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AI Super-Resolution-Based Subfilter Modeling for Finite-Rate-Chemistry Flows: A Jet Flow Case Study
Technical Paper
2023-01-0200
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Large-eddy simulation (LES) can be a very important tool to support and accelerate the energy transition to green technologies and thus play a significant role in the fight against climate change. However, especially LES of reactive flows is still challenging, e.g., with respect to emission prediction, and perfect subfilter models do not yet exist. Recently, new subfilter models based on physics-informed generative adversarial networks (GANs), called physics-informed enhanced super-resolution GANs (PIESRGANs), have been developed and successfully applied to a wide range of flows, including decaying turbulence, sprays, and finite-rate-chemistry flows. This technique, based on AI super-resolution, allows for the systematic derivation of accurate subfilter models from direct numerical simulation (DNS) data, which is critical, e.g., for the development of efficient energy devices based on advanced fuels. This paper describes a case study demonstrating PIESRGANA for a finite-rate chemical methane jet flow using transfer learning. A priori and a posteriori results are presented and discussed. Since the training process is very crucial for the successful application of this new LES technique, a detailed description of possible strategies is provided.
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Citation
Bode, M., "AI Super-Resolution-Based Subfilter Modeling for Finite-Rate-Chemistry Flows: A Jet Flow Case Study," SAE Technical Paper 2023-01-0200, 2023, https://doi.org/10.4271/2023-01-0200.Also In
References
- Farazi , S. , Hinrichs , J. , Davidovic , M. , Falkenstein , T. et al. Numerical Investigation of Coal Particle Stream Ignition in Oxy-Atomsphere Fuel 241 2019 477 487
- Gauding , M. , Wang , L. , Goebbert , J.H. , Bode , M. et al. On The Self-Similarity of Line Segments in Decaying Homogeneous Isotropic Turbulence Computers & Fluids 180 2019 206 217
- Gauding , M. , Bode , M. , Denker , D. , Brahami , Y. et al. On The Combined Effect of Internal and External Intermittency in Turbulent Non-Premixed Jet Flames Proceedings of the Combustion Institute 38 2021 2767 2774
- Gauding , M. , Bode , M. , Brahami , Y. , Varea , E. et al. Self-Similarity of Turbulent Jet Flows with Internal and External Intermittency Journal of Fluid Mechanics 919 2021
- Falkenstein , T. , Kang , S. , Cai , L. , Bode , M. et al. DNS Study of the Global Heat Release Rate During Early Flame Kernel Development Under Engine Conditions Combustion and Flame 213 2020 455 466
- Falkenstein , T. , Rezchikova , A. , Langer , R. , Bode , M. et al. The Role of Differential Diffusion During Early Flame Kernel Development Under Engine Conditions - Part I: Analysis of the Heat-Release-Rate Response Combustion and Flame 221 2020 502 515
- Falkenstein , T. , Chu , H. , Bode , M. , Kang , S. et al. The Role of Differential Diffusion During Early Flame Kernel Development Under Engine Conditions - Part II: Effect of Flame Structure and Geometry Combustion and Flame 221 2020 516 529
- Pope , S.B. Turbulent Flows Cambridge Cambridge University Press, UK 2000
- Pitsch , H. Large-Eddy Simulation of Turbulent Combustion Annual Review of Fluid Mechanics 38 2006 453 482
- Smagorinsky , J. General Circulation Experiments with the Primitive Equations: I. The Basic Experiment Monthly Weather Review 91 3 1963 99 164
- Hinton , G. , Deng , L. , Yu , D. , Dahl , G. et al. Deep neural networks for acoustic modeling in speech recognition IEEE Signal processing magazine 29 2012
- Wang , X. , Yu , K. , Wu , S. , Gu , J. et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks Lecture Notes in Computer Science 11133 2019 63 79
- Dong , C. , Loy , C.C. , He , K. , and Tang , X. Learning a Deep Convolutional Network for Image Super-Resolution European Conference on Computer Vision 184 199 2014
- Vinyals , O. , Babuschkin , I. , Czarnecki , W. , Mathieu , M. et al. Grandmaster Level in StarCraft II using Multi-Agent Reinforcement Learning Nature 575 2019 350 354
- Bhati , A. , Wan , S. , Alfe , D. , Clyde , A. et al. Pandemic drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- And Physics-Based Simulations on High Performance Computers Interface Focus 20210018 2021
- Fukami , K. , Maulik , R. , Ramachandra , N. , Fukagata , K. et al. Global Field Reconstruction from Sparse Sensors with Voronoi Tessellation-Assisted Deep Learning Nature Machine Intelligence 3 2021 945 951
- Liang , Y. , Pope , S.B. , and Pepiot , P. A Pre-Partitioned Adaptive Chemistry Methodology for the Efficient Implementation of Combustion Chemistry in Particle Pdf Methods Combustion and Flame 162 2015 3236 3253
- Bode , M. , Collier , N. , Bisetti , F. , and Pitsch , H. Adaptive Chemistry Lookup Tables for Combustion Simulations using Optimal B-Spline Interpolants Combustion Theory and Modelling 23 2019 674 699
