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Title: An approach to the automatic generation of reduced chemical mechanisms using Computational Singular Perturbation (CSP) and Rate-Controlled Constrained Equilibrium (RCCE)
Author: Stefan, Andreea
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
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Computer simulations using accurate chemical kinetic models are increasingly being used to support the development of combustion technologies and fuels. This action is essential for the reduction of hazardous and green-house gas emissions as well as for efficiency improvement of combustion applications. Consequently, the need to incorporate detailed chemistry in the simulation of combustion processes resulted in an increased interest in developing effective tools for mechanism reduction, from both accuracy and efficiency point of view. In this work, the Rate-Controlled Constrained Equilibrium (RCCE) and the Computational Singular Perturbation (CSP) methods are combined in order to generate an automatic technique for chemical kinetics reduction. The former method identifies the steady-state species and fast reactions while the latter simplifies the kinetics of complex reacting systems. The non steady state species provided by CSP represent the constraints employed in the RCCE code which systematically reduces the detailed mechanism. The benefits of combining the two reduction methods are briefly assessed for H2-air and C2H2-air chemical mechanisms and other two laminar premixed flames are thoroughly investigated i.e. the CH4-air and C3H8-air flames. The detailed chemical kinetics mechanisms used for describing the later two gas mixtures consist of 53 species and 325 reactions and 118 species and $665$ reactions, respectively. The direct numerical solution of 1-D laminar premixed flames is computed using the premixed flame code, providing accurate data for the proposed methodology of detailed chemistry reduction for methane-air and propane-air mixtures. The computational work involves the investigation of several chemical reduced models for each of the above gas mixtures in order to test the potential of the synergy between the two chemical mechanism reduction methodologies. These models are obtained by gradually increasing the number of constraints used for RCCE (resulting in reduced schemes with 12, 16 and 20 constraints for the methane/air flame and 15, 25, 35 and 45 constraints for the propane/air flame) as well as varying the equivalence ratio in the range of (0.8-1.2). The comparison with the results obtained from direct numerical simulations shows that the reduced models containing 20 constraints for the methane case and 45 constraints for the propane case provide good predictions of the laminar flame structure, including steady-state minor species both at stoichiometric and rich/lean mixtures, as well as adequate values of the corresponding burning velocities. Very good agreement with the detailed kinetic model as well as a significant computational time gain are observed. The study also derived a reduced chemical model that can predict flames of various mixture composition at specific pressure value. This reduced chemical model uses the same set of constraints for various equivalence ratio cases and it is able to predict global variables and species concentration within acceptable accuracy limits. The computation time by using this RCCE-CSP scheme is reduced to a third of the simulation time required in the case of applying the direct integration method. Overall, the results suggest that the combined RCCE-CSP is potentially a very reliable time-scale separation method for deriving low-dimensional models by the use of a fully automatic reduction algorithm. Since the proposed technique is an approach to automatically generate reduced chemical models and requires minor computations, it is recommended for the simplification of large detailed chemical kinetic mechanisms as well as for applications to turbulent combustion due to its potential for tabulation.
Supervisor: Rigopoulos, Stelios Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available