The use of global uncertainty methods for the evaluation of combustion mechanisms

Complex chemical mechanisms are increasingly used within models describing a range of important chemical processes. Parameters describing the rates of chemical steps and thermodynamics may be highly uncertain, influencing the uncertainty in final model predictions. Local sensitivity analysis is traditionally employed within commercial modelling packages but may not be appropriate for highly uncertain data within non-linear models. There is a need for global uncertainty techniques such as Morris and Monte Carlo methods that can be applied efficiently for computationally expensive models. This paper presents the development of such techniques, along with application to a kinetic mechanism describing the influence of fuel trace elements such as sulphur-containing compounds, on the formation of nitrogen oxide in combustion devices. The analysis evaluates the parameters from within the current sulphur scheme that drive uncertainties in predicted relative changes in nitrogen oxide concentrations when sulphur compounds are added to the fuel. The overall performance of the mechanism is evaluated in comparison with available experimental profiles and the level of agreement between different methods for importance ranking of the rate parameters is highlighted. The use of fitted model representations is also discussed as an alternative method for determining importance ranking, and highlights non-linear interactions between parameters. Finally, possible improvements to the chemical scheme are tested within a Monte Carlo framework under lean flame conditions, where the current mechanism performs the least well with respect to experimental results.