Increasingly stringent regulations on emissions in the gas turbine industry require novel designs to minimize the environmental impact of oxides of nitrogen (NOx). The development of advanced low-NOx technologies depends on accurate and reliable thermochemical mechanisms to achieve emissions targets. However, current combustion models have high levels of uncertainty in kinetic rates that, when propagated through calculations, yield significant variations in predictions. A recent study identified and optimized nine elementary reactions involved in CH formation to accurately capture its concentration and improve prompt-NO predictions. The current work quantifies the uncertainty on peak CH concentration and NOx emissions generated by these nine reaction rates only, when propagated through the San Diego mechanism. Various non-intrusive spectral methods are used to study atmospheric alkane-air flames. 1st- and 2nd-order total-order expansions and tensor-product expansions are compared against a reference Monte Carlo analysis to assess the ability of the different techniques to accurately quantify the effect of uncertainties on the quantities of interest. Sparse grids, subsets of the full tensor-product expansion, are shown to retain the advantages of tensor formulation compared to total-order expansions while requiring significantly fewer collocation points to develop a surrogate model. The high resolution per dimension can capture complex probability distributions witnessed in radical species concentrations. The uncertainty analysis of lean to rich flames demonstrated a high variability in NOx predictions reaching up to 400 % of nominal predictions. Wider concentration intervals were observed in rich conditions where prompt-NOx is the dominant contributor to emissions. The high variability and scale of uncertainty in NOx emissions originating from these nine elementary reactions demonstrate the need for future experiments and data assimilation to constrain current models to accurately capture CH for robust NOx emissions predictions.

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