Juan Camilo Giraldo Delgado, Khalid Alhazmi, Inna Gorbatenko, Deanna A. Lacoste, S. Mani Sarathy
{"title":"利用深度强化学习和神经自回归模型控制层流预混火焰的热声不稳定性","authors":"Juan Camilo Giraldo Delgado, Khalid Alhazmi, Inna Gorbatenko, Deanna A. Lacoste, S. Mani Sarathy","doi":"10.1016/j.proci.2024.105223","DOIUrl":null,"url":null,"abstract":"Thermoacoustic instabilities pose challenges for several combustion applications, such as rockets, ramjets, aeroengines and boilers. The mitigation of these instabilities requires decoupling unsteady heat release and acoustics of the system. While existing strategies rely in theoretical approaches, this paper introduces a fully data-driven approach for modelling and control of systems with sustained pressure oscillations. A nonlinear autoregressive model (NARX) with neural networks was trained on experimental data obtained from a laminar premixed flame exhibiting a thermoacoustic instability at 166 Hz. The NARX model showed good prediction capabilities using closed-loop measurements. Furthermore, given the limitations that traditional control techniques face for nonlinear systems, this work explores the application of offline reinforcement learning for tuning the parameters of a phase-shift controller. The reinforcement learning model is trained using the NARX model as the environment. The study demonstrates the potential of reinforcement learning for control of thermoacoustic instabilities and shows that the parameters suggested by the model fall in the range where the thermoacoustic instability can be reduced.","PeriodicalId":408,"journal":{"name":"Proceedings of the Combustion Institute","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Controlling thermoacoustic instability of a laminar premixed flame with deep reinforcement learning and neural autoregressive models\",\"authors\":\"Juan Camilo Giraldo Delgado, Khalid Alhazmi, Inna Gorbatenko, Deanna A. Lacoste, S. Mani Sarathy\",\"doi\":\"10.1016/j.proci.2024.105223\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Thermoacoustic instabilities pose challenges for several combustion applications, such as rockets, ramjets, aeroengines and boilers. The mitigation of these instabilities requires decoupling unsteady heat release and acoustics of the system. While existing strategies rely in theoretical approaches, this paper introduces a fully data-driven approach for modelling and control of systems with sustained pressure oscillations. A nonlinear autoregressive model (NARX) with neural networks was trained on experimental data obtained from a laminar premixed flame exhibiting a thermoacoustic instability at 166 Hz. The NARX model showed good prediction capabilities using closed-loop measurements. Furthermore, given the limitations that traditional control techniques face for nonlinear systems, this work explores the application of offline reinforcement learning for tuning the parameters of a phase-shift controller. The reinforcement learning model is trained using the NARX model as the environment. The study demonstrates the potential of reinforcement learning for control of thermoacoustic instabilities and shows that the parameters suggested by the model fall in the range where the thermoacoustic instability can be reduced.\",\"PeriodicalId\":408,\"journal\":{\"name\":\"Proceedings of the Combustion Institute\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Combustion Institute\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1016/j.proci.2024.105223\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Combustion Institute","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.proci.2024.105223","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Controlling thermoacoustic instability of a laminar premixed flame with deep reinforcement learning and neural autoregressive models
Thermoacoustic instabilities pose challenges for several combustion applications, such as rockets, ramjets, aeroengines and boilers. The mitigation of these instabilities requires decoupling unsteady heat release and acoustics of the system. While existing strategies rely in theoretical approaches, this paper introduces a fully data-driven approach for modelling and control of systems with sustained pressure oscillations. A nonlinear autoregressive model (NARX) with neural networks was trained on experimental data obtained from a laminar premixed flame exhibiting a thermoacoustic instability at 166 Hz. The NARX model showed good prediction capabilities using closed-loop measurements. Furthermore, given the limitations that traditional control techniques face for nonlinear systems, this work explores the application of offline reinforcement learning for tuning the parameters of a phase-shift controller. The reinforcement learning model is trained using the NARX model as the environment. The study demonstrates the potential of reinforcement learning for control of thermoacoustic instabilities and shows that the parameters suggested by the model fall in the range where the thermoacoustic instability can be reduced.
期刊介绍:
The Proceedings of the Combustion Institute contains forefront contributions in fundamentals and applications of combustion science. For more than 50 years, the Combustion Institute has served as the peak international society for dissemination of scientific and technical research in the combustion field. In addition to author submissions, the Proceedings of the Combustion Institute includes the Institute''s prestigious invited strategic and topical reviews that represent indispensable resources for emergent research in the field. All papers are subjected to rigorous peer review.
Research papers and invited topical reviews; Reaction Kinetics; Soot, PAH, and other large molecules; Diagnostics; Laminar Flames; Turbulent Flames; Heterogeneous Combustion; Spray and Droplet Combustion; Detonations, Explosions & Supersonic Combustion; Fire Research; Stationary Combustion Systems; IC Engine and Gas Turbine Combustion; New Technology Concepts
The electronic version of Proceedings of the Combustion Institute contains supplemental material such as reaction mechanisms, illustrating movies, and other data.