(27d) Reactive Flow Simulation Based On Automated Mechanism Generation and On-the-Fly Mechanism Reduction: A Demonstrative Study Conference: AIChE Annual MeetingYear: 2013Proceeding: 2013 AIChE Annual MeetingGroup: Catalysis and Reaction Engineering DivisionSession: Reaction Path Analysis Time: Sunday, November 3, 2013 - 4:30pm-4:50pm Authors: Zhang, S., Rutgers, The State University of New Jersey Androulakis, I. P., Rutgers, The State University of New Jersey Ierapetritou, M., Rutgers, The State University of New Jersey Broadbelt, L. J., Northwestern University The detailed chemical kinetic models are becoming increasingly important in combustion research and development. Kinetic models allow us to study the reaction mechanisms and predict the chemical kinetics of combustion process under complex conditions. In recent years, great progress has been made in developing detailed chemical kinetic models for large alkanes and complex realistic fuels such as biofuels. Computational automation approaches for the construction of chemical kinetic models has been studied extensively in the literature.[2-10] However, the computational cost of automated mechanism generation can be tremendous due to large and complex reactant structure, large number of possible reactions, and required accuracy of the estimation of thermodynamic properties and rate constants. Therefore, the ability to reduce the size and scale of the generated mechanisms is usually preferred in the computer construction of reaction mechanisms. To limit the size of generated mechanisms, the rate-based mechanism generation scheme  was developed to identify the kinetically significant species and reactions when constructing the reaction network automatically. The reduction of generated mechanisms based on flux analysis was also explored previously by combining the element flux analysis with the automated mechanism generation process. In our previous work, we have successfully developed the on-the-fly mechanism reduction approach based on the element flux analysis. The on-the-fly reduction approach was applied to effectively reduce the mechanism size and computational costs in complex reactive flow simulations and CFD calculations.[14, 15] However, the proposed approach is based on the detailed full mechanism to identify redundant species and reactions. Moreover, although the chemistry calculation is simplified, the computation for transport is not reduced since all the species are involved in transport calculations. Therefore, in order to further deal with the issues in both mechanism generation and reduction, in this work, we are focusing on the incorporation of automated mechanism generation and flux-based on-the-fly mechanism reduction to establish a novel framework for the reacting flow simulations. In the proposed simulation framework, no actual detailed mechanism is used. Instead, the automated mechanism generator is used to generate an instantaneous mechanism including all possible species and reactions based on the current conditions. Element flux analysis is then performed to remove any species and reactions that are not kinetically important at the current point. The chemistry and transport equations are then solved for the reduced mechanism. Once the current point is solved, the system progresses to the next time point and the mechanism generator will generate the new mechanism based on the previous reduced mechanism and the new conditions, which is then reduced by the on-the-fly flux analysis again. By simultaneously performing the on-the-fly mechanism generation and reduction, the system is always described by a reduced mechanism with minimum redundancy. The simulation is done without a detailed mechanism but using a series of locally accurate reduced mechanisms. The proposed simulation framework is demonstrated in a plug-flow reactor (PFR) model with alkane oxidation reactions. This novel scheme provides a new approach for performing reactive flow simulations resulting in reduced computational cost and thus allowing for realistic flow simulations. References 1. Pitz, W.J. and C.J. Mueller, Recent progress in the development of diesel surrogate fuels. Progress in Energy and Combustion Science, 2011. 37(3): p. 330-350. 2. Prickett, S.E. and M.L. Mavrovouniotis, Construction of complex reaction systems°ªI. Reaction description language. Computers & Chemical Engineering, 1997. 21(11): p. 1219-1235. 3. Di Maio, F.P. and P.G. Lignola, KING, a KInetic Network Generator. Chemical Engineering Science, 1992. 47(9¨C11): p. 2713-2718. 4. Broadbelt, L.J., S.M. Stark, and M.T. Klein, Computer Generated Pyrolysis Modeling: On-the-Fly Generation of Species, Reactions, and Rates. Industrial & Engineering Chemistry Research, 1994. 33(4): p. 790-799. 5. Broadbelt, L.J., S.M. Stark, and M.T. Klein, Termination of Computer-Generated Reaction Mechanisms: Species Rank-Based Convergence Criterion. Industrial & Engineering Chemistry Research, 1995. 34(8): p. 2566-2573. 6. Broadbelt, L.J., S.M. Stark, and M.T. Klein, Computer generated reaction modelling: Decomposition and encoding algorithms for determining species uniqueness. Computers & Chemical Engineering, 1996. 20(2): p. 113-129. 7. Susnow, R.G., et al., Rate-Based Construction of Kinetic Models for Complex Systems. The Journal of Physical Chemistry A, 1997. 101(20): p. 3731-3740. 8. De Witt, M.J., D.J. Dooling, and L.J. Broadbelt, Computer Generation of Reaction Mechanisms Using Quantitative Rate Information: Application to Long-Chain Hydrocarbon Pyrolysis. Industrial & Engineering Chemistry Research, 2000. 39(7): p. 2228-2237. 9. Grenda, J.M., et al., Application of Computational Kinetic Mechanism Generation to Model the Autocatalytic Pyrolysis of Methane. Industrial & Engineering Chemistry Research, 2003. 42(5): p. 1000-1010. 10. Van Geem, K.M., et al., Automatic reaction network generation using RMG for steam cracking of n-hexane. AIChE Journal, 2006. 52(2): p. 718-730. 11. Klinke, D.J. and L.J. Broadbelt, Mechanism reduction during computer generation of compact reaction models. AIChE Journal, 1997. 43(7): p. 1828-1837. 12. Androulakis, I.P., J.M. Grenda, and J.W. Bozzelli, Time-integrated pointers for enabling the analysis of detailed reaction mechanisms. AIChE Journal, 2004. 50(11): p. 2956-2970. 13. He, K., I.P. Androulakis, and M.G. Ierapetritou, On-the-fly reduction of kinetic mechanisms using element flux analysis. Chemical Engineering Science, 2010. 65(3): p. 1173-1184. 14. Zhang, S., et al., Comparison of Biodiesel Performance Based on HCCI Engine Simulation Using Detailed Mechanism with On-the-fly Reduction. Energy & Fuels, 2012. 26(2): p. 976-983. 15. Zhang, S., I.P. Androulakis, and M.G. Ierapetritou, A Hybrid Kinetic Mechanism Reduction Scheme based on the On-the-fly Reduction and Quasi-steady-state Approximation. Chemical Engineering Science, 2013.