Applications and prospects of machine learning in turbulent combustion and engines

AN Jian; CHEN Yuxuan; SU Xingyu; ZHOU Hua; REN Zhuyin

doi:10.16511/j.cnki.qhdxxb.2023.25.001

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Journal of Tsinghua University(Science and Technology) ›› 2023, Vol. 63 ›› Issue (4) : 462-472. DOI: 10.16511/j.cnki.qhdxxb.2023.25.001

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Applications and prospects of machine learning in turbulent combustion and engines

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Abstract

[Significance] With the development of combustion science, large amounts of data containing various kinds of effective physical information are generated by numerical simulation and experimental measurement. Traditional research methods mainly apply model-based physical rules to illustrate such information. However, as the amount of data increases, data-driven methods have gradually gained research attention. Due to the remarkable success of machine learning (ML) techniques in data analysis and processing, they also offer a new way of processing large amounts of data in the field of combustion. [Progress] This study reviews the applications of ML in turbulent combustion, including chemical reactions, combustion modeling, engine performance prediction and optimization, and combustion instability prediction and control. The challenges and future prospects are also discussed. In the area of chemical reactions, the use of ML has been successfully demonstrated for the simplification and optimization of chemical mechanisms as well as for the efficient representation of chemical systems. Similarly, ML applications have produced encouraging results for modeling subgrid-scale processes and for parameterizing PDFs, often outperforming physics-based closure models in a priori studies. However, caution should be exercised in extrapolating these findings to a posteriori applications. Moreover, further studies are necessary to examine the performance of these data-driven models that are typically generated for specific operating conditions in practical applications. To address the limitations of regression models, physics-informed neural networks provide avenues for incorporating physical principles and other fundamental consistencies that are necessary for enabling robust combustion simulations. As for applications in engines, robust intelligent control via ML has only become feasible for combustion experiments in recent years, mainly due to the developments of deep learning. As such, these methods are still not feasible for commercial applications. This is largely caused by the lack of confidence in ML models under unseen conditions, especially in safety-critical applications, and by the large amounts of online training required for the convergence of current ML methods. [Conclusions and Prospects] Given such a background, robustness study is still a top priority. Although many successful studies on the combination between ML and combustion research have been accomplished, the conceptualization of combustion problems in ML frameworks remains a laborious task. Formulating the problem into an ML framework is a prerequisite for the issue to be successfully solved using ML. Clarifying the combustion problem and carefully selecting and preprocessing the obtained data are important. In addition, the careful selection of the ML model, the loss function, and the training and tuning of the model are necessary components for building a predictive model. Moreover, the ML models exhibit various degrees of predictive uncertainties, which are exacerbated by the lack of interpretability in complex models. Therefore, there is an urgent need to establish ML methods with physical insights. More attempts, such as sample construction method, modeling method, and uncertainty quantification or sensitivity analysis, should be conducted to effectively verify the performance of the model. This ensures that the model abides by the laws of physics and that it can accurately represent the simulated system. The holistic combination of data-driven methods with physical insights could have profound impacts on all areas of combustion science and technology, such as data-assisted modeling and simulation techniques, in situ control and optimization strategies, and data-driven screening of alternative fuels.

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turbulent combustion / machine learning (ML) / large eddy simulation / neural networks / intelligent control

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AN Jian, CHEN Yuxuan, SU Xingyu, ZHOU Hua, REN Zhuyin. Applications and prospects of machine learning in turbulent combustion and engines[J]. Journal of Tsinghua University(Science and Technology). 2023, 63(4): 462-472 https://doi.org/10.16511/j.cnki.qhdxxb.2023.25.001

