Ultrafast two-dimensional x-ray imager with temporal fiducial pulses for laser-produced plasmas

It is challenging to make an ultrafast diagnosis of the temporal evolution of small and short-lived plasma in two dimensions. To overcome this difficulty, we have developed a well-timed diagnostic utilizing an x-ray streak camera equipped with a row of multi-pinhole arrays. By processing multiple sets of one-dimensional streaked image data acquired from various pinholes, we are capable of reconstructing high-resolution two-dimensional images with a temporal resolution of 38 ps and a spatial resolution of 18 μm. The temporal fiducial pulses accessed from external sources can advance the precise timing and accurately determine the arrival time of the laser. Moreover, it can correct the nonlinear sweeping speed of the streak camera. The effectiveness of this diagnostic has been successfully verified at the Shenguang-II laser facility,providing an indispensable tool for observing complex physical phenomena, such as the implosion process of laser-fusion experiments.

A multifractal model for linking Lagrangian and Eulerian velocity structure functions

A multifractal model is developed to connect the Lagrangian multifractal dimensions with their Eulerian counterparts. We propose that the characteristic time scale of a Lagrangian quantity should be the Lagrangian time scale, and it should not be the Eulerian time scale which was widely used in previous studies on Lagrangian statistics. Using the present model, we can obtain the scaling exponents of Lagrangian velocity structure functions from the existing data or models of scaling exponents of Eulerian velocity structure functions. This model is validated by comparing its prediction with the results of experiments, direct numerical simulations, and the previous theoretical models. The comparison shows that the proposed model can better predict the scaling exponents of Lagrangian velocity structure functions, especially for orders larger than 6.

High speed ghost imaging based on a heuristic algorithm and deep learning

We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.

Critical damage value of AZ31B magnesium alloy with different temperatures and strain rates

The uniaxial tensile tests were carried out at temperature ranging from room temperature(RT)to 523 K under strain rates of 0.001,0.010 and 0.100 s^(-1) to obtain the flow stress of AZ31 B magnesium alloy.The tensile tests were simulated by the finite element method(FEM)to calculate the critical damage values(Dc)of the CockcroftLatham criterion to predict the occurrence of fracture defects.Finally,the effects of temperature and strain rate on Dc were analyzed to predict the critical damage value’s evolution.The results demonstrate that the critical damage value changes in the range of 0.188-0.718.It increases as strain rate decreases at constant temperature,and increases with the increase of temperature under certain strain rate,but is more sensitive to the temperature.