Mechanism optimization with a novel objective function: Surface matching with joint dependence on physical condition parameters.

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]