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Application of neural dynamic optimization to combustion-instability control

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  • Fichera, A.
  • Pagano, A.

Abstract

The suppression of thermoacoustic combustion instabilities represents one of the main goals in the design of reliable high-performances combustion chambers. Unstable dynamics arise when a non-linear coupling is established between the acoustic field and the flame front generating high-amplitude and low-frequency pressure and heat release oscillations, associated with the excitation of the combustor's natural modes. Temperature and pressure peaks due to these phenomena are particularly harmful for the structural damage they can cause as well as for performance degradations and increase of pollutant emissions. Due to the non-linear nature of the phenomenon, relevant problems arise when it is necessary to define model-based control-systems. The aim of this study is to define a control strategy, based on the application of recent results in the field of neural control of non-linear systems. The proposed strategy is an application of an innovative neural-network-based technique, namely Neural Dynamic Optimization, which is able to exploit the potential of optimal control strategies in dealing with complex non-linear systems and the flexibility and the generalisation properties of neural networks. Reported simulations show the satisfactory performance of the proposed controller in suppressing undesired thermoacoustic combustion instabilities.

Suggested Citation

  • Fichera, A. & Pagano, A., 2006. "Application of neural dynamic optimization to combustion-instability control," Applied Energy, Elsevier, vol. 83(3), pages 253-264, March.
    • Handle: RePEc:eee:appene:v:83:y:2006:i:3:p:253-264
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    References listed on IDEAS

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    1. Cammarata, L. & Fichera, A. & Pagano, A., 2002. "Neural prediction of combustion instability," Applied Energy, Elsevier, vol. 72(2), pages 513-528, June.
    2. Fichera, A. & Losenno, C. & Pagano, A., 2001. "Experimental analysis of thermo-acoustic combustion instability," Applied Energy, Elsevier, vol. 70(2), pages 179-191, October.
    3. Fichera, A. & Losenno, C. & Pagano, A., 2001. "Clustering of chaotic dynamics of a lean gas-turbine combustor," Applied Energy, Elsevier, vol. 69(2), pages 101-117, June.
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    1. Zhang, Zhiguo & Zhao, Dan & Ni, Siliang & Sun, Yuze & Wang, Bing & Chen, Yong & Li, Guoneng & Li, S., 2019. "Experimental characterizing combustion emissions and thermodynamic properties of a thermoacoustic swirl combustor," Applied Energy, Elsevier, vol. 235(C), pages 463-472.
    2. Sun, Yang & Wang, Ligang & Xu, Cheng & Van herle, Jan & Maréchal, François & Yang, Yongping, 2020. "Enhancing the operational flexibility of thermal power plants by coupling high-temperature power-to-gas," Applied Energy, Elsevier, vol. 263(C).
    3. Sun, Yuze & Rao, Zhuming & Zhao, Dan & Wang, Bing & Sun, Dakun & Sun, Xiaofeng, 2020. "Characterizing nonlinear dynamic features of self-sustained thermoacoustic oscillations in a premixed swirling combustor," Applied Energy, Elsevier, vol. 264(C).
    4. Wu, Gang & Lu, Zhengli & Pan, Weichen & Guan, Yiheng & Li, Shihuai & Ji, C.Z., 2019. "Experimental demonstration of mitigating self-excited combustion oscillations using an electrical heater," Applied Energy, Elsevier, vol. 239(C), pages 331-342.
    5. Laera, D. & Campa, G. & Camporeale, S.M., 2017. "A finite element method for a weakly nonlinear dynamic analysis and bifurcation tracking of thermo-acoustic instability in longitudinal and annular combustors," Applied Energy, Elsevier, vol. 187(C), pages 216-227.

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