A methodology for derivation of RCCE-reduced mechanisms via CSP

Koniavitis, Panos; Rigopoulos, Stelios; Jones, W.P.

doi:10.1016/j.combustflame.2017.05.010

Cited by 26 publications

(23 citation statements)

References 47 publications

(63 reference statements)

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“…The most important comparison here is between the RCCE and ANN results, as the objective of the latter is to approximate the former, while the agreement with experiments is subject to further factors associated with the LES and micromixing modelling. We can see that the profiles acquired through the ANN simulation reproduce very It must be noted that the RCCE-reduced mechanism has been shown to be very accurate compared with direct integration in [9] where it was derived. Also, a very similar RCCEreduced CH 4 mechanism, comprising 18 constraints and based on the GRI 3.0 mechanism, was employed in [55] to simulate the Sandia series of flames and produced results very close to direct integration of the full mechanism.…”

Section: Resultsmentioning

confidence: 79%

“…The details of the RCCE methodology have been thoroughly described in [9,8,6,7] and are only summarised here. In RCCE the leading, kinetically controlled species are described from chemical kinetics without approximations, while the minimisation of free energy is employed to calculate the fast, equilibrated species by imposing a constrained equilibrium state -a state where the leading species are constrained to retain their current values.…”

Section: Kinetics and Mechanism Reductionmentioning

confidence: 99%

“…In RCCE the leading, kinetically controlled species are described from chemical kinetics without approximations, while the minimisation of free energy is employed to calculate the fast, equilibrated species by imposing a constrained equilibrium state -a state where the leading species are constrained to retain their current values. The selection of the constrained species can be based on a quantitative analysis from methods such as LOI [3,27,28] or CSP [9]; here we employ a CSP-based approach described in [9]. The dynamics of the major, constrained species are described via the following equation:…”

Section: Kinetics and Mechanism Reductionmentioning

confidence: 99%

“…In this section, the methodology described above will be applied to the tabulation of an RCCE-reduced CH 4 combustion mechanism, itself derived via a CSP-based methodology from the detailed mechanism GRI 1.2 [47]. The methodology and the mechanism are discussed in detail in [9], where it is also shown that the predictions of the RCCE-reduced mechanism are in excellent agreement with the detailed mechanism over a wide range of problems including flamelets with varying strain rates, premixed flames and ignition. The following 16 species are retained as RCCE constraints in the mechanism:…”

Section: Ann Tabulation Of An Rcce-reduced Mechanism For Ch 4 Combustionmentioning

confidence: 99%

“…Reduced mechanisms can help in bringing down the computational time required for direct integration of combustion mechanisms. In recent years, systematic methods for deriving reduced mechanisms have been developed; these include, among others, Computational Singular Perturbation (CSP) [1], Intrinsic Low Dimensional Manifolds (ILDM [2], Level of Importance (LOI) [3], Computer Assisted Reduction Mechanism Code (CARM) [4,5], Rate-Controlled Constrained Equilibrium (RCCE) [6,7,8,9] and Directed Relation Graph (DRG) [10]. Reviews of this field can be found in [11,10,12,13].…”

Section: Introductionmentioning

confidence: 99%

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Tabulation of combustion chemistry via Artificial Neural Networks (ANNs): Methodology and application to LES-PDF simulation of Sydney flame L

Franke

Chatzopoulos

Rigopoulos

2017

Combustion and Flame

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118

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In this work, a methodology for the tabulation of combustion mechanisms via Artificial Neural Networks (ANNs) is presented. The objective of the methodology is to train the ANN using samples generated via an abstract problem, such that they span the composition space of a family of combustion problems. The abstract problem in this case is an ensemble of laminar flamelets with an artificial pilot in mixture fraction space to emulate ignition, of varying strain rate up to well into the extinction range. The composition space thus covered anticipates the regions visited in a typical simulation of a non-premixed flame. The ANN training consists of two-stage process: clustering of the composition space into subdomains using the Self-Organising Map (SOM) and regression within each subdomain via the multilayer Perceptron (MLP). The approach is then employed to tabulate a mechanism of CH 4-air combustion, based on GRI 1.2 and reduced via Rate-Controlled Constrained Equilibrium (RCCE) and Computational Singular Perturbation (CSP). The mechanism is then applied to simulate the Sydney Flame L, a turbulent non-premixed flame that features significant levels of local extinction and re-ignition. The flow field is resolved through Large Eddy Simulation (LES), while the transported Probability Density Function (PDF) approach is employed for modelling the turbulence-chemistry interaction and solved numerically via the Stochastic Fields method. Results demonstrate reasonable agreement with experiments, indicating that the SOM-MLP approach provides a good representation of the composition space, while the great savings in CPU time allow for a simulation to be performed with a comprehensive combustion model, such as the LES-PDF, with modest CPU resources such as a workstation.

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Section: Resultsmentioning

confidence: 79%