Hostname: page-component-68c7f8b79f-7mrzp Total loading time: 0 Render date: 2025-12-17T23:10:19.914Z Has data issue: false hasContentIssue false
  • English
  • Français

Enhancement of large-eddy simulations for the prediction of an intake flow rig using sequential data assimilation

Published online by Cambridge University Press:  24 November 2025

Lucas Villanueva Open the ORCID record for Lucas Villanueva [Opens in a new window] ,
Karine Truffin ,
Jacques Borée Open the ORCID record for Jacques Borée [Opens in a new window]  and
Marcello Meldi Open the ORCID record for Marcello Meldi [Opens in a new window]
Lucas Villanueva*
Affiliation:
Institut Pprime, CNRS - ISAE-ENSMA - Université de Poitiers, 11 Bd. Marie et Pierre Curie, Site du Futuroscope, Poitiers CEDEX 9 TSA 41123, 86073, France
Karine Truffin
Affiliation:
IFP Energies nouvelles, Institut Carnot IFPEN Transports Energie, 1-4 avenue de Bois-Préau, Rueil Malmaison 92852, France
Jacques Borée
Affiliation:
Institut Pprime, CNRS - ISAE-ENSMA - Université de Poitiers, 11 Bd. Marie et Pierre Curie, Site du Futuroscope, Poitiers CEDEX 9 TSA 41123, 86073, France
Marcello Meldi
Affiliation:
Univ. Lille, CNRS, ONERA, Arts et Métiers ParisTech, Centrale Lille, UMR 9014- LMFL- Laboratoire de Mécanique des fluides de Lille- Kampé de Feriet, Lille F 59000, France
*
Corresponding author: Lucas Villanueva, lucas.villanueva.pro@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

A data assimilation (DA) strategy based on an ensemble Kalman filter (EnKF) is used to enhance the predictive capabilities of scale-resolving numerical tools for the analysis of flows exhibiting cyclic behaviour. More precisely, an ensemble of numerical runs using large-eddy simulations (LES) for a compressible intake flow rig is augmented via the integration of high-fidelity data. This observation is in the form of instantaneous velocity measurements, which are sampled at localised sensors in the physical domain. Two objectives are targeted. The first objective is the calibration of an unsteady inlet condition suitable to capture the cyclic flow investigated. The second objective is the analysis of the synchronisation of the LES velocity field with the available observations. In order to reduce the computational costs required for this analysis, a hyper-localisation procedure (HLEnKF) is proposed and integrated in the library CONES, tailored to perform fast online DA. The proposed strategy performs a satisfactory calibration of the inlet conditions, and its robustness is assessed using two different prior distributions for the free parameters optimised in this task. The DA state estimation is efficient in obtaining accurate local synchronisation of the inferred velocity fields with the observed data. The modal analysis of the kinetic energy field provides additional insight into the improved reconstruction quality of the velocity field. Thus, the HLEnKF shows promising features for the calibration and synchronisation of scale-resolved turbulent flows, opening perspectives of applications for complex phenomena using advanced tools such as digital twins.

