373 research outputs found
Nanostructures in a ferritic and an oxide dispersion strengthened steel induced by dynamic plastic deformation
Mixing and separation of liquid-liquid two-phase in a novel cyclone reactor of isobutane alkylation catalyzed by ionic liquid
To improve the existing problems of the traditional isobutane alkylation catalyzed by ionic liquid reactors, a novel liquid-liquid cyclone reactor has been designed for the liquid-liquid heterogeneous reaction. Compared with the traditional hydrocyclone, the novel cyclone reactor consists of two inlets for light phase and heavy phase respectively. The light phase is injected into the reactor through two symmetric tangential slots in the inlet, while the heavy phase inlet is the axial entry with guide vane. The trajectory and residence time distribution (RTD) of the light phase could influence the reaction process and the products quality. In order to study the contact-mixing and separation mechanism of liquid-liquid in the novel cyclone reactor, the trajectory and residence time distribution in the reactor were investigated. The simulation using species transport equation and experiment were performed under oil-water system. The tangential and radial dispersion process of oil was observed in the simulation. The simulation results show that the mean residence time of the oil is between 0.6s~1.0s under different operating parameters. The oil flow in the reactor is not a smooth flow or a complete mixing flow judging from the dimensionless variance. The separation efficiency in simulated method was higher than 99%. The volume fraction of water in the overflow mixture was lower than 5%. And the deviation between the simulated and experimental results was no more than 5%, which indicates that the simulated results are reliable and accurate
Engineering mobility in quasiperiodic lattices with exact mobility edges
We investigate the effect of an additional modulation parameter on
the mobility properties of quasiperiodic lattices described by a generalized
Ganeshan-Pixley-Das Sarma model with two on site modulation parameters. For the
case with bounded quasiperiodic potential, we unveil the existence of
self-duality relation, independent of . By applying Avila's global
theory, we analytically derive Lyapunov exponents in the whole parameter space,
which enables us to determine mobility edges or anomalous mobility edges
exactly. Our analytical results indicate that the mobility edge equation is
described by two curves and their intersection with the spectrum gives the true
mobility edge. Tuning the strength parameter can change the spectrum
of the quasiperiodic lattice, and thus engineers the mobility of quasi-periodic
systems, giving rise to completely extended, partially localized, and
completely localized regions. For the case with unbounded quasiperiodic
potential, we also obtain the analytical expression of the anomalous mobility
edge, which separates localized states from critical states. By increasing the
strength parameter , we find that the critical states can be destroyed
gradually and finally vanishes.Comment: 10 pages,6 figure
Nanoscale lamellae in an oxide dispersion strengthened steel processed by dynamic plastic deformation
Oxide dispersion-strengthened steel PM2000 after dynamic plastic deformation: nanostructure and annealing behaviour
MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network
Picking the first arrival times of prestack gathers is called First Arrival
Time (FAT) picking, which is an indispensable step in seismic data processing,
and is mainly solved manually in the past. With the current increasing density
of seismic data collection, the efficiency of manual picking has been unable to
meet the actual needs. Therefore, automatic picking methods have been greatly
developed in recent decades, especially those based on deep learning. However,
few of the current supervised deep learning-based method can avoid the
dependence on labeled samples. Besides, since the gather data is a set of
signals which are greatly different from the natural images, it is difficult
for the current method to solve the FAT picking problem in case of a low Signal
to Noise Ratio (SNR). In this paper, for hard rock seismic gather data, we
propose a Multi-Stage Segmentation Pickup Network (MSSPN), which solves the
generalization problem across worksites and the picking problem in the case of
low SNR. In MSSPN, there are four sub-models to simulate the manually picking
processing, which is assumed to four stages from coarse to fine. Experiments on
seven field datasets with different qualities show that our MSSPN outperforms
benchmarks by a large margin.Particularly, our method can achieve more than
90\% accurate picking across worksites in the case of medium and high SNRs, and
even fine-tuned model can achieve 88\% accurate picking of the dataset with low
SNR
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