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Paola Cinnella

Paola2

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"

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:

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

 

P. Cinnella joins the editorial board of "Advanced Modeling and Simulation in Engineering Sciences" (Springer)

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 (january 2025-now):

 

Positions available in my group

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)

PhDs (ongoing)

Interns (ongoing)

External team members and collaborators

Fluid Dynamics Laboratory, ENSAM, Paris

Ongoing collaborations with companies

Safran Tech

ArianeGroup

Airbus Operations

Other collaborations

Gwangju Institute of Science and Technology (Korea)

Université Libre de Bruxelles

Politecnico di Bari, Centro di Eccellenza Meccanica Computazionale

Technical University Dresden (Germany)

AArhus University (Danemark)

Former members (last 5 years)

Master interns:

 PhDs:

 

ABOUT ME

(click below to see more)

Research: Computational Fluid Dynamics, Compressible flows of ideal and real gases, data-driven modeling and uncertainty quantification of turbulent flows.

Education

1995 : Master degree in Mechanical Engineering « summa cum laude », Politecnico di Bari (Italy)

1996 : DEA (Master of Science) in Mechanics from Ecole Nationale Supérieure des Arts et Métiers (ENSAM), France

1999 : PhD in ‘Engineering of Thermal Machines', Politecnico di Bari

1999 : PhD in ‘Fluid Mechanics’ at ENSAM, « summa cum laude » (très honorable, felicitations du jury)

2006 : Habilitation à Diriger des Recherches, Université Pierre et Marie Curie, France

Professional experience

1999- 2000 : Attaché temporaire d’Enseignement et de Recherche (Lecturer) at l’ENSAM.

2000- 2001: Postdoc at Dipartimento di Ingegneria Meccanica e Gestionale, Politecnico di Bari, Italy.

2001-2008: Assistant professor, Università del Salento, Lecce, Italy

2008- 2014 : Professor, ENSAM, Laboratoire DynFluid

2014- 2015 : Associate professor, Università del Salento, Italy

2015-2020: Professor, ENSAM, Laboratoire DynFluid

2020-présent : Professor, Sorbonne Université, Institut Jean Le Rond D’Alembert

Main responsibilities (last 5 years)

2016-2019: Coordinator of the ENSAM Research Network « Computational Science and Engineering »

2018-2020: Member and vice-President of the board of Directors (Conseil d’Administration) of Arts et Métiers Paris Tech. Vice-President.

March 2022-: Member of the Counsel of Department of undergraduate studies in Mechanics, Sorbonne University.

October 2022-: Assistant of the vice-dean for Research and Innovation of the Faculty of Sciences and Engineering (Engineering portfolio).

Main editorial and dissemination activities (last 5 years)

Editor-in-Chief, Computers & Fluids, Mai 2022-present

Associate Editor, International Journal of Heat and Fluid Flow, March 2023-

Associate Editor, Advanced modeling and simulation in engineering sciences, January 2025-

Co-Editor of the Fluid Mechanics Webinar collection, Cassyni platform, November 2024-

Member of the Editorial Board of “Scientific Reports” (Springer-Nature), Mechanical Engineering panel, November 2021-2024.

Member of the Editorial Advisory Board of “Flow, Turbulence and Combustion” (Springer), December 2021-.

Member of the Scientific committee of the Symposium of Applied Aerodynamics of AAAF since 2010.

Scientific Secretary (2022-present), Member of the Scientific committee (2012-present) and of the Executive Board (2018-present) of the International Conference of Computational Fluid Dynamics (ICCFD, https://www.iccfd.org/)

Coordinator of the ERCOFTAC Special Interest Group 54 "Machine Learning in Fluid Dynamics" (https://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/)

Teaching

Principal teacher of several courses in fluid mechanics (Fundamentals of Fluid Mechanics, Hydraulics, Aerodynamics, Gasdynamics, Computational Fluid Dynamics, Turbulence, Turbulence modelling), applied mathematics (Numerical Analysis, Fundamentals of Statistics, Calculus, Uncertainty Quantification) and energetics (Thermal Power Systems, Renewable Energies) since 2001 (more than 4000 hours of teaching experience, Bachelor, Master of Engineering, Master of Science and Doctoral levels). Most of my courses are taught in French and in English. In the past, I also gave courses in Italian and Spanish.

