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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/

PI (with P. Gallinari and T. Sayadi) of the AI for Fluids and Climate Collaborative Acceleration Program in the frame of PostGenAI@Paris Cluster program.

Executive Committee Member, SCAI (Sorbonne Cluster for Artificial Intelligence"

Editor in Chief, "Computers & Fluids"

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

Associate Editor, "Advanced modeling and simulation in engineering sciences"

Co-editor of the Fluid Dynamics Collection, Cassyni Scientific Webinar platform.

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

 NEWS:

 

New intern positions available

Check below ↓

New paper alert!

Salihoglu, M., Liapi, A., Belme, A., Brenner, P., Pont, G., & Cinnella, P. (2025). P-adaptation of successive correction k-exact finite volume schemes for compressible flows. Journal of Computational Physics, 114330. https://doi.org/10.1016/j.jcp.2025.114330

 

 

Positions available in my group

  • Various Postdoc positions funded in the PostGenAI@Paris Cluster program, collaborative acceleration program in AI for Fluids and Climate!
  • Postdoc position coming soon in the frame of the MIDENGRAD ANR project on data-driven transition modeling for hypersonic flows
  • PhD position soon available in the frame of the REALISE Marie Curie Doctoral Network https://www.unipg.it/news/ricerca?layout=scheda&idNews=4377 The PhD will develop hybrid Computational Fluid Dynamics/Machine Learning methodologies for discovering the thermodynamics and rheology of magmas from sparse observations of products. The PhD will be cosupervised with ISTEP (Institut de Sciences de la Terre de Paris) et INGP (Institut de Physique du Globe de Paris), and he/she will have the opportunity for secondments at Istituto Nazionale di Geofisica e Vulcanologia in Pisa, Italy. The candidate should have strong background in fluid mechanics and computational methods. He/she cannot have spent more than 12 months in France over the last three years. Contact me for more info and to check eligibity!
  • Two intern positions (5 months)

 

Older news

Check out the following Books to which I recently contributed:

- "Machine Learning for Fluid Dynamics", ed. M.A. Mendez and A. Parente, von Karman Institute for Fluid Dynamics (coming soon)

- "Data Driven Analysis and Modeling of Turbulent Flows", ed. K. Duraisamy https://shop.elsevier.com/books/data-driven-analysis-and-modeling-of-turbulent-flows/duraisamy/978-0-323-95043-5

 

New papers published (january 2025-now):

  • Gloerfelt X., Cinnella, P. (2025). High-fidelity investigation of vortex shedding from a highly loaded turbine blade. J. Turbomachinery, 1-13, https://doi.org/10.1115/1.4067438
  • Gloerfelt X., Hake L., Bienner A., Matar C., Cinnella P., aus der Wiesche S. (2025). Roughness Effects on Dense-Gas Turbine Flow: Comparison of Experiments and Simulations, 147, 1-14. https://doi.org/10.1115/1.4067443
  • Cherroud, S., Merle, X., Cinnella, P., Gloerfelt, X. (2025). Space-dependent aggregation of stochastic data-driven turbulence models, Journal of Computational Physics. 527,113793, https://doi.org/10.1016/j.jcp.2025.113793
  • Raje, P., Parish, E., Hickey, J. P., Cinnella, P., & Duraisamy, K. (2025). Recent developments and research needs in turbulence modeling of hypersonic flows. Physics of Fluids, 37(3).
  • Matar C., Gloerfelt X., Cinnella P. (2025). Cost-effective multi-fidelity strategy for the optimization of high-Reynolds number turbine flows guided by LES. Aerospace Science and Technology. 164, 110426, https://doi.org/10.1016/j.ast.2025.110426
  • Hake, L., aus der Wiesche, S., Sundermeier, S., Passmann, M., Bienner, A., Gloerfelt, X., and Cinnella, P. (June 4, 2025). "INVESTIGATION OF A TRANSONIC DENSE GAS FLOW OVER AN IDEALIZED BLADE VANE CONFIGURATION." ASME. J. Turbomach.147(12): 121007. doi: https://doi.org/10.1115/1.4068834
  •  Oulghelou M., Cherroud S., Merle X., Cinnella P. (2025). Machine-learning-assisted blending of data-driven turbulence models. Flow, Turbulence and Combustion 115: 1095–1132. https://doi.org/10.1007/s10494-025-00661-8
  • Salihoglu, M., Liapi, A., Belme, A., Brenner, P., Pont, G., & Cinnella, P. (2025). P-adaptation of successive correction k-exact finite volume schemes for compressible flows. Journal of Computational Physics, 542: 114330. https://doi.org/10.1016/j.jcp.2025.114330

 

 

Sci-Fi-Turbo HORIZON Project kicked off January 1st, 2024: https://scifiturbo.eu/

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)

  • Mattia Fabrizio CIARLATANI "Data-driven turbulence models for high-fidelity aerodynamic optimization"
  • Alexis DORANGE "Time and space adaptation of finite volume schemes for scale resolving simulations of compressible flows"

PhDs (ongoing)

  • 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.
  • Emanuele TAMBURINI: "Multi-fidelity machine learning surrogates for turbomachinery fluid dynamic design". Funding: Sci-Fi-Turbo Horizon Project.

Interns (ongoing)

  • Rebecca STANCIU "Data-driven turbulence models for transitional turbomachinery flows"

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

Gwangju Institute of Science and Technology (Korea)

  • Prof. Solkeun JEE, PhD Yeji  Yun: Data-driven turbulence modeling for separated flows

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 (last 5 years)

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.
  • Matthias RUESSEL: "Data-driven modeling of sediment bed rheology". Visiting student, TU Dresden
  • Yann FRANCESCHETTI "Machine learning for shape optimization in fluid dynamics". Sorbonne University.

 PhDs:

  • Soufiane CHERROUD: "Self-adaptive Bayesian learning of data-driven turbulence models". Funding: SMI Doctoral School fellowship
  • 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".
  • 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.
  • Cécile ROQUES: "Machine Learning modelling of turbulent flows in turbomachinery". Funding: CIFRE/Safran Tech.

Postdocs:

  • Mourad OULGHELOU. "Bayesian methods for consistent data-driven turbulence modeling". Funding: Sorbonne Institute of Computation and Data Science
  • Marc SCHOULER. "Mesh adaptation and multi-fidelity methods for high-accurate shape optimization in turbomachinery". Funding: Sci-Fi-Turbo Horizon Project.

Interns:

  •  Johnson LIU. "Data-driven turbulence models for shape optimization". Funding: Sci-Fi-Turbo Horizon Project.

 

ABOUT ME

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