Publications Details
Inference and combination of missing data sets for investigation of H2O2 thermal decomposition rate uncertainty
Prescribing uncertainty measures to rate expressions is crucial for performing useful predictive combustion computations. Raw experimental measurement data, and its associated noise and uncertainty, is typically unavailable for most reported investigations of elementary reaction rates, making the direct derivation of the desired joint uncertainty structure of the parameters in rate expressions difficult. To approximate this uncertainty structure we construct an inference procedure, relying on maximum entropy and approximate Bayesian computation methods, and using a two-level nested Markov Chain Monte Carlo algorithm, to arrive at a joint density on rate parameters and missing data. This method employs the reported context of a specific experiment to construct a set of hypothetical experimental data profiles consistent with the reported statistics of the data, in the form of error bars on rate constants at the experimental temperatures. Bayesian inference can then be performed using these consistent data sets as evidence to determine the joint posterior density on the rate parameters for any choice of chemical model. The method is also used to demonstrate the combination of missing data from different experiments for the generation of consensus rate expressions using these multiple sources of experimental evidence.