3D convolutional selective autoencoder for instability detection in combustion systems
highlights•A novel semi-supervised deep learning architecture for early detection of combustion instability.•Capturing transition from a stable to an unstable regime using spatiotemporal data (snippets of flame videos).•Demonstration of excellent generalization capability for different operating con...
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| Vydané v: | Energy and AI Ročník 4; s. 100067 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.06.2021
Elsevier |
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| ISSN: | 2666-5468, 2666-5468 |
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| Abstract | highlights•A novel semi-supervised deep learning architecture for early detection of combustion instability.•Capturing transition from a stable to an unstable regime using spatiotemporal data (snippets of flame videos).•Demonstration of excellent generalization capability for different operating conditions.•Verification of the identified precursors of transitions using physics-based understanding.
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While analytical solutions of critical (phase) transitions in dynamical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical systems. A key example is thermoacoustic instability in combustion, where prediction or early detection of the onset of instability is a hard technical challenge, which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries. The instabilities arising in combustion chambers of engines are mathematically too complex to model. To address this issue in a data-driven manner instead, we propose a novel deep learning architecture called 3D convolutional selective autoencoder (3D-CSAE) to detect the evolution of self-excited oscillations using spatiotemporal data, i.e., hi-speed videos taken from a swirl-stabilized combustor (laboratory surrogate of gas turbine engine combustor). 3D-CSAE consists of filters to learn, in a hierarchical fashion, the complex visual and dynamic features related to combustion instability from the training videos (i.e., two spatial dimensions for the image frames and the third dimension for time). We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions. We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video. The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability. The machine learning-driven results are verified with physics-based off-line measures. Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions. |
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| AbstractList | highlights•A novel semi-supervised deep learning architecture for early detection of combustion instability.•Capturing transition from a stable to an unstable regime using spatiotemporal data (snippets of flame videos).•Demonstration of excellent generalization capability for different operating conditions.•Verification of the identified precursors of transitions using physics-based understanding.
[Display omitted]
While analytical solutions of critical (phase) transitions in dynamical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical systems. A key example is thermoacoustic instability in combustion, where prediction or early detection of the onset of instability is a hard technical challenge, which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries. The instabilities arising in combustion chambers of engines are mathematically too complex to model. To address this issue in a data-driven manner instead, we propose a novel deep learning architecture called 3D convolutional selective autoencoder (3D-CSAE) to detect the evolution of self-excited oscillations using spatiotemporal data, i.e., hi-speed videos taken from a swirl-stabilized combustor (laboratory surrogate of gas turbine engine combustor). 3D-CSAE consists of filters to learn, in a hierarchical fashion, the complex visual and dynamic features related to combustion instability from the training videos (i.e., two spatial dimensions for the image frames and the third dimension for time). We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions. We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video. The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability. The machine learning-driven results are verified with physics-based off-line measures. Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions. While analytical solutions of critical (phase) transitions in dynamical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical systems. A key example is thermoacoustic instability in combustion, where prediction or early detection of the onset of instability is a hard technical challenge, which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries. The instabilities arising in combustion chambers of engines are mathematically too complex to model. To address this issue in a data-driven manner instead, we propose a novel deep learning architecture called 3D convolutional selective autoencoder (3D-CSAE) to detect the evolution of self-excited oscillations using spatiotemporal data, i.e., hi-speed videos taken from a swirl-stabilized combustor (laboratory surrogate of gas turbine engine combustor). 3D-CSAE consists of filters to learn, in a hierarchical fashion, the complex visual and dynamic features related to combustion instability from the training videos (i.e., two spatial dimensions for the image frames and the third dimension for time). We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions. We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video. The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability. The machine learning-driven results are verified with physics-based off-line measures. Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions. |
| ArticleNumber | 100067 |
| Author | Gangopadhyay, Tryambak Ramanan, Vikram Akintayo, Adedotun K Boor, Paige Sarkar, Soumalya Chakravarthy, Satyanarayanan R Sarkar, Soumik |
| Author_xml | – sequence: 1 givenname: Tryambak surname: Gangopadhyay fullname: Gangopadhyay, Tryambak organization: Department of Mechanical Engineering, Iowa State University, Ames, IA, USA – sequence: 2 givenname: Vikram surname: Ramanan fullname: Ramanan, Vikram organization: Indian Institute of Technology Madras, Chennai, India – sequence: 3 givenname: Adedotun surname: Akintayo fullname: Akintayo, Adedotun organization: Intel Corporation, Folsom, CA, USA – sequence: 4 givenname: Paige surname: K Boor fullname: K Boor, Paige organization: Department of Mechanical Engineering, Iowa State University, Ames, IA, USA – sequence: 5 givenname: Soumalya surname: Sarkar fullname: Sarkar, Soumalya organization: Raytheon Technologies, East Hartford, CT, USA – sequence: 6 givenname: Satyanarayanan R surname: Chakravarthy fullname: Chakravarthy, Satyanarayanan R organization: Indian Institute of Technology Madras, Chennai, India – sequence: 7 givenname: Soumik surname: Sarkar fullname: Sarkar, Soumik email: soumiks@iastate.edu organization: Department of Mechanical Engineering, Iowa State University, Ames, IA, USA |
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| CitedBy_id | crossref_primary_10_1016_j_ast_2023_108354 crossref_primary_10_1016_j_energy_2023_129782 crossref_primary_10_1016_j_ast_2025_109994 crossref_primary_10_1016_j_proci_2025_105796 crossref_primary_10_1115_1_4069002 crossref_primary_10_2514_1_B38780 crossref_primary_10_2514_1_B39287 crossref_primary_10_3390_en14248468 crossref_primary_10_1016_j_apenergy_2024_123224 crossref_primary_10_3390_s23031236 crossref_primary_10_3390_s25154613 crossref_primary_10_1016_j_engappai_2022_105591 crossref_primary_10_1016_j_joei_2024_101733 crossref_primary_10_1016_j_ast_2025_110243 crossref_primary_10_1016_j_fuel_2024_133393 crossref_primary_10_1177_17568277221139974 crossref_primary_10_3389_feart_2022_1029160 crossref_primary_10_1016_j_combustflame_2023_113182 |
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| Keywords | 3D deep learning Instability precursors Combustion instability Hi-speed video analytics Gas turbine engines Physics-based validation Convolutional autoencoder Early detection |
| Language | English |
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| SubjectTerms | 3D deep learning Combustion instability Convolutional autoencoder Early detection Gas turbine engines Hi-speed video analytics Instability precursors Physics-based validation |
| Title | 3D convolutional selective autoencoder for instability detection in combustion systems |
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