A Priori Modeling of NO Formation with Principal Component Analysis and the Convolutional Neural Network in the Context of Large Eddy Simulation

[1]  T. Poinsot,et al.  Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates , 2018, Combustion and Flame.

[2]  H. Najm,et al.  Analysis of NO structure in a methane–air edge flame , 2009 .

[3]  Elnaz Jahani Heravi,et al.  Guide to Convolutional Neural Networks , 2017 .

[4]  Zuo-hua Huang,et al.  Combustion and emission characteristics of a direct-injection diesel engine fueled with diesel-diethyl adipate blends , 2007 .

[5]  Ray W. Grout,et al.  Deep learning for presumed probability density function models , 2019, Combustion and Flame.

[6]  Stephen B. Pope,et al.  Empirical low-dimensional manifolds in composition space , 2012 .

[7]  H. Pitsch,et al.  Modeling of radiation and nitric oxide formation in turbulent nonpremixed flames using a flamelet/progress variable formulation , 2008 .

[8]  Alessandro Parente,et al.  Combustion modeling using principal component analysis , 2009 .

[9]  Guillaume Blanquart,et al.  A proposed modification to Lundgren's physical space velocity forcing method for isotropic turbulence , 2013 .

[10]  H. Pitsch,et al.  Large-eddy simulation of a turbulent piloted methane/air diffusion flame (Sandia flame D) , 2000 .

[11]  C. Hasse,et al.  Machine Learning of ignition delay times under dual-fuel engine conditions , 2020 .

[12]  A. W. Vreman,et al.  Premixed and nonpremixed generated manifolds in large-eddy simulation of Sandia flame D and F , 2008 .

[13]  O. Colin,et al.  NO Relaxation Approach (NORA) to predict thermal NO in combustion chambers , 2011 .

[14]  Xue-Song Bai,et al.  Effect of split fuel injection on heat release and pollutant emissions in partially premixed combustion of PRF70/air/EGR mixtures , 2015 .

[15]  Denis Veynante,et al.  Turbulent combustion modeling , 2002, VKI Lecture Series.

[16]  S. Correa A Review of NOx Formation Under Gas-Turbine Combustion Conditions , 1993 .

[17]  J. Mi,et al.  Optimization of the Global Reaction Mechanism for MILD Combustion of Methane Using Artificial Neural Network , 2020, Energy & Fuels.

[18]  S. Pope,et al.  Simulations of a turbulent non-premixed flame using combined dimension reduction and tabulation for combustion chemistry , 2013 .

[19]  J. Wen,et al.  Large Eddy Simulation of a Syngas Jet Flame: Effects of Preferential Diffusion and Turbulence–Chemistry Interaction , 2019, Energy & Fuels.

[20]  S. C. Hill,et al.  Modeling of nitrogen oxides formation and destruction in combustion systems , 2000 .

[21]  Stelios Rigopoulos,et al.  Tabulation of combustion chemistry via Artificial Neural Networks (ANNs): Methodology and application to LES-PDF simulation of Sydney flame L , 2017 .

[22]  Jianren Fan,et al.  A lower-dimensional approximation model of turbulent flame stretch and its related quantities with machine learning approaches , 2020 .

[23]  Jianren Fan,et al.  A priori assessment of convolutional neural network and algebraic models for flame surface density of high Karlovitz premixed flames , 2021 .

[24]  Marcus S. Day,et al.  Simulation of nitrogen emissions in a premixed hydrogen flame stabilized on a low swirl burner , 2013 .

[25]  Opeoluwa Owoyele,et al.  Toward computationally efficient combustion DNS with complex fuels via principal component transport , 2017 .

[26]  R. Blint,et al.  Relative importance of nitric oxide formation mechanisms in laminar opposed-flow diffusion flames , 1991 .

[27]  Tabulation of NOx chemistry for Large-Eddy Simulation of non-premixed turbulent flames , 2009 .

[28]  V. Knop,et al.  Modelling and speciation of nitrogen oxides in engines , 2013 .

[29]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[30]  M. J. Scott,et al.  Premixed turbulent flame instability and NO formation in a lean-burn swirl burner , 1998 .

[31]  T. Passot,et al.  Numerical simulation of compressible homogeneous flows in the turbulent regime , 1987, Journal of Fluid Mechanics.

[32]  James A. Miller,et al.  Modeling nitrogen chemistry in combustion , 2018, Progress in Energy and Combustion Science.

