Bayesian models for the determination of resonant frequencies in a DI diesel engine

A time series method for the determination of combustion chamber resonant frequencies is outlined. This technique employs the use of Markov-chain Monte Carlo (MCMC) to infer parameters in a chosen model of the data. The development of the model is included and the resonant frequency is characterised as a function of time. Potential applications for cycle-by-cycle analysis are discussed and the bulk temperature of the gas and the trapped mass in the combustion chamber are evaluated as a function of time from resonant frequency information.

► Propose the use of Bayesian statistics in engine research.
► Demonstrate Bayesian model development to analyse in-cylinder pressure signals.
► Locate the resonant frequency of in-cylinder pressure signals.
► Establish a connection between frequency, temperature and trapped mass.
► Confirm the importance of considering inter-cycle variability.