Dynamic data-driven stability map prediction in combustion systems

Pritthi Chattopadhyay, Sudeepta Mondal, Achintya Mukhopadhyay, Asok Ray

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Prediction of thermoacoustic instabilities is a critical issue for both design and operation of combustion systems. Sustained high-amplitude pressure oscillations cause mechanical stresses in the structural components of the combustor, leading to thermomechanical damage. This paper proposes a dynamic data-driven method to construct stability maps of combustion systems as a function of pertinent process parameters. Given the knowledge of a combustion system's behavior at certain operating conditions, a Bayesian nonparametric method has been adopted to predict the system stability for operating conditions at which experiments have not been conducted. The proposed method also quantifies the uncertainty in prediction, resulting from measurement noise, insufficient training data, inaccurate parameter estimates etc. The proposed method has been validated in a laboratory environment with experimental data of pressure time-series from a lean-premixed swirl-stabilized combustor apparatus.

    Original languageEnglish (US)
    Title of host publication53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017
    PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
    ISBN (Print)9781624105111
    StatePublished - Jan 1 2017
    Event53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017 - Atlanta, Georgia
    Duration: Jul 10 2017Jul 12 2017

    Publication series

    Name53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017

    Other

    Other53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017
    CountryGeorgia
    CityAtlanta
    Period7/10/177/12/17

    Fingerprint

    Combustors
    Thermoacoustics
    System stability
    Time series
    Experiments
    Uncertainty

    All Science Journal Classification (ASJC) codes

    • Engineering(all)

    Cite this

    Chattopadhyay, P., Mondal, S., Mukhopadhyay, A., & Ray, A. (2017). Dynamic data-driven stability map prediction in combustion systems. In 53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017 (53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017). American Institute of Aeronautics and Astronautics Inc, AIAA.
    Chattopadhyay, Pritthi ; Mondal, Sudeepta ; Mukhopadhyay, Achintya ; Ray, Asok. / Dynamic data-driven stability map prediction in combustion systems. 53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017. American Institute of Aeronautics and Astronautics Inc, AIAA, 2017. (53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017).
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    title = "Dynamic data-driven stability map prediction in combustion systems",
    abstract = "Prediction of thermoacoustic instabilities is a critical issue for both design and operation of combustion systems. Sustained high-amplitude pressure oscillations cause mechanical stresses in the structural components of the combustor, leading to thermomechanical damage. This paper proposes a dynamic data-driven method to construct stability maps of combustion systems as a function of pertinent process parameters. Given the knowledge of a combustion system's behavior at certain operating conditions, a Bayesian nonparametric method has been adopted to predict the system stability for operating conditions at which experiments have not been conducted. The proposed method also quantifies the uncertainty in prediction, resulting from measurement noise, insufficient training data, inaccurate parameter estimates etc. The proposed method has been validated in a laboratory environment with experimental data of pressure time-series from a lean-premixed swirl-stabilized combustor apparatus.",
    author = "Pritthi Chattopadhyay and Sudeepta Mondal and Achintya Mukhopadhyay and Asok Ray",
    year = "2017",
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    Chattopadhyay, P, Mondal, S, Mukhopadhyay, A & Ray, A 2017, Dynamic data-driven stability map prediction in combustion systems. in 53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017. 53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017, American Institute of Aeronautics and Astronautics Inc, AIAA, 53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017, Atlanta, Georgia, 7/10/17.

    Dynamic data-driven stability map prediction in combustion systems. / Chattopadhyay, Pritthi; Mondal, Sudeepta; Mukhopadhyay, Achintya; Ray, Asok.

    53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017. American Institute of Aeronautics and Astronautics Inc, AIAA, 2017. (53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    AB - Prediction of thermoacoustic instabilities is a critical issue for both design and operation of combustion systems. Sustained high-amplitude pressure oscillations cause mechanical stresses in the structural components of the combustor, leading to thermomechanical damage. This paper proposes a dynamic data-driven method to construct stability maps of combustion systems as a function of pertinent process parameters. Given the knowledge of a combustion system's behavior at certain operating conditions, a Bayesian nonparametric method has been adopted to predict the system stability for operating conditions at which experiments have not been conducted. The proposed method also quantifies the uncertainty in prediction, resulting from measurement noise, insufficient training data, inaccurate parameter estimates etc. The proposed method has been validated in a laboratory environment with experimental data of pressure time-series from a lean-premixed swirl-stabilized combustor apparatus.

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    Chattopadhyay P, Mondal S, Mukhopadhyay A, Ray A. Dynamic data-driven stability map prediction in combustion systems. In 53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017. American Institute of Aeronautics and Astronautics Inc, AIAA. 2017. (53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017).