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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]

The prediction accuracy of chemical kinetics models can be improved efficiently by using automatic model optimization techniques. In the optimization, an objective function, which quantifies the differences between model responses and experimental data for quantities of interest, is minimized by calibrating the reaction rate parameters of a model within their uncertainty limits. Consequently, the values of the model predictions become closer to those of the measurements. Typically, a point-wise objective function, which is based on function components separately for each measurement over the investigated domain, is used in the model optimization. Quantities of interest are often functions of various physical condition parameters, such as temperature, pressure, and equivalence ratio. However, the point-wise objective function does not consider the correlation between data and their corresponding physical conditions. Thus, in this work, a new objective function is proposed, which uses a surface-matching (SM) method. It evaluates the similarity between surface shapes of the predicted and measured values, which is quantified in form of two user-defined physical condition parameters. By minimizing this function, the joint dependence of model predictions on physical conditions is optimized in conjunction with the point-wise model prediction accuracy. A chemical mechanism of oxymethylene ethers is optimized in this work as an example. The model is calibrated with the point-wise, curve-matching (CM)-based, and SM-based objective functions. The optimized models are compared and the results are discussed. It is shown that the optimization with the SM-based objective function yields improved results for certain cases compared to using the point-wise objective function. This model also provides the best prediction accuracy in terms of joint physical condition dependence. In addition, a better overall performance is achieved by adjusting the ratios between the component functions in the objective function, which demonstrates that the definition of objective functions plays a crucial role for model optimization. [ABSTRACT FROM AUTHOR]

Abstract Thermochemical properties play an important role for accurate predictions of ignition characteristics using chemical kinetic models due to their impact on energy balances and chemical equilibrium. Therefore, the acquisition and incorporation of accurate thermochemical data have been a major objective of many recent modeling studies. As an addition to these studies, the present work investigates the impact of species thermochemical data and of their uncertainties on the prediction of fuel combustion. Sensitivity analysis is used first to evaluate the effects of enthalpies of formation, standard entropies, and heat capacities of single species on ignition characteristics employing a recent diethyl ether model (Sakai et al., 2017). Modified strategies for uncertainty quantification, which have been traditionally used to quantify prediction uncertainties due to rate parameter uncertainties, are then applied to quantify the joint impact of uncertainties in the thermochemical data on the ignition delay times. Large prediction uncertainties are found in the negative temperature coefficient (NTC) regime, where different reaction channels compete with each other and thus, small shifts of chemical equilibria of reactions can strongly affect the reaction pathway. Moreover, automatic calibration of enthalpies of formation within their uncertainty limits is performed for a 2-methylhexane model (Mohamed et al., 2016) in order to demonstrate the potential for model improvement in terms of the thermochemical parameters. This optimization process significantly improves ignition delay time predictions at the conditions of interest. Overall, the results show that the predicted uncertainties, and thus the potential for optimization, are high at conditions where multiple reaction channels are competing each other, while they are relatively lower when the reaction pathway is unique. [ABSTRACT FROM AUTHOR]