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Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels

Energies (Basel), 2024-06, Vol.17 (11), p.2604

2024

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Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames

International journal of engine research, 2020-01, Vol.21 (1), p.151-168

2020

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Thermal radiation modeling using the LES–ODT framework for turbulent combustion flows

International journal of heat and mass transfer, 2017-01, Vol.104, p.1300-1316

2017

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A Data-Based Hybrid Chemistry Acceleration Framework for the Low-Temperature Oxidation of Complex Fuels

Energies (Basel), 2024-02, Vol.17 (3), p.734

2024

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Experiment-Based Modeling of Turbulent Flames with Inhomogeneous Inlets

Flow, turbulence and combustion, 2022-04, Vol.108 (4), p.1043-1067

2022

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A framework for data-based turbulent combustion closure: A posteriori validation

Combustion and flame, 2019-12, Vol.210, p.279-291

2019

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A data-based hybrid model for complex fuel chemistry acceleration at high temperatures

Combustion and flame, 2021-01, Vol.223, p.142-152

2021

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Combustion chemistry acceleration with DeepONets

Fuel (Guildford), 2024-06, Vol.365, p.131212, Article 131212

2024

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A framework for data-based turbulent combustion closure: A priori validation

Combustion and flame, 2019-08, Vol.206, p.490-505

2019

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Derived scalar statistics from multiscalar measurements via surrogate composition spaces

Combustion and flame, 2023-04, Vol.250, p.112641, Article 112641

2023

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Direct numerical simulation of autoignition in non-homogeneous hydrogen-air mixtures

Combustion and flame, 2003-08, Vol.134 (3), p.169-191

2003

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Principal component transport in turbulent combustion: A posteriori analysis

Combustion and flame, 2015-05, Vol.162 (5), p.1919-1933

2015

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Asymptotic analysis of steady two-reactant premixed flames using a step-function reaction rate model

Combustion and flame, 2016-10, Vol.172, p.280-288

Echekki, Tarek

2016

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In the rain with and without an umbrella? The Reynolds transport theorem to the rescue

European journal of physics, 2020-01, Vol.41 (1), p.15002

Echekki, Tarek

2020

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Nonlinear reduction of combustion composition space with kernel principal component analysis

Combustion and flame, 2014-01, Vol.161 (1), p.118-126

2014

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Toward computationally efficient combustion DNS with complex fuels via principal component transport

Combustion theory and modelling, 2017-07, Vol.21 (4), p.770-798

2017

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The reconstruction of thermo-chemical scalars in combustion from a reduced set of their principal components

Combustion and flame, 2015-05, Vol.162 (5), p.1650-1652

2015

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A novel principal component analysis-based acceleration scheme for LES–ODT: An a priori study

Combustion and flame, 2013-05, Vol.160 (5), p.898-908

2013

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Physics-Informed Neural Networks for Turbulent Combustion: Toward Extracting More Statistics and Closure from Point Multiscalar Measurements

Energy & fuels, 2023-11, Vol.37 (22), p.17484-17498

2023

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Analysis of the contribution of curvature to premixed flame propagation

Combustion and flame, 1999-07, Vol.118 (1), p.308-311

1999

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ML for reacting flows _ editorial

Applications in energy and combustion science, 2023-12, Vol.16, p.100208, Article 100208

2023

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An ANN based hybrid chemistry framework for complex fuels

Fuel (Guildford), 2019-04, Vol.241, p.625-636

2019

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Principal component analysis based combustion model in the context of a lifted methane/air flame: Sensitivity to the manifold parameters and subgrid closure

Combustion and flame, 2022-10, Vol.244, p.112134, Article 112134

2022

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Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion

Combustion and flame, 2022-02, Vol.236, p.111814, Article 111814

2022

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Deep Learning of Joint Scalar PDFs in Turbulent Flames from Sparse Multiscalar Data

Combustion science and technology, 2023-11, p.1-22

2023

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Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels

Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames

Thermal radiation modeling using the LES–ODT framework for turbulent combustion flows

A Data-Based Hybrid Chemistry Acceleration Framework for the Low-Temperature Oxidation of Complex Fuels

Experiment-Based Modeling of Turbulent Flames with Inhomogeneous Inlets

A framework for data-based turbulent combustion closure: A posteriori validation

A data-based hybrid model for complex fuel chemistry acceleration at high temperatures

Combustion chemistry acceleration with DeepONets

A framework for data-based turbulent combustion closure: A priori validation

Derived scalar statistics from multiscalar measurements via surrogate composition spaces

Direct numerical simulation of autoignition in non-homogeneous hydrogen-air mixtures

Principal component transport in turbulent combustion: A posteriori analysis

Asymptotic analysis of steady two-reactant premixed flames using a step-function reaction rate model

In the rain with and without an umbrella? The Reynolds transport theorem to the rescue

Nonlinear reduction of combustion composition space with kernel principal component analysis

Toward computationally efficient combustion DNS with complex fuels via principal component transport

The reconstruction of thermo-chemical scalars in combustion from a reduced set of their principal components

A novel principal component analysis-based acceleration scheme for LES–ODT: An a priori study

Physics-Informed Neural Networks for Turbulent Combustion: Toward Extracting More Statistics and Closure from Point Multiscalar Measurements

Analysis of the contribution of curvature to premixed flame propagation

ML for reacting flows _ editorial

An ANN based hybrid chemistry framework for complex fuels

Principal component analysis based combustion model in the context of a lifted methane/air flame: Sensitivity to the manifold parameters and subgrid closure

Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion

Deep Learning of Joint Scalar PDFs in Turbulent Flames from Sparse Multiscalar Data

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