武器装备体系结构描述方法

在明确武器装备体系相关概念、特点的基础上,总结比较了目前体系结构描述方法和工具的优缺点,针对新形式对武器装备体系结构描述方法的选择提出了要求,并指出了武器装备体系要解决的“短板”问题.     

探空火箭级间段的CAD系统

本文在CADKEY绘图软件包的支持下,开发了加筋圆柱结构CAD系统—Hcadx.此系统通过菜单引导用户的操作,可用经验公式或解析法作轴压稳定分析,用罚函数法优化参数,可以在不退出Hcadx的情况下启动CADKEY,自动绘制设计草图。此草图经过适当地编辑、修改、便可成为工程中能接受的工作图纸。     

气氧甲烷溅板式层板喷注器燃烧特性试验分析

采用试验方法分别对单喷嘴和多喷嘴溅板式层板喷注器燃烧特性进行研究,考察不同工况下喷注器面板热载情况,得到不同混合比、不同结构参数对燃烧室压力分布和燃烧效率的影响规律。试验结果表明:混合比对多喷嘴喷注器燃烧室压力影响不大,但对燃烧效率有较大影响。对于单喷嘴喷注器,燃烧效率随着混合比的增加而增高。相同条件下,增大扩张角及出口层宽度有利于提高喷注器燃烧效率,但会引起喷注面板热载增大,各喷注器喷注面板均产生不同程度的变形或失效。该结果对于正确指导层板式喷注器的设计以及筛选喷注器结构具有重要意义。     

大功率IGBT器件及其组合多时间尺度动力学表征研究综述

在提出电力电子器件及其组合多时间尺度动力学表征需求的前提下,以目前常用的全控型电力电子器件——绝缘栅双极晶体管(Insulated Gate Bipolar Transistor,IGBT)为例,系统分析并归纳了目前在IGBT及其组合多时间尺度动力学表征研究方面的进展和成果,包括作为基础的大功率IGBT及其组合多时间尺度电热瞬态建模方法、基于模型的大功率IGBT模块失效量化表征方法以及用于辅助分析的IGBT组合多速率仿真方法。此外,介绍了基于IGBT多时间尺度模型的装置应用设计案例。从建模方法、可靠性评估、仿真手段以及应用设计四个方面系统全面地阐述了大功率IGBT及其组合多时间尺度的动力学表征方法,可为电力电子混杂系统的精确设计提供电力电子器件层面的理论和技术支撑。     

信息知识库中的数据可用性恢复策略

信息知识库在工业生产中能够提高生产效率、降低资源消耗,但数据失效频率高。针对多节点失效重构的纠删码策略较少,且未充分考虑失效数据重构时各参与节点间的链路关系,导致重构效率较低。针对信息知识库数据失效,提出一种新的重构策略。根据节点的数据处理能力,选取数据处理能力最高的新生节点为路由节点;依据路由节点与候选供应节点及剩余空闲节点的链路带宽,确定供应节点及新生节点,从而构建数据重构网络拓扑,提高失效数据重构效率。实验结果证实,与传统纠删码策略性能比较,该方法有较短的重构时间和较高的重构成功率。     

MF-CFI: A fused evaluation index for camouflage patterns based on human visual perception

The evaluation index of camouflage patterns is important in the field of military application. It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the human visual mechanism. In order to make the evaluation method more computationally intelligent, a Multi-Feature Camouflage Fused Index (MF-CFI) is proposed based on the comparison of grayscale, color and texture features between the target and the background. In order to verify the effectiveness of the proposed index, eye movement experiments are conducted to compare the proposed index with existing indexes including Universal Image Quality Index (UIQI), Camouflage Similarity Index (CSI) and Structural Similarity (SSIM). Twenty-four different simulated targets are designed in a grassland background, 28 observers participate in the experiment and record the eye movement data during the observation process. The results show that the highest Pearson correlation coefficient is observed between MF-CFI and the eye movement data, both in the designed digital camouflage patterns and large-spot camouflage patterns. Since MF-CFI is more in line with the detection law of camouflage targets in human visual perception, the proposed index can be used for the comparison and parameter optimization of camouflage design algorithms.     

Multi-objective design optimization of composite submerged cylindrical pressure hull for minimum buoyancy and maximum buckling load capacity

This paper presents the design optimization of composite submersible cylindrical pressure hull subjected to 3 MPa hydrostatic pressure. The design optimization study is conducted for cross-ply layups [0s/90t/0u], [0s/90t/0u]s, [0s/90t]s and [90s/0t]s considering three uni-directional composites, i.e. Carbon/Epoxy, Glass/Epoxy, and Boron/Epoxy. The optimization study is performed by coupling a Multi-Objective Genetic Algorithm (MOGA) and Analytical Analysis. Minimizing the buoyancy factor and maximizing the buckling load factor are considered as the objectives of the optimization study. The objectives of the optimization are achieved under constraints on the Tsai-Wu, Tsai-Hill and Maximum Stress composite failure criteria and on buckling load factor. To verify the optimization approach, optimization of one particular layup configuration is also conducted in ANSYS with the same objectives and constraints.     

Buckling analysis of embedded graphene oxide powder-reinforced nanocomposite shells

In this study, the buckling analysis of a Graphene oxide powder reinforced (GOPR) nanocomposite shell is investigated. The effective material properties of the nanocomposite are estimated through Halpin-Tsai micromechanical scheme. Three distribution types of GOPs are considered, namely uniform, X and O. Also, a first-order shear deformation shell theory is incorporated with the principle of virtual work to derive the governing differential equations of the problem. The governing equations are solved via Galerkin's method, which is a powerful analytical method for static and dynamic problems. Comparison study is performed to verify the present formulation with those of previous data. New results for the buckling load of GOPR nanocomposite shells are presented regarding for different values of circumfer-ential wave number. Besides, the influences of weight fraction of nanofillers, length and radius to thickness ratios and elastic foundation on the critical buckling loads of GOP-reinforced nanocomposite shells are explored.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.     

Size dependent free vibration analysis of functionally graded piezoelectric micro/nano shell based on modified couple stress theory with considering thickness stretching effect

Higher-order shear and normal deformation theory is used in this paper to account thickness stretching effect for free vibration analysis of the cylindrical micro/nano shell subjected to an applied voltage and uniform temperature rising. Size dependency is included in governing equations based on the modified couple stress theory. Hamilton's principle is used to derive governing equations of the cylindrical micro/nano shell. Solution procedure is developed using Navier technique for simply-supported boundary conditions. The numerical results are presented to investigate the effect of significant parameters such as some dimensionless geometric parameters, material properties, applied voltages and temperature rising on the free vibration responses.