Session: 04-45 Combustion dynamics - modeling III

Paper Number: 122656

122656 - Bayesian Data Assimilation in Cold Flow Experiments on an Industrial Thermoacoustic Rig 

Thermoacoustic oscillations cause noise and vibrations in rocket and aero engines and can severely damage them. Most current thermoacoustic models represent the physics and are qualitatively accurate but do not give quantitatively-accurate predictions. This is because thermoacoustic behaviour is extremely sensitive to model parameters. In this study, we assimilate experimental data from the non-reacting SCARLET (SCaled Acoustic Rig for Low Emission Technologies) test rig using physics-based Bayesian inference. As a first step, we model the complex geometries of the combustor with a qualitatively-accurate 1D low order network model. At the first level of Bayesian inference, we assimilate experimental data to improve the parameter values by minimizing the negative log posterior likelihood of the parameters of each model, given the prior assumptions and the data. At the second level of inference, we find the best model by comparing the log marginal likelihoods of candidate models. We apply Laplace’s method accelerated with first and second order adjoint methods to assimilate data efficiently. The first order adjoint is used for rapid data assimilation and optimization. The first and second order adjoints are used for inverse uncertainty quantification. At the end we derive an improved physical model of the rig, with corresponding model and experimental uncertainties, to provide more quantitatively accurate predictions by knowing all sensitivities of our model parameters.

Presenting Author: Jingquan Zheng University of Cambridge

Presenting Author Biography: Jingquan Zheng is a first year PhD student at the Department of Engineering, University of Cambridge

Authors:

Jingquan Zheng University of Cambridge
André Fischer Rolls-Royce Deutschland
Claus Lahiri Rolls-Royce Deutschland
Matthew Yoko University of Cambridge
Matthew Juniper University of Cambridge