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SUMMARY:Ushnish Sengupta\, Listening for instabilities with probabilistic 
 machine learning - Ushnish Sengupta\, Engineering Dept.
DTSTART:20190711T120000Z
DTEND:20190711T130000Z
UID:TALK127297@talks.cam.ac.uk
CONTACT:Rachel Furner
DESCRIPTION:Thermoacoustic instabilities are a peculiar phenomenon arising
  from feedback loops between heat release and acoustics. These can be extr
 emely dangerous in high-energy density combustors such as jet engines\, ga
 s turbines or rocket engines. I will describe how we are employing probabi
 listic machine learning techniques to predict the stability of a laborator
 y-scale combustor from far-field noise data. I will also try to convince y
 ou that the uncertainties that we obtain from our Bayesian techniques\, in
  addition to the predictions\, makes our diagnostic more trustworthy and l
 eads to better decisions. Finally\, there will be a brief glimpse into our
  attempts at applying these techniques to sensor data from systems far big
 ger than our little experimental rig.
LOCATION:Newnham Terrace Seminar Room\, Darwin 
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