References

[1] JORDAN M I, MITCHELL T M. Machine learning:Trends, perspectives, and prospects[J]. Science, 2015, 349(6245):255-260.
[2] BRUNTON S L, PROCTOR J L, KUTZ J N. Discovering governing equations from data by sparse identification of nonlinear dynamical systems[J]. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(15):3932-3937.
[3] RUDY S H, BRUNTON S L, PROCTOR J L, et al. Data-driven discovery of partial differential equations[J]. Science Advances, 2017, 3(4):e1602614.
[4] LUSCH B, KUTZ J N, BRUNTON S L. Deep learning for universal linear embeddings of nonlinear dynamics[J]. Nature Communications, 2018, 9(1):4950.
[5] RAISSI M, YAZDANI A, KARNIADAKIS G E. Hidden fluid mechanics:Learning velocity and pressure fields from flow visualizations[J]. Science, 2020, 367(6481):1026-1030.
[6] VINUESA R, BRUNTON S L. Enhancing computational fluid dynamics with machine learning[J]. Nature Computational Science, 2022, 2(6):358-366.
[7] DURAISAMY K, IACCARINO G, XIAO H. Turbulence modeling in the age of data[J]. Annual Review of Fluid Mechanics, 2019, 51:357-377.
[8] BRUNTON S L, NOACK B R, KOUMOUTSAKOS P. Machine learning for fluid mechanics[J]. Annual Review of Fluid Mechanics, 2020, 52:477-508.
[9] CURRAN H J. Developing detailed chemical kinetic mechanisms for fuel combustion[J]. Proceedings of the Combustion Institute, 2019, 37(1):57-81.
[10] JI W Q, DENG S L. Autonomous discovery of unknown reaction pathways from data by chemical reaction neural network[J]. The Journal of Physical Chemistry A, 2021, 125(4):1082-1092.
[11] ZENG J Z, CAO L Q, XU M Y, et al. Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation[J]. Nature Communications, 2020, 11(1):5713.
[12] SU X Y, JI W Q, AN J, et al. Kinetics parameter optimization vianeural ordinary differential equations[J/OL].(2022-09-05)[2022-09-08]. https://doi.org/10.48550/arxiv.2209.01862.
[13] CHRISTO F C, MASRI A R, NEBOT E M. Artificial neural network implementation of chemistry with pdf simulation of H₂/CO₂ flames[J]. Combustion and Flame, 1996, 106(4):406-427.
[14] CHRISTO F C, MASRI A R, NEBOT E M, et al. An integrated PDF/neural network approach for simulating turbulent reacting systems[J]. Symposium (International) on Combustion, 1996, 26(1):43-48.
[15] BLASCO J A, FUEYO N, DOPAZO C, et al. Modelling the temporal evolution of a reduced combustion chemical system with an artificial neural network[J]. Combustion and Flame, 1998, 113(1-2):38-52.
[16] BLASCO J, FUEYO N, DOPAZO C, et al. A self-organizing-map approach to chemistry representation in combustion applications[J]. Combustion Theory and Modelling, 2000, 4(1):61-76.
[17] AN J, HE G Q, LUO K H, et al. Artificial neural network based chemical mechanisms for computationally efficient modeling of hydrogen/carbon monoxide/kerosene combustion[J]. International Journal of Hydrogen Energy, 2020, 45(53):29594-29605.
[18] WAN K D, BARNAUD C, VERVISCH L, et al. Machine learning for detailed chemistry reduction in DNS of a syngas turbulent oxy-flame with side-wall effects[J]. Proceedings of the Combustion Institute, 2021, 38(2):2825-2833.
[19] ALQAHTANI S, ECHEKKI T. A data-based hybrid model for complex fuel chemistry acceleration at high temperatures[J]. Combustion and Flame, 2021, 223:142-152.
[20] ZHANG T H, YI Y X, XU Y F, et al. A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics[J]. Combustion and Flame, 2022, 245:112319.
[21] BARWEY S, RAMAN V. A neural network-inspired matrix formulation of chemical kinetics for acceleration on GPUs[J]. Energies, 2021, 14(9):2710.
[22] BUCHHEIT K, OWOYELE O, JORDAN T, et al. The stabilized explicit variable-load solver with machine learning acceleration for the rapid solution of stiff chemical kinetics[J]. CoRR:1905.09395, 2019.
[23] POPE S B. Small scales, many species and the manifold challenges of turbulent combustion[J]. Proceedings of the Combustion Institute, 2013, 34(1):1-31.
[24] CHEN Z X, IAVARONE S, GHIASI G, et al. Application of machine learning for filtered density function closure in MILD combustion[J]. Combustion and Flame, 2021, 225:160-179.
[25] DE FRAHAN M T H, YELLAPANTULA S, KING R, et al. Deep learning for presumed probability density function models[J]. Combustion and Flame, 2019, 208:436-450.
[26] RANADE R, ECHEKKI T. A framework for data-based turbulent combustion closure:A posteriori validation[J]. Combustion and Flame, 2019, 210:279-291.
[27] ECHEKKI T, MIRGOLBABAEI H. Principal component transport in turbulent combustion:A posteriori analysis[J]. Combustion and Flame, 2015, 162(5):1919-1933.
[28] MALIK M R, COUSSEMENT A, ECHEKKI T, et al. Principal component analysis based combustion model in the context of a lifted methane/air flame:Sensitivity to the manifold parameters and subgrid closure[J]. Combustion and Flame, 2022, 244:112134.
