A Global Sensitivity Study of Sulfur Chemistry in a Premixed Methane Flame Model Using HDMR

The use of complex mechanisms is increasing within models describing a range of important chemical processes, including combustion. Parameters describing chemical reaction rates and thermodynamics can often be very uncertain. Highlighting the main parameters contributing to predictive uncertainty is an important part of model development. However, due to the computational cost of the models and their nonlinearity, traditional methods for sensitivity analysis are often not suitable. The high-dimensional model representation HDMR) method was developed to express the input-output relationship of a complex model with a high-dimensional input space. A fully functional surrogate model call be constructed with low computational effort. First- and second-order sensitivity indices can then be calculated in all automatic way, over large input parameter ranges, These provide all importance ranking for the input parameters An extension to the existing set of HDMR tools is developed in this work, where the number of HMDR component functions can be reduced to explore large dimensional input spaces very efficiently. The HDMR tools are demonstrated for a case study of a one-dimensional low-pressure premixed methane flame model doped with trace sulfur and nitrogen species. Uncertainties in rate constants and thermodynamic data are considered, leading to a study of 176 input parameters. Using the new HDMR tools, the use of screening methods such as the Morris method, which aim to identify unimportant parameters beforehand, can generally be avoided However, in certain cases, a combination of a screening method and HDMR is computationally more efficient than using HDMR alone. The final ranking of important parameters is shown to be critically dependent on the uncertainty ranges chosen due to the nonlinearity of the model. The study demonstrates that the proposed HDMR method provides a powerful tool for general application to global sensitivity analysis of complex chemical mechanisms. (c) 2008 Wiley Periodicals Inc. Int J Chem Kinet 40: 742-753, 2008