Paola Cinnella
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:
P. Cinnella appointed co-editor (with R. Martinuzzi) of the Cassyni Webinar collection in Fluid Mechanics: https://cassyni.com/c/fluid-dynamics
P. Cinnella joins the steering board of SCAI: Sorbonne Center for Artificial Intelligence
New papers published (september 2024-now):
- Passiatore, D., Gloerfelt, X., Sciacovelli, L., Pascazio, G., & Cinnella, P. (2024). Direct numerical simulation of subharmonic second-mode breakdown in hypersonic boundary layers with finite-rate chemistry. International Journal of Heat and Fluid Flow, 109, 109505. https://doi.org/10.1016/j.ijheatfluidflow.2024.109505
- Ivagnes, A., Tonicello, N., Cinnella, P., & Rozza, G. (2024). Enhancing non-intrusive reduced-order models with space-dependent aggregation methods. Acta Mechanica, 1-30. https://doi.org/10.1007/s00707-024-04007-9
- Bienner, A., Gloerfelt, X. & Cinnella, P. (2024). Influence of large-scale freestream turbulence on bypass transition in air and organic vapour flows, Journal of Fluid Mechanics, 997, A56, https://doi.org/10.1017/jfm.2024.567
- Bienner, A., Gloerfelt, X. & Cinnella, P. (2024). Investigation of transonic flows through an idealized ORC turbine vane using Delayed Detached Eddy simulations. Applied Thermal Energy, 261, 124951, https://doi.org/10.1016/j.applthermaleng.2024.124951
- 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
- Yagoubi, M., Danan, D., Leyli-Abadi, M., Brunet, J. P., Mazari, J. A., Bonnet, F., Gmati, M., Cinnella, P., Gallinari, P. & Schoenauer, M. (2024). Neurips 2024 ml4cfd competition: Harnessing machine learning for computational fluid dynamics in airfoil design. arXiv preprint arXiv:2407.01641.
- Liapi, A., Salihoglu, M., Belme, A. C., Brenner, P., Limare, A., Pont, G., & Cinnella, P. (2024). Adaptive Grid Refinement for High-Order Finite Volume Simulations of Unsteady Compressible and Turbulent Flows. International Journal of Computational Fluid Dynamics, 38(2-3), 155-178. https://doi.org/10.1080/10618562.2024.2431670
- 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
Positions available in my group
- Coming soon...
Older news
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)
- 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.
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)
- Johnson LIU. "Data-driven turbulence models for shape optimization". Funding: Sci-Fi-Turbo Horizon Project.
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.
ABOUT ME
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