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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"

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:

 


Positions available in my group


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

PhDs (ongoing)

Dynamics of dense gases

Machine learning for turbulent flows

Advanced numerical methods for compressible flow

Master Interns (ongoing)

 

External team members and collaborators

Fluid Dynamics Laboratory, ENSAM, Paris

Ongoing collaborations with companies

Safran Tech

ArianeGroup

Airbus Operations

Other collaborations 

Université Libre de Bruxelles

Politecnico di Bari, Centro di Eccellenza Meccanica Computazionale

Technical University Dresden (Germany)

AArhus University (Danemark)

Former members

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)

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

Associate Editor, Computers & Fluids, Mai 2022-

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

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. Edeling W.N., Schmelzer M., Dwight R., Cinnella P. Bayesian predictions of Reynolds-Averaged Navier-Stokes uncertainties using Maximum a Posteriori estimates. AIAA J 56(5) :2018-2029 (2018). DOI10.2514/1.J056287.
  2. Edeling W.N., Iaccarino G., Cinnella P., « Data-free et data-driven RANS predictions with quantified uncertainty », Flow, Turbulence and Combustion, Vol. 100, No. 3, pp. 593-616, 2018
  3. Bufi E., Cinnella P. « Preliminary design method for dense-gas supersonic axial turbine stages », ASME Journal of Engineering for Gas Turbines and Power, Vol. 140, No. 11, pp. 112605, 2018. DOI 10.1115/1.4039837
  4. Sciacovelli L, Cinnella P, Gloerfelt X., «A priori tests of RANS models for turbulent channel flows of a dense gas», Flow, Turbulence and Combustion, Vol. 101, pp. 295–315, 2018.
  5. 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.
  6. 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.
  7. Xiao H., Cinnella P., «Quantification of Model Uncertainty in RANS Simulations: A Review», Progress in Aerospace Sciences, Vol. 108, pp. 1-31, 2019.
  8. 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.
  9. 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
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Serafino A., B. Obert, Vergé L., Cinnella P., « Robust optimization of an Organic Rankine Cycle for geothermal application », Renewable Energy, 161, 2020.
  15. Serafino A., Obert B., Cinnella P., « Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics”, Algorithms, 13:248, 2020.
  16. 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.
  17. 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
  18. 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
  19. 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
  20. 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.
  21. Passiatore D., Sciacovelli L., Cinnella P., Pascazio G., “Thermochemical nonequilibrium effects in turbulent hypersonic boundary layers”. Journal of Fluid Mechanics, 941:A21, 2022.
  22. 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
  23. 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.
  24. 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.
  25. Passiatore D., Sciacovelli L., Cinnella P., Pascazio G., "Shock impingement on a transitional hypersonic high-enthalpy boundary layer". Physical Review Fluids, 8, 044601, 2023.
  26. 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.
  27. 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.
  28. 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
  29. Gloerfelt X., Bienner A., Cinnella P., « High-subsonic boundary-layer flows of an organic vapour”, Journal of Fluid Mechanics, Vol. 971, A633, 2023.
  30. Cinnella, P. and Gloerfelt, X., "Insights into the turbulent flow of dense gases through high-fidelity simulations". Computers & Fluids, Vol. 267, 106067, 2023.
  31. 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
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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

 Invited lectures and schools

  1. Merle, P. Cinnella, “An introduction to Bayesian methods for the calibration of CFD models (part 1)”. Lecture series on Uncertainty Quantification in Computational Fluid Dynamics. Lecture Series 2018-2019, STO-AVT 326, 17-19 Octobre 2018, von Karman Institute for Fluid Dynamics
  2. Merle, P. Cinnella, “Application examples: calibration of RANS models and thermodynamic models for real gases (part 2)”. Lecture series on Uncertainty Quantification in Computational Fluid Dynamics. Lecture Series 2018-2019, STO-AVT 326, 17-19 Octobre 2018, von Karman Institute for Fluid Dynamics
  3. 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
  4. 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)
  5. 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
  6. 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.
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. Cinnella, « Machine-learning-assisted modeling of turbulence: current status and perspectives», 14th ETMM Symposium, Barcelona, Spain, September 6th -8th, 2023
  13. 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. 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/