
Organization
The Mechanics-Energetics Department offers original and multidisciplinary research thanks to the expertise of some twenty permanent staff, researchers, teacher-researchers and engineers, organized into two complementary teams: DATAFLOT (DAta science, TrAnsition, FLuid instabiLity, contrOl & Turbulence) relying on data-augmented modeling and artificial intelligence, and studying fluid dynamics, instabilities and turbulence, and COMET (COuplages MultiphysiquEs et Transferts) focusing on the understanding of complex coupled fluid phenomena, involved in energy conversion and storage, heat transfers as well as energy efficiency optimization.
Annelies Braffort. L’héritage scientifique de Patrice Dalle : le traitement automatique des langues des signes au service de l’enseignement en LSF. La main de Thôt : théories, enjeux et pratiques de la traduction, A paraître, 11. ⟨hal-04256752⟩
Thibaut Soulard, Joe Raad, Fatiha Saïs. Validation temporelle explicable de faits par la découverte de contraintes temporelles complexes dans les graphes de connaissances. 35es Journées francophones d’Ingénierie des Connaissances (IC 2024) @ Plate-Forme Intelligence Artificielle (PFIA 2024), Jul 2024, La Rochelle, France. pp.62-71. ⟨hal-04650739⟩
P. Pico, L. Kahouadji, S. Shin, J. Chergui, Damir Juric, et al.. Drop encapsulation and bubble bursting in surfactant-laden flows in capillary channels. Physical Review Fluids, 2024, 9 (3), pp.034001. ⟨10.1103/PhysRevFluids.9.034001⟩. ⟨hal-04647251⟩
Charles Pradeau, Severine Estival, Virginie Postal, Virginie Laurier, Céline Maugard, et al.. A pilot rating system to evaluate the quality of goal attainment scales used as outcome measures in rehabilitation. Neuropsychological Rehabilitation, 2024, pp.1-32. ⟨10.1080/09602011.2024.2343150⟩. ⟨hal-04648129⟩
Lyse Brichet, Nathan Carbonneau, Elian Bernard, Romane Braun, Lucas Méthivier, et al.. Universal scaling laws in turbulent Rayleigh-Bénard convection with and without roughness. 2024. ⟨hal-04434081v2⟩
Roger Ballester Claret, Nicolò Fabbiane, Christian Fagiano, Cédric Julien, Didier Lucor. Aeroelastic reliability based optimisation of composite plates via dual-space surrogate modeling. 20th International Forum on Aeroelasticity and Structural Dynamics Conference (IFASD 2024), Jun 2024, La Haye, Netherlands. pp.17. ⟨hal-04645831⟩
Clément Morand, Aurélie Névéol, Anne-Laure Ligozat. MLCA: a tool for Machine Learning Life Cycle Assessment. 2024 International Conference on ICT for Sustainability (ICT4S), Jun 2024, Stockholm, Sweden. ⟨hal-04643414⟩