Fully resolved direct numerical simulations are conducted to study the effects of fluctuating freestream flow on the drag force and wake characteristics of a stationary spherical particle. Sinusoidal fluctuation around a mean value is adopted as the freestream velocity. The interaction between the particle and fluctuating flow is computed by the direct-forcing immersed boundary method. We principally consider the relative difference between the computed mean drag and the drag law of a uniform flow past the particle and the properties of drag fluctuation in different freestream fluctuation directions. For the influence of streamwise fluctuating inflow, the relative mean drag difference increases with the particle Reynolds number. At small or intermediate particle scale ratios, the relative mean drag difference is very close to zero, indicating that the classical drag law can be used in these cases, while a large particle scale ratio can induce a notable increase in the relative mean drag difference at a large particle Reynolds number and high fluctuation intensity. For the transverse fluctuating inflow, generally, there is an evident increase in the mean drag coefficient when the particle scale ratio is small. Compared with the streamwise fluctuation case, the drag fluctuation intensity is a little smaller with the transverse fluctuating inflow. An explicit empirical drag fluctuation law is obtained by fitting the data for streamwise fluctuating inflow. The wake characteristics are also analyzed, and they are found to be strongly dependent on the direction of inflow fluctuation.
With the deepened exploration and development of petroleum, pre-stack inversion can predict reservoir and petroleum distribution by extracting elastic parameters by virtue of the gathers. Because of the abundant information, pre-stack inversion has gradually become a focus of research. Existing studies indicate that pre-stack amplitude variation with angle simultaneous inversion has a favourable application effect in conventional shallow sandstone gas reservoirs, but has poor impact in deeply hidden-type reservoirs. For improved the precision of description and prediction of hidden-type reservoirs, the Shahejie Formation in the Gangbei District of the Gangzhong Oil Field was used as an example, and a method of predicting complicated structure-lithology reservoir based on improved Fatti reflection coefficient approximation formula was proposed in this study. Based on petrophysical analysis and coordinate transformation theory, with elastic parameters which were obtained by the new technology, the indicative factors of reservoir sensitivity were established, resulting in forming a series of accurate reservoir prediction methods. Results show that the improved inversion method can accurately describe sand bodies in a complicated structure-lithology reservoir. Indicative factors of reservoir sensitivity show favourable linear correlation with sand bodies and it can indicate reservoir distribution more effectively than the conventional method. Prediction results are in agreement with actual drilling results. The study provides a theoretical reference for identifying complicated structure-lithology reservoirs.
Drag force acting on a particle is vital for the accurate simulation of turbulent multiphase flows, but the robust drag model is still an open issue. Fully resolved direct numerical simulation (DNS) with an immersed boundary method is performed to investigate the drag force on saltating particles in wall turbulence over a sediment bed. Results show that, for saltating particles, the drag force along the particle trajectories cannot be estimated accurately by traditional drag models originally developed for an isolated particle that depends on the particle-wall separation distance or local volume fraction in addition to the particle Reynolds number. The errors between the models and DNS are especially clear during the descending phase of the particles. Through simple theoretical analysis and DNS data fitting, we present a corrected factor using the classical, particle Reynolds number dependent drag force model as the benchmark model. The new drag model, which takes the particle vertical velocity into account, can reasonably predict the mean drag force obtained by DNS along a particle trajectory.
Abstract. In reference [1][2][3][4] discuss the stack's shared technology, the most frequently-used in stack's shared technology is the double-ended stack. But traditional double-ended stack always define the maximum storage space of it, which causes the wasting or lacking of storage space. Here we discuss and implement the dynamic storage technology of double-ended stack at first. The dynamic storage structure of the Double-ended stack compared with the traditional structure has following advantages. First, the free space in the dynamic double-ended stack is always kept in a certain range, which makes less waste of storage space. Second, the technology can achieve dynamic expansion of storage space and automatic recovery of storage space when the program keeps running.
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