JFM classification

Turbulent Flows: Turbulence control Mathematical Foundations: Computational methods Wakes/Jets: Shear layers

Information

Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 1023 , 25 November 2025 , A47
Check for updates
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Afailal, A.H. 2021 Numerical simulation of non-reactive aerodynamics in internal combustion engines using a hybrid RANS/LES approach. PhD thesis, Université de Pau et des pays de l’Adour, France.Google Scholar
Afailal, A.H., Galpin, J., Velghe, A. & Manceau, R. 2019 Development and validation of a hybrid temporal LES model in the perspective of applications to internal combustion engines. Oil Gas Sci. Technol. – Revue d’IFP Energies nouvelles 74, 56.10.2516/ogst/2019031CrossRefGoogle Scholar
Annand, W.J.D. & Roe, G.E. 1974 Gas Flow in the Internal Combustion Engine: Power, Performance, Emission Control, and Silencing. GT Foulis.Google Scholar
Asch, M., Bocquet, M. & Nodet, M. 2016 Data Assimilation: Methods, Algorithms, and Applications. Society for Industrial and Applied Mathematics.10.1137/1.9781611974546CrossRefGoogle Scholar
Ayache, S., Dawson, J.R., Triantafyllidis, A., Balachandran, R. & Mastorakos, E. 2010 Experiments and large-eddy simulations of acoustically forced bluff-body flows. Intl J. Heat Fluid Flow 31 (5), 754–766.10.1016/j.ijheatfluidflow.2010.04.003CrossRefGoogle Scholar
Ben Ali, M.Y., Tissot, G., Aguinaga, S., Heitz, D. & Mémin, E. 2022 Mean wind flow reconstruction of a high-rise building based on variational data assimilation using sparse pressure measurements. J. Wind Engng Ind. Aerodyn. 231, 105204.10.1016/j.jweia.2022.105204CrossRefGoogle Scholar
Carrassi, A., Bocquet, M., Bertino, L. & Evensen, G. 2018 Data assimilation in the geosciences: an overview of methods, issues, and perspectives. WIREs Clim. Change 9, e535.10.1002/wcc.535CrossRefGoogle Scholar
Chandramouli, P., Memin, E. & Heitz, D. 2020 4D large scale variational data assimilation of a turbulent flow with a dynamics error model. J. Comput. Phys. 412, 109446.10.1016/j.jcp.2020.109446CrossRefGoogle Scholar
Dellenback, P.A., Metzger, D.E. & Neitzel, G.P. 1988 Measurements in turbulent swirling flow through an abrupt axisymmetric expansion. AIAA J. 26 (6), 669–681.10.2514/3.9952CrossRefGoogle Scholar
Ding, Z., Truffin, K. & Jay, S. 2024 a Cause-and-effect chain analysis of combustion cyclic variability in a spark-ignition engine using large-eddy simulation, part I: from tumble compression to flame initiation. Combust. Flame 267, 113566.10.1016/j.combustflame.2024.113566CrossRefGoogle Scholar
Ding, Z., Truffin, K. & Jay, S. 2024 b Cause-and-effect chain analysis of combustion cyclic variability in a spark-ignition engine using large-eddy simulation, part II: origins of flow variations from intake. Combust. Flame 267, 113565.10.1016/j.combustflame.2024.113565CrossRefGoogle Scholar
Ephrati, S., Franken, A., Luesink, E., Cifani, P. & Geurts, B. 2025 Continuous data assimilation closure for modeling statistically steady turbulence in large-eddy simulation. Phys. Rev. Fluids 10, 013801.10.1103/PhysRevFluids.10.013801CrossRefGoogle Scholar
Evensen, G. 2009 The ensemble Kalman filter for combined state and parameter estimation – Monte Carlo techniques for data assimilation in large systems. IEEE Control Syst. 29, 83–104.10.1109/MCS.2009.932223CrossRefGoogle Scholar
Evensen, G. & Van Leeuwen, P.J. 2000 An ensemble Kalman smoother for nonlinear dynamics. Mon. Weath. Rev. 128 (6), 1852–1867.10.1175/1520-0493(2000)128<1852:AEKSFN>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Evensen, G., Vossepoel, F.C. & Van Leeuwen, P.J. 2022 Data assimilation fundamentals: a unified formulation of the state and parameter estimation problem. In Springer Textbooks in Earth Sciences, Geography and Environment 1. Springer International Publishing.Google Scholar
