(27d) Reactive Flow Simulation Based On Automated Mechanism Generation and On-the-Fly Mechanism Reduction: A Demonstrative Study

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.[1] 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.[11] To limit the size of generated mechanisms, the
rate-based mechanism generation scheme [7] 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.[12]

In our previous work,[13] 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.