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High-speed planar imaging of OH radicals in turbulent flames assisted by deep learning

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Abstract

High-speed planar imaging of key combustion species, like hydroxyl radicals (OH), is crucial for understanding the complex chemistry–turbulence interactions in turbulent flames. However, conducting high-speed (kHz) diagnostics is challenging due to the requirements on advanced optical system, including both fast lasers and cameras. In this paper, we report a computational imaging method to artificially achieve higher diagnosing rates based on experimental data at relatively low rates. Sequencies of planar laser-induced fluorescence (PLIF) of OH recorded at 100 kHz in a turbulent flame were first down sampled to 50 kHz, 33.3 kHz and 20 kHz, respectively, and then used as a data source to train several networks. The accuracies of the models were assessed by comparing the predicted images with those from laser measurements. It was found that, among the models tested, convolutional long short-term memory network (CONV-LSTM) can provide the best predictions and is reliable in predicting consecutive images with higher repetition rates. The model can also generate consecutive OH-PLIF images at 200 kHz based on the 100 kHz experimental data. This work sheds light on the hybrid of deep learning-based computational methods with conventional high-speed laser diagnosing techniques, which can potentially increase the temporal resolution of planar optical measurement in turbulent flows, and significantly reduce the computational timing.

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References

  1. K. Kohse-Höinghaus et al., Combustion at the focus: laser diagnostics and control. Proc. Combust. Inst. 30(1), 89–123 (2005)

    Article  Google Scholar 

  2. V. Sick, High speed imaging in fundamental and applied combustion research. Proc. Combust. Inst. 34(2), 3509–3530 (2013)

    Article  Google Scholar 

  3. R.K. Hanson, J.M. Seitzman, P.H. Paul, Planar laser-fluorescence imaging of combustion gases. Appl. Phys. B 50(6), 441–454 (1990)

    Article  ADS  Google Scholar 

  4. G. Grünefeld, M. Schütte, P. Andresen, Simultaneous multiple-line Raman/Rayleigh/LIF measurements in combustion. Appl. Phys. B 70(2), 309–313 (2000)

    Article  ADS  Google Scholar 

  5. U. Retzer et al., Burst-mode OH/CH2O planar laser-induced fluorescence imaging of the heat release zone in an unsteady flame. Opt. Express 26(14), 18105–18114 (2018)

    Article  ADS  Google Scholar 

  6. M.N. Slipchenko, T.R. Meyer, S. Roy, Advances in burst-mode laser diagnostics for reacting and nonreacting flows. Proc. Combust. Inst. 38(1), 1533–1560 (2021)

    Article  Google Scholar 

  7. M.E. Smyser et al., Compact burst-mode Nd:YAG laser for kHz–MHz bandwidth velocity and species measurements. Opt. Lett. 43(4), 735–738 (2018)

    Article  ADS  Google Scholar 

  8. C. Yang, H. Tang, G. Magnotti, High-speed 1D Raman analyzer for temperature and major species measurements in a combustion environment. Opt. Lett. 45(10), 2817–2820 (2020)

    Article  ADS  Google Scholar 

  9. S. Roy et al., 100-ps-pulse-duration, 100-J burst-mode laser for kHz–MHz flow diagnostics. Opt. Lett. 39(22), 6462–6465 (2014)

    Article  ADS  Google Scholar 

  10. M.N. Slipchenko et al., Quasi-continuous burst-mode laser for high-speed planar imaging. Opt. Lett. 37(8), 1346–1348 (2012)

    Article  ADS  Google Scholar 

  11. C. Fu et al., Experimental investigation on an acoustically forced flame with simultaneous high-speed LII and stereo PIV at 20 kHz. Appl. Opt. 58(10), C104–C111 (2019)

    Article  Google Scholar 

  12. J.B. Michael et al., 100 kHz thousand-frame burst-mode planar imaging in turbulent flames. Opt Lett 39(4), 739–742 (2014)

    Article  ADS  Google Scholar 

  13. T.A. McManus et al., Spatio-temporal characteristics of temperature fluctuations in turbulent non-premixed jet flames. Proc. Combust. Inst. 35(2), 1191–1198 (2015)

    Article  Google Scholar 

  14. R.A. Patton et al., Multi-kHz temperature imaging in turbulent non-premixed flames using planar Rayleigh scattering. Appl. Phys. B 108(2), 377–392 (2012)

    Article  ADS  Google Scholar 

  15. S. Roy et al., 100-kHz-rate gas-phase thermometry using 100-ps pulses from a burst-mode laser. Opt Lett 40(21), 5125–5128 (2015)

