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@ARTICLE{AR-CFD-19-76, author = {Lapeyre, C. and Misdariis, A. and Cazard, N. and Veynante, D. and Poinsot, T. }, title = {Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates}, year = {2019}, number = {May}, volume = {203}, pages = {255-264}, doi = {10.1016/j.combustflame.2019.02.019}, journal = {Combustion and Flame}, abstract = {This work presents a new approach for premixed turbulent combustion modeling based on convolutional neural networks (CNN).1 We first propose a framework to reformulate the problem of subgrid flame surface density estimation as a machine learning task. Data needed to train the CNN is produced by direct numerical simulations (DNS) of a premixed turbulent flame stabilized in a slot-burner configuration. A CNN inspired from a U-Net architecture is designed and trained on the DNS fields to estimate subgrid-scale wrinkling. It is then tested on an unsteady turbulent flame where the mean inlet velocity is increased for a short time and the flame must react to a varying turbulent incoming flow. The CNN is found to efficiently extract the topological nature of the flame and predict subgrid-scale wrinkling, outperforming classical algebraic models.}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0010218019300835}}

@ARTICLE{AR-CMGC-19-81, author = {Foltz, G.R. and Brandt, P. and Richter, I. and Rodrìguez-Fonseca, B. and Hernandez, F. and Dengler, M. and Rodrigues, R.R. and Schmidt, J.O. and Yu, L. and Lefevre, N. and Cotrim Da Cunha, L. and McPhaden, M.J. and Araujo, M. and Karstensen, J. and Hahn, J. and Martin-Rey, M. and Patricola, C.M. and Poli, P. and Hummels, R. and Perez, R.C. and Hatje, V. and Lübbecke, J.-F. and Polo, I. and Lumpkin, R. and Bourles, B. and Asuquo, F.E. and Lehodey, P. and Conchon, A. and Chang, P. and Dandin, P. and Schmid, C. and Sutton, A. and Giordani, H. and Xue, Y. and Illig, S. and Losada, T. and Grodsky, S.A. and Gasparin, F. and Lee, T. and Mohino, E. and Nobre, P. and Wanninkhof, R. and Keenlyside, N. and Garcon, V. and Sanchez-Gomez, E. and Nnamchi, H.C. and Drévillon, M. and Storto, A. and Remy, E. and Lazar, A. and Speich, S. and Goes, M. and Dorrington, T. and Johns, W.E. and Moum, J.N. and Robinson, C. and Perruche, C. and de Souza, R.B. and Gaye, A.T. and Lopez-Parages, J. and Monerie, P.A. and Castellanos, P. and Benson, N.U. and Hounkonnou, M.N. and Trotte Duha, J. and Laxenaire, R. and Reul, N. }, title = {The tropical atlantic observing system}, year = {2019}, number = {206}, volume = {6}, doi = {10.3389/fmars.2019.00206}, journal = {Frontiers in Marine Science}, pdf = {https://cerfacs.fr/wp-content/uploads/2019/05/GLOBC-Foltz_etal_2019_TAOS_Frontiers.pdf}}

@ARTICLE{AR-CFD-19-53, author = {Felden, A. and Pepiot, P. and Esclapez, L. and Riber, E. and Cuenot, B. }, title = {Including analytically reduced chemistry (ARC) in CFD applications}, year = {2019}, number = {May}, volume = {158}, pages = {444-459}, doi = {10.1016/j.actaastro.2019.03.035}, journal = {Acta Astronautica}, abstract = {Reacting numerical simulations today are often based on either fitted global reaction schemes, comprised of a few empirical reactions, or pre-tabulated laminar flame solutions computed with detailed chemistry. Although both methods can accurately predict global quantities such as laminar flame speed and burnt gas composition, they have significant limitations. In particular, neither are able to directly and adequately describe the complexity of pollutant chemistry. In the context of reducing harmful emissions of the next generation of aeronautical combustors, however, including these needed additional kinetic details in combustion simulations is becoming essential. Direct integration of detailed chemistry in accurate turbulent combustion models is not a viable option in the foreseeable future, because of excessive computational demands and numerical stiffness. In this context, Analytically Reduced Chemistry (ARC) represents an attractive compromise between accuracy and effic iency, and is already employed in relatively complex Direct Numerical Simulations (DNS) and Large Eddy Simulations (LES). ARCs are knowledge-based compact mechanisms retaining only the most relevant kinetic information as extracted directly, and without fitting, from detailed chemical models using specialized reduction techniques (important species identification through graph search, lumping of species with similar features, short-living species identification, etc.). In recent years, several multi-step efficient and automated reduction tools have been developed, enabling the easy generation of ARCs with minimum input and knowledge from the user. The main objective of this paper is to present a review of ARCs for fuels ranging from methane to aviation kerosene surrogates, recently derived with such a multi-step automated reduction tool: YARC. Information about the applicability and range of validity of each derived mechanism are given, along with further references. Each on e was specifically derived to be convenient to use in CFD; in particular, the stiffness was regarded as a key factor and the final number of transported species never exceeds thirty. In a final section, the great potential of the methodology is illustrated in a multi-phase, reactive LES application where the fuel is a real multi-component transportation fuel. To that end, an ARC based on a Jet A described by the novel Hybrid Chemistry (HyChem) approach is coupled with the Dynamically Thickened Flame LES (DTFLES) model and directly integrated into the LES solver AVBP. A Lagrangian spray description is used. Results are compared to experimental data in terms of temperature and major species (CO 2 , H 2 O, CO, NO) mass fractions, leading to very satisfying results.}, keywords = {Analytically Reduced Chemistry, database , CFD applications}}

@ARTICLE{AR-CMGC-19-59, author = {Guo, R. and Deser, C. and Terray, L. and Lehner, F. }, title = {Human influence on winter precipitation trends (1921–2015) over North America and Eurasia revealed by dynamical adjustment}, year = {2019}, volume = {46}, pages = {3426-3434}, doi = {10.1029/2018GL081316}, journal = {Geophysical Research Letters}, pdf = {https://cerfacs.fr/wp-content/uploads/2019/04/Globc-Article-Guo_et_al_GRL_2019.pdf}}

@ARTICLE{AR-CFD-19-60,author = {Malé, Q. and Staffelbach, G. and Vermorel, O. and Misdariis, A. and Ravet, F. and Poinsot, T. },title = {Large Eddy Simulation of pre-chamber ignition in an internal combustion engine}, year = {2019}, doi = {10.1007/s10494-019-00026-y}, journal = {Flow Turbulence and Combustion}, abstract = {Using homogeneous lean mixtures is an efficient way to reduce fuel consumption and pollutant emissions in internal combustion engines. However, lean combustion requires breakthrough technologies to induce reliable ignition and fast combustion. One of these technologies uses pre-chamber to create multiple hot turbulent jets and provide ignition sites for the lean mixture. In this paper, the behaviour of a pre-chamber ignition system used to ignite the main chamber of a real engine is studied using large eddy simulation with direct integration of analytically reduced chemistry using the dynamic thickened flame model. The large eddy simulation allows to analyze the flow entering and leaving the pre-chamber, to measure the cooling and quenching effects introduced by the hot gas passages through the ducts connecting pre- and main chambers and to analyze the ignition and combustion sequences. For the case studied here, small amount of flame kernels are exhausted from the pre-chamb er. Hot products penetrate the main chamber, disperse and mix with the fresh reactants and lead to ignition. The combustion in the main chamber starts in a distributed reaction mode before reaching a flamelet propagation mode.}, keywords = {Pre-chamber ignition, Turbulent jet ignition, Internal combustion engines, Large eddy simulation}}