多层衍射元件的浮雕深度和基底材料的优化分析

在分析单层和多层衍射元件衍射效率的基础上,推导出光学系统中多层衍射元件浮雕深度和基底材料的一般关系式,并对这些变量进行优化,得到阿贝数的值大致在20～80之间,β的合理值在0.25～0.6之间,当2个衍射元件的阿贝数v1＜v2,且差值比较大时,阿贝数小的衍射元件的基底材料选择光学玻璃中的火石玻璃,阿贝数大的衍射元件的基底材料选择光学玻璃中的冕牌玻璃。根据这个方法,设计了3组可实现宽波段高衍射效率的不同材料、不同浮雕深度的组合,使得每组组合中两层衍射元件的衍射效率都在99%以上。     

强流电子束入射叠靶能量沉积计算

计算了多脉冲相对论强流电子束入射钽-石墨叠靶的能量沉积和轫致辐射谱。能量沉积采用Geant4程序计算,轫致辐射谱根据基本的辐射理论和蒙特卡罗方法计算。结果显示,各层的热区能量沉积呈由大到小的递减分布,截面轫致辐射分布和电子束径向分布主要受钽层的影响。石墨层的低能量沉积率和高热容能改善叠靶的性能。对于单脉冲,钽-石墨层厚比为1∶1时,石墨能全部吸收相邻钽层的热沉积,轫致辐射效率为35.4%;4脉冲情况下,钽-石墨层厚比应为1∶13,总轫致辐射效率降到19.9%。考虑轫致辐射剂量和质量,钽-石墨两者的厚度比为1∶5时,钽层的总厚度应为1.2 mm;当钽-石墨层厚比为1∶10时,钽层的总厚应降到0.7 mm。     

Convective transport of nanoparticles in multi-layer fluid flow

Technologically, multi-layer fluid models are important in understanding fluid-fluid or fluid-nanoparticle interactions and their effects on flow and heat transfer characteristics. However, to the best of the authors’ knowledge, little attention has been paid to the study of three-layer fluid models with nanofluids. Therefore, a three-layer fluid flow model with nanofluids is formulated in this paper. The governing coupled nonlinear differential equations of the problem are non-dimensionalized by using appropriate fundamental quantities. The resulting multi-point boundary value problem is solved numerically by quasi-linearization and Richardson’s extrapolation with modified boundary conditions. The effects of the model parameters on the flow and heat transfer are obtained and analyzed. The results show that an increase in the nanoparticle concentration in the base fluid can modify the fluid-velocity at the interface of the two fluids and reduce the shear not only at the surface of the clear fluid but also at the interface between them. That is, nanofluids play a vital role in modifying the flow phenomena. Therefore, one can use nanofluids to obtain the desired qualities for the multi-fluid flow and heat transfer characteristics.     

93W杆式弹超高速撞击多层Q345钢靶毁伤及微观分析

为了解杆式弹超高速撞击多层薄钢靶的破坏过程及毁伤机理,开展了克级93W杆式弹正撞击多层Q345钢靶实验及数值模拟研究,通过扫描电子显微镜（scanning electron microscope,SEM）及金相显微镜,分析了超高速撞击实验后靶板材料的微观组织及成分。结果表明,超高速撞击作用下,靶板呈现出“翻唇”穿孔变形、花瓣状塑性变形、撕裂、撞击成坑及鼓包等破坏模式。靶板前3层毁伤以超高速穿孔为主,孔洞数目多但面积小,后几层靶板毁伤孔洞数目少且孔径呈先增大后减小趋势。微观分析表明靶材在强冲击压力下发生晶粒碎化、熔化及再结晶,撞击过程中会形成微孔聚集与微裂纹,可见靶板失效主要是熔融混合物冷却过程中产生的热应力与切应力下的剪切撕裂综合作用的结果。     

Detection of liner surface defects in solid rocket motors using multilayer perceptron neural networks

Debonding problems along the propellant/liner/insulation interface are a critical point to the integrity and one of the major causes of structural failures of solid rocket motors. Current solutions are typically restricted to methods for assessing the integrity of the rocket motors structure and visually inspecting their components. In this context, this paper presents an improved algorithm to detect liner surface defects that may compromise the bonding between the solid propellant and the insulation. The use of Local Binary Patterns (LBP) provides a structural and statistical approach to texture analysis of liner sample images. Along with color information extraction, these two methods allow the representation of image pixels by feature vectors that are further processed by a Multilayer Perceptron (MLP) neural network classifier. The MLP neural network analyzes liner sample images and classifies each pixel into one of three classes: non-defect, foreign object, and defect. Several tests were executed varying different parameters to find the optimal MLP configuration, and as a result, the best classification accuracy of 99.08%, 90.66%, and 99.48% was achieved for the corresponding classes. Moreover, the defect size estimate showed that the MLP classifier correctly identified defects less than 1 mm long, with a relatively small number of training examples. Positive results indicate that the algorithm can identify liner surface defects with a performance similar to human inspectors and has the potential to assist or even automate the liner inspection process of solid rocket motors.     

