Tabulation of combustion chemistry via Artificial Neural Networks (ANNs): Methodology and application to LES-PDF simulation of Sydney flame L.
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| Title: | Tabulation of combustion chemistry via Artificial Neural Networks (ANNs): Methodology and application to LES-PDF simulation of Sydney flame L. |
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| Authors: | Franke, Lucas L.C.1, Chatzopoulos, Athanasios K.1, Rigopoulos, Stelios1 s.rigopoulos@imperial.ac.uk |
| Source: | Combustion & Flame. Nov2017, Vol. 185, p245-260. 16p. |
| Subjects: | Combustion chambers, Artificial neural networks, Laminar flow, Mixtures, Strains & stresses (Mechanics), Large eddy simulation models |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Combustion & Flame is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Tabulation of combustion chemistry via Artificial Neural Networks (ANNs): Methodology and application to LES-PDF simulation of Sydney flame L. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Franke%2C+Lucas+L%2EC%2E%22">Franke, Lucas L.C.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Chatzopoulos%2C+Athanasios+K%2E%22">Chatzopoulos, Athanasios K.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Rigopoulos%2C+Stelios%22">Rigopoulos, Stelios</searchLink><relatesTo>1</relatesTo><i> s.rigopoulos@imperial.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Combustion+%26+Flame%22">Combustion & Flame</searchLink>. Nov2017, Vol. 185, p245-260. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Combustion+chambers%22">Combustion chambers</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Laminar+flow%22">Laminar flow</searchLink><br /><searchLink fieldCode="DE" term="%22Mixtures%22">Mixtures</searchLink><br /><searchLink fieldCode="DE" term="%22Strains+%26+stresses+%28Mechanics%29%22">Strains & stresses (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Large+eddy+simulation+models%22">Large eddy simulation models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Combustion & Flame is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.combustflame.2017.07.014 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 245 Subjects: – SubjectFull: Combustion chambers Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Laminar flow Type: general – SubjectFull: Mixtures Type: general – SubjectFull: Strains & stresses (Mechanics) Type: general – SubjectFull: Large eddy simulation models Type: general Titles: – TitleFull: Tabulation of combustion chemistry via Artificial Neural Networks (ANNs): Methodology and application to LES-PDF simulation of Sydney flame L. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Franke, Lucas L.C. – PersonEntity: Name: NameFull: Chatzopoulos, Athanasios K. – PersonEntity: Name: NameFull: Rigopoulos, Stelios IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 00102180 Numbering: – Type: volume Value: 185 Titles: – TitleFull: Combustion & Flame Type: main |
| ResultId | 1 |