    Article  ADS  Google Scholar 

  16. J.D. Miller et al., Spatiotemporal analysis of turbulent jets enabled by 100-kHz, 100-ms burst-mode particle image velocimetry. Exp. Fluids 57(12), 192 (2016)

    Article  Google Scholar 

  17. J.J. Philo, M.D. Frederick, C.D. Slabaugh, 100 kHz PIV in a liquid-fueled gas turbine swirl combustor at 1 MPa. Proc. Combust. Inst. 38(1), 1571–1578 (2021)

    Article  Google Scholar 

  18. B. Peterson et al., An experimental study of the detailed flame transport in a SI engine using simultaneous dual-plane OH-LIF and stereoscopic PIV. Combust. Flame 202, 16–32 (2019)

    Article  Google Scholar 

  19. I. Boxx et al., High-speed laser diagnostics for the study of flame dynamics in a lean premixed gas turbine model combustor. Exp. Fluids 52(3), 555–567 (2012)

    Article  Google Scholar 

  20. R. Wellander, M. Richter, M. Aldén, Time-resolved (kHz) 3D imaging of OH PLIF in a flame. Exp. Fluids 55(6), 1764 (2014)

    Article  Google Scholar 

  21. S.D. Hammack et al., CH PLIF and PIV implementation using C-X (0,0) and intra-vibrational band filtered detection. Appl. Phys. B (2018). https://doi.org/10.1007/s00340-017-6883-8

    Article  Google Scholar 

  22. J. Sjöholm et al., Ultra-high-speed pumping of an optical parametric oscillator (OPO) for high-speed laser-induced fluorescence measurements. Measur Sci Technol 20(2), 025306 (2009)

    Article  ADS  Google Scholar 

  23. J. Sjöholm et al., Simultaneous visualization of OH, CH, CH2O and toluene PLIF in a methane jet flame with varying degrees of turbulence. Proc. Combust. Inst. 34(1), 1475–1482 (2013)

    Article  Google Scholar 

  24. Z. Wang et al., Ultra-high-speed PLIF imaging for simultaneous visualization of multiple species in turbulent flames. Opt. Express 25(24), 30214–30228 (2017)

    Article  ADS  Google Scholar 

  25. J.D. Miller et al., Ultrahigh-frame-rate OH fluorescence imaging in turbulent flames using a burst-mode optical parametric oscillator. Opt. Lett. 34(9), 1309–1311 (2009)

    Article  ADS  Google Scholar 

  26. W. Zhang et al., Generating planar distributions of soot particles from luminosity images in turbulent flames using deep learning. Appl. Phys. B (2021). https://doi.org/10.1007/s00340-020-07571-9

    Article  Google Scholar 

  27. C.-S. Liu, R.-C. Song, S.-J. Fu, Design of a laser-based autofocusing microscope for a sample with a transparent boundary layer. Appl. Phys. B 125(11), 199 (2019)

    Article  ADS  Google Scholar 

  28. W. Zhang et al., 100 kHz CH2O imaging realized by lower speed planar laser-induced fluorescence and deep learning. Opt. Express 29(19), 30857–30877 (2021)

    Article  ADS  Google Scholar 

  29. T. Li, Z. Zhang, H. Chen, Predicting the combustion state of rotary kilns using a Convolutional Recurrent Neural Network. J. Process Control 84, 207–214 (2019)

    Article  Google Scholar 

  30. Z.C. Lipton, J. Berkowitz, C. Elkan, A critical review of recurrent neural networks for sequence learning.. 2015. arXiv e-prints. arXiv:1506.00019

  31. S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  32. J. Chung, et al., Empirical evaluation of gated recurrent neural networks on sequence modeling., 2014. arXiv e-prints. arXiv:1412.3555.

  33. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  34. Z. Leihong et al., Restoration of single pixel imaging in atmospheric turbulence by fourier filter and CGAN. Appl. Phys. B (2021). https://doi.org/10.1007/s00340-021-07596-8

    Article  Google Scholar 

  35. J. Li et al., Object identification in computational ghost imaging based on deep learning. Appl. Phys. B (2020). https://doi.org/10.1007/s00340-020-07514-4

    Article  Google Scholar 

  36. S. Hong, et al. PSIque: Next sequence prediction of satellite images using a convolutional sequence-to-sequence network, 2017. .arXiv:1711.10644

  37. X. Shi, et al., Convolutional LSTM network: a machine learning approach for precipitation nowcasting, 2015. arXiv:1506.04214

  38. S. Kim, et al., DeepRain: ConvLSTM network for precipitation prediction using multichannel radar data, 2017. arXiv:1711.02316

  39. C. Finn, I. Goodfellow, S. Levine, Unsupervised learning for physical interaction through video prediction, 2016. arXiv:1605.07157

