高平均功率电光开关晶体热畸变分析

 深入分析了方板构型的电光开关晶体在高功率载荷条件下的热畸变行为,讨论了光强分布对热效应的影响。以KDP晶体为例,分别计算了激光束光强为高斯分布和均匀分布时晶体的温升、相应的热应力分布、波前畸变以及热退偏。结果表明,光强的分布形式对波前畸变和热退偏的影响是不同的。相对于光强均匀分布的激光束,高斯光束减缓了光斑边沿处的温度梯度,产生的热应力较小,因此可以减弱热退偏效应;另一方面,在光束口径范围内,高斯光束产生了附加的温度分布非均匀性,因而波前畸变会大一些。     

Direct numerical simulation of stationary particles in homogeneous turbulence decay: Application of the k–ε model

This study focuses on understanding how the presence of particles, in homogeneous turbulence decay, affects the dissipation of dissipation coefficient within the volume averaged dissipation transport equation. In developing this equation, the coefficient for dissipation of dissipation was assumed to be the sum of the single phase coefficient and an additional coefficient that is related to the effects of the dispersed phase. Direct numerical simulation was used to isolate the effect of stationary particles in homogeneous turbulent decay at low Reynolds numbers (Re_L = 3.3 and 12.5). The particles were positioned at each grid point and modeled as point forces and a comparison was made between a 64³ and 128³ domain. The results show that the dissipation of dissipation coefficient correlates well with a dimensionless parameter called the momentum coupling factor.     

Lower bounds on the minimum average distance of binary codes

Let β(n,M) denote the minimum average Hamming distance of a binary code of length n and cardinality M. In this paper we consider lower bounds on β(n,M). All the known lower bounds on β(n,M) are useful when M is at least of size about 2ⁿ⁻¹/n. We derive new lower bounds which give good estimations when size of M is about n. These bounds are obtained using a linear programming approach. In particular, it is proved that lim_n→∞β(n,2n)=5/2. We also give a new recursive inequality for β(n,M).     

原子的共振激发—速率方程与半经典理论的比较

本文由半经典理论推得了原子电离率随脉冲空间传输出现周期性起伏这一新的相干作用结果,这点与速率方程的预计相差甚远.文中定量说明了利用速率方程对同位素分离过程进行分析时产生的系统误差,为实验分析工作提供了依据.     

Inference on superimposed subcritical Galton-Watson processes with immigration

Summary In this paper we consider the superimposed processZ generated by two independent subcritical Galton-Watson processesX ₁ andX ₂, with immigration, by the relationZ=X ₁ +X ₂. The seemingly second order autoregressive relation, that is identified inZ, is exploted towards proposing CAN estimators for the parameters ofZ,X ₁ andX ₂, based on only a partial realisation ofZ, using time series techniques. The results of this paper are motivated by a time series approach for studying specific branching processes due to Venkataraman (1982,Adv. Appl. Prob.,14, 1–20).     

Sample average approximation of expected value constrained stochastic programs

We propose a sample average approximation (SAA) method for stochastic programming problems with expected value constraints. Such problems arise, for example, in portfolio selection with constraints on conditional value-at-risk (CVaR). We provide a convergence analysis and a statistical validation scheme for the proposed method.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

Average coherence with respect to complementary measurements

We investigate the average coherence with respect to a complete set of complementary measurements.By using a Wigner-Yanase skew information-based coherence measure introduced in Luo and Sun(2017 Phys.Rev.A 96,022130),we evaluate the average coherence of a state with respect to any complete set of mutually unbiased measurements and general symmetric informationally complete measurements,respectively.We also establish analytically the relations among these average coherences.     

Dispersion in retentive pillar array columns

The method of volume averaging is applied to estimate the Taylor–Aris dispersion tensor of solute advected in columns consisting of ordered pillar arrays with wall retention of the type used in chromatographic separation. The appropriate closure equations are derived and solved in a unit cell with periodic boundary conditions to obtain the dispersion tensor (or the reduced plate height) as a function of the Peclet number (reduced velocity); pillar pattern, shape and size; partition coefficient; and resistance to mass transfer. The contributions of the velocity profile, the wall adsorption, and the mass transfer resistance to the dispersion tensor are identified and delineated. The model is verified by comparing its predictions and obtaining favorable agreement with results of direct numerical simulations and with experimental data for columns containing ordered pillars. The model is then used to study the effect of pillars’ shape and pattern on the longitudinal dispersion coefficient (plate height).     

Nano-Fabrication by Cathodic Plasma Electrolysis

Cathodic plasma electrolytic (CPE) techniques are new groups of coating processes, which can be used for fabrication of nanostructured layers on surface of a wide range of metallic substrates. The most exciting visible feature of these atmospheric-based plasma techniques is continuous sparking on processed surface inside an electrolyte. Unlike the anodic part of plasma electrolysis (usually known as plasma electrolytic oxidation (PEO) or micro arc oxidation (MAO)), which is commonly used for oxidation of light metals/alloys such as aluminum, titanium and magnesium, CPE techniques can clean and coat different metals and alloys such as steel, copper, and light metals/alloys with formation of wide range of nanostructures including complex carbides, carbonitrides, intermetallics, and even oxides. It has been observed that the properties of obtained layers depend on the characteristics of achieved nanostructures such as average size, distribution and average coordination number of nanocrystallites. Furthermore, the properties of the processed surface can be tailored by tailoring the nanostructure characteristics. There is limited literature available on the mechanism of CPE and its connection to the morphology of nanostructured layers. This article addresses the two important aspects of CPE, namely characterization of nanostructured layers and mechanism of cathodic plasma electrolysis, which are reviewed in accordance to the morphology of fabricated nanostructures.