[Assimilation de données en combustion]
Lʼassimilation de données, encore inappliquée en combustion, combine mesures et simulations en tenant compte des incertitudes afin dʼaméliorer la prévision numérique dʼun système. Dans le contexte des turbines à gaz, lʼassimilation de données peut être utilisée par exemple pour améliorer lʼestimation de lʼallumage et de la propagation de la flamme, grâce à une exploitation plus poussée de données telles que des images ou des mesures ponctuelles. Une première illustration de lʼassimilation de données est présentée pour la prédiction de la propagation dʼun front de flamme dans un cas test simple. Dans cet exemple, les positions du front de flamme au cours du temps sont assimilées pour caler les paramètres dʼun modèle de flamme prémélangée. La capacité de prédiction des simulations obtenues par assimilation de données est finalement démontrée dans un cas réel de propagation de feux naturels.
Data assimilation is a sophisticated technique, yet not available in combustion, that combines measurements to model simulation and account for uncertainties in order to improve the numerical prediction of a system. In the context of gas turbines, data assimilation may be used for example to improve the prediction of flame ignition and propagation by a smart analysis of images and measurements. A first illustration of data assimilation is given in a simple case, where synthetic time-evolving positions of the flame front are assimilated to calibrate parameters of a premixed flame model. Its successful application in the context of natural fire propagation assesses the predictive capacity of the technique and the resulting higher fidelity in the data-driven simulations.
Mots-clés : Combustion, Assimilation de données, Propagation de feux
Mélanie C. Rochoux 1, 2, 3 ; Bénédicte Cuenot 1 ; Sophie Ricci 4 ; Arnaud Trouvé 5 ; Blaise Delmotte 4, 5 ; Sébastien Massart 4 ; Roberto Paoli 4 ; Ronan Paugam 6
@article{CRMECA_2013__341_1-2_266_0,
author = {M\'elanie C. Rochoux and B\'en\'edicte Cuenot and Sophie Ricci and Arnaud Trouv\'e and Blaise Delmotte and S\'ebastien Massart and Roberto Paoli and Ronan Paugam},
title = {Data assimilation applied to combustion},
journal = {Comptes Rendus. M\'ecanique},
pages = {266--276},
publisher = {Elsevier},
volume = {341},
number = {1-2},
year = {2013},
doi = {10.1016/j.crme.2012.10.011},
language = {en},
}
TY - JOUR AU - Mélanie C. Rochoux AU - Bénédicte Cuenot AU - Sophie Ricci AU - Arnaud Trouvé AU - Blaise Delmotte AU - Sébastien Massart AU - Roberto Paoli AU - Ronan Paugam TI - Data assimilation applied to combustion JO - Comptes Rendus. Mécanique PY - 2013 SP - 266 EP - 276 VL - 341 IS - 1-2 PB - Elsevier DO - 10.1016/j.crme.2012.10.011 LA - en ID - CRMECA_2013__341_1-2_266_0 ER -
%0 Journal Article %A Mélanie C. Rochoux %A Bénédicte Cuenot %A Sophie Ricci %A Arnaud Trouvé %A Blaise Delmotte %A Sébastien Massart %A Roberto Paoli %A Ronan Paugam %T Data assimilation applied to combustion %J Comptes Rendus. Mécanique %D 2013 %P 266-276 %V 341 %N 1-2 %I Elsevier %R 10.1016/j.crme.2012.10.011 %G en %F CRMECA_2013__341_1-2_266_0
Mélanie C. Rochoux; Bénédicte Cuenot; Sophie Ricci; Arnaud Trouvé; Blaise Delmotte; Sébastien Massart; Roberto Paoli; Ronan Paugam. Data assimilation applied to combustion. Comptes Rendus. Mécanique, Combustion, spray and flow dynamics for aerospace propulsion, Volume 341 (2013) no. 1-2, pp. 266-276. doi : 10.1016/j.crme.2012.10.011. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2012.10.011/
[1] Inverse Problem Theory, Methods for Data Fitting and Parameter Estimation, Elsevier, 1987
[2] Unified notation for data assimilation: Operational, sequential and variational, Journal of the Meteorological Society of Japan, Volume 75 (1997) no. 1B, pp. 181-189
[3] F. Bouttier, P. Courtier, Data assimilation concepts and methods, ECMWF, Meteorological Training Course Lecture Series, March 1999.
