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We present new groups and group values for the gas‐phase thermochemistry of ethers, polyethers, and acetals suited for combustion modeling. Our investigation comprises fuel species, their primary radicals, peroxy radicals, and hydroperoxide species. In total, 45 species are used for the parameterization of 14 groups, six of which are newly introduced here. Presently, calculated thermochemistry at the DLPNO‐CCSD(T)/CBS(cc‐pVTZ, cc‐PVQZ) // B3LYP‐D3BJ/def2‐TZVP level of theory is combined with thermochemistry from the literature. Validation of the new group values against the quantum mechanical results gives deviations of 1.22 kcal/mol, 2.36 cal/(mol·K), and 1.20 cal/(mol·K) for heats of formation, standard entropies, and heat capacities, respectively. The new thermochemistry is tested with a recent OME2 and OME3 chemical kinetic model, which shows large sensitivities to the updated values in the intermediate temperature regime. This highlights the importance of using updated thermochemistry in future chemical kinetic modeling studies of oxygenated methyl ethers (OMEs) and OME‐related structures. [ABSTRACT FROM AUTHOR]

The uncertainties of chemical kinetic model parameters induce uncertainties in model predictions. Automatic optimization and uncertainty minimization techniques have been developed to constrain these uncertainties based on sets of experimental target data for quantities of interest. While such methods were frequently used to optimize models for relatively well-studied systems with large numbers of available targets, only few of these experimental data points may be of crucial importance. In addition, for novel fuel candidates such as biofuels and synthetic fuels, the number of available measurements is generally limited. Thus, an important aspect to be explored in this context is the number of experimental data points required to achieve a certain degree of uncertainty reduction, and the determination of optimal experimental conditions for these. To target this question, a model-based experimental design framework based on the criterion of D-optimality is used in the present work to automatically identify these optimal conditions. As an example, the auto-ignition of dimethyl ether is investigated. The majority of experiments with high priority cover the intermediate- and low-temperature regimes, where the employed prior model exhibits the largest prediction uncertainties. It is also found that 90 % of the maximum observed reduction of average prediction uncertainty in ignition delay times can be achieved based on only the ten most informative experiments alone. The results thus demonstrate that few well-selected measurements allow for efficient model uncertainty reduction, and the employed approach provides an effective means of identifying the optimal conditions, which is useful for further experimental investigation. On the other hand, the inclusion of more experiments into the calibration process still provides additional benefit in terms of the posterior uncertainties of a number of important model parameters, which points to the importance of taking such data into account in case of their availability. [ABSTRACT FROM AUTHOR]

Many studies apply sensitivity analysis to explore the impact of reaction kinetic parameters on model predictions. The importance of thermochemical and transport data is often assumed to be relatively low. While this is true for specific combustion properties of hydrocarbons, the role of thermochemical and transport data in combustion processes of nitrogen-containing molecules remains to be investigated. Thus, this work applies adjoint sensitivity analysis to the complete set of parameters in combustion models, i.e., kinetics, thermodynamics, and transport data. This integral approach increases the number of parameters considered in the sensitivity analysis drastically. Compared to forward sensitivity analysis, the adjoint approach is very efficient for a large number of parameters, and analysis with several thousand parameters can be performed in seconds. Nitrogen oxide formation in methane/air flames and laminar burning velocities of ammonia/air flames are considered as prediction targets. Sensitivity analysis results for kinetic, thermochemical, and transport data are compared by jointly considering all appearing parameter uncertainties. The comparison reveals that, due to their importance for the equilibrium constants of elementary reactions, the optimization potential of thermodynamic properties is often similarly high as that of the kinetics parameters. Transport parameters are found to be of the lowest priority for the model development due to their low uncertainties, even though high sensitivities are determined for several of them. More specifically, the analysis for the laminar burning velocities of ammonia/air flames reveals a high optimization potential for parameters in the N 2 -amine chemistry, including the molar heat capacities of N 2 H 2 , N 2 H 3 , and NH. Interestingly, analyses with different mechanisms reveal strongly diverging results, especially regarding the importance of reactions with OH, which is uncommon when considering the combustion of hydrocarbons. [ABSTRACT FROM AUTHOR]