VOF （volume of fluid） method has been used to make the numerical simulation of freak wave come true. The comparisons between the numerical results and linear theoretical results corresponding to Eq.（5） have been c...VOF （volume of fluid） method has been used to make the numerical simulation of freak wave come true. The comparisons between the numerical results and linear theoretical results corresponding to Eq.（5） have been carried out to show that the numerical results have a better exhibition of nonlinear characteristics. Wavelet analysis method has been adopted to investigate the time-frequency energy spectrum of simulation freak waves and the results reveal strong nonlinear interaction enables energy to be transferred to high harmonics during the progress of its formation. Varying water depth can enhance the nonlinear interaction, making much more energy be transferred to high harmonics and freak waves with higher asymmetry be generated.展开更多
The new technique that combines wave superposition with the fast Fourier transformation was introduced to simulate the nodal three-dimension relevant wind velocity time series of spatial structures. The wind velocity ...The new technique that combines wave superposition with the fast Fourier transformation was introduced to simulate the nodal three-dimension relevant wind velocity time series of spatial structures. The wind velocity field where the spatial structure is located is assumed to be homogeneous. The wind’s power spectral density is divided into frequency spectral function and coherency function and the spectral functions are transformed as the superposition coefficients. The wavelet analysis has excellent localized characters in both time and frequency domains, which not only makes wind velocity time series analysis more accurate, but also can focus on any detail of the objective signal series. The discrete wavelet transformation was adopted to decompose and reconstruct the discrete wind velocity time series. The stability of wavelet analysis for the wind velocity time series was also proved.展开更多
Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to th...Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to the intrinsic features of GPR signals and wavelet time–frequency analysis,an optimal wavelet basis named GPR3.3 wavelet is constructed via an improved biorthogonal wavelet construction method to quantitatively analyse the GPR signal.A new quantitative analysis method based on the biorthogonal wavelet(the QAGBW method)is proposed and applied in the analysis of analogue and measured signals.The results show that compared with the Bayesian frequency-domain blind deconvolution and with existing wavelet bases,the QAGBW method based on optimal wavelet can limit the disturbance from factors such as the coherence of reflected waves and echo noise,improve the quantitative analytical precision of the GPR signal,and match the minimum thickness for quantitative analysis with the vertical resolution of GPR detection.展开更多
文摘VOF （volume of fluid） method has been used to make the numerical simulation of freak wave come true. The comparisons between the numerical results and linear theoretical results corresponding to Eq.（5） have been carried out to show that the numerical results have a better exhibition of nonlinear characteristics. Wavelet analysis method has been adopted to investigate the time-frequency energy spectrum of simulation freak waves and the results reveal strong nonlinear interaction enables energy to be transferred to high harmonics during the progress of its formation. Varying water depth can enhance the nonlinear interaction, making much more energy be transferred to high harmonics and freak waves with higher asymmetry be generated.
文摘The new technique that combines wave superposition with the fast Fourier transformation was introduced to simulate the nodal three-dimension relevant wind velocity time series of spatial structures. The wind velocity field where the spatial structure is located is assumed to be homogeneous. The wind’s power spectral density is divided into frequency spectral function and coherency function and the spectral functions are transformed as the superposition coefficients. The wavelet analysis has excellent localized characters in both time and frequency domains, which not only makes wind velocity time series analysis more accurate, but also can focus on any detail of the objective signal series. The discrete wavelet transformation was adopted to decompose and reconstruct the discrete wind velocity time series. The stability of wavelet analysis for the wind velocity time series was also proved.
基金Projects(51678071,51278071)supported by the National Natural Science Foundation of ChinaProjects(14KC06,CX2015BS02)supported by Changsha University of Science&Technology,China
文摘Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to the intrinsic features of GPR signals and wavelet time–frequency analysis,an optimal wavelet basis named GPR3.3 wavelet is constructed via an improved biorthogonal wavelet construction method to quantitatively analyse the GPR signal.A new quantitative analysis method based on the biorthogonal wavelet(the QAGBW method)is proposed and applied in the analysis of analogue and measured signals.The results show that compared with the Bayesian frequency-domain blind deconvolution and with existing wavelet bases,the QAGBW method based on optimal wavelet can limit the disturbance from factors such as the coherence of reflected waves and echo noise,improve the quantitative analytical precision of the GPR signal,and match the minimum thickness for quantitative analysis with the vertical resolution of GPR detection.
