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

Schedule

Last publications

  • Article dans une revue

    Ying Wang, Anne Sergent, Didier Saury, Denis Lemonnier, Patrice Joubert. Gas radiation effect on a turbulent thermal plume in a confined cavity using direct numerical simulation. International Journal of Thermal Sciences, 2025, 213, pp.109820. ⟨10.1016/j.ijthermalsci.2025.109820⟩. ⟨hal-04997739⟩

    COMET

    Year of publication

    Available in free access

  • Communication dans un congrès

    Gen Wu, Nicolas Grenier, Caroline Nore. Two-fluid Compressible Flows with Multiresolution Adaptive Mesh Refinement. 9th ECCOMAS, APMTAC, Jun 2024, Lisbon, Portugal. ⟨hal-04955216⟩

    COMET

    Year of publication

    Available in free access

  • Thèse

    Michele Quattromini. Graph Neural Networks for fluid mechanics : data-assimilation and optimization. Machine Learning [cs.LG]. Université Paris-Saclay; Politecnico di Bari, 2024. English. ⟨NNT : 2024UPAST161⟩. ⟨tel-04964013⟩

    DATAFLOT

    Year of publication

    Available in free access

  • Thèse

    Thibault Monsel. Deep Learning for Partially Observed Dynamical Systems. Discrete Mathematics [cs.DM]. Université Paris-Saclay, 2024. English. ⟨NNT : 2024UPASG113⟩. ⟨tel-04952358⟩

    AO, AO, DATAFLOT, DATAFLOT

    Year of publication

    Available in free access

  • Article dans une revue

    Yohann Duguet. Intermittency in Transitional Shear Flows. Entropy, 2021, 23 (3), pp.280. ⟨10.3390/e23030280⟩. ⟨hal-04938938⟩

    DATAFLOT

    Year of publication

    Available in free access

  • Thèse

    Nemo Malhomme. Statistical learning for climate models. Machine Learning [stat.ML]. Université Paris-Saclay, 2024. English. ⟨NNT : 2024UPAST165⟩. ⟨tel-04939186⟩

    DATAFLOT, DATAFLOT

    Year of publication

    Available in free access

  • Communication dans un congrès

    Soufiane Mrini, Anne Sergent, Francesca Chillà, Julien Salort, Didier Lucor. Addressing turbulent convection experimental data challenges in PINNs with appropriate physical sampling. Joint event Euromech Colloquium on Data-Driven Fluid Dynamics/2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, Apr 2025, London, United Kingdom. ⟨hal-04924450⟩

    COMET, DATAFLOT

    Year of publication

    Available in free access

  • Communication dans un congrès

    Marie-Christine Volk, Didier Lucor, Anne Sergent, Michael Mommert, Christian Bauer, et al.. A PINN Methodology for Temperature Field Inference in the PIV Measurement Plane: Case of Rayleigh-Bénard Convection. Joint event Euromech Colloquium on Data-Driven Fluid Dynamics/2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, Apr 2025, London, United Kingdom. ⟨hal-04924426⟩

    COMET, DATAFLOT

    Year of publication

    Available in free access

  • Communication dans un congrès

    Jai Kumar, Anne Sergent, Francesca Chillà, Julien Salort, Didier Lucor. Bridging Experimental shadowgraphs and DNS in Turbulent Convection Using physically-informed U-Net. Joint event Euromech Colloquium on Data-Driven Fluid Dynamics/2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, Apr 2025, London, United Kingdom. ⟨hal-04924440⟩

    COMET, DATAFLOT

    Year of publication

    Available in free access

  • Pré-publication, Document de travail

    Nathan Carbonneau, Julien Salort, Yann Fraigneau, Anne Sergent. Influence of wind on heat transfer in turbulent convection with roughness. 2025. ⟨hal-04928422⟩

    ASARD, COMET

    Year of publication

    Available in free access