The University of Southampton
University of Southampton Institutional Repository

Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes

Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes
Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes
Premixed flames exhibit different asymptotic regimes of interaction between heat release and turbulence depending on their respective length scales. At high Karlovitz number, the dilatation caused by heat release does not have any relevant effect on turbulent kinetic energy with respect to non-reacting flow, while at low Karlovitz number, the mean shear is a sink of turbulent kinetic energy, and counter-gradient transport is observed. This latter phenomenon is not well captured by closure models commonly used in Large Eddy Simulations that are based on gradient diffusion. The massive amount of data available from Direct Numerical Simulation (DNS) opens the possibility to develop data-driven models able to represent physical mechanisms and non-linear features present in both these regimes. In this work, the databases are formed by DNSs of two planar hydrogen/air flames at different Karlovitz numbers corresponding to the two asymptotic regimes. In this context, the Generative Adversarial Network (GAN) gives the possibility to successfully recognize and reconstruct both gradient and counter-gradient phenomena if trained with databases where both regimes are included. Two GAN models were first trained each for a specific Karlovitz number and tested using the same dataset in order to verify the capability of the models to learn the features of a single asymptotic regime and assess its accuracy. In both cases, the GAN models were able to reconstruct the Reynolds stress subfilter scales accurately. Later, the GAN was trained with a mixture of both datasets to create a model containing physical knowledge of both combustion regimes. This model was able to reconstruct the subfilter scales for both cases capturing the interaction between heat release and turbulence closely to the DNS as shown from the turbulent kinetic budget and barycentric maps.
generative adversarial network, machine learning, Premixed flames, turbulent combustion modeling
0010-2202
3923-3946
Grenga, T.
be0eba30-74b5-4134-87e7-3a2d6dd3836f
Nista, L.
3c47ceab-cf95-418e-a8b3-b1926403faf1
Schumann, C.
9246a4bc-a8df-4663-a033-2468bfe0db59
Karimi, A.N.
3430b3d6-6a7e-467c-8365-425927ab7651
Scialabba, G.
3e339b54-1898-4900-8643-7038115dbdcb
Attili, A.
37ee3caf-698f-4311-8020-cb9f4485def3
Pitsch, H.
3dc0eb6e-deca-4742-98a1-f0cdd62ff8b8
Grenga, T.
be0eba30-74b5-4134-87e7-3a2d6dd3836f
Nista, L.
3c47ceab-cf95-418e-a8b3-b1926403faf1
Schumann, C.
9246a4bc-a8df-4663-a033-2468bfe0db59
Karimi, A.N.
3430b3d6-6a7e-467c-8365-425927ab7651
Scialabba, G.
3e339b54-1898-4900-8643-7038115dbdcb
Attili, A.
37ee3caf-698f-4311-8020-cb9f4485def3
Pitsch, H.
3dc0eb6e-deca-4742-98a1-f0cdd62ff8b8

Grenga, T., Nista, L., Schumann, C., Karimi, A.N., Scialabba, G., Attili, A. and Pitsch, H. (2022) Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes. Combustion Science and Technology, 195 (15), 3923-3946. (doi:10.1080/00102202.2022.2041624).

Record type: Article

Abstract

Premixed flames exhibit different asymptotic regimes of interaction between heat release and turbulence depending on their respective length scales. At high Karlovitz number, the dilatation caused by heat release does not have any relevant effect on turbulent kinetic energy with respect to non-reacting flow, while at low Karlovitz number, the mean shear is a sink of turbulent kinetic energy, and counter-gradient transport is observed. This latter phenomenon is not well captured by closure models commonly used in Large Eddy Simulations that are based on gradient diffusion. The massive amount of data available from Direct Numerical Simulation (DNS) opens the possibility to develop data-driven models able to represent physical mechanisms and non-linear features present in both these regimes. In this work, the databases are formed by DNSs of two planar hydrogen/air flames at different Karlovitz numbers corresponding to the two asymptotic regimes. In this context, the Generative Adversarial Network (GAN) gives the possibility to successfully recognize and reconstruct both gradient and counter-gradient phenomena if trained with databases where both regimes are included. Two GAN models were first trained each for a specific Karlovitz number and tested using the same dataset in order to verify the capability of the models to learn the features of a single asymptotic regime and assess its accuracy. In both cases, the GAN models were able to reconstruct the Reynolds stress subfilter scales accurately. Later, the GAN was trained with a mixture of both datasets to create a model containing physical knowledge of both combustion regimes. This model was able to reconstruct the subfilter scales for both cases capturing the interaction between heat release and turbulence closely to the DNS as shown from the turbulent kinetic budget and barycentric maps.

This record has no associated files available for download.

More information

Accepted/In Press date: 15 January 2022
e-pub ahead of print date: 10 March 2022
Additional Information: Funding Information: Simulations were performed with computing resources granted by RWTH Aachen University under project rwth0658. The authors gratefully acknowledge the computing resources from the DEEP-EST project, which received funding from the European Union’s Horizon 2020 research and innovation program under the grant agreement no. 754304. We thank Mr Sedona for the support in the porting of the application to DEEP-EST. The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation program under the Center of Excellence in Combustion (CoEC) project, grant agreement no. 952181.
Keywords: generative adversarial network, machine learning, Premixed flames, turbulent combustion modeling

Identifiers

Local EPrints ID: 485183
URI: http://eprints.soton.ac.uk/id/eprint/485183
ISSN: 0010-2202
PURE UUID: 9eb6d6f3-ce31-48a9-b1d4-51978d2df587
ORCID for T. Grenga: ORCID iD orcid.org/0000-0002-9465-9505

Catalogue record

Date deposited: 30 Nov 2023 17:58
Last modified: 06 Jun 2024 02:16

Export record

Altmetrics

Contributors

Author: T. Grenga ORCID iD
Author: L. Nista
Author: C. Schumann
Author: A.N. Karimi
Author: G. Scialabba
Author: A. Attili
Author: H. Pitsch

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×