扬声器布置在Rijke管型燃烧器入口和出口处,输出不同频率和声压级的声波可以对热声不稳定进行开环主动控制。试验发现在热声管燃料入口燃料管路布置扬声器是一种有效的热声不稳定主动控制方法,扬声器发出的声波可以使热声不稳定的压力...扬声器布置在Rijke管型燃烧器入口和出口处,输出不同频率和声压级的声波可以对热声不稳定进行开环主动控制。试验发现在热声管燃料入口燃料管路布置扬声器是一种有效的热声不稳定主动控制方法,扬声器发出的声波可以使热声不稳定的压力振荡有所衰减,同时也会产生新的和声波同频的压力振荡;在某些工况下,和声波同频的压力振荡幅值甚至会高于热声不稳定的压力振荡幅值;在该试验中,当扬声器声波频率为50 Hz、声压级为100 d B时,控制效果最好,达到38.55%。布置在热声管开口端的扬声器也是一种有效的热声不稳定主动控制方法,在该试验中,当扬声器声波频率为300 Hz、声压级为115 d B时,控制效果最好,此时控制效果可达34.58%。展开更多
Thermoacoustic instability phenomena often encounter in gas turbine combustors,especially for the premixed combustor design,with many possible detrimental results.As a classical experiment,the Rijke tube is the simple...Thermoacoustic instability phenomena often encounter in gas turbine combustors,especially for the premixed combustor design,with many possible detrimental results.As a classical experiment,the Rijke tube is the simplest and the most effective illustration to study the thermoacoustic instability.This paper investigates the active control approach of the thermoacoustic instability in a horizontal Rijke tube.What’s more,the radial basis function(RBF)neural network is adopted to estimate the complex unknown continuous nonlinear heat release rate in the Rijke tube.Then,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 origin.Finally,simulation results demonstrate the feasibility of the design method.展开更多
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 lar...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 ratios.This brings in new challenges of suppressing high-amplitude pressure oscillations(e.g.,ther-moacoustic instabilities(TAI))in combustors.These 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 failures.Thus,prediction of thermoacoustic instabilities is a critical issue for both design and operation of combustion systems.From this perspective,it is important to identify operating conditions which can potentially lead to thermoacoustic instabilities.In this regard,data-driven approaches have shown considerable success in predicting the instability map as a function of operating conditions.However,often the available data are limited to learn such a relationship efficiently in a data-driven approach for a practical combustion system.In 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 obtain.The 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 domains.The 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,even though the available data are significantly展开更多
文摘扬声器布置在Rijke管型燃烧器入口和出口处,输出不同频率和声压级的声波可以对热声不稳定进行开环主动控制。试验发现在热声管燃料入口燃料管路布置扬声器是一种有效的热声不稳定主动控制方法,扬声器发出的声波可以使热声不稳定的压力振荡有所衰减,同时也会产生新的和声波同频的压力振荡;在某些工况下,和声波同频的压力振荡幅值甚至会高于热声不稳定的压力振荡幅值;在该试验中,当扬声器声波频率为50 Hz、声压级为100 d B时,控制效果最好,达到38.55%。布置在热声管开口端的扬声器也是一种有效的热声不稳定主动控制方法,在该试验中,当扬声器声波频率为300 Hz、声压级为115 d B时,控制效果最好,此时控制效果可达34.58%。
基金This research was supported by the National Natural Science Foundation of China under Grant No.61973060the Science Center Program of National Natural Science Foundation of China under Grant No.62188101.
文摘Thermoacoustic instability phenomena often encounter in gas turbine combustors,especially for the premixed combustor design,with many possible detrimental results.As a classical experiment,the Rijke tube is the simplest and the most effective illustration to study the thermoacoustic instability.This paper investigates the active control approach of the thermoacoustic instability in a horizontal Rijke tube.What’s more,the radial basis function(RBF)neural network is adopted to estimate the complex unknown continuous nonlinear heat release rate in the Rijke tube.Then,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 origin.Finally,simulation results demonstrate the feasibility of the design method.
基金The work reported in this paper has been supported in part by the U.S.Air Force Office of Scientific Research(AFOSR)under Grant nos.FA9550-15-1-0400 and FA9550-18-1-0135 in the area of dynamic data-driven application systems(DDDAS).The authors are grateful to Profes-sor Domenic Santavicca at Penn State,who kindly provided the exper-imental data on the combustor apparatus.Any opinions,findings and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies.
文摘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 ratios.This brings in new challenges of suppressing high-amplitude pressure oscillations(e.g.,ther-moacoustic instabilities(TAI))in combustors.These 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 failures.Thus,prediction of thermoacoustic instabilities is a critical issue for both design and operation of combustion systems.From this perspective,it is important to identify operating conditions which can potentially lead to thermoacoustic instabilities.In this regard,data-driven approaches have shown considerable success in predicting the instability map as a function of operating conditions.However,often the available data are limited to learn such a relationship efficiently in a data-driven approach for a practical combustion system.In 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 obtain.The 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 domains.The 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,even though the available data are significantly
