Turbulent premixed flame modeling using artificial neural networks based chemical kinetics

2009 ◽  
Vol 32 (1) ◽  
pp. 1605-1611 ◽  
Author(s):  
Baris A. Sen ◽  
Suresh Menon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Kinetics ◽  
Premixed Flame ◽  
Turbulent Premixed Flame ◽  
Artificial Neural ◽  
Turbulent Premixed

Artificial Neural Networks for Modeling of Chemical Source Terms in CFD Simulations of Turbulent Reactive Flows

2014 ◽  
Vol 555 ◽  
pp. 395-400 ◽  
Author(s):  
Ionuţ Porumbel ◽  
Andreea Cristina Petcu ◽  
Florin Gabriel Florean ◽  
Constantin Eusebiu Hritcu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Algorithm ◽  
Stokes Equations ◽  
Combustion Model ◽  
Source Terms ◽  
Reactive Flows ◽  
Turbulent Reactive Flows ◽  
Artificial Neural ◽  
Turbulent Premixed

The main goal of the work presented here was to develop, implement and test a highly efficient numerical algorithm for the evaluation of the chemical reaction source terms that appear in the Navier - Stokes equations when a turbulent, premixed, reactive flow is simulated using a finite rate chemistry combustion model. The approach was based on employing Artificial Neural Networks (ANN) that were designed, trained and incorporated into an existing LEM – LES numerical algorithm. Two numerical simulations of reacting flows have been carried out using several techniques for the estimation of the LES filtered reaction rate for the chemical species in laminar and turbulent, premixed, reactive flows, and the results were compared in terms of numerical accuracy and computational speed. It was concluded that the ANN approach provides a significant speedup of the numerical simulation while preserving acceptable accuracy.

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