Simple calibrated nonlinear excitation regime two-line atomic fluorescence thermometry

Fang, Bolang; Zhang, Zhenrong; Li, Guohua; Tao, Bo; Wang, Sheng; Hu, Zhiyun; Song, Mingying

doi:10.1364/ol.44.000227

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2013) and the temperature field measured by two-line atomic fluorescence spectroscopy (Hult, Burns & Kaminski 2005; Fang et al. 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Finally, the successful demonstration of our proposed model is just an example application of deep learning in predicting complex flows. It should be emphasized that this method can also be applied to predict the evolution of any other physical parameters/fields of reactive/non-reactive flows, such as the velocity field measured by particle image velocimetry (Baum et al 2013) and the temperature field measured by two-line atomic fluorescence spectroscopy (Hult, Burns & Kaminski 2005;Fang et al 2019).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

2019

View full text Add to dashboard Cite

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.

show abstract

“…2013) and the temperature field measured by two-line atomic fluorescence spectroscopy (Hult, Burns & Kaminski 2005; Fang et al. 2019).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

show abstract

Linearly excited indium fluorescence imaging for temporally resolved high-precision flame thermometry

Fang

Luan

et al. 2020

Opt. Lett.

View full text Add to dashboard Cite

Two-line atomic fluorescence (TLAF) is a promising technique for two-dimensional (2D) flame thermometry. However, it suffers either from a low signal-to-noise ratio (SNR) when excited in the linear regime or a quenching effect and nonlinear behavior in the nonlinear regime. This work aims to develop a new TLAF modality, which can overcome the aforementioned limitations based on a specifically designed laser source that can generate long pulses ( ∼ 400 n s ) with a moderate energy of ∼ 0.9 µ J and operate at a repetition rate up to ∼ 22 k H z . A proof-of-concept experiment was conducted and linearly excited fluorescence images with an SNR up to ∼ 14 were obtained within 1 ms acquisition time by synchronizing the laser with the microchannel plate (MCP) of a 10 Hz-rate intensified camera. The SNR achieved was comparable to that of a traditional nonlinear TLAF implementation and superior to a conventional linear TLAF approach. This approach offers a novel solution for recording linearly excited indium fluorescence images and is expected to make TLAF a temporally resolved and high-precision 2D thermometry for the first time.

show abstract

Simple calibrated nonlinear excitation regime two-line atomic fluorescence thermometry

Cited by 2 publications

References 18 publications

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

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

Linearly excited indium fluorescence imaging for temporally resolved high-precision flame thermometry

Contact Info

Product

Resources

About