Kinetics parameter optimization of the ethylene chemistry in the AramcoMech 2.0 mechanism (493 species and 2716 reactions) was carried out against a large collection of indirect (1440 data points in 153 data sets) and direct (936 data points in 58 data sets) experimental data. The indirect data collection consisted of ignition delay time measurements in shock tubes covering a temperature range of 930-2230 K and a pressure range of 0.28-63.3 atm, and laminar burning velocity measurements at preheat temperatures from 298 to 650 K, and pressures from 0.5 to 10 atm. Due to the large size of the model and the data collection, direct optimization was not feasible, therefore, we applied the recently proposed Reduction-Assisted Parameter Optimization-Based Model Development (RAPOD) procedure. First, using the Simulation Error Minimization Connectivity Method (SEM-CM), a reduced mechanism with 75 species and 612 reactions was obtained that performs similarly to the detailed mechanism regarding the indirect measurements used. This smaller model could be simulated around 50 times faster enabling efficient optimization with moderate computational effort on the large number of experimental targets. Then, influential reactions of the reduced model were identified using the novel PCALIN method, which is based on principal component analysis of the local sensitivity matrix scaled with experimental data uncertainty and parameter uncertainty. The Arrhenius parameters (ln A, n, E/R) in 18 reactions were optimized within their prior uncertainty domain against the data collection. Finally, the optimized parameters were transferred to the original AramcoMech 2.0 mechanism, whose performance was shown to improve in a similar fashion as that of the reduced model. The uncertainties of the model results were considerably reduced due to the significant reduction of the uncertainties of most of the optimized rate coefficients.

Continue reading...