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.
Artur Gesla, Yohann Duguet, Patrick Le Quéré, Laurent Martin Witkowski. Subcritical axisymmetric solutions in rotor-stator flow. Physical Review Fluids, 2024, 9 (8), pp.083903. ⟨10.1103/PhysRevFluids.9.083903⟩. ⟨hal-04693180⟩
Grégory Bana, Fabrice Lamadie, Sophie Charton, Tojonirina Randriamanantena, Didier Lucor, et al.. BYG-drop: a tool for enhanced droplet detection in liquid–liquid systems through machine learning and synthetic imaging. Frontiers in Chemical Engineering, 2024, 6, ⟨10.3389/fceng.2024.1415453⟩. ⟨hal-04676144⟩
Roger Ballester Claret, Ludovic Coelho, Christian Fagiano, Cédric Julien, Didier Lucor, et al.. Reliability based optimisation of composite plates under aeroelastic constraints via adapted surrogate modelling and genetic algorithms. Composite Structures, 2024, 347, pp.118461. ⟨10.1016/j.compstruct.2024.118461⟩. ⟨hal-04671263⟩
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⟩
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⟩
Éric Falcon, Marc Lefranc, François Pétrélis, Chi-tuong Pham. Recueil des contributions de la 27e Rencontre du Non-Linéaire (Paris 2024). 27e Rencontre du Non-Linéaire 2024, 2024, 978-2-9576145-3-0. ⟨hal-04643241⟩