  The 100 MW cryogenic liquid oxygen/hydrogen multi-injector combustor BKD
operated by the DLR Institute of Space Propulsion is a research platform that
allows the study of thermoacoustic instabilities under realistic conditions,
representative of small upper stage rocket engines. We use data from BKD
experimental campaigns in which the static chamber pressure and fuel-oxidizer
ratio are varied such that the first tangential mode of the combustor is
excited under some conditions. We train an autoregressive Bayesian neural
network model to forecast the amplitude of the dynamic pressure time series,
inputting multiple sensor measurements (injector pressure/ temperature
measurements, static chamber pressure, high-frequency dynamic pressure
measurements, high-frequency OH* chemiluminescence measurements) and future
flow rate control signals. The Bayesian nature of our algorithms allows us to
work with a dataset whose size is restricted by the expense of each
experimental run, without making overconfident extrapolations. We find that the
networks are able to accurately forecast the evolution of the pressure
amplitude and anticipate instability events on unseen experimental runs 500
milliseconds in advance. We compare the predictive accuracy of multiple models
using different combinations of sensor inputs. We find that the high-frequency
dynamic pressure signal is particularly informative. We also use the technique
of integrated gradients to interpret the influence of different sensor inputs
on the model prediction. The negative log-likelihood of data points in the test
dataset indicates that predictive uncertainties are well-characterized by our
Bayesian model and simulating a sensor failure event results as expected in a
dramatic increase in the epistemic component of the uncertainty.