Galmiche, B., Mazellier, N., Halter, F. & Foucher, F. 2014 Turbulence characterization of a high-pressure high-temperature fan-stirred combustion vessel using LDV, PIV and TR-PIV measurements. Exp. Fluids 55 (1), 1636.10.1007/s00348-013-1636-xCrossRefGoogle Scholar
Garnier, E., Adams, N. & Sagaut, P. 2009 Large Eddy Simulation for Compressible Flows. Springer Science & Business Media.10.1007/978-90-481-2819-8CrossRefGoogle Scholar
Ge, J., Rolland, J. & Vassilicos, J.C. 2023 The production of uncertainty in three-dimensional Navier–Stokes turbulence. J. Fluid Mech. 977, A17.10.1017/jfm.2023.967CrossRefGoogle Scholar
Ghosh, S., Mons, V., Sipp, D. & Schmid, P.J. 2024 A robust computational framework for variational data assimilation of mean flows with sparse measurements corrupted by strong outliers. J. Comput. Phys. 508, 113008.10.1016/j.jcp.2024.113008CrossRefGoogle Scholar
Graftieaux, L., Michard, M. & Grosjean, N. 2001 Combining PIV, POD and vortex identification algorithms for the study of unsteady turbulent swirling flows. Meas. Sci. Technol. 12 (9), 1422–1429.10.1088/0957-0233/12/9/307CrossRefGoogle Scholar
He, C., Zeng, X., Wang, P., Wen, X. & Liu, Y. 2024 Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction. J. Fluid Mech. 978, A14.10.1017/jfm.2023.972CrossRefGoogle Scholar
Houtekamer, P.L. & Mitchell, H.L. 2001 A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Weath. Rev. 129 (1), 123–137.10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Hunt, B.R., Kostelich, E.J. & Szunyogh, I. 2007 Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Phys. D: Nonlinear Phenom. 230 (1–2), 112–126.10.1016/j.physd.2006.11.008CrossRefGoogle Scholar
Kalman, R.E. 1960 A new approach to linear filtering and prediction problems. J. Basic Engng 82, 35–45.10.1115/1.3662552CrossRefGoogle Scholar
Labahn, J.W., Wu, H., Harris, S.R., Coriton, B., Frank, J.H. & Ihme, M. 2020 Ensemble Kalman filter for assimilating experimental data into large-eddy simulations of turbulent flows. Flow Turbul. Combust. 104 (4), 861–893.10.1007/s10494-019-00093-1CrossRefGoogle Scholar
Lamberti, G., García-Sánchez, C., Sousa, J. & Gorlé, C. 2018 Optimizing turbulent inflow conditions for large-eddy simulations of the atmospheric boundary layer. J. Wind Engng Ind. Aerodyn. 177, 32–44.10.1016/j.jweia.2018.04.004CrossRefGoogle Scholar
Le Provost, M. & Eldredge, J.D. 2021 Ensemble Kalman filter for vortex models of disturbed aerodynamic flows. Phys. Rev. Fluids 6 (5), 050506.10.1103/PhysRevFluids.6.050506CrossRefGoogle Scholar
Leite, C.R., Brequigny, P., Borée, J. & Foucher, F. 2024 Comparative analysis of cycle-to-cycle variabilities and combustion development in an optical spark-ignition engine fueled by pure hydrogen and propane: insights from chemiluminescence and PIV. In 21st International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics. University of Coimbra.Google Scholar
Leite, C.R., Laignel, M., Brequigny, P., Borée, J. & Foucher, F. 2023 Experimental combustion analysis in a gasoline baseline hydrogen-fueled internal combustion engine at ultra-lean conditions. SAE Tech. Paper 2023-24-0073.Google Scholar
Massey, J.C., Langella, I. & Swaminathan, N. 2019 A scaling law for the recirculation zone length behind a bluff body in reacting flows. J. Fluid Mech. 875, 699–724.10.1017/jfm.2019.475CrossRefGoogle Scholar
Meldi, M. 2018 Augmented prediction of turbulent flows via sequential estimators: sensitivity of state estimation to density of time sampling for available observation. Flow Turbul. Combust. 101, 389–412.10.1007/s10494-018-9967-6CrossRefGoogle Scholar
Meldi, M. & Poux, A. 2017 A reduced order model based on Kalman filtering for sequential data assimilation of turbulent flows. J. Comput. Phys. 347, 207–234.10.1016/j.jcp.2017.06.042CrossRefGoogle Scholar