[4] Overview of global data assimilation development in numerical weather-prediction centers, Quarterly Journal of the Royal Meteorological Society, Volume 131 (2005), pp. 3215-3233
[5] Special Issue on the Revolution in Global Ocean Forecasting. GODAE: 10 Years of Achievement, Oceanography Society, 2009 (vol. 22)
[6] Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, 2003
[7] A wildland fire model with data assimilation, Mathematics and Computers in Simulation, Volume 79 (2008), pp. 584-606
[8] Applied Optimal Estimation, MIT Press, Cambridge, MA, 1974
[9] Suboptimal schemes for atmospheric data assimilation based on the Kalman filter, Monthly Weather Review, Volume 122 (1994), pp. 2530-2557
[10] Assimilation of observations—An introduction, Journal of the Meteorological Society of Japan, Volume 75 (1997) no. 1B, pp. 191-209
[11] Theoretical and Numerical Combustion, R.T. Edwards, 2005
[12] R.G. Rehm, R.J. McDermott, Fire front propagation using the level-set method, NIST, Technical Report 1611, 2009.
[13] Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release, Journal of Geophysical Research, Volume 110 (2005), p. D24311
[14] R.C. Rothermel, A mathematical model for predicting fire spread in wildland fuels, USDA Forest Service, Research Paper INT-115, Intermountain Forest and Range Experiment, Ogden, UT:40, 1972.
- Plasma-Assisted Partial Methane Oxidation. Part I: One-Dimensional Statistical Modeling of a Dielectric Barrier Discharge Reactor, The Journal of Physical Chemistry C (2025) | DOI:10.1021/acs.jpcc.4c06369
- Application of incremental 4D-Var for real-time prediction of a fire enclosure oscillatory dynamics, Journal of Physics: Conference Series, Volume 2885 (2024) no. 1, p. 012060 | DOI:10.1088/1742-6596/2885/1/012060
- Hydrogen-Fueled Stationary Combustion Systems, Hydrogen for Future Thermal Engines (2023), p. 269 | DOI:10.1007/978-3-031-28412-0_7
- A computational framework combining a basic turbulent combustion model, self-similar chemistry behavior, and a wall thermal law adapted for large-eddy simulations of a cyclically operating constant volume combustion chamber, Combustion and Flame, Volume 244 (2022), p. 112212 | DOI:10.1016/j.combustflame.2022.112212
- Real-time wildfire monitoring with a fleet of UAVs, Robotics and Autonomous Systems, Volume 152 (2022), p. 104071 | DOI:10.1016/j.robot.2022.104071
- Constrained-temperature solutions of coflow laminar diffusion flames, Proceedings of the Combustion Institute, Volume 38 (2021) no. 2, p. 1905 | DOI:10.1016/j.proci.2020.06.034
- EnKF-Based Real-Time Prediction of Wildfire Propagation, The Proceedings of 11th Asia-Oceania Symposium on Fire Science and Technology (2020), p. 713 | DOI:10.1007/978-981-32-9139-3_52
- Transient flow prediction in an idealized aneurysm geometry using data assimilation, Computers in Biology and Medicine, Volume 115 (2019), p. 103507 | DOI:10.1016/j.compbiomed.2019.103507
- Vegetation fire spread database: 85 wood wool shaving experiments at laboratory scale, Fire Safety Journal, Volume 109 (2019), p. 102870 | DOI:10.1016/j.firesaf.2019.102870
- Combined state and parameter estimation in level-set methods, Journal of Computational Physics, Volume 399 (2019), p. 108950 | DOI:10.1016/j.jcp.2019.108950
- A compact shock-focusing geometry for detonation initiation: Experiments and adjoint-based variational data assimilation, Combustion and Flame, Volume 183 (2017), p. 144 | DOI:10.1016/j.combustflame.2017.03.014
- A Luenberger observer for reaction–diffusion models with front position data, Journal of Computational Physics, Volume 300 (2015), p. 288 | DOI:10.1016/j.jcp.2015.07.044
- Towards predictive data-driven simulations of wildfire spread – Part II: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread, Natural Hazards and Earth System Sciences, Volume 15 (2015) no. 8, p. 1721 | DOI:10.5194/nhess-15-1721-2015
- 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, Natural Hazards and Earth System Sciences, Volume 14 (2014) no. 11, p. 2951 | DOI:10.5194/nhess-14-2951-2014
Cité par 14 documents. Sources : Crossref
Commentaires - Politique
Vous devez vous connecter pour continuer.
S'authentifier