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A Genetic Algorithm‐Based Method for the Automatic Reduction of Reaction Mechanisms
Authors:N Sikalo  O Hasemann  C Schulz  A Kempf  I Wlokas
Institution:1. Institute for Combustion and Gas Dynamics–Fluid Dynamics (IVG), University of Duisburg‐Essen, , 47048 Duisburg, Germany;2. Institute for Combustion and Gas Dynamics–Reactive Fluids (IVG), University of Duisburg‐Essen, , 47048 Duisburg, Germany;3. Center for Nanointegration Duisburg‐Essen (CENIDE), University of Duisburg‐Essen, , 47048 Duisburg, Germany;4. Center for Computational Sciences and Simulation (CCSS), University of Duisburg‐Essen, , 47048 Duisburg, Germany
Abstract:An automatic method for the reduction of chemical kinetic mechanisms under specific physical or thermodynamic conditions is presented. The method relies on the genetic algorithm search logic to gradually reduce the number of reactions from the detailed mechanism while still preserving its ability to describe the overall chemistry at an acceptable error. Accuracy of the reduced mechanism is determined by comparing its solution to the solution obtained with the full mechanism under the same initial and/or physical conditions. However, not only the chemical accuracy and the size of the mechanism are considered but also the time for its solution which helps to avoid stiff and slow converging mechanisms, thus preferring the fast solutions. The reduction method is demonstrated for a detailed mechanism for methane combustion, GRI‐Mech 3.0, which was reduced from 325 reactions and 53 species to 58 reactions and 26 species, and for an iron oxide formation mechanism from iron pentacarbonyl doped flames by Wlokas et al. (Int J Chem Kinet 2013, 45(8), 487–498),  originally consisting of 144 reactions and 34 species, which was reduced to 37 reactions and 24 species. The performance of the reduced mechanisms is shown for homogeneous constant pressure reactors and for burner‐stabilized flames. The results show a good agreement between reduced and full mechanisms for both the reactor and flame cases. The presented method is flexible and can be easily adjusted to either yield more accurate (but bigger) or smaller (but less accurate) reduced mechanisms, depending on the user's preference.
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