It has been shown that much dynamic information is hidden in the pressure fluctuation signals of a gas-solid fluidized bed. Unfortunately, due to the random and capricious nature of this signal, it is hard to realize ...It has been shown that much dynamic information is hidden in the pressure fluctuation signals of a gas-solid fluidized bed. Unfortunately, due to the random and capricious nature of this signal, it is hard to realize reliable analysis using traditional signal processing methods such as statistical analysis or spectral analysis, which is done in Fourier domain. Information in different frequency band can be extracted by using wavelet analysis. On the evidence of the composition of the pressure fluctuation signals, energy of low frequency (ELF) is proposed to show the transition of fluidized regimes from bubbling fluidization to turbulent fluidization. Plots are presented to describe the fluidized bed's evolution to help identify the state of different flow regimes and provide a characteristic curve to identify the fluidized status effectively and reliably.展开更多
The optimization of velocity field is the core issue in reservoir seismic pressure prediction. For a long time, the seismic processing velocity analysis method has been used in the establishment of pressure prediction...The optimization of velocity field is the core issue in reservoir seismic pressure prediction. For a long time, the seismic processing velocity analysis method has been used in the establishment of pressure prediction velocity field, which has a long research period and low resolution and restricts the accuracy of seismic pressure prediction;This paper proposed for the first time the use of machine learning algorithms, based on the feasibility analysis of wellbore logging pressure prediction, to integrate the CVI velocity inversion field, velocity sensitive post stack attribute field, and AVO P-wave and S-wave velocity reflectivity to obtain high-precision seismic P and S wave velocities. On this basis, high-resolution formation pore pressure and other parameters prediction based on multi waves is carried out. The pressure prediction accuracy is improved by more than 50% compared to the P-wave resolution of pore pressure prediction using only root mean square velocity. Practice has proven that the research method has certain reference significance for reservoir pore pressure prediction.展开更多
ZnSe multipod-based structures,including tetrapod-like microrods,long microwires,and short nanorods,are selectively prepared by atmospheric pressure thermal evaporation of ZnSe nanoparticles without using any catalyst...ZnSe multipod-based structures,including tetrapod-like microrods,long microwires,and short nanorods,are selectively prepared by atmospheric pressure thermal evaporation of ZnSe nanoparticles without using any catalyst.The morphologies could be well controlled by simply adjusting the deposition position.The phase structures,morphologies,and optical properties of the products are investigated by X-ray diffraction(XRD),scanning electron microscopy(SEM),high-resolution transmission electron microscopy(TEM),and photoluminescence(PL) spectroscopy.A vapor-liquid mechanism is proposed for the formation of ZnSe multipod-based structures.The presented route is expected to be applied to the synthesis of other Ⅱ-Ⅵ groups or other group's semiconductor materials with controllable morphologies.展开更多
文摘It has been shown that much dynamic information is hidden in the pressure fluctuation signals of a gas-solid fluidized bed. Unfortunately, due to the random and capricious nature of this signal, it is hard to realize reliable analysis using traditional signal processing methods such as statistical analysis or spectral analysis, which is done in Fourier domain. Information in different frequency band can be extracted by using wavelet analysis. On the evidence of the composition of the pressure fluctuation signals, energy of low frequency (ELF) is proposed to show the transition of fluidized regimes from bubbling fluidization to turbulent fluidization. Plots are presented to describe the fluidized bed's evolution to help identify the state of different flow regimes and provide a characteristic curve to identify the fluidized status effectively and reliably.
文摘The optimization of velocity field is the core issue in reservoir seismic pressure prediction. For a long time, the seismic processing velocity analysis method has been used in the establishment of pressure prediction velocity field, which has a long research period and low resolution and restricts the accuracy of seismic pressure prediction;This paper proposed for the first time the use of machine learning algorithms, based on the feasibility analysis of wellbore logging pressure prediction, to integrate the CVI velocity inversion field, velocity sensitive post stack attribute field, and AVO P-wave and S-wave velocity reflectivity to obtain high-precision seismic P and S wave velocities. On this basis, high-resolution formation pore pressure and other parameters prediction based on multi waves is carried out. The pressure prediction accuracy is improved by more than 50% compared to the P-wave resolution of pore pressure prediction using only root mean square velocity. Practice has proven that the research method has certain reference significance for reservoir pore pressure prediction.
文摘ZnSe multipod-based structures,including tetrapod-like microrods,long microwires,and short nanorods,are selectively prepared by atmospheric pressure thermal evaporation of ZnSe nanoparticles without using any catalyst.The morphologies could be well controlled by simply adjusting the deposition position.The phase structures,morphologies,and optical properties of the products are investigated by X-ray diffraction(XRD),scanning electron microscopy(SEM),high-resolution transmission electron microscopy(TEM),and photoluminescence(PL) spectroscopy.A vapor-liquid mechanism is proposed for the formation of ZnSe multipod-based structures.The presented route is expected to be applied to the synthesis of other Ⅱ-Ⅵ groups or other group's semiconductor materials with controllable morphologies.