  40. W. Lotter, G. Kreiman, D. Cox, Deep predictive coding networks for video prediction and unsupervised learning, 2016. arXiv:1605.08104

  41. V. Patraucean, A. Handa, R. Cipolla, Spatio-temporal video autoencoder with differentiable memory, 2015. arXiv:1511.06309

  42. Z. Wang et al., Investigation of OH and CH2O distributions at ultra-high repetition rates by planar laser induced fluorescence imaging in highly turbulent jet flames. Fuel 234, 1528–1540 (2018)

    Article  Google Scholar 

  43. R. Hanson, Combustion diagnostics: planar imaging techniques. Symp (Int) Combust 21, 1677–1691 (1988)

    Article  Google Scholar 

  44. J. Rosell et al., Multi-species PLIF study of the structures of turbulent premixed methane/air jet flames in the flamelet and thin-reaction zones regimes. Combust. Flame 182, 324–338 (2017)

    Article  Google Scholar 

  45. B. Zhou et al., Distributed reactions in highly turbulent premixed methane/air flames: Part I Flame structure characterization. Combust. Flame 162(7), 2937–2953 (2015)

    Article  Google Scholar 

  46. B. Zhou et al., Simultaneous multi-species and temperature visualization of premixed flames in the distributed reaction zone regime. Proc. Combust. Inst. 35(2), 1409–1416 (2015)

    Article  Google Scholar 

  47. D.-A. Clevert, T. Unterthiner, S. Hochreiter, Fast and accurate deep network learning by exponential linear units (elus) 2015. arXiv preprint arXiv:1511.07289

  48. Klambauer, G., et al. Self-normalizing neural networks. In 31st Annual Conference on Neural Information Processing Systems, NIPS 2017, December 4, 2017 - December 9, 2017. 2017. Long Beach, CA, United states: Neural information processing systems foundation.

  49. V. Nair, G.E. Hinton, Rectified linear units improve Restricted Boltzmann machines. In 27th International Conference on Machine Learning, ICML 2010, June 21, 2010 - June 25, 2010. 2010. Haifa, Israel: Unavailable.

  50. I. Sutskever, O. Vinyals, Q.V. Le, Sequence to sequence learning with neural networks. In 28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014, December 8, 2014–December 13, 2014. 2014. Montreal, QC, Canada: Neural information processing systems foundation.

  51. N. Srivastava, E. Mansimov, R. Salakhutdinov. Unsupervised learning of video representations using LSTMs. In 32nd International Conference on Machine Learning, ICML 2015, July 6, 2015–July 11, 2015. 2015. Lile, France: International Machine Learning Society (IMLS).

  52. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)

    Article  ADS  Google Scholar 

  53. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)

    Article  ADS  Google Scholar 

  54. D.P. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, 2014. arXiv:1412.6980

  55. W. Zhou et al., Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  56. J. Redmon, et al. You Only Look Once: Unified, Real-Time Object Detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016.

  57. H. Belmabrouk, M. Michard, Taylor length scale measurement by laser Doppler velocimetry. Exp. Fluids 25(1), 69–76 (1998)

    Article  Google Scholar 

  58. J.I. Liang, J. Piper, J.Y. Tang, Erosion and dilation of binary images by arbitrary structuring elements using interval coding. Pattern Recogn. Lett. 9(3), 201–209 (1989)

    Article  ADS  Google Scholar 

  59. W. Lorensen, H. Cline, Marching cubes: a high resolution 3d surface construction algorithm. ACM SIGGRAPH Comput. Graph. 21, 163 (1987)

    Article  Google Scholar 

  60. W. Kesheng, O. Ekow, S. Arie. Optimizing connected component labeling algorithms. In Proc. SPIE. 2005.

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Acknowledgements

This research was financially supported by the National Natural Science Foundation of China under Grant No. 52006137, as well as Shanghai Sailing Program, under Grant No. 19YF1423400.

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Authors and Affiliations

  1. China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai, 200240, China

    Hao Guo, Wei Zhang, Xiangyu Nie & Xue Dong

  2. School of Mechanical Engineering and Centre for Energy Technology (CET), The University of Adelaide, Adelaide, SA, 5005, Australia

    Zhiwei Sun

  3. Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, People’s Republic of China

    Bo Zhou

  4. Division of Combustion Physics, Lund University, P. O. Box 118, 221 00, Lund, Sweden

    Zhenkan Wang & Mattias Richter

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  1. Hao Guo
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Correspondence to Xue Dong.

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Guo, H., Zhang, W., Nie, X. et al. High-speed planar imaging of OH radicals in turbulent flames assisted by deep learning. Appl. Phys. B 128, 52 (2022). https://doi.org/10.1007/s00340-021-07742-2

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