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Adaptive Neural Network Control of Thermoacoustic Instability in rijke tube: A Fully Actuated System Approach

Journal of Systems Science & Complexity 2022年第2期35卷 586-603页

作者： ZHAO Yuzhuo MA Dan MA Hongwei State Key Laboratory of Synthetical Automation for Process Industries and College of Information Science and Engineering Northeastern UniversityShenyang 110819China College of Information Science and Engineering Northeastern UniversityShenyang 110819China

Thermoacoustic instability phenomena often encounter in gas turbine combustors,especially for the premixed combustor design,with many possible detrimental *** a classical experiment,the rijke tube is the simplest and the most effective illustration to study the thermoacoustic *** paper investigates the active control approach of the thermoacoustic instability in a horizontal rijke ***’s more,the radial basis function(RBF)neural network is adopted to estimate the complex unknown continuous nonlinear heat release rate in the rijke ***,based on the proposed second-order fully actuated system model,the authors present an adaptive neural network controller to guarantee the flow velocity fluctuation and pressure fluctuation to converge to a small region of the ***,simulation results demonstrate the feasibility of the design method.

关键词： Adaptive neural network control fully actuated system nonlinear system rijke tube thermoacoustics instability

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Transfer learning of deep neural networks for predicting thermoacoustic instabilities in combustion systems

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Energy and AI 2021年第3期5卷 339-350页

作者： Sudeepta Mondal Ashesh Chattopadhyay Achintya Mukhopadhyay Asok Ray Department of Mechanical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Mechanical Engineering Rice UniversityHoustonTX 77005USA Department of Mathematics The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Mechanical Engineering Jadavpur UniversityIndia

The intermittent nature of operation and unpredictable availability of renewable sources of energy(e.g.,wind and solar)would require the combustors in fossil-fuel power plants,sharing the same grid,to operate with large turn-down *** brings in new challenges of suppressing high-amplitude pressure oscillations(e.g.,ther-moacoustic instabilities(TAI))in *** pressure oscillations are usually self-sustained,as they occur within a feedback loop,and may induce severe thermomechanical stresses in structural components of combus-tors,which often lead to performance degradation and even system ***,prediction of thermoacoustic instabilities is a critical issue for both design and operation of combustion *** this perspective,it is important to identify operating conditions which can potentially lead to thermoacoustic *** this regard,data-driven approaches have shown considerable success in predicting the instability map as a function of operating ***,often the available data are limited to learn such a relationship efficiently in a data-driven approach for a practical combustion *** this work,a proof-of-concept demonstration of transfer learning is provided,whereby a deep neural network trained on relatively inexpensive experiments in an electrically heated rijke tube has been adapted to predict the unstable operating conditions for a swirl-stabilized lean-premixed laboratory scaled combustor,for which data are expensive to *** operating spaces and underlying flow physics of these two combustion systems are different,and hence this work presents a strong case of using transfer learning as a potential data-driven solution for transferring knowledge across *** results show that the knowledge transfer from the electrically heated rijke tube apparatus helps in formulating an accurate data-driven surrogate model for predicting the unstable operating conditions in the swirl-stabilized combustor,

关键词： Thermoacoustic instabilities Deep learning Transfer learning rijke tube Lean-premixed combustion

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