UTILIZATION OF BASIC MULTI-LAYER PERCEPTRON ARTIFICIAL NEURAL NETWORKS TO RESOLVE TURBULENT FINE STRUCTURE CHEMICAL KINETICS APPLIED TO A CFD MODEL OF A METHANE/AIR PILOTED JET FLAME

Ryno LAUBSCHER

doi:https://doi.org/10.18186/journal-of-thermal-engineering.381838

Article Details

UTILIZATION OF BASIC MULTI-LAYER PERCEPTRON ARTIFICIAL NEURAL NETWORKS TO RESOLVE TURBULENT FINE STRUCTURE CHEMICAL KINETICS APPLIED TO A CFD MODEL OF A METHANE/AIR PILOTED JET FLAME

Authors:

Ryno LAUBSCHER

Publisher:

Yildiz Technical University

Journal:

Journal of Thermal Engineering 2018 Vol.4 Issue 2, pp.1828-1846

Document Type:

Article

Publication Date:

12/2017

Document Language:

Turkish

Document ID (Org No.):

2000506236

Frequency:

Bimonthly

eISSN:

2148-7847

DOI:

https://doi.org/10.18186/journal-of-thermal-engineering.381838

Abstract:

 


This work
investigates and proposes an alternative chemistry integration approach to be
used with the eddy dissipation concept (EDC) advanced combustion model. The
approach uses basic multi-layer perceptron (MLP) artificial neural networks
(ANNs) as a chemistry integrator for the reactions that take place in the fine
structure regions created by the turbulence field. The ANNs are therefore
utilised to predict the incremental species changes that occur in these fine
structure regions as a function of the initial species composition, temperature
and the residence time of the mixture in the fine structure regions. The
chemistry integration approach for the EDC model was implemented to model a
piloted methane/air turbulent jet diffusion flame (Sandia Flame D) at a
Reynolds number of 22400. To prove the concept, a five-step methane combustion
mechanism was used to model the chemical reactions of the experimental flame.
The results of the new approach were benchmarked against experimental data and
the simulation results using the standard integration approaches in Fluent. It
was shown that once the ANNs are well-trained (in-sample error minimised as
best possible), it can predict the species mass fractions with relative
accuracy in a manner that is both time and computer-memory efficient compared
with using traditional integration procedures.

.

Article Details

UTILIZATION OF BASIC MULTI-LAYER PERCEPTRON ARTIFICIAL NEURAL NETWORKS TO RESOLVE TURBULENT FINE STRUCTURE CHEMICAL KINETICS APPLIED TO A CFD MODEL OF A METHANE/AIR PILOTED JET FLAME

Authors: Ryno LAUBSCHER

Publisher: Yildiz Technical University

Journal: Journal of Thermal Engineering 2018 Vol.4 Issue 2, pp.1828-1846

Document Type: Article

Publication Date: 12/2017

Document Language: Turkish

Document ID (Org No.): 2000506236

Frequency: Bimonthly

eISSN: 2148-7847

DOI: https://doi.org/10.18186/journal-of-thermal-engineering.381838

Issues

Authors:

Ryno LAUBSCHER

Publisher:

Yildiz Technical University

Journal:

Journal of Thermal Engineering 2018 Vol.4 Issue 2, pp.1828-1846

Document Type:

Article

Publication Date:

12/2017

Document Language:

Turkish

Document ID (Org No.):

2000506236

Frequency:

Bimonthly

eISSN:

2148-7847

DOI:

https://doi.org/10.18186/journal-of-thermal-engineering.381838