[33]  L. Vervisch,et al.  Machine learning for detailed chemistry reduction in DNS of a syngas turbulent oxy-flame with side-wall effects , 2020, Proceedings of the Combustion Institute.

[34]  Bing Liu,et al.  Experimental study on engine performance and emissions for an engine fueled with natural gas-hydrogen mixtures , 2006 .

[35]  Jun-Kui Mao,et al.  LES investigation of two frequency effects on acoustically forced premixed flame , 2016 .

[36]  U. Maas,et al.  Modeling of NO formation based on ILDM reduced chemistry , 2002 .

[37]  Scott Klasky,et al.  Terascale direct numerical simulations of turbulent combustion using S3D , 2008 .

[38]  H. Curran,et al.  Comparative Chemical Kinetic Analysis and Skeletal Mechanism Generation for Syngas Combustion with NOx Chemistry , 2019, Energy & Fuels.

[39]  L. Vervisch,et al.  Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall effects , 2020, Combustion and Flame.

[40]  Jianren Fan,et al.  2-D and 3-D measurements of flame stretch and turbulence–flame interactions in turbulent premixed flames using DNS , 2021, Journal of Fluid Mechanics.

[41]  Tianfeng Lu,et al.  A criterion based on computational singular perturbation for the identification of quasi steady state species: A reduced mechanism for methane oxidation with NO chemistry , 2008 .

[42]  N. Peters Turbulent Combustion: Subject Index , 2000 .

[43]  Nedunchezhian Swaminathan,et al.  Application of machine learning for filtered density function closure in MILD combustion , 2021, Combustion and Flame.

[44]  Yiliang Chen,et al.  PDF modelling of turbulent non-premixed combustion with detailed chemistry , 2004 .

[45]  Luc Vervisch,et al.  DNS and approximate deconvolution as a tool to analyse one-dimensional filtered flame sub-grid scale modelling , 2017 .

[46]  T. Echekki,et al.  A framework for data-based turbulent combustion closure: A priori validation , 2019, Combustion and Flame.

[47]  Hua Zhou,et al.  An analytic model for the effects of nitrogen dilution and premixing characteristics on NOx formation in turbulent premixed hydrogen flames , 2017 .

[48]  L. Vervisch,et al.  Modelling nitrogen oxide emissions in turbulent flames with air dilution: Application to LES of a non-premixed jet-flame , 2014 .

[49]  Jacqueline H. Chen,et al.  Direct numerical simulations of a high Karlovitz number laboratory premixed jet flame – an analysis of flame stretch and flame thickening , 2017, Journal of Fluid Mechanics.

[50]  Stelios Rigopoulos,et al.  A chemistry tabulation approach via Rate-Controlled Constrained Equilibrium (RCCE) and Artificial Neural Networks (ANNs), with application to turbulent non-premixed CH4/H2/N2 flames , 2013 .

[51]  A. Kronenburg,et al.  Modeling extinction and reignition in turbulent flames , 2005 .

[52]  R. Fox,et al.  Hybrid finite-volume/transported PDF simulations of a partially premixed methane–air flame , 2004 .

[53]  Second-order conditional moment closure modeling, of turbulent piloted Jet diffusion flames , 2004 .

[54]  K. Okazaki,et al.  Effect of CO2 Reactivity on NOx Formation and Reduction Mechanisms in O2/CO2 Combustion , 2012 .

[55]  Santosh J. Shanbhogue,et al.  Investigations into the Impact of the Equivalence Ratio on Turbulent Premixed Combustion Using Particle Image Velocimetry and Large Eddy Simulation Techniques: “V” and “M” Flame Configurations in a Swirl Combustor , 2016 .

[56]  Weeratunge Malalasekera,et al.  Eulerian particle flamelet modeling of a bluff-body CH4/H2 flame , 2007 .

[57]  Jorge Cadima,et al.  Principal component analysis: a review and recent developments , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[58]  T. Echekki,et al.  A framework for data-based turbulent combustion closure: A posteriori validation , 2019 .

[59]  J. Larfeldt,et al.  Skeletal Methane-Air Reaction Mechanism for Large Eddy Simulation of Turbulent Microwave-Assisted Combustion , 2017 .

[60]  Hessam Mirgolbabaei,et al.  Principal component transport in turbulent combustion: A posteriori analysis , 2015 .

[61]  Y. Minamoto,et al.  Data driven analysis and prediction of MILD combustion mode , 2021 .

[62]  Jianren Fan,et al.  Predictive models for flame evolution using machine learning: A priori assessment in turbulent flames without and with mean shear , 2021 .