摘要
为解决传统方法在判断离心压气机动态失稳过程中因信号强非线性导致误判错判,针对其动态时序属非线性信号,基于分形理论提出自适应变分模态分解(adaptive variational mode decomposition with fractal,AFVMD)方法以同时实现降噪与非线性特征提取,采用相空间重构法还原系统动力学结构.以某离心压气机失稳过程中叶轮动态压力数据为对象,验证所提出算法的优越性,分析其吸引子状态.结果表明:在处理具有非线性特征的含噪信号时,AFVMD比小波降噪具有更好的降噪效果与特征提取能力;相空间将失速发展过程可视化,最小流量状态所对应的相空间呈现“毛球状”;随失速的发展,相空间将逐渐发散;经小波与AFVMD方法预处理的信号所对应相形对失速过程更加敏感;通过经AFVMD处理的信号进行重构可更早捕获失速征兆,其更小的最大Lyapunov指数表明该方法提升了流动混沌系统的可预测性,为压气机失稳分析、预测提供新思路与方法.
A novel signal processing method named adaptive variational mode decomposition with the fractal(AFVMD),which is based on variational mode decomposition and fractal theory,is proposed in this paper for solving a problem that it is easy to misjudge the working conditions of the centrifugal compressor.The measured signal of a compressor is unstable,so a traditional method is used to analyze the nonlinear phenomenon of the stall flutter.Owing to the fact that the robustness of VMD method is strong and its combination with the fractal dimension can accurately describe self-similarity and fractal characteristics of a measured signal,the proposed AFVMD method can not only achieve noise reduction,but also extract nonlinear feature from a complex signal.Taking the dynamic pressure data of the impeller during the instability of a centrifugal compressor as an object to verify the effectiveness and superiority of the proposed AFVMD method,the results are obtained as follows.Firstly,compared with the wavelet noise reduction method,the proposed AFVMD method has both noise reduction and feature extraction functions,and the compressor pressure pulsation spectrum has more significant stall characteristics.Secondly,none of the traditional nonlinear analysis methods can reflect the stall process,so the chaotic phase space attractor is used to visualize the flow field changes.Due to the reasonable choice of the delay time and the embedding dimension,the physical information originally mixed in the signal is separated,so that the attractor phase diagram method has a better process of judging the flow stall than the frequency spectrum method.The results show that the proposed AFVMD method can judge the compressor about to enter into the deep surge earlier.Thirdly,In order to quantify the superiority of the proposed method,if the process of surging and the occurrence of deep wheezing can be predicted in advance,the largest Lyapunov exponent is used as an evaluation index.The above results show that the largest Lyapunov exponent of the proposed AFVMD is smallest for illustrating that the signal has more accurate flow field nonlinear information,which improves the predictability of the signal.
作者
许子非
缪维跑
李春
金江涛
李蜀军
Xu Zi-Fei;Miao Wei-Pao;Li Chun;Jin Jiang-Tao;Li Shu-Jun(Energy and Power Engineering Institute,University of Shanghai for Science and Technology,Shanghai 200093,China;Shanghai Key Laboratory of Multiphase Flow and Heat Transfer for Power Engineering,Shanghai 200093,China)
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2020年第24期338-346,共9页
Acta Physica Sinica
基金
国家自然科学基金(批准号:51976131,51676131)
上海市“科技创新心动计划”地方院校能力建设项目(批准号:19060502200)资助的课题.