PUBLICATIONS

Some recent publications are given below (international journal publications and some Invited lectures, last 5 years). For a more complete list see my ReserchGate page:

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

 Most publications are downloadable from ResearchGate, or from the open repository: https://hal.science/search/index?q=cinnella

International peer-reviewer journals

  1. Gloerfelt X., Cinnella P., «Large Eddy Simulation requirements for the flow over periodic hills», Flow, Turbulence and Combustion, Vol. 103, No. 1, pp. 55-91, 2019.
  2. Merle X., Cinnella P., «Robust prediction of dense gas flows under uncertain thermodynamic models», Reliability Engineering and System Safety, Vol. 183, No. 3, pp. 400-421, 2019.
  3. Xiao H., Cinnella P., «Quantification of Model Uncertainty in RANS Simulations: A Review», Progress in Aerospace Sciences, Vol. 108, pp. 1-31, 2019.
  4. Menasria A., Brenner P., Cinnella P., «Improving the treatment of near-wall regions for multiple-correction k-exact schemes», Computers & Fluids, Vol. 181, pp. 116-134, 2019.
  5. Schmeltzer M., Dwight R., Cinnella P., «Discovery of Algebraic Reynolds-stress Models using Sparse Symbolic Regression», Flow, Turbulence and Combustion,Vol. 104, No. 2–3, pp. 579–603, 2019
  6. Hoarau J.-Ch., Cinnella P., Gloerfelt X., «Large Eddy Simulation of turbomachinery flows using a high-order Implicit Residual Smoothing scheme», Computers & Fluids, Vol. 198, pp. 104395, 2020.
  7. De Zordo-Banliat M., Merle X., Dergham G., Cinnella P., «Bayesian model-scenario averaged preditions of compressor cascade flows under uncertain turbulence models», Computers & Fluids, Vol. 201, pp. 104473, 2020.
  8. Gloerfelt X., Robinet J.C., Sciacovelli L., Cinnella P., Grasso F., «Dense gas effects on compressible boundary layer stability», Journal of Fluid Mechanics, Vol. 893, pp. A19, 2020.
  9. Sciacovelli L., Gloerfelt X., Passiatore D., Cinnella P., Grasso F., «Numerical investigation of high-speed boundary layers of dense gases», Flow, Turbulence and Combustion, Vol.105, pp. 555–579, 2020.
  10. Serafino A., B. Obert, Vergé L., Cinnella P., « Robust optimization of an Organic Rankine Cycle for geothermal application », Renewable Energy, 161, 2020.
  11. Serafino A., Obert B., Cinnella P., « Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics”, Algorithms, 13:248, 2020.
  12. Hoarau J.-Ch., Cinnella P., Gloerfelt X., « Large Eddy Simulations of strongly non ideal compressible flows through a transonic cascade », Energies 14(3):772, 2021.
  13. Passiatore D., Sciacovelli L., Cinnella P., Pascazio G., “Finite-rate chemistry effects in turbulent hypersonic boundary layers: A direct numerical simulation study”. Phys. Rev. Fluids 6, 054604, 2021
  14. Sciacovelli L., Passiatore D., Cinnella P., Pascazio G., Sciacovelli L., Passiatore D., Cinnella P., Pascazio G., “Assessment of a high-order shock-capturing central-difference scheme for hypersonic turbulent flow simulations”, Computers & Fluids 230(11):105134, 2021
  15. De Zordo-Banliat M., Merle X., Dergham G., Cinnella P., “Estimates of turbulence modeling uncertainties in NACA65 cascade flow predictions by Bayesian Model-Scenario Averaging”, International Journal of Numerical Methods for Heat and Fluid Flow, October 2021, à paraître. DOI: 10.1108/HFF-08-2021-0524
  16. Ben Hassan-Saidi I., Schmelzer M., Cinnella P., Grasso F., “CFD-driven Symbolic Identification of Algebraic Reynolds-Stress Models”, Journal of Computational Physics, 457:111037, 2022.
  17. Passiatore D., Sciacovelli L., Cinnella P., Pascazio G., “Thermochemical nonequilibrium effects in turbulent hypersonic boundary layers”. Journal of Fluid Mechanics, 941:A21, 2022.
  18. Cherroud S., Merle X., Cinnella P., Gloerfelt X., “Sparse Bayesian Learning of Explicit Algebraic Reynolds-Stress models for turbulent separated flows”, International Journal of Heat and Fluid Flow, Volume 98, December 2022, 109047
  19. Serafino A., Obert B., Cinnella P., “Multi-Fidelity Robust Design Optimization of an ORC Turbine for High Temperature Waste Heat Recovery”, Energy. Vol. 269, 126538.
  20. Matar C., Cinnella P., Gloerfelt X., Reinker F., aus der Wiesche S., “Investigation of non-ideal gas flows around a circular cylinder”. Energy. Volume 268, 1 April 2023, 126563.
  21. Passiatore D., Sciacovelli L., Cinnella P., Pascazio G., "Shock impingement on a transitional hypersonic high-enthalpy boundary layer". Physical Review Fluids, 8, 044601, 2023.
  22. Hake L., aus der Wiesche S., Sundermeier S., Cakievski L., Baumer J., Cinnella P., Matar C., Gloerfelt G., “Hot-wire anemometry in high subsonic organic vapor flows”. ASME Journal of Turbomachinery, 145(9): 091010, 2023.
  23. Amarloo A., Cinnella P., Iosifidis A., Forooghi P., Abkar M., « Data-driven Reynolds stress models based on the frozen treatment of Reynolds stress tensor and Reynolds force vector”. Physics of Fluids 1 July 2023; 35 (7): 075154.
  24. Stöcker, Y., Golla, C., Jain, R., Frölich, J., Cinnella, P., “DNS‑Based Turbulent Closures for Sediment Transport Using Symbolic Regression”. Flow, Turbulence and Combustion, https://doi.org/10.1007/s10494-023-00482-7, 2023
  25. Gloerfelt X., Bienner A., Cinnella P., « High-subsonic boundary-layer flows of an organic vapour”, Journal of Fluid Mechanics, Vol. 971, A633, 2023.
  26. Cinnella, P. and Gloerfelt, X., "Insights into the turbulent flow of dense gases through high-fidelity simulations". Computers & Fluids, Vol. 267, 106067, 2023.
  27. Matar, C. Gloerfelt, X. and Cinnella, P., “Numerical investigation of transonic non-ideal gas flows around a circular cylinder at high Reynolds number”, Flow, Turbulence and Combustion, 2023. https://doi.org/10.1007/s10494-023-00496-1. 
  28. Bienner, C., Gloerfelt, X., Cinnella, P., “Leading-edge effects on freestream turbulence induced transition of an organic vapor”, Flow, Turbulence and Combustion, 2023. https://doi.org/10.1007/s10494-023-00499-y.
  29. 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.
  30. 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. In press.
  31. 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.
  32. 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.
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38.  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
  39. 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
  40. 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
  41. 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
  42. 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).