- D’Alessio , G. , Parente , A. , Stagni , A. , and Cuoci , A. Adaptive Chemistry Via Pre-Partitioning of Composition Space and Mechanism Reduction Combustion and Flame 211 2020 68 82
- Chung , W.T. , Mishra , A.A. , Perakis , N. , and Ihme , M. Data-Assisted Combustion Simulations with Dynamic Submodel Assignment using Random Forests Combustion and Flame 227 2021 172 185
- Lapeyre , C.J. , Misdariis , A. , Cazard , N. , Veynante , D. et al. Training Convolutional Neural Networks to Estimate Turbulent Sub-Grid Scale Reaction Rates Combustion and Flame 203 2019 255 264
- Henry de Frahan , M.T. , Yellapantula , S. , King , R. , Day , M.S. et al. Deep Learning for Presumed Probability Density Function Models Combustion and Flame 208 2019 436 450
- Wan , K. , Hartl , S. , Vervisch , L. , Domingo , P. et al. Combustion Regime Identification from Machine Learning Trained by Raman/Rayleigh Line Measurements Combustion and Flame 219 2020 268 274
- Bode , M. , Gauding , M. , Kleinheinz , K. , and Pitsch , H. Deep Learning at Scale for Subgrid Modeling in Turbulent Flows: Regression and Reconstruction LNCS 11887 2019 541 560
- Bode , M. , Gauding , M. , Lian , Z. , Denker , D. et al. Using Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks for Subfilter Modeling in Turbulent Reactive Flows Proceedings of the Combustion Institute 38 2021 2617 2625
- Gauding , M. and Bode , M. Using Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Reconstruct Mixture Fraction Statistics of Turbulent Jet Flows Jagode , H. , Anzt , H. , Ltaief , H. and Luszczek , P. High Performance Computing 11203 Springer International Publishing 2021 138 153
- Bode , M. AI Super-Resolution: Application to Turbulence and Combustion Swaminathan , N. and Parente , A. Machine Learning and Its Application to Reacting Flows Springer 2023
- Bode , M. Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Large-Eddy Simulations of ECN Spray C SAE Technical Paper 2022-01-0503 2022 10.4271/2022-01-0503
- Bode , M. Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors arXiv preprint 2022
- Bode , M. , Gauding , M. , Goeb , D. , Falkenstein , T. et al. Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-Like Flame Kernel Direct Numerical Simulation Data arXiv preprint 2022
- Bode , M. Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of An Accelerated Simulation Workflow arXiv preprint 2022
- Bode , M. , et al. Development of Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks for Subfilter Modeling arXiv preprint 2021
- Bode , M. Accelerating Complex Simulations with AI Super-Resolution-Based Subfilter Modeling arXiv preprint 2022
- Hirschfelder , J.O. , Curtiss , C.F. , and Bird , R.B. Molecular Theory of Gases and Liquids New York John Wiley and Sons 1954
- Denker , D. , Attili , A. , Boschung , J. et al. Dissipation Element Analysis of Non-Premixed Jet Flames Journal of Fluid Mechanics 904 2020 A4
- Denker , D. , Attili , A. , Gauding , M. , Niemietz , K. et al. A New Modeling Approach for Mixture Fraction Statistics Based on Dissipation Elements Proceedings of the Combustion Institute 38 2021 2681 2689
- Desjardins , O. , Blanquart , G. , Balarac , G. , and Pitsch , H. High Order Conservative Finite Difference Scheme for Variable Density Low Mach Number Turbulent Flows Journal of Computational Physics 227 2008 7125 7159
- Bode , M. , Deshmukh , A.Y. , Falkenstein , T. , Kang , S. et al. Hybrid Scheme for Complex Flows on Staggered Grids and Application to Multiphase Flows Journal of Computational Physics 474 2023 108478
- Bode , M. , Davidovic , M. , and Pitsch , H. Towards Clean Propulsion with Synthetic Fuels: Computational Aspects and Analysis High-Performance Scientific Computing Springer Nature 2019 185 207
- Bode , M. , Falkenstein , T. , Kang , S. , and Pitsch , H. http://www.fz-juelich.de/ias/jsc/EN/Expertise/High-Q-Club/CIAO/ 2015
- Falgout , R.D. and Yang , U.M. Hypre: A Library of High Performance Preconditioners Sloot , P.M.A. , Hoekstra , A.G. , Tan , C.J.K. and Dongarra , J.J. Computational Science - ICCS 2002 Springer Berlin Heidelberg 2002 632 641
- Henson , V.E. and Yang , U.M. BoomerAMG: A Parallel Algebraic Multigrid Solver and Preconditioner Applied Numerical Mathematics 41 2002 155 177
- Jiang , G.-S. and Shu , C.-W. Efficient Implementation of Weighted ENO Schemes Journal of Computational Physics 126 1996 202 228
- Strang , G. On the Construction and Comparison of Difference Schemes SIAM Journal on Numerical Analysis 5 1968 506 517
- Hindmarsh , A.C. , Brown , P.N. , Grant , K.E. , Lee , S.L. et al. SUNDIALS: Suite of Nonlinear and Differential/Algebraic Equation Solvers ACM Transactions on Mathematical Software 31 2005 363 396
- Brown , P.N. , Byrne , G.D. , and Hindmarsh , A.C. VODE: A Variable-Coefficient ODE Solver SIAM Journal on Scientific and Statistical Computing 10 1989 1038 1051
- Jolicoeur-Martineau , A. The Relativistic Discriminator: A Key Element Missing from Standard GAN arXiv preprint 2018
- Hutter , F. , Kotthoff , L. , and Vanschoren , J. Automated Machine Learning - Methods Challenges, Springer Systems 2019