[29] MIRGOLBABAEI H, ECHEKKI T. A novel principal component analysis-based acceleration scheme for LES-ODT:An a priori study[J]. Combustion and Flame, 2013, 160(5):898-908.
[30] GITUSHI K M, RANADE R, ECHEKKI T. Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion[J]. Combustion and Flame, 2022, 236:111814.
[31] YELLAPANTULA S, PERRY B A, GROUT R W. Deep learning-based model for progress variable dissipation rate in turbulent premixed flames[J]. Proceedings of the Combustion Institute, 2021, 38(2):2929-2938.
[32] YAO S, WANG B, KRONENBURG A, et al. Conditional scalar dissipation rate modeling for turbulent spray flames using artificial neural networks[J]. Proceedings of the Combustion Institute, 2021, 38(2):3371-3378.
[33] NIKOLAOU Z M, CHRYSOSTOMOU C, MINAMOTO Y, et al. Evaluation of a neural network-based closure for the unresolved stresses in turbulent premixed V-flames[J]. Flow, Turbulence and Combustion, 2021, 106(2):331-356.
[34] SCHOEPPLEIN M, WEATHERITT J, SANDBERG R, et al. Application of an evolutionary algorithm to LES modelling of turbulent transport in premixed flames[J]. Journal of Computational Physics, 2018, 374:1166-1179.
[35] LAPEYRE C J, MISDARIIS A, CAZARD N, et al. Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates[J]. Combustion and Flame, 2019, 203:255-264.
[36] REN J H, WANG H O, LUO K, et al. A priori assessment of convolutional neural network and algebraic models for flame surface density of high Karlovitz premixed flames[J]. Physics of Fluids, 2021, 33(3):036111.
[37] RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks:A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378:686-707.
[38] KARNIADAKIS G E, KEVREKIDIS I G, LU L, et al. Physics-informed machine learning[J]. Nature Reviews Physics, 2021, 3(6):422-440.
[39] CAI S Z, MAO Z P, WANG Z C, et al. Physics-informed neural networks (PINNs) for fluid mechanics:A review[J]. Acta Mechanica Sinica, 2021, 37(12):1727-1738.
[40] AN J, WANG H Y, LIU B, et al. A deep learning framework for hydrogen-fueled turbulent combustion simulation[J]. International Journal of Hydrogen Energy, 2020, 45(35):17992-18000.
[41] ANGIKATH SHAMSUDHEEN F, YALAMANCHI K, YOO K H, et al. Machine learning techniques for classification of combustion events under homogeneous charge compression ignition (HCCI) conditions[R]. New York, USA:SAE, 2020.
[42] KODAVASAL J, ABDUL MOIZ A, AMEEN M, et al. Using machine learning to analyze factors determining cycle-to-cycle variation in a spark-ignited gasoline engine[J]. Journal of Energy Resources Technology, 2018, 140(10):102204.
[43] MARIANI V C, OCH S H, DOS SANTOS COELHO L, et al. Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models[J]. Applied Energy, 2019, 249:204-221.
[44] JOHNSON R, KACZYNSKI D, ZENG W, et al. Prediction of combustion phasing using deep convolutional neural networks[R]. New York, USA:SAE, 2020.
[45] CHEN C Y, WU J, WEI J S, et al. The virtual boosted DISI engine model development based on artificial neural networks[R]. New York, USA:SAE, 2022.
[46] WONG P K, TAM L M, LI K, et al. Engine idle-speed system modelling and control optimization using artificial intelligence[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering, 2010, 224(1):55-72.
[47] WONG K I, WONG P K, CHEUNG C S, et al. Modeling and optimization of biodiesel engine performance using advanced machine learning methods[J]. Energy, 2013, 55:519-528.
[48] WONG P K, TAM L M, KE L. Automotive engine power performance tuning under numerical and nominal data[J]. Control Engineering Practice, 2012, 20(3):300-314.
[49] BENDU H, DEEPAK B B V L, MURUGAN S. Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN-PSO[J]. Applied Energy, 2017, 187:601-611.
[50] LIEUWEN T C, YANG V. Combustion instabilities in gas turbine engines:Operational experience, fundamental mechanisms, and modeling[M]. Reston:American Institute of Aeronautics and Astronautics, 2005.
[51] SHARIFI V, KEMPF A M, BECK C. Large-eddy simulation of acoustic flame response to high-frequency transverse excitations[J]. AIAA Journal, 2019, 57(1):327-340.
[52] SU X Y, JI W Q, ZHANG L, et al. Neural differential equations for inverse modeling in model combustors[OL].(2022-07-24)[2022-09-08]. https://doi.org/10.48550/arXiv.2107.11510.
[53] ZHANG L, XUE Y, XIE Q, et al. Analysis and neural network prediction of combustion stability for industrial gases[J]. Fuel, 2021, 287:119507.
[54] ZHANG L, LI S, XUE Y, et al. Neural network PID control for combustion instability[J]. Combustion Theory and Modelling, 2022, 26(2):383-398.
[55] ZHANG L, SU X Y, ZHOU H, et al. Active control of multiple neural networks for oscillating combustion[J]. AIAA Journal, 2022, 60(6):3821-3833.