The key to failure prevention for aero-engine lies in performance prediction and the exhaust gas temperature margin(EGTM)is used as the most important degradation parameter to obtain the operating performance of the a...The key to failure prevention for aero-engine lies in performance prediction and the exhaust gas temperature margin(EGTM)is used as the most important degradation parameter to obtain the operating performance of the aero-engine.Because of the complex environment interference,EGTM always has strong randomness,and the state space based degradation model can identify the noisy observation from the true degradation state,which is more close to the actual situations.Therefore,a state space model based on EGTM is established to describe the degradation path and predict the remaining useful life(RUL).As one of the most effective methods for both linear state estimation and parameter estimation,Kalman filter(KF)is applied.Firstly,with EGTM degradation data,state space model approach is used to set up a state space model for aero-engine.Secondly,RUL of aero-engine is analyzed,and expected RUL and distribution of RUL are determined.Finally,the sate space model and KF algorithm are applied to an example of CFM-56aero-engine.The expected RUL is predicted,and corresponding probability density distribution(PDF)and cumulative distribution function(CDF)are given.The result indicates that the accuracy of RUL prediction reaches 7.76%ahead 580 flight cycles(FC),which is more accurate than linear regression,and therefore shows the validity and rationality of the proposed method.展开更多
This study employs a new SATES(Self-Adaptive Turbulence Eddy Simulation)-FGM(Flamelet Generated Manifold)-CRN(Chemical Reactor Network)coupling method to numerically predict the combustion pollutions of CO and NO_(x)t...This study employs a new SATES(Self-Adaptive Turbulence Eddy Simulation)-FGM(Flamelet Generated Manifold)-CRN(Chemical Reactor Network)coupling method to numerically predict the combustion pollutions of CO and NO_(x)together in a methane/air turbulent diffusion flame(Sandia Flame D).Two SATES models are developed based on the underlying realizable k-εand BSL k-ωturbulence models.The prediction accuracy of the combustion field and the CO pollutant distribution are compared and analyzed by coupling two SATES models and two RANS(Reynolds-Averaged Navier-Stokes)models with FGM combustion model.Furthermore,CRN is utilized to construct the NO_(x)distribution characteristics for different scales and rules using the unsteady high-fidelity combustion field results obtained from SATES-FGM.The results demonstrate that SATES-FGM can accurately predict the turbulent diffusion flame and improve the sensitivity of different RANS models to flow patterns in the framework of the SATES method.However,the results show a large deviation in predicting the main combustion zone.The SATES-FGM method can efficiently and accurately simulate flow fields of the free-jet turbulent flame.Additionally,it performs well in predicting the pollution products associated with combustion process,such as CO,while the SATES-CRN coupling method can accurately predict the post-combustion pollutants like NO_(x).The number of CRN zones can be adjusted to fit the combustor.Excessive reaction zones not only reduce the efficiency but also result in a deviation in the NO_(x)prediction.The unsteady SATES-CRN coupling method is better suited for complex partitioning rules.The developed SATES-FGM-CRN method can offer a new and efficient approach to simultaneously predict the distributions of CO and NO_(x)pollutions.展开更多
针对退役动力电池规模大、单体筛选复杂、重组后动态特性差异大以及寿命损耗加剧等问题,该文考虑电池模组的功能状态(state of function,SOF)特性,提出基于数字孪生技术的退役电池模组筛选方法。首先,通过电压、电流、荷电状态(state of...针对退役动力电池规模大、单体筛选复杂、重组后动态特性差异大以及寿命损耗加剧等问题,该文考虑电池模组的功能状态(state of function,SOF)特性,提出基于数字孪生技术的退役电池模组筛选方法。首先,通过电压、电流、荷电状态(state of charge,SOC)及健康状态(state of health,SOH)等参量表征SOF特性,估计梯次利用过程中SOF动态安全裕度;其次,搭建耦合物理模型、信息流及数字孪生映射体的电池模组筛选架构,提出基于生成对抗网络(generative adversarial networks,GAN)与长短期记忆网络(long short-term memory,LSTM)的电池数据缺失及偏移预测方法,优化退役动力电池模组表征SOF的多性能参量;最后,采用k-means算法对综合考虑SOH及SOF特性的退役电池模组进行聚类筛选。仿真结果表明:所提筛选方法可以提高退役动力电池动态一致性,并延长梯次利用过程中电池的运行寿命。展开更多
文摘The key to failure prevention for aero-engine lies in performance prediction and the exhaust gas temperature margin(EGTM)is used as the most important degradation parameter to obtain the operating performance of the aero-engine.Because of the complex environment interference,EGTM always has strong randomness,and the state space based degradation model can identify the noisy observation from the true degradation state,which is more close to the actual situations.Therefore,a state space model based on EGTM is established to describe the degradation path and predict the remaining useful life(RUL).As one of the most effective methods for both linear state estimation and parameter estimation,Kalman filter(KF)is applied.Firstly,with EGTM degradation data,state space model approach is used to set up a state space model for aero-engine.Secondly,RUL of aero-engine is analyzed,and expected RUL and distribution of RUL are determined.Finally,the sate space model and KF algorithm are applied to an example of CFM-56aero-engine.The expected RUL is predicted,and corresponding probability density distribution(PDF)and cumulative distribution function(CDF)are given.The result indicates that the accuracy of RUL prediction reaches 7.76%ahead 580 flight cycles(FC),which is more accurate than linear regression,and therefore shows the validity and rationality of the proposed method.
