Fluid Mechanics - Energetics

Fluid Mechanics – Energetics

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.

Coordination

Research teams

News

Last publications

  • Article dans une revue

    A. Gesla, Y. Duguet, P. Le Quéré, Laurent Martin Witkowski. On the origin of circular rolls in rotor-stator flow. Journal of Fluid Mechanics, 2024, 1000, pp.A47. ⟨10.1017/jfm.2024.1011⟩. ⟨hal-04902902⟩

    COMET, DATAFLOT

    Year of publication

    Available in free access

  • Communication dans un congrès

    Vincent Blot, Alexandra Lorenzo de Brionne, Ines Sellami, Olivier Trassard, Isabelle Beau, et al.. Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, Carole H. Sudre (University College London); Raghav Mehta (Imperial College London); Cheng Ouyang (Oxford University); Chen Qin (Imperial College London); Marianne Rakic (Massachusetts Institute of Technology); William M. Wells (Harvard Medical School), 2024, Marrakesh, Morocco. pp.183-193, ⟨10.1007/978-3-031-73158-7_17⟩. ⟨hal-04895227⟩

    DATAFLOT

    Year of publication

  • Article dans une revue

    D. Panda, L. Kahouadji, A M Abdal, L S Tuckerman, Seungwon Shin, et al.. Drop Medusa: Direct numerical simulations of high-frequency Faraday waves on spherical drops. Physical Review Fluids, 2024, 9 (11), pp.110514. ⟨10.1103/PhysRevFluids.9.110514⟩. ⟨hal-04859680⟩

    COMET

    Year of publication

    Available in free access

  • Article dans une revue

    Yohann Duguet. Puffing along. Nature Physics, 2024, 20 (8), pp.1227-1227. ⟨10.1038/s41567-024-02565-2⟩. ⟨hal-04795934⟩

    DATAFLOT

    Year of publication

    Available in free access

  • Poster de conférence

    Thibault Monsel, Onofrio Semeraro, Lionel Mathelin, Guillaume Charpiat. Time and State Dependent Neural Delay Differential Equations. ML-DE@ECAI 2024 : Machine Learning Meets Differential Equations: From Theory to Applications, Sep 2024, Santiago de compostela, Galicia, Spain. ⟨hal-04794800⟩

    AO, DATAFLOT

    Year of publication

    Available in free access

  • Article dans une revue

    Thomas Boeck, Mattias Brynjell-Rahkola, Yohann Duguet. Energy stability of magnetohydrodynamic flow in channels and ducts. Journal of Fluid Mechanics, 2024, 987, pp.A33. ⟨10.1017/jfm.2024.393⟩. ⟨hal-04795942⟩

    DATAFLOT

    Year of publication

    Available in free access

  • Article dans une revue

    P. Kashyap, Yohann Duguet, O. Dauchot. Linear stability of turbulent channel flow with one-point closure. Physical Review Fluids, 2024, 9 (6), pp.063906. ⟨10.1103/PhysRevFluids.9.063906⟩. ⟨hal-04795968⟩

    DATAFLOT

    Year of publication

    Available in free access

  • Article dans une revue

    Mattias Brynjell-Rahkola, Yohann Duguet, Thomas Boeck. Chaotic edge regimes in magnetohydrodynamic channel flow: An alternative path towards the tipping point. Physical Review Research, 2024, 6 (3), pp.033066. ⟨10.1103/PhysRevResearch.6.033066⟩. ⟨hal-04795955⟩

    DATAFLOT

    Year of publication

    Available in free access

  • Article dans une revue

    Thomas Boeck, Mattias Brynjell-Rahkola, Yohann Duguet. Energy stability analysis of MHDMagnétohydrodynamique flow in a rectangular duct. PAMM, 2024, 24 (2), pp.e202400041. ⟨10.1002/pamm.202400041⟩. ⟨hal-04795978⟩

    DATAFLOT

    Year of publication

    Available in free access

  • Article dans une revue

    Yanis Zatout, Adrien Toutant, Onofrio Semeraro, Lionel Mathelin, Françoise Bataille. A Priori Reconstruction of Thermal-Large Eddy Simulation (T-LES) by Deep Learning Reconstruction a Priori de Champs de Simulations Des Grandes Echelles Thermiques Par Apprentissage Profond. Entropie : thermodynamique – énergie – environnement – économie, 2023, 4 (3), ⟨10.21494/ISTE.OP.2023.1015⟩. ⟨hal-04751095⟩

    DATAFLOT

    Year of publication

    Available in free access