Moldovan, G., Mariotti, A., Lehnasch, G., Cordier, L., Salvetti, M.V. & Meldi, M. 2024 Multigrid sequential data assimilation for the large eddy simulation of a massively separated bluff-body flow. Comput. Fluids 281, 106385.10.1016/j.compfluid.2024.106385CrossRefGoogle Scholar
Mons, V., Chassaing, J.C., Gomez, T. & Sagaut, P. 2016 Reconstruction of unsteady viscous flows using data assimilation schemes. J. Comput. Phys. 316, 255–280.10.1016/j.jcp.2016.04.022CrossRefGoogle Scholar
Mons, V., Du, Y. & Zaki, T. 2021 Ensemble-variational assimilation of statistical data in large-eddy simulation. Phys. Rev. Fluids 6, 104607.10.1103/PhysRevFluids.6.104607CrossRefGoogle Scholar
Mons, V. & Marquet, O. 2021 Linear and nonlinear sensor placement strategies for mean flow reconstruction via data assimilation. J. Fluid Mech. 923, A1.10.1017/jfm.2021.488CrossRefGoogle Scholar
Moussie, T., Errante, P. & Meldi, M. 2024 Statistical inference of upstream turbulence intensity for the flow around a bluff body with massive separation. Flow Turbul. Combust. 113, 853–889.10.1007/s10494-024-00573-zCrossRefGoogle Scholar
Nicoud, E. 2018 Quantifying combustion robustness in GDI engines by large-eddy simulation. PhD thesis, Université de Paris-Saclay, France.Google Scholar
Nicoud, F. & Ducros, F. 1999 Subgrid-scale stress modelling based on the square of the velocity gradient tensor. Flow Turbul. Combust. 62 (3), 183–200.10.1023/A:1009995426001CrossRefGoogle Scholar
Nishi, Y. & Doan, P.V. 2013 Hybrid boundary condition combined with data assimilation for simulations of free surface flows using lattice Boltzmann method. Comput. Fluids 88, 108–114.10.1016/j.compfluid.2013.08.010CrossRefGoogle Scholar
Pera, C. & Angelberger, C. 2011 Large eddy simulation of a motored single-cylinder engine using system simulation to define boundary conditions: methodology and validation. SAE Intl J. Engines 4 (1), 948–963.10.4271/2011-01-0834CrossRefGoogle Scholar
Plogmann, J., Brenner, O. & Jenny, P. 2024 Variational assimilation of sparse time-averaged data for efficient adjoint-based optimization of unsteady RANS simulations. Comput. Meth. Appl. Mech. Engng 427, 117052.10.1016/j.cma.2024.117052CrossRefGoogle Scholar
Poinsot, T. & Veynante, D. 2011 Theoretical and Numerical Combustion. R.T. Edwards, Inc.Google Scholar
Pope, S.B. 2000 Turbulent Flows. Cambridge University Press.Google Scholar
Rasheed, A., San, O. & Kvamsdal, T. 2020 Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access 8, 21980–22012.10.1109/ACCESS.2020.2970143CrossRefGoogle Scholar
Reflox, A. et al. 2011 CEDRE software. Aerosp. Lab 2, 1–10.Google Scholar
Rochoux, M.C., Ricci, S., Lucor, D., Cuenot, B. & Trouve, A. 2015 Towards predictive data-driven simulations of wildfire spread – part I: reduced-cost ensemble Kalman filter based on a polynomial chaos surrogate model for parameter estimation. Nat. Hazards Earth Syst. Sci. 14, 2951–2973.10.5194/nhess-14-2951-2014CrossRefGoogle Scholar
Sagaut, P. 2005 Large-Eddy Simulation for Incompressible Flows. An Introduction. Springer-Verlag.Google Scholar
Semeraro, C., Lezoche, M., Panetto, H. & Dassisti, M. 2021 Digital twin paradigm: a systematic literature review. Comput. Ind. 130, 103469.10.1016/j.compind.2021.103469CrossRefGoogle Scholar
Sirkes, Z. & Tziperman, E. 1997 Finite difference of adjoint or adjoint of finite difference? Mon. Weath. Rev. 125, 3373–3378.10.1175/1520-0493(1997)125<3373:FDOAOA>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Smagorinsky, J. 1963 General circulation experiments with the primitive equations: I. The basic experiment. Mon. Weath. Rev. 91 (3), 99–164.10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;22.3.CO;2>CrossRefGoogle Scholar
Sousa, J. & Gorlé, C. 2019 Computational urban flow predictions with Bayesian inference: validation with field data. Build. Environ. 154, 13–22.10.1016/j.buildenv.2019.02.028CrossRefGoogle Scholar
