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Combustion Science and Technology Volume 194, 2022 - Issue 8
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Research Article

Deep Residual Networks for Flamelet/progress Variable Tabulation with Application to a Piloted Flame with Inhomogeneous Inlet

Maximilian HansingerInstitute for Thermodynamics, University Bundeswehr Munich, Neubiberg, GermanyCorrespondence[email protected]
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Yipeng GeInstitute for Thermodynamics, University Bundeswehr Munich, Neubiberg, GermanyORCID Iconhttps://orcid.org/0000-0003-2176-7932View further author information
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Michael PfitznerInstitute for Thermodynamics, University Bundeswehr Munich, Neubiberg, GermanyView further author information
Pages 1587-1613 | Received 23 Jan 2020, Accepted 09 Sep 2020, Published online: 24 Sep 2020

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Paola Breda, Chunkan Yu, Ulrich Maas & Michael Pfitzner. (2022) Reaction-Diffusion Manifolds including differential diffusion applied to methane/air combustion in strong extinction regimes. Combustion Theory and Modelling 26:3, pages 451-481.
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Articles from other publishers (12)

Anxiong Liu, Tianjie Ding, Runzhi Liu, Stelios Rigopoulos & Kun Luo. (2024) Machine learning tabulation of thermochemistry for turbulent dimethyl ether (DME) flames. Fuel 360, pages 130338.
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Kaimeng Li, Pourya Rahnama, Ricardo Novella & Bart Somers. (2023) Combining flamelet-generated manifold and machine learning models in simulation of a non-premixed diffusion flame. Energy and AI 14, pages 100266.
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Xi Chen, Cédric Mehl, Thibault Faney & Florent Di Meglio. (2023) Clustering-Enhanced Deep Learning Method for Computation of Full Detailed Thermochemical States via Solver-Based Adaptive Sampling. Energy & Fuels 37:18, pages 14222-14239.
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Cédric Mehl & Damien Aubagnac-Karkar. (2023) On-the-fly accuracy evaluation of artificial neural networks and hybrid method to improve the robustness of neural network accelerated chemistry solving. Physics of Fluids 35:6.
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Thomas Readshaw, W. P. Jones & Stelios Rigopoulos. (2023) On the incorporation of conservation laws in machine learning tabulation of kinetics for reacting flow simulation. Physics of Fluids 35:4.
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Cheng Chi, Xiaopeng Xu & Dominique Thévenin. (2022) Efficient premixed turbulent combustion simulations using flamelet manifold neural networks: A priori and a posteriori assessment. Combustion and Flame 245, pages 112325.
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Junsu Shin, Maximilian Hansinger, Michael Pfitzner & Markus Klein. (2022) A Priori Analysis on Deep Learning of Filtered Reaction Rate. Flow, Turbulence and Combustion 109:2, pages 383-409.
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A. Khalifa, M. Breuer & J. Gollwitzer. (2022) Neural-network based approach for modeling wall-impact breakage of agglomerates in particle-laden flows applied in Euler–Lagrange LES. International Journal of Heat and Fluid Flow 94, pages 108897.
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Lei Zhou, Yuntong Song, Weiqi Ji & Haiqiao Wei. (2022) Machine learning for combustion. Energy and AI 7, pages 100128.
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Opeoluwa Owoyele & Pinaki Pal. (2022) ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers. Energy and AI 7, pages 100118.
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Thomas Readshaw, Tianjie Ding, Stelios Rigopoulos & W. P. Jones. (2021) Modeling of turbulent flames with the large eddy simulation–probability density function (LES–PDF) approach, stochastic fields, and artificial neural networks. Physics of Fluids 33:3.
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Lorenzo Angelilli, Pietro Paolo CiottoliRiccardo Malpica GalassiFrancisco E. Hernandez Perez, Mattia Soldan, Zhen Lu, Mauro ValoraniHong G. Im. (2021) Large eddy simulation with flamelet progress variable approach combined with artificial neural network acceleration. Large eddy simulation with flamelet progress variable approach combined with artificial neural network acceleration.

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