 

Invited lectures and schools

  1. Cinnella, “Quantification and reduction of epistemic uncertainties in flow simulations: tackling the turbulence modeling dilemma”. Journée Scientifique de l’Association ARISTOTE “Mécanique déterministe ou incertitudes : Où en est-on avec F= M γ ? - Ça passe ou ça casse ?”, Ecole Polytechnique, Palaiseau, 21 Février 2019
  2. Cinnella, “Data-driven discovery and uncertainty quantification of turbulence models for Fluid Dynamics”, Séminaire « Saisir le Mouvement », initialement prévu à l’Institut Henri Poincaré le 30/4/2020 et reporté au 25/11/2020. https://seminaire.phimeca.com/ (online)
  3. Cinnella, « Real-Gas Effects in High-Speed Turbulent Flows: From Power Plants to Hypersonic Vehicles”, Fluid Mechanics Seminar, Stanford University (online), 4 may 2021. https://web.stanford.edu/group/fpc/cgi-bin/fpcwiki/uploads/Main/HomePage/fmseminar-spring2021.pdf
  4. Cinnella, “Introduction to Bayesian Calibration and Bayesian Model Averaging”. Ecole d’été CEA/EDF/INRIA "Multi-fidelity, multi-level, model selection/aggregation: how the presence of several versions of a code can improve the prediction of complex phenomena. », Paris, 14-18 june 2021.
  5. Cinnella, “Data-driven symbolic identification of turbulence models and perspectives for the quantification of model-form uncertainties”, Keynote Lecture, Symposium on Model-Consistent Data-driven Turbulence Modeling, June 22nd 2021 (online). http://turbgate.engin.umich.edu/symposium/assets/files/pdfs21/SymposiumAgenda.pdf
  6. Cinnella, « Bayesian machine learning for turbulence model discovery and uncertainty quantification”, Invited Lecture, IUTAM Symposium on Data-driven modeling and optimization in fluid mechanics, 15-17 June 2022, Aarhus, Danemark. https://conferences.au.dk/iutam/invited-speakers
  7. Cinnella, « Bayesian machine learning for data-enhanced CFD», NASA Advanced Supercomputing Division, Advanced Modeling and Simulation (AMS) Seminars, Online, December 8th, 2022.
    https://www.nas.nasa.gov/pubs/ams/2022/12-08-22.html
  8. Cinnella, « Synergy of high-fidelity simulations and machine learning for turbulence modelling», Keynote Lecture at VKI Symposium of PhD Research, Von Karman Institut for Fluid Dynamics, Rhode-Saint-Genèse, March 9th, 2023. https://www.researchgate.net/publication/369113084_Synergy_of_high-fidelity_simulations_and_machine_learning_for_turbulence_modelling?channel=doi&linkId=640a193e66f8522c3890736d&showFulltext=true
  9. Cinnella, « Turbulence modeling: artificial vs human intelligence », Workshop: data-driven methods in fluid mechanics, Leeds Institut for Fluid Dynamics, UK, 30-31st March 2023. https://www.eventbrite.co.uk/e/workshop-data-driven-methods-in-fluid-dynamics-tickets-427809177767
  10. Cinnella, « Machine-learning-assisted modeling of turbulence: current status and perspectives», 14th ETMM Symposium, Barcelona, Spain, September 6th -8th, 2023
  11. Cinnella, “ Data-driven turbulence Modeling,”, von Karman Institute / ULB Lecture Series : « Machine Learning for Fluid Mechanics: Analysis, Modeling, Control and Closures”, Brussels, 29 January-2 February 2024. https://www.datadrivenfluidmechanics.com/