Thobois, L. 2006 Intèrêt et faisabilité de la simulation aux grandes échelles dans les moteurs automobiles. PhD thesis, CERFACS, France.Google Scholar
Thobois, L., Rymer, G., Souléres, T. & Poinsot, T. 2004 Large-eddy simulation in IC engine geometries. In 2004 SAE Fuels & Lubricants Meeting & Exhibition. 01-1854. SAE.10.4271/2004-01-1854CrossRefGoogle Scholar
Thobois, L., Rymer, G., Souleres, T., Poinsot, T. & Van den Heuvel, B. 2005 Large-eddy simulation for the prediction of aerodynamics in IC engines. Intl J. Vehicle Des. 39 (4), 368–382.10.1504/IJVD.2005.008468CrossRefGoogle Scholar
Valero, M.M. & Meldi, M. 2025 An immersed boundary method using online sequential data assimilation. J. Comput. Phys. 524, 113697.10.1016/j.jcp.2024.113697CrossRefGoogle Scholar
Véras, P., Métais, O., Balarac, G., Georges, D., Bombenger, A. & Ségoufin, C. 2023 Reconstruction of proper numerical inlet boundary conditions for draft tube flow simulations using machine learning. Comput. Fluids 254, 105792.10.1016/j.compfluid.2023.105792CrossRefGoogle Scholar
Villanueva, L. 2024 Développement d’outils d’assimilation de données pour l’estimation augmentée d’écoulements internes. PhD thesis, ISAE-ENSMA Ecole Nationale Supérieure de Mécanique et d’Aérotechique, France.Google Scholar
Villanueva, L., Truffin, K. & Meldi, M. 2024 Synchronization and optimization of large eddy simulation using an online ensemble Kalman filter. Intl J. Heat Fluid Flow 110, 109597.10.1016/j.ijheatfluidflow.2024.109597CrossRefGoogle Scholar
Villanueva, L., Valero, M.M., Glumac, A.Š. & Meldi, M. 2023 Augmented state estimation of urban settings using on-the-fly sequential data assimilation. Comput. Fluids 269, 106118.10.1016/j.compfluid.2023.106118CrossRefGoogle Scholar
Voisine, M., Thomas, L., Borée, J. & Rey, P. 2011 Spatio-temporal structure and cycle to cycle variations of an in-cylinder tumbling flow. Exp. Fluids 50 (5), 1393–1407.10.1007/s00348-010-0997-7CrossRefGoogle Scholar
Wang, M. & Zaki, T.A. 2022 Synchronization of turbulence in channel flow. J. Fluid Mech. 943, A4.10.1017/jfm.2022.397CrossRefGoogle Scholar
Wang, M. & Zaki, T.A. 2025 Variational data assimilation in wall turbulence: from outer observations to wall stress and pressure. J. Fluid Mech. 1008, A26.10.1017/jfm.2025.132CrossRefGoogle Scholar
Zauner, M., Mons, V., Marquet, O. & Leclaire, B. 2022 Nudging-based data assimilation of the turbulent flow around a square cylinder. J. Fluid Mech. 937, A38.10.1017/jfm.2022.133CrossRefGoogle Scholar
Zhang, X.-L., Xiao, H., Gomez, T. & Coutier-Delgosha, O. 2020 Evaluation of ensemble methods for quantifying uncertainties in steady-state CFD applications with small ensemble sizes. Comput. Fluids 203, 104530.10.1016/j.compfluid.2020.104530CrossRefGoogle Scholar
Zhang, X.-L., Xiao, H., Luo, X. & He, G. 2022 Ensemble Kalman method for learning turbulence models from indirect observation data. J. Fluid Mech. 949, A26.10.1017/jfm.2022.744CrossRefGoogle Scholar
Zhang, X.-L., Zhang, F., Li, Z., Yang, X. & He, G. 2024 a Large-eddy simulation-based shape optimization for mitigating turbulent wakes of a bluff body using the regularized ensemble Kalman method. J. Fluid Mech. 1001, A31.10.1017/jfm.2024.1090CrossRefGoogle Scholar
Zhang, X.-L., Zhang, L. & He, G. 2024 b Parallel ensemble Kalman method with total variation regularization for large-scale field inversion. J. Comput. Phys. 509, 113059.10.1016/j.jcp.2024.113059CrossRefGoogle Scholar
Zheng, J., Fisher, A., Lahiri, C., Yoko, M. & Juniper, M. 2024 Bayesian data assimilation in cold flow experiments on an industrial thermoacoustic rig. In ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition. GT2024-122656. American Society of Mechanical Engineers.Google Scholar