Modelling the Temporal Evolution of a Reduced Combustion Chemical System With an Artificial Neural Network

Blasco, J.A.; Fueyo, Norberto; Dopazo, César; Ballester, Javier

doi:10.1016/s0010-2180(97)00211-3

Cited by 140 publications

(74 citation statements)

References 9 publications

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“…The fundamentals of ANNs and their applicability to modeling combustion chemistry have been described in multiple publications [12][13][14][15][16]. Mathematically, the neural network is a universal approximator for non-linear functions that predicts an output value (or values) based on a set of input parameters and a training procedure.…”

Section: The Artificial Neural Network (Ann)mentioning

confidence: 99%

Fast Prediction of HCCI Combustion with an Artificial Neural Network Linked to a Fluid Mechanics Code

Aceves

Flowers

Chen

et al. 2006

SAE Technical Paper Series

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We have developed an artificial neural network (ANN) based combustion model and have integrated it into a fluid mechanics code (KIVA3V) to produce a new analysis tool (titled KIVA3V-ANN) that can yield accurate HCCI predictions at very low computational cost. The neural network predicts ignition delay as a function of operating parameters (temperature, pressure, equivalence ratio and residual gas fraction). KIVA3V-ANN keeps track of the time history of the ignition delay during the engine cycle to evaluate the ignition integral and predict ignition for each computational cell. After a cell ignites, chemistry becomes active, and a two-step chemical kinetic mechanism predicts composition and heat generation in the ignited cells. KIVA3V-ANN has been validated by comparison with isooctane HCCI experiments in two different engines.The neural network provides reasonable predictions for HCCI combustion and emissions that, although typically not as good as obtained with the more physically representative multi-zone model, are obtained at a much reduced computational cost. KIVA3V-ANN can perform reasonably accurate HCCI calculations while requiring only 10% more computational effort than a motored KIVA3V run. It is therefore considered a valuable tool for evaluation of engine maps or other performance analysis tasks requiring multiple individual runs.

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Section: The Artificial Neural Network (Ann)mentioning

confidence: 99%

Fast Prediction of HCCI Combustion with an Artificial Neural Network Linked to a Fluid Mechanics Code

Aceves

Flowers

Chen

et al. 2006

SAE Technical Paper Series

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“…Recently, artificial neural networks (ANN) have been used to improve computational times and memory for reduced chemical mechanisms (Christo et al, 1996;Blasco, et al, 1998) in full combustor simulations. As described by Blasco et al (1998), an ANN consists of interconnected layers of non-linear processing elements, which resemble biological neurons.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…As described by Blasco et al (1998), an ANN consists of interconnected layers of non-linear processing elements, which resemble biological neurons. This network stores the information in the neuron interconnections (with weights).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The learning algorithm utilized is of lesser importance in terms of the overall ANN accuracy. Georgia Tech used both a three-layer scaled conjugate gradient (SCG) back propagation network (Christo et al, 1996) and a Levenberg-Marquardt back propagation network (Blasco et al, 1998). Tests were also performed to determine the optimum input-output range and transformations for the ANN.…”

Section: Oh Contour Predictions At 6 Msec Using Direct Integration Anmentioning

confidence: 99%

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Les Software for the Design of Low Emission Combustion Systems for Vision 21 Plants

Smith¹,

Cannon²,

Adumitroaie³

et al. 2005

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In this project, an advanced computational software tool was developed for the design of low emission combustion systems required for Vision 21 clean energy plants. Vision 21 combustion systems, such as combustors for gas turbines, combustors for indirect fired cycles, furnaces and sequestrian-ready combustion systems, will require innovative low emission designs and low development costs if Vision 21 goals are to be realized. The simulation tool will greatly reduce the number of experimental tests; this is especially desirable for gas turbine combustor design since the cost of the high pressure testing is extremely costly. In addition, the software will stimulate new ideas, will provide the capability of assessing and adapting low-emission combustors to alternate fuels, and will greatly reduce the development time cycle of combustion systems.The revolutionary combustion simulation software is able to accurately simulate the highly transient nature of gaseous-fueled (e.g. natural gas, low BTU syngas, hydrogen, biogas etc.) turbulent combustion and assess innovative concepts needed for Vision 21 plants. In addition, the software is capable of analyzing liquid-fueled combustion systems since that capability was developed under a concurrent Air Force Small Business Innovative Research (SBIR) program. The complex physics of the reacting flow field are captured using 3D Large Eddy Simulation (LES) methods, in which large scale transient motion is resolved by time-accurate numerics, while the small scale motion is modeled using advanced subgrid turbulence and chemistry closures. In this way, LES combustion simulations can model many physical aspects that, until now, were impossible to predict with 3D steady-state Reynolds Averaged Navier-Stokes (RANS) analysis, i.e. very low NO x emissions, combustion instability (coupling of unsteady heat and acoustics), lean blowout, flashback, autoignition, etc. LES methods are becoming more and more practical by linking together tens to hundreds of PCs and performing parallel computations with fine grids (millions of cells). Such simulations, performed in a few weeks or less, provide a very cost-effective complement to experimental testing. In 5 years, these same calculations can be performed in 24 hours or less due to the expected increase of computing power and improved numerical techniques.This project was a four-year program. During the first year, the project included the development and implementation of improved chemistry (reduced GRI mechanism), subgrid turbulence (localized dynamic), and subgrid combustion-turbulence interaction (Linear Eddy) models into the CFD-ACE+ code. University expertise (Georgia Tech and University of California, Berkeley) was utilized to help develop and implement these advanced submodels into the unstructured, parallel CFD flow solver, CFD-ACE+. Efficient numerical algorithms that rely on in situ look-up tables or artificial neural networks were implemented for chemistry calculations. In the second year, the combustion LES software was ev...

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“…Yang et al [12] and Ramadhas et al [13] used ANNs for predicting the cetane number for the mixtures of fuels, while Lee et al [14] used ANNs for modelling the range of fuel injection to the engine cylinder chamber. ANNs were also used in the combustion process models to reduce the cost of the algorithm calculation [15] - [21], and for determining specific fuel consumption [22,23], combustion process temperature [24], air/fuel equivalence ratio [25], the emission of carbon oxide and hydrocarbons [26] - [28], and even failures of piston engines [29].…”

Section: Introductionmentioning

confidence: 99%

ANN based evaluation of the NOx concentration in the exhaust gas of a marine two-stroke diesel engine

Kowalski¹

2009

Polish Maritime Research

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Modelling the Temporal Evolution of a Reduced Combustion Chemical System With an Artificial Neural Network

Cited by 140 publications

References 9 publications

Fast Prediction of HCCI Combustion with an Artificial Neural Network Linked to a Fluid Mechanics Code

Fast Prediction of HCCI Combustion with an Artificial Neural Network Linked to a Fluid Mechanics Code

Les Software for the Design of Low Emission Combustion Systems for Vision 21 Plants

ANN based evaluation of the NOx concentration in the exhaust gas of a marine two-stroke diesel engine

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