  12. Cinnella “Data-driven correction and uncertainty quantification of turbulence models using Bayesian learning and multi-model ensembles”. SEMINAR++ Scientific Machine Learning (Semester Programme), CWI, Amsterdam, the Netherlands. 6-7 November 2023. https://www.cwi.nl/en/events/cwi-research-semester-programs/research-programmes-in-2023/research-semester-programme-on-scientific-machine-learning/seminar-scientific-machine-learning-semester-programme/
  13. P. Cinnella, “ Data-driven turbulence Modeling,”, von Karman Institute / ULB Lecture Series : « Machine Learning for Fluid Mechanics: Analysis, Modeling, Control and Closures”, Brussels, 29 January-2 February 2024. https://www.datadrivenfluidmechanics.com/
  14. P. Cinnella, “Synergy of high-fidelity simulations and machine learning for turbulence modelling”, Aerospace Department Seminar Series, TU Delft, The Netherlands, 26th jan 2024.
  15. P. Cinnella, “The potential of machine learning for data-driven turbulence modeling”. Plenary lecture at 58th International Conference on Applied Aerodynamics, Orleans, 27-29 March 2024.
  16. P. Cinnella, “Transition in Hypersonic Boundary Layers and Data-Driven Turbulent Models,”. VKI and STO-AVT-358 Lecture Series: Advanced Computational Fluid Dynamics Methods for Hypersonic Flows, Brussels, 25-29 March 2024.
  17. P. Cinnella, “Bayesian machine learning and multi-model ensembles for data-driven turbulent flow prediction”, Workshop on Scientific Machine Learning, Strasbourg, 8-12 July 2024. https://irma.math.unistra.fr/~micheldansac/SciML2024/index.html
  18. P. Cinnella, “High-fidelity simulations and machine learning for improved turbomachinery design”, Webinaire du Laboratoire de Mécanique des Fluides de Lille, 16 Mai 2024.
  19. P. Cinnella, “Learning to model: symbolic regression and aggregation of experts for turbulence modeling”, Séminaire de l’Institut Jacques-Louis Lions, 28 June 2024.
  20. P. Cinnella, “High-fidelity simulations and machine learning for improved turbomachinery design”. China Academy of Science, Frontiers in Science: A Youth Led Vanguard International Symposium: “1st Artificial Intelligence for Fluid Dynamics and Turbomachinery ", November 7-11 2024, Hong Kong (given online on Nov 8)
  21. P. Cinnella, “Scientific machine learning for fluid flow modeling and design”, Séminaire « Le machine learning au pays des équations», Institut Henri Poincaré le 21/11/2024. https://seminaire.phimeca.com/
  22. P.Cinnella et al., “Quantification and reduction of RANS model uncertainties through regional Bayesian calibration and model mixtures”, Keynote lecture, Minisymposium “Bayesian inference for synthesis of model and data in Fluid Mechanics”, 77th Annual Meeting of the Division of Fluid Dynamics, 24-26 November 2024, Salt Lake City, US. https://meetings.aps.org/Meeting/DFD24/Session/X01.3
  23. P. Cinnella, “Bayesian Machine Learning for Fluid Dynamic Design”, ANSYS TECHTalk, Webinar, 16 January 2025.
  24. P. Cinnella, “Advancing turbulence and transition modeling through high-fidelity simulation and scientific machine learning”, Ecole de Mécanique des Fluides Numérique SMAI/GAMNI/CEA, Institut Poincaré, 28 January 2025. http://www.cmap.polytechnique.fr/~allaire/gamni/semihp-en.html
  25. P. Cinnella, “Bayesian machine learning for fluid dynamic design”, Keynote lecture, 3rd Workshop on Data-Driven Fluid Dynamics, 17-19 March 2025, Nagoya, Japan. https://www.seas.ucla.edu/fluidflow/workshop2025