基金financially supported by the National Natural Science Foundation of China(No.52376114 and No.92041001)the Jiangsu Provincial Natural Science Foundation(BK20200069)the National Science and Technology Major Project(J2019-III-0015-0059)。
文摘This study employs a new SATES(Self-Adaptive Turbulence Eddy Simulation)-FGM(Flamelet Generated Manifold)-CRN(Chemical Reactor Network)coupling method to numerically predict the combustion pollutions of CO and NO_(x)together in a methane/air turbulent diffusion flame(Sandia Flame D).Two SATES models are developed based on the underlying realizable k-εand BSL k-ωturbulence models.The prediction accuracy of the combustion field and the CO pollutant distribution are compared and analyzed by coupling two SATES models and two RANS(Reynolds-Averaged Navier-Stokes)models with FGM combustion model.Furthermore,CRN is utilized to construct the NO_(x)distribution characteristics for different scales and rules using the unsteady high-fidelity combustion field results obtained from SATES-FGM.The results demonstrate that SATES-FGM can accurately predict the turbulent diffusion flame and improve the sensitivity of different RANS models to flow patterns in the framework of the SATES method.However,the results show a large deviation in predicting the main combustion zone.The SATES-FGM method can efficiently and accurately simulate flow fields of the free-jet turbulent flame.Additionally,it performs well in predicting the pollution products associated with combustion process,such as CO,while the SATES-CRN coupling method can accurately predict the post-combustion pollutants like NO_(x).The number of CRN zones can be adjusted to fit the combustor.Excessive reaction zones not only reduce the efficiency but also result in a deviation in the NO_(x)prediction.The unsteady SATES-CRN coupling method is better suited for complex partitioning rules.The developed SATES-FGM-CRN method can offer a new and efficient approach to simultaneously predict the distributions of CO and NO_(x)pollutions.
文摘针对退役动力电池规模大、单体筛选复杂、重组后动态特性差异大以及寿命损耗加剧等问题,该文考虑电池模组的功能状态(state of function,SOF)特性,提出基于数字孪生技术的退役电池模组筛选方法。首先,通过电压、电流、荷电状态(state of charge,SOC)及健康状态(state of health,SOH)等参量表征SOF特性,估计梯次利用过程中SOF动态安全裕度;其次,搭建耦合物理模型、信息流及数字孪生映射体的电池模组筛选架构,提出基于生成对抗网络(generative adversarial networks,GAN)与长短期记忆网络(long short-term memory,LSTM)的电池数据缺失及偏移预测方法,优化退役动力电池模组表征SOF的多性能参量;最后,采用k-means算法对综合考虑SOH及SOF特性的退役电池模组进行聚类筛选。仿真结果表明:所提筛选方法可以提高退役动力电池动态一致性,并延长梯次利用过程中电池的运行寿命。
基金supported in part by the National Natural Science Foundation of China(Nos.52376114,92041001)the Natural Science Foundation of Jiangsu Province(No.BK20200069)the National Science and Technology Major Projects(Nos.J2019-Ⅲ-0015-0059,2017-Ⅲ-0005-0029).
