The role of sensitivity and uncertainty analysis in combustion modelling

Abstract

Models play an important role in improving our understanding of combustion processes and more and more are able to assist in the design of advanced energy conversion devices. Due to constant improvements in computing power and techniques such as automatic kinetic mechanism generation, we have the ability to represent combustion processes with increasing levels of detail. This is particularly true for kinetic processes where complex mechanisms are being developed which describe the oxidation of both conventional and alternative fuels. These mechanisms may comprise of up to hundreds of species and thousands of reactions with thermo-kinetic data derived from a wide variety of sources including direct measurements, global combustion experiments, and theoretical calculations. However, significant uncertainties in the data used to parametrise combustion models still exist. These input uncertainties propagate through models of combustion devices leading to uncertainties in the prediction of key combustion properties. In order to improve confidence in these models to the extent where they can successfully be used in design, input uncertainties need to be reduced as far as possible. This requires focussing efforts on those parameters which drive predictive uncertainty, which may be identified through sensitivity analysis. The paper will describe the methodologies available for the sensitivity and uncertainty analysis of combustion models with examples focussed on chemical kinetics. It will then discuss how such techniques can be incorporated into strategies for model improvement and will try to provide some future perspectives on how we can proceed in this direction as a research community.