Noise is generated in a two-dimensional mixing layer due to the growing of instability waves and vortex pairings. The adjoint-based control methodology has shown to be a robust tool to suppress noise radiation. The mo...Noise is generated in a two-dimensional mixing layer due to the growing of instability waves and vortex pairings. The adjoint-based control methodology has shown to be a robust tool to suppress noise radiation. The mode decomposition algorithms such as the compressible version of proper orthogonal decomposition （POD） and dynamic mode decomposition （DMD） are employed to analyze the spatial/spatial-temporal coherent structures for a consecutive data sets of the controlled mixing layer and its uncontrolled counterpart. The analyses of POD indicate that the y-direction body force control mainly modify the most energetic spatial structures, and increase the uniformity of the flow. The analyses of DMD show us prevalent frequencies and corresponding mode structures, and the stability characteristics of each mode can be obtained from DMD-spectrum. The spectral signatures illustrate that a lot of neutral/slightly damping modes emerging in uncontrolled flow within the frequency range （w 〈 0.4） are suppressed due to control, relevant spatial-temporal structures are also varied, which is coincident with the change of far-field noise spectra. From the view of mode decomposition, the action of control redistribute the energy for frequency components of ~ 〈 0.4 by weakening nonlinearities and regularizing corresponding dynamic structures in streamwise direction, and thus suppress the noise radiation. Moreover, the POD- and DMD-analysis in this study demon- strate that DMD can serve as an important supplement for POD in analyzing a time-resolved physical process.展开更多
局部均值分解(Local mean decomposition,LMD)方法是一种新的自适应时频分析方法,但在其实现过程中会发生模态混淆现象,使分析结果失真。通过数值试验得到了LMD对白噪声的滤波器组结构,并在此基础上,针对模态混淆现象提出总体局部均值分...局部均值分解(Local mean decomposition,LMD)方法是一种新的自适应时频分析方法,但在其实现过程中会发生模态混淆现象,使分析结果失真。通过数值试验得到了LMD对白噪声的滤波器组结构,并在此基础上,针对模态混淆现象提出总体局部均值分解(Ensemble local mean decomposition,ELMD)方法。在该方法中添加不同的白噪声到目标信号,分别对加噪后的信号进行LMD分解,最后将多次分解结果的平均值作为最终的分解结果。对仿真信号和试验转子局部碰摩信号进行分析,结果表明ELMD方法能有效地克服原LMD方法的模态混淆现象。展开更多
高频噪声压制是高分辨率地震数据处理中提高信噪比的关键性问题.本文针对f-x(频率-空间)反褶积空间预测滤波器无法处理非平稳、非线性信号的缺点,提出了一种基于高通滤波的频率-空间域经验模态分解(Empirical Mode Decomposition in the...高频噪声压制是高分辨率地震数据处理中提高信噪比的关键性问题.本文针对f-x(频率-空间)反褶积空间预测滤波器无法处理非平稳、非线性信号的缺点,提出了一种基于高通滤波的频率-空间域经验模态分解(Empirical Mode Decomposition in the frequency-space domain,f-xEMD)压制地震剖面中高频噪声的方法.该方法采用全域高通滤波从原始数据中分离出含有部分有效信号的高频数据,将其变换到f-x域,然后在滑动的短窗口内提取每一个频率的空变数据序列进行EMD分解得到高频复本征模态函数(Intrinsic Mode Function,IMF)IMF1,将所有频率的IMF1序列反Fourier变换到时间域得到噪声剖面,将其与原始数据相减,达到高频噪声压制的目的.该方法可克服传统EMD分解方法中的模态混叠现象,保护陡倾角反射同相轴;压制后的噪声剖面中不包含有效信号能量,地震剖面的信噪比得到了提高.模拟数据和实际数据处理结果充分证明了该方法的有效性.展开更多
基金supported by the National Natural Science Foundation of China (11072238, 11232011)111 project (B07033)
文摘Noise is generated in a two-dimensional mixing layer due to the growing of instability waves and vortex pairings. The adjoint-based control methodology has shown to be a robust tool to suppress noise radiation. The mode decomposition algorithms such as the compressible version of proper orthogonal decomposition （POD） and dynamic mode decomposition （DMD） are employed to analyze the spatial/spatial-temporal coherent structures for a consecutive data sets of the controlled mixing layer and its uncontrolled counterpart. The analyses of POD indicate that the y-direction body force control mainly modify the most energetic spatial structures, and increase the uniformity of the flow. The analyses of DMD show us prevalent frequencies and corresponding mode structures, and the stability characteristics of each mode can be obtained from DMD-spectrum. The spectral signatures illustrate that a lot of neutral/slightly damping modes emerging in uncontrolled flow within the frequency range （w 〈 0.4） are suppressed due to control, relevant spatial-temporal structures are also varied, which is coincident with the change of far-field noise spectra. From the view of mode decomposition, the action of control redistribute the energy for frequency components of ~ 〈 0.4 by weakening nonlinearities and regularizing corresponding dynamic structures in streamwise direction, and thus suppress the noise radiation. Moreover, the POD- and DMD-analysis in this study demon- strate that DMD can serve as an important supplement for POD in analyzing a time-resolved physical process.
文摘局部均值分解(Local mean decomposition,LMD)方法是一种新的自适应时频分析方法,但在其实现过程中会发生模态混淆现象,使分析结果失真。通过数值试验得到了LMD对白噪声的滤波器组结构,并在此基础上,针对模态混淆现象提出总体局部均值分解(Ensemble local mean decomposition,ELMD)方法。在该方法中添加不同的白噪声到目标信号,分别对加噪后的信号进行LMD分解,最后将多次分解结果的平均值作为最终的分解结果。对仿真信号和试验转子局部碰摩信号进行分析,结果表明ELMD方法能有效地克服原LMD方法的模态混淆现象。
文摘高频噪声压制是高分辨率地震数据处理中提高信噪比的关键性问题.本文针对f-x(频率-空间)反褶积空间预测滤波器无法处理非平稳、非线性信号的缺点,提出了一种基于高通滤波的频率-空间域经验模态分解(Empirical Mode Decomposition in the frequency-space domain,f-xEMD)压制地震剖面中高频噪声的方法.该方法采用全域高通滤波从原始数据中分离出含有部分有效信号的高频数据,将其变换到f-x域,然后在滑动的短窗口内提取每一个频率的空变数据序列进行EMD分解得到高频复本征模态函数(Intrinsic Mode Function,IMF)IMF1,将所有频率的IMF1序列反Fourier变换到时间域得到噪声剖面,将其与原始数据相减,达到高频噪声压制的目的.该方法可克服传统EMD分解方法中的模态混叠现象,保护陡倾角反射同相轴;压制后的噪声剖面中不包含有效信号能量,地震剖面的信噪比得到了提高.模拟数据和实际数据处理结果充分证明了该方法的有效性.
