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

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Schedule

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Last publications

  • Communication dans un congrès

    Onofrio Semeraro, Michele A Bucci, Remy Hosseinkhan-Boucher, Sergio Chibbaro, Alexandre Allauzen, et al.. On the use of entropy-based metrics for data-driven modeling and reinforcement learning control. Joint event Euromech Colloquium on Data-Driven Fluid Dynamics/2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, Apr 2025, Londres, United Kingdom. ⟨hal-05379611⟩

    DATAFLOT

    Year of publication

    HAL publication

  • Communication dans un congrès

    Onofrio Semeraro, Michele Alessandro Bucci, Lionel Mathelin, Luigi Marra, Amine Saibi. From robotics to fluid dynamics: opportunities and pitfalls of Reinforcement Learning in flow control. iTi Workshop on Structure and control of wall-bounded turbulent flows, Jul 2025, Bertinoro, Italy. ⟨hal-05379587⟩

    BioInfo, DATAFLOT

    Year of publication

    Available in free access

    HAL publication

  • Communication dans un congrès

    Andrea Palumbo, Onofrio Semeraro, Luigi de Luca. Transition to turbulence in planar synthetic jets: numerical simulations and coherent structures eduction. Coherent structures and instabilities in transitional and turbulent wall-bounded flows, Euromech Colloquium 658, Sep 2025, Bari, Italy. ⟨hal-05379576⟩

    DATAFLOT

    Year of publication

    Available in free access

    HAL publication

  • Communication dans un congrès

    Michele Quattromini, Michele Alessandro Bucci, Stefania Cherubini, Onofrio Semeraro. Mean flow data assimilation using physics-constrained Graph Neural Networks. Coherent structures and instabilities in transitional and turbulent wall-bounded flows, Euromech Colloquium 658, Sep 2025, Bari, Italy. ⟨hal-05379581⟩

    DATAFLOT

    Year of publication

    Available in free access

    HAL publication

  • Communication dans un congrès

    Amine Saibi, Lionel Mathelin, Onofrio Semeraro. Actor-Critic methods for model-free control of spatially evolving flows. Joint event Euromech Colloquium on Data-Driven Fluid Dynamics/2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, Apr 2025, London, United Kingdom. ⟨hal-05379604⟩

    DATAFLOT

    Year of publication

    Available in free access

    HAL publication

  • Communication dans un congrès

    Riccardo Margheritti, Onofrio Semeraro, Maurizio Quadrio, Giacomo Boracchi. Physics-Based Region Clustering to Boost Inference on Computational Fluid Dynamics Flow Fields. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track, Sep 2025, Porto, Portugal. pp.3-20, ⟨10.1007/978-3-032-06129-4_1⟩. ⟨hal-05379496⟩

    DATAFLOT

    Year of publication

    Available in free access

    HAL publication

  • Poster de conférence

    Nathan Carbonneau, Julien Salort, Anne Sergent. Small coherent structures in rough turbulent convection. 27e Rencontre du Non-Linéaire, Mar 2024, Paris, France. ⟨hal-05349336⟩

    COMET

    Year of publication

    Available in free access

    HAL publication

  • Article dans une revue

    Daniele Noto, Alexandre Allauzen, Sergio Chibbaro. An efficient training method to learn a model of turbulence. The European Physical Journal Plus, 2024, 139 (3), pp.298. ⟨10.1140/epjp/s13360-024-05056-8⟩. ⟨hal-05356011⟩

    AO, DATAFLOT

    Year of publication

    HAL publication

  • Communication dans un congrès

    Nathan Carbonneau, Julien Salort, Yann Fraigneau, Anne Sergent. Petites structures cohérentes en convection turbulente rugueuse. GdR Navier-Stokes 2.00, Jun 2024, Nantes, France. ⟨hal-05353110⟩

    ASARD, COMET

    Year of publication

    HAL publication

  • Article dans une revue

    Michele Quattromini, Michele Alessandro Bucci, Stefania Cherubini, Onofrio Semeraro. Active learning of data-assimilation closures using graph neural networks. Theoretical and Computational Fluid Dynamics, 2025, 39, pp.17. ⟨10.1007/s00162-025-00737-1⟩. ⟨hal-05379430⟩

    DATAFLOT

    Year of publication

    Available in free access

    HAL publication

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