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
DOIs
StatePublished - 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

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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