Concentrated steady vorticities of the Euler equation on 2-d domains and their linear stability

We consider concentrated vorticities for the Euler equation on a smooth domain $\mathrm{\Omega }?{\mathbf{R}}^2$ in the form of

$\omega =\sum _{j=1}^N{\omega }_j{\chi }_{{\mathrm{\Omega }}_j},|{\mathrm{\Omega }}_j|=\pi r_j^2,\underset{{\mathrm{\Omega }}_j}{\int }{\omega }_{j}d\mu ={\mu }_j\ne 0,$

supported on well-separated vortical domains ${\mathrm{\Omega }}_j$, $j=1,\dots ,N$, of small diameters $O(r_j)$. A conformal mapping framework is set up to study this free boundary problem with ${\mathrm{\Omega }}_j$ being part of unknowns. For any given vorticities ${\mu }_1,\dots ,{\mu }_N$ and small $r_1,\dots ,r_N\in {\mathbf{R}}^{+}$, through a perturbation approach, we obtain such piecewise constant steady vortex patches as well as piecewise smooth Lipschitz steady vorticities, both concentrated near non-degenerate critical configurations of the Kirchhoff–Routh Hamiltonian function. When vortex patch evolution is considered as the boundary dynamics of $?{\mathrm{\Omega }}_j$, through an invariant subspace decomposition, it is also proved that the spectral/linear stability of such steady vortex patches is largely determined by that of the 2N-dimensional linearized point vortex dynamics, while the motion is highly oscillatory in the 2N-codim directions corresponding to the vortical domain shapes.     

Eye-safe compact scanning LIDAR technology

A 1.5-μm eye-safe, 3-D scanning, and compact Mie LIght Detection and Ranging (LIDAR) is presented. The transmitter of the LIDAR is based on a KTA optical parameter oscillator (OPO) resonator. For detecting return signals, an InGaAs APD is used. The all solid-state OPO laser transmitter has the feature of small volume and lightweight, which allows a 165-lb compact eye-safe scanning LIDAR to be constructed. A system simulation using our own model was conducted to direct the system development. A method to solve the problem with small active area APD detectors was developed and described. The preliminary field-test measurement results indicated that the LIDAR has the capability to detect aerosols and clouds in lower atmospheres up to three dimensions.     

氢化丁苯共聚物结构的核磁共振波谱表征Ⅱ.微观结构的定量计算

在氢化丁苯共聚物¹³C-NMR谱脂肪碳部分谱带归属的基础上,根据各谱带的主要来源,推导出六个二单元浓度的计算公式。计算出二单元、一单元的相对含量,各结构单元的数均序列长度、嵌段含量和其它结构参数。加氢前后的¹H-和¹³C-NMR谱的组成计算结果基本一致。初步探讨了作为粘度指数改进剂的氢化丁苯共聚物微观结构与性能的关系,为合成提供了依据。     

HIRFL–CSR加速器中束流与真空中剩余气体的碰撞损失

研究了重离子加速器中束流与真空中剩余气体的碰撞损失过程和碰撞截面,在依据大量实验数据的基础上,提出了一组计算离子一原子的电荷交换截面的经验公式.以兰州重离子加速器HDRFL及冷却储存环CSR为例,给出了依据碰撞截面的公式计算束流在加速器真空中的传输效率的方法,并计算了在不同真空度下HIRFL的ECR源轴向注入束运线、注入器SFC、前束运线、主加速器SSC和后束运线等不同加速阶段及CSR的传输效率,并提出合理的真空度要求.HIRFL的真空分布测量和束流的损失测量证明了该计算方法的可靠性.     

非线性光纤方向耦合器孤子动力特性分析

利用变分方法,导出了双曲正割型光孤子在非线性光纤耦合器中传输时满足的动力学方程组,分析了该方程组的平衡点性态,讨论了光孤子的开关特性.指出参数|β|<2π.是能实现完全的开关操作的必要条件.     

Optimization of the surface texture for silicon carbide sliding in water

Surface texturing has been recognized as an effective means to improve the tribological performances of sliding surfaces. Usually, generation additional hydrodynamic pressure to increase the load carrying capacity is regarded as the most significant effect of surface texture. In the case of silicon carbide sliding against identical material in water, the experimental results indicate that surface texture is also helpful to improve the running-in progress to smooth the contact surfaces, showing another reason to result in low friction. Based on the consideration of enhancing the generation of hydrodynamic pressure and improving running-in progress, a surface texture pattern, which was combined with large (circle, 350 μm in diameter) and small (rectangular, 40 μm in length) dimples, was designed to maximize the texture effect on the load carrying capacity of SiC surfaces sliding in water. The friction coefficient of such textured surface was evaluated and compared with that of untextured and those only with large or small dimples only. The friction reduction mechanisms of the patterns with different dimples in size are discussed.     

一类多维指数分布的参数估计

考虑生存函数为(F)(x1,x2,…,xn)=P{X1＞x1,…,Xn＞xn}=exp{-[n∑i=1(xi/θi)1/δ]δ}(0＜xi＜∞,0＜δ≤ 1,0＜θi＜∞,i=(1,n))的一类多维指数分布,给出了它的密度函数的表示式,并讨论了它的性质.提出了相关参数δ的估计(^δ),证明了(^δ)有相合性和渐近正态性,得到了(^δ)的渐近方差σ2δ.最后还给出了若干随机模拟的结果.