Abstract
To suppress excessive thermoacoustic instabilities in the gas turbine, it must be possible to predict pressure changes in the combustion chamber. The time-series data of acoustic pressure fluctuations in the Rijke type burner under external sound source interference were studied combined via nonlinear theory, and a new data-driven model for predicting internal sound pressure fluctuations under such conditions was established. An improved particle swarm optimization (PSO) algorithm was proposed to optimize the parameters of the support vector regression (SVR) model, and the parameter optimization time required for the improved PSO algorithm is only 3/5 of that before the improvement. The results show that at least 0.94 ms ahead, the improved data-driven model can accurately predict sound pressure oscillation signals. The improved PSO-SVR model proved to be more accurate than the Multilayer Perceptron (MLP) model and Gaussian process regression (GPR) model in predicting the fluctuation of sound pressure under variable conditions and can provide effective guidance for predicting and eliminating the thermoacoustic oscillations in the actual combustion chambers.
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References
Zhang, S., Jiang, X., Lv, G., et al. 2016. SO2, NOx, HF, HCl and PCDD/Fs emissions during Co-combustion of bituminous coal and pickling sludge in a drop tube furnace. Fuel 186: 91–99. https://doi.org/10.1016/j.fuel.2016.08.061.
Ahn, S.Y., Go, S.M., Lee, K.Y., et al. 2011. The characteristics of NO production mechanism on flue gas recirculation in oxy-firing condition. Applied Thermal Engineering 31: 1163–1171. https://doi.org/10.1016/j.applthermaleng.2010.12.013.
Joo, S., Yoon, J., Kim, J., et al. 2015. NOx emissions characteristics of the partially premixed combustion of H2/CO/CH4 syngas using artificial neural networks. Applied Thermal Engineering 80: 436–444. https://doi.org/10.1016/j.applthermaleng.2015.01.057.
Poinsot, T. 2017. Prediction and control of combustion instabilities in real engines. Proceedings of the Combustion Institute 36: 1–28. https://doi.org/10.1016/j.proci.2016.05.007.
Huang, Y., and Yang, V. 2009. Dynamics and stability of lean-premixed swirl-stabilized combustion. Progress in Energy and Combustion Science 35: 293–364. https://doi.org/10.1016/j.pecs.2009.01.002.
George, N.V., and Panda, G. 2012. A robust filtered-s LMS algorithm for nonlinear active noise control. Applied Acoustics 73: 836–841. https://doi.org/10.1016/j.apacoust.2012.02.005.
Zhao, H., Zeng, X., He, Z., et al. 2013. Nonlinear adaptive filter-based simplified bilinear model for multichannel active control of nonlinear noise processes. Applied Acoustics 74: 1414–1421. https://doi.org/10.1016/j.apacoust.2013.05.010.
Rayleigh, B. 1878. The explanation of certain acoustical phenomena. Nature 18: 319–321. https://doi.org/10.1038/018319a0.
Lieuwen, T., and Zinn, B.T. 1998. The role of equivalence ratio oscillations in driving combustion instabilities in low NOx gas turbines. Symposium (International) on Combustion 27: 1809–1816. https://doi.org/10.1016/s0082-0784(98)80022-2.
Huang, Y., Sung, H.-G., Hsieh, S.-Y., et al. 2003. Large-eddy simulation of combustion dynamics of lean-premixed swirl-stabilized combustor. Journal of Propulsion and Power 19: 782–794. https://doi.org/10.2514/2.6194.
Zhao, D., and Li, L. 2015. Effect of choked outlet on transient energy growth analysis of a thermoacoustic system. Applied Energy 160: 502–510. https://doi.org/10.1016/j.apenergy.2015.09.078.
Li, S., Li, Q., Tang, L., et al. 2016. Theoretical and experimental demonstration of minimizing self-excited thermoacoustic oscillations by applying anti-sound technique. Applied Energy 181: 399–407. https://doi.org/10.1016/j.apenergy.2016.08.069.
Hield, P.A., and Brear, M.J. 2008. Comparison of open and choked premixed combustor exits during thermoacoustic limit cycle. The American Institute of Aeronautics and Astronautics 46: 517–526. https://doi.org/10.2514/1.32650.
Zhao, D., and Morgans, A.S. 2009. Tuned passive control of combustion instabilities using multiple Helmholtz resonators. Journal of Sound and Vibration 320: 744–757. https://doi.org/10.2514/6.2007-3423.
Tran, N., Ducruix, S., and Schuller, T. 2009. Passive control of the inlet acoustic boundary of a swirled burner at high amplitude combustion instabilities. Journal of Engineering for Gas Turbines and Power 131: 051502. https://doi.org/10.1115/1.3078206.
Tran, N., Ducruix, S., and Schuller, T. 2009. Damping combustion instabilities with perforates at the premixer inlet of a swirled burner. Proceedings of the Combustion Institute 32: 2917–2924. https://doi.org/10.1016/j.proci.2008.06.123.
Zhao, D., Morgans, A.S., and Dowling, A.P. 2011. Tuned passive control of acoustic damping of perforated liners. The American Institute of Aeronautics and Astronautics 49: 725–734. https://doi.org/10.2514/1.J050613.
