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The uncertainties existing in the parameters of chemical kinetic models have a non-negligible influence on the model predictions. It is necessary to conduct a quantitative uncertainty analysis to explore the influence of each parameter on chemical mechanism predictions. To comprehensively consider the effect of the uncertainties of reaction rate parameters, thermodynamic parameters, and transport parameters on model predictions, local sensitivity analysis, local-sensitivity-based uncertainty analysis (LSUA), and random-sampling high dimensional model representation (RS-HDMR) method were coupled to investigate the uncertainty propagation of the chemical kinetic parameters to the calculated laminar flame speed of dimethyl ether under a wide range of conditions using a detailed mechanism. First, the uncertainty analysis was conducted using the local sensitivity analysis and the LSUA method under a wide range of operating conditions to identify the important operating conditions and chemical kinetic parameters. It is found that the prediction uncertainty of laminar flame speed is more obvious under the conditions of high dilution ratio, high pressure, and large equivalence ratio than that under other conditions. According to the results of LSUA, the prediction uncertainty is mainly from the reaction rate coefficients and thermodynamic data. Then, the uncertainty propagation from the significant parameters to the calculated laminar flame speed under important conditions was analysed using the RS-HDMR method. To reduce the huge computational cost of the RS-HDMR method, the backpropagation artificial neural network was employed. The RS-HDMR results indicate that the reaction H + O 2  = O + OH has the highest sensitivity coefficient under the whole investigated conditions, which is different from the results using the LSUA method. The non-linear relationship between the rate coefficient and the predicted laminar flame speed is responsible for the discrepancy. Furthermore, it is found that the sensitivity coefficient of the input parameters strongly depends on the operating conditions.

Comprehensive influence of uncertainty propagation of chemical kinetic parameters on laminar flame speed prediction: a case study of dimethyl ether