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 less for the latter.展开更多
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 less for the latter.