Tao, C., and Zhou, H. 2020. Correlation analysis of oxy-fuel jet in cross-flow on thermoacoustic instability in a model gas turbine combustor. Aerospace Science and Technology 106: 106184. https://doi.org/10.1016/j.ast.2020.106184.
Zhou, H., Tao, C., Liu, Z., et al. 2020. Optimal control of turbulent premixed combustion instability with annular micropore air jets. Aerospace Science and Technology 98: 105650. https://doi.org/10.1016/j.ast.2019.105650.
Oh, S., Ji, H. and Kim, Y. 2017. FDF-based combustion instability analysis for stabilization effects of a slotted plate in a multiple flame combustor. Aerospace Science and Technology 70: 95–107. https://doi.org/10.1016/j.ast.2017.07.045.
Palies, P., Durox, D., Schuller, T., et al. 2010. The combined dynamics of swirler and turbulent premixed swirling flames. Combustion and Flame 157: 1698–1717. https://doi.org/10.1016/j.combustflame.2010.02.011.
Kwon, M., Oh, S., and Kim, Y. 2018. Numerical analysis for attenuation effects of perforated plates on thermoacoustic instability in the multiple flame combustor. Applied Thermal Engineering 132: 321–332. https://doi.org/10.1016/j.applthermaleng.2017.12.081.
Balusamy, S., Li, L.K.B., Han, Z., et al. 2015. Nonlinear dynamics of a self-excited thermoacoustic system subjected to acoustic forcing. Proceedings of the Combustion Institute 35: 3229–3236. https://doi.org/10.1016/j.proci.2014.05.029.
Cammarata, L., Fichera, A., and Pagano, A. 2002. Neural prediction of combustion instability. Applied Energy 72: 513–528. https://doi.org/10.1016/S0306-2619(02)00024-7.
Fichera, A., and Pagano, A. 2006. Application of neural dynamic optimization to combustion-instability control. Applied Energy 83: 253–264. https://doi.org/10.1016/j.apenergy.2005.01.008.
Sarkar, S., Chakravarthy, S.R., Ramanan, V., et al. 2016. Dynamic data-driven prediction of instability in a swirl-stabilized combustor. International Journal of Spray and Combustion Dynamics 8: 235–253. https://doi.org/10.1177/1756827716642091.
Matthaiou, I., Khandelwal, B., Antoniadou, I., et al. Using Gaussian Processes to model combustion dynamics, in: Proceedings of the 24th international congress on sound and vibration, London, July 2017, pp. 23–27.
Takens, F. 1981. Detecting strange attractors in turbulence. Berlin Heidelberg: Springer.
Kabiraj, L., Sujith, R.I., and Wahi, P. 2012. Bifurcations of self-excited ducted laminar premixed flames. Journal of Engineering for Gas Turbines and Power 134: 031502. https://doi.org/10.1115/1.4004402.
Grassberger, P., and Procaccia, I. 1983. Measuring the strangeness of strange attractors. Physica D: Nonlinear Phenomena 9: 189–208. https://doi.org/10.1016/0167-2789(83)90298-1.
Hu, L., Zhang, T., Chen, H., et al. 2020. Amplitude death phenomenon and modulation of thermoacoustic oscillation in cryogenic systems. AIP Advances. 10: 045134. https://doi.org/10.1063/1.5144001.
Hu, L., Liu, Q., Yang, P., et al. 2020. Identification of nonlinear characteristics of thermoacoustic oscillations in helium piping systems. International Communications in Heat and Mass Transfer. https://doi.org/10.1016/j.icheatmasstransfer.2020.104999.
Kim, H.S., Eykholt, R., and Salas, J.D. 1999. Nonlinear dynamics, delay times, and embedding windows. Physica D: Nonlinear Phenomena 127: 48–60. https://doi.org/10.1016/S0167-2789(98)00240-1.
Mukherjee, S., Osuna, E., and Girosi, F. 1997. Nonlinear prediction of chaotic time series using support vector machines, Neural Networks Signal Process. In Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop. Amelia Island, FL, USA, 24–26 September 1997. https://doi.org/10.1109/NNSP.1997.622433.
Zhou, H., Zheng, L., and Cen, K. 2010. Computational intelligence approach for NOx emissions minimization in a coal-fired utility boiler. Energy Conversion and Management 51: 580–586. https://doi.org/10.1016/j.enconman.2009.11.002.
Barbieri, R., Barbieri, N., and De Lima, K.F. 2015. Some applications of the PSO for optimization of acoustic filters. Applied Acoustics 89: 62–70. https://doi.org/10.1016/j.apacoust.2014.09.007.
El Hamzaoui, Y., Rodríguez, J.A., Hernández, J.A., et al. 2015. Optimization of operating conditions for steam turbine using an artificial neural network inverse. Applied Thermal Engineering 75: 648–657. https://doi.org/10.1016/j.applthermaleng.2014.09.065.
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This work was supported by the National Science Fund for Distinguished Young Scholars (No. 51825605).
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Liu, ZH., Zhou, H., Tao, CF. et al. Prediction of sound pressure fluctuations in the start-up phase of thermoacoustic oscillations under external perturbation. Waste Dispos. Sustain. Energy 3, 21–30 (2021). https://doi.org/10.1007/s42768-020-00065-6
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DOI: https://doi.org/10.1007/s42768-020-00065-6