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Online in situ prediction of 3-D flame evolution from its history 2-D projections via deep learning

Published online by Cambridge University Press:  18 July 2019

Jianqing Huang Open the ORCID record for Jianqing Huang [Opens in a new window] ,
Hecong Liu  and
Weiwei Cai Open the ORCID record for Weiwei Cai [Opens in a new window]
Jianqing Huang
Affiliation:
Key Lab of Education Ministry for Power Machinery and Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
Hecong Liu
Affiliation:
Key Lab of Education Ministry for Power Machinery and Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
Weiwei Cai*
Affiliation:
Key Lab of Education Ministry for Power Machinery and Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
*
†Email address for correspondence: [email protected]
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Abstract

Online in situ prediction of 3-D flame evolution has been long desired and is considered to be the Holy Grail for the combustion community. Recent advances in computational power have facilitated the development of computational fluid dynamics (CFD), which can be used to predict flame behaviours. However, the most advanced CFD techniques are still incapable of realizing online in situ prediction of practical flames due to the enormous computational costs involved. In this work, we aim to combine the state-of-the-art experimental technique (that is, time-resolved volumetric tomography) with deep learning algorithms for rapid prediction of 3-D flame evolution. Proof-of-concept experiments conducted suggest that the evolution of both a laminar diffusion flame and a typical non-premixed turbulent swirl-stabilized flame can be predicted faithfully in a time scale on the order of milliseconds, which can be further reduced by simply using a few more GPUs. We believe this is the first time that online in situ prediction of 3-D flame evolution has become feasible, and we expect this method to be extremely useful, as for most application scenarios the online in situ prediction of even the large-scale flame features are already useful for an effective flame control.

JFM classification

Mathematical Foundations: Computational methods Reacting Flows: Combustion Reacting Flows: Flames
Type
JFM Rapids
Information
Journal of Fluid Mechanics , Volume 875 , 25 September 2019 , R2
Copyright
© 2019 Cambridge University Press 

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Huang et al. supplementary movie 1

The evolution of Flame #1

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The evolution of Flame #2

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