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Paola Cinnella

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Paola CINNELLA, Professeur

Téléphone: 01.44.27.54.65
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Adresse physique: Campus de Jussieu, Tour 55-65, bureau N° 516
Adresse courrier: Institut Jean le Rond d'Alembert Université Pierre et Marie Curie
Boîte 162, Tour 55-65, 4 place Jussieu, 75252 Paris Cedex 05.

https://www.linkedin.com/in/paola-cinnella-7469ba11/

https://www.researchgate.net/profile/Paola-Cinnella

 

Member of the "Combustion, Clean Energies and Turbulence" team of d'Alembert http://www.dalembert.upmc.fr/frt/

Coordinator of the LearnFluidS "Machine-LEARNing for FLUID flow Simulations" (https://www.researchgate.net/project/LearnFluidS-Machine-LEARNing-for-FLUID-Simulations) project team of the Sorbonne Institute for Computational Science and Data (https://iscd.sorbonne-universite.fr/)

Editor in Chief, "Computers & Fluids"

Associate Editor, "International Journal of Heat and Fluid Flow"

Scientific Secretary: ICCFD, International Conference in Computational Fluid Dynamics, conference series. https://www.iccfd.org/

 NEWS:

Kick-Off of the SciFiTurbo Horizon Project:

New papers published:

  • Buffa, V., Salaün, W., & Cinnella, P. (2024). Influence of posture during gliding flight in the flying lizard Draco volans. Bioinspiration & Biomimetics. In press https://doi.org/10.1088/1748-3190/ad1dbb
  • Bienner, A., Gloerfelt, X., Yalçin, Ö., Cinnella, P.,  « Multiblock parallel high-order implicit residual smoothing time scheme for compressible Navier–Stokes equations”. Computers & Fluids, 269, 30 January 2024, 106138. https://doi.org/10.1016/j.compfluid.2023.106138
  • Passiatore, D., Sciacovelli, L. Cinnella, P., Pascazio, G., "Evaluation of a high-order central-difference solver for highly compressible flows out of thermochemical equilibrium". Computers & Fluids, 269, 30 January 2024, 106137. https://doi.org/10.1016/j.compfluid.2023.106137
  • Sciacovelli, L., Cannici, A., Passiatore, D., Cinnella, P., "A priori tests of turbulence models for compressible flows", 2023. Accepted for publication in the International Journal of Numerical Methods for Heat and Fluid Flows.
  • De Zordo-Banliat, M., Dergham, G., Merle, X., Cinnella, P., "Space-dependent turbulence model aggregation using machine learning". Journal of Computational Physics, 497, 15 January 2024, 112628. https://doi.org/10.1016/j.jcp.2023.112628

 

Positions available in my group

  • Two PhD positions available in the frame of the Sci-Fi-Turbo European project!! See below for details:
    PHD_SciFiTurbo_PHDs.pdf

Older news

P. Cinnella appointed Editor in Chief of Computers & Fluids 

Computers & Fluids, published by Elsevier, has been running since 1973, and is one of the oldest Journals in Computational Fluid Dynamics.

Best paper award ASME TURBO EXPO 2022

Our paper presented at the 2022 Turbo Expo in Rotterdam "Hot-Wire Anemometry in High Subsonic Organic Vapor Flows", ASME Paper GT2022‐81686, was chosen as one of the Best Papers by the Controls, Diagnostics & Instrumentation Committee of the American Society of Mechanical Engineers (ASME) Turbo Expo Technical Conference. The paper results from a collaboration with Technical University of Muenster (Germany) and our team in the frame of ANR-DFG project REGAL-ORC, whereby the German team developed hot-wire anemometry for organic vapour with the support of high-fidelity numerical simulations by PhD candidate Camille Matar

 

TEAM

Postdocs (ongoing)

Machine learning for turbulent flows

  • Mourad OULGHELOU. "Bayesian methods for consistent data-driven turbulence modeling". Funding: Sorbonne Institute of Computation and Data Science

PhDs (ongoing)

Dynamics of dense gases

  • Camille MATAR: "Simulation of transitional non-ideal gas flows in ORC turbines by RANS / LES multi-fidelity coupling". Funding: SMAER Doctoral School fellowship.
  • Aurélien BIENNER. "Real-gas effects on freestream transition and losses in ORC turbine flows". Funding: ANR-DFG Project "Regal-ORC".

Machine learning for turbulent flows

  • Paul CALVI. "Data-driven turbulence models for highly compressible flows". Funding: CEA.
  • Louenas ZEMMOUR: "Data-driven laminar-turbulent transition models for turbomachinery flows". Funding: Sorbonne Institute of Computation and Data Science.
  • Cécile ROQUES: "Machine Learning modelling of turbulent flows in turbomachinery". Funding: CIFRE/Safran Tech.

Advanced numerical methods for compressible flow

  • Ariadni LIAPI: "Adaptive mesh refinement of RANS/LES simulations in aerodynamics". Funding: Civil Aviation Direction, MAMBO Collaborative Project.
  • Mikail SALIHOGLU: "Strategies for the h/p adaptation of k-exact finite volume schemes based on successive corrections". Funding: Civil Aviation Direction, MAMBO Collaborative Project.

Master Interns (ongoing)

  • Matthias RUESSEL: "Data-driven modeling of sediment bed rheology". Visiting student, TU Dresden

 

External team members and collaborators

Fluid Dynamics Laboratory, ENSAM, Paris

  • Prof. Xavier GLOERFELT: HPC, scale-resolving simulations of turbulent flow, high-order schemes
  • Dr. Xavier MERLE: Bayesian methods, data-driven turbulence modelling, uncertainty quantification
  • Dr. Luca SCIACOVELLI: HPC, scale-resolving simulations of real-gas turbulent flows, hypersonic flows

Ongoing collaborations with companies

Safran Tech

  • Dr. Grégory DERGHAM: Data-driven turbulence model aggregation for turbomachinery problems

ArianeGroup

  • Dr. Pierre BRENNER, David PUECH, Jean COLLINET, Alexandre LIMARE: k-exact finite volume schemes, mesh adaptation, RANS/LES simulations. MAMBO Project

Airbus Operations

  • Dr. Grégoire PONT: k-exact finite volume schemes, mesh adaptation, RANS/LES simulations. MAMBO Project

Other collaborations 

Université Libre de Bruxelles

  • Prof. Alessandro PARENTE, PhD Candidate Léo COTTELEER: Data-driven turbulence modeling for environmental flows

Politecnico di Bari, Centro di Eccellenza Meccanica Computazionale

  • Prof. Giuseppe PASCAZIO: Hypersonic flow models

Technical University Dresden (Germany)

  • Prof. Jochen FROELICH: Turbulence models for sediment transport

AArhus University (Danemark)

  • Prof. Mahdi ABKHAR, PhD Candidate Ali AMARLOO: Data-driven turbulence models for flows over rough surfaces.

Former members

Master interns:

  • Niklas NEHER: "Data-driven turbulence model corrections for sediment transport problems". Visiting from TU Dresden.
  • Louenas ZEMMOUR: "Bayesian aggregation of data-driven turbulence model corrections". Sorbonne University.
  • Paul CALVI: "Data-driven turbulence modelling for highly compressible flows". Sorbonne University.
  • Antoine RULLIER: "Deep-learning enhanced turbulence modelling". Centrale Supélec/Sorbonne University.

 PhDs:

  • Soufiane CHERROUD: "Self-adaptive Bayesian learning of data-driven turbulence models". Funding: SMI Doctoral School fellowship

 

ABOUT ME

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