Modelling matrix damage and fibre–matrix interfacial decohesion in composite laminates via a multi-fibre multi-layer representative volume element (MRVE)

A three-dimensional multi-fibre multi-layer micromechanical finite element model was developed for the prediction of mechanical behaviour and damage response of composite laminates. Material response and micro-scale damage mechanism of cross-ply, [0/90]_ns, and angle-ply, [±45]_ns, glass-fibre/epoxy laminates were captured using multi-scale modelling via computational micromechanics. The framework of the homogenization theory for periodic media was used for the analysis of the proposed ‘multi-fibre multi-layer representative volume element’ (M²RVE). Each layer in M²RVE was represented by a unit cube with multiple randomly distributed, but longitudinally aligned, fibres of equal diameter and with a volume fraction corresponding to that of each lamina (equal in the present case). Periodic boundary conditions were applied to all the faces of the M²RVE. The non-homogeneous stress–strain fields within the M²RVE were related to the average stresses and strains by using Gauss’ theorem in conjunction with the Hill–Mandal strain energy equivalence principle. The global material response predicted by the M²RVE was found to be in good agreement with experimental results for both laminates. The model was used to study effect of matrix friction angle and cohesive strength of the fibre–matrix interface on the global material response. In addition, the M²RVE was also used to predict initiation and propagation of fibre–matrix interfacial decohesion and propagation at every point in the laminae.     

金属正交波纹夹芯结构的动态压缩响应

通过理论和数值方法,对冲击载荷下金属正交波纹夹芯结构的动态压缩响应进行了研究。考虑材料应变率影响,建立了金属正交波纹夹芯结构动态响应的理论模型,同时对它的动态压缩响应进行了有限元模拟。结果表明,考虑材料应变率影响的理论模型的预测结果与有限元模拟结果吻合较好。进一步对多层正交波纹夹芯结构的动态压缩响应进行了数值模拟,获得了不同速度冲击下的变形模式,分析了层数对其动态响应的影响。研究发现,通过增加层数能够有效地增强结构的缓冲吸能能力,但层数超过4层以后增强效果不明显。

     

多层感知器信用评模型及预警研究

本文利用多层感知器 ( MLP)原理建立神经网络信用评价模型 ,用来对我国 2 0 0 0年 1 0 6家上市公司进行信用评级 ,并进一步对我国 2 0 0 1年公布的 1 3家预亏公司进行预警研究 .按照各上市公司的经营状况分为“好”、“差”两类 ,每一类由 5 3家上市公司构成数据样本 .对于每一家上市公司 ,主要考虑其经营状况的四个财务指标 :每股收益 ,每股净资产 ,净资产收益率和每股现金流量 .仿真结果表明 ,本文所建立的神经网络信用评价模型有很高的分类准确率 ,达到 98.1 1 % .又由于该信用评价模型有很强的适应能力 ,故可以进一步用来对企业的财务危机进行预警研究 .预警实证分析表明 ,该信用评价模型对我国 2 0 0 1年公布的 1 3家预亏公司进行预警分析 ,预警准确率达到 1 0 0 % .此外 ,文中还给出 MLP网络模型的学习算法和步骤     

Optical Filters Using Optical Multi-Layer Structures for Optical Communication Systems

Abstract

In this article, light reflection spectrums for different one-dimensional multi-layer structures are obtained. Optical reflectivities for periodic, superperiodic, and quasiperiodic structures have been calculated using the transfer matrix method. PBGs of each structure have been obtained using the optical reflectivity pattern. From a comparison of reflectivity and standard dense wavelength division multiplexing grids, it is demonstrated that narrow and dense band filters can be built from generalized Fibonacci quasiperiodic structures. Reflectivity of these structures contains bands that meet the ITU-T dense wavelength division multiplexing standard. Reflectivity of other multi-layer structures has poor or less potential to be used in dense wavelength division multiplexing systems.