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Ushnish Sengupta, Listening for instabilities with probabilistic machine learning

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Thermoacoustic instabilities are a peculiar phenomenon arising from feedback loops between heat release and acoustics. These can be extremely dangerous in high-energy density combustors such as jet engines, gas turbines or rocket engines. I will describe how we are employing probabilistic machine learning techniques to predict the stability of a laboratory-scale combustor from far-field noise data. I will also try to convince you that the uncertainties that we obtain from our Bayesian techniques, in addition to the predictions, makes our diagnostic more trustworthy and leads to better decisions. Finally, there will be a brief glimpse into our attempts at applying these techniques to sensor data from systems far bigger than our little experimental rig.

This talk is part of the CEDSG-AI4ER series.

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