TY - GEN
T1 - Dynamic data-driven stability map prediction in combustion systems
AU - Chattopadhyay, Pritthi
AU - Mondal, Sudeepta
AU - Mukhopadhyay, Achintya
AU - Ray, Asok
N1 - Funding Information:
This work has been supported in part by the U.S. Air Force Office of Scientific Research (AFOSR) under Grant No. FA9550-15-1-0400. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies. The authors are thankful to Professor Domenic Santavica at Penn State, who has kindly provided the experimental data.
PY - 2017
Y1 - 2017
N2 - 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.
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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U2 - 10.2514/6.2017-4893
DO - 10.2514/6.2017-4893
M3 - Conference contribution
AN - SCOPUS:85088773107
SN - 9781624105111
T3 - 53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017
BT - 53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - 53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017
Y2 - 10 July 2017 through 12 July 2017
ER -