军事软毁伤效应综合评估与决策分析

军事打击不但可以直接毁伤敌方政治、军事目标,而且可以造成敌方社会秩序混乱、经济发展受挫、人民心理恐慌等军事软毁伤效应。军事软毁伤效应在未来信息化战争中很有可能达成显著的战略目标,因此,研究军事软毁伤效应具有重要而现实的意义。依据软毁伤理论,建立了军事软毁伤效应的综合评估指标体系,给出了一种军事软毁伤效应的综合评估模型,并设计了综合评估决策支持系统。     

基于失效树的某型无坐力炮弹储存可靠性分析

通过对某型无坐力炮弹战斗部的结构分析和功能分析,以弹着目标不爆炸为顶事件建立了失效树,并根据该型弹药的功能构件在储存期间的特点选择了失效树的简化原则,建立了系统可靠度模型。确定了由储存因素导致弹着目标不爆炸这一事件的4种主要失效模式。并通过分析其不同的储存期失效机理和影响因素,提出该型炮弹的储存策略。     

美国非法证据排除规则与德国证据运用禁止规则之比较

非法获得的证据能否作为定案的依据 ,历来为各国刑事证据理论所关注。美国和德国作为英美法系和大陆法系的典型代表 ,分别确立了非法证据排除规则和证据运用禁止规则。研究了两国排除规则的内涵、发展历史、适用范围及存在差异     

基于功能树的导弹打击系统目标瞄准点选择方法

针对导弹打击系统目标的问题,建立了基于目标功能损伤度的瞄准点选择方法;在对毁伤树方法进行改进的基础上,提出了描述目标结构与工作流程的目标功能树模型,构造出体现目标整体功能下降的优化目标函数,采用模拟退火算法搜索最优解。通过算例对不同的瞄准点选择方案进行比较,发现考虑目标的功能结构特点时,得到的瞄准点明显优于其它方案。     

基于均匀实验设计的温压战斗部爆炸冲击波毁伤仿真研究

以温压战斗部爆炸冲击波对地下目标的毁伤为背景,设计毁伤仿真模拟的基本方案,在对影响冲击波毁伤因素合理分析的基础上,利用均匀试验设计方法设计以装药密度、空气温度、大气压强以及混凝土密度为影响因素的均匀试验方案,最后通过正态分布检验图和Bootstrap法等方法对毁伤参数的统计特性进行研究。结果表明,固定条件下温压战斗部爆炸冲击波对地下目标的毁伤中,毁伤参数值具有明显的正态分布特性,对后续结合现场试验数据确定毁伤参数真值和毁伤试验鉴定提供了一定的指导和借鉴作用。     

数据挖掘故障诊断专家系统

为了及时准确地对防空导弹飞行试验故障进行诊断,克服传统专家系统获取专家知识的瓶颈,立足于防空导弹飞行试验历史数据,利用数据挖掘的方法获得飞行试验故障模式判定决策树,进而得出导弹故障判定规则,形成故障诊断专家知识库,构建防空导弹故障诊断专家系统.这使数据挖掘和专家系统两者的优点得到充分发挥.     

Damage modeling of ballistic penetration and impact behavior of concrete panel under low and high velocities

This work presents a numerical simulation of ballistic penetration and high velocity impact behavior of plain and reinforced concrete panels. This paper is divided into two parts. The first part consists of numerical modeling of reinforced concrete panel penetrated with a spherical projectile using concrete damage plasticity (CDP) model, while the second part focuses on the comparison of CDP model and Johnson-Holmquist-2 (JH-2) damage model and their ability to describe the behavior of concrete panel under impact loads. The first and second concrete panels have dimensions of 1500 mm × 1500 mm × 150 mm and 675 mm × 675 mm × 200 mm, respectively, and are meshed using 8-node hexahedron solid elements. The impact object used in the first part is a spherical projectile of 150 mm diameter, while in the second part steel projectile of a length of 152 mm is modeled as rigid element. Failure and scabbing characteristics are studied in the first part. In the second part, the com-parison results are presented as damage contours, kinetic energy of projectile and internal energy of the concrete. The results revealed a severe fracture of the panel and high kinetic energy of the projectile using CDP model comparing to the JH-2 model. In addition, the internal energy of concrete using CDP model was found to be less comparing to the JH-2 model.     

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.     

Weapon effectiveness and the shapes of damage functions

This paper provides a review of methods of assessing a fragmentation weapon's effectiveness against a point target or an area target with keeping the focus on the necessity of using the Carleton damage function with the correct shape factor.First,cookie-cutter damage functions are redefined to preserve the shape factor of and to have the same lethal area as the corresponding Carleton damage function.Then,closed-form solutions of the effectiveness methods are obtained by using those cookie-cutter damage functions and the Carleton damage function.Finally,the closed-form solutions are applied to calculate the probability of damaging a point target and the expected fractional damage to an area target for several attack scenarios by using cookie-cutter damage functions and the Carleton damage functions with different shape factors.The comparison of the calculation results shows that using cookie-cutter damage functions or the Carleton damage function with a wrong shape factor results in quite signifi-cant differences from using the original Carleton damage function with a correct shape factor when weapon's delivery error deviations are less than or comparable to the lengths of the lethal area and the aim point is far from a target.The effectiveness methods improved in this paper will be useful for mission planning utilizing the precision-guided munitions in circumstances where the collateral damage should be reduced.     

An interface shear damage model of chromium coating/steel substrate under thermal erosion load

The Cr-plated coating inside a gun barrel can effectively improve the barrel's erosion resistance and thus increase the service life.However,due to the cyclic thermal load caused by high-temperature gun-powder,micro-element damage tends to occur within the Cr coating/steel substrate interface,leading to a gradual deterioration in macro-mechanical properties for the material in the related region.In order to mimic this cyclic thermal load and,thereby,study the thermal erosion behavior of the Cr coating on the barrel's inner wall,a laser emitter is utilized in the current study.With the help of in-situ tensile test and finite element simulation results,a shear stress distribution law of the Cr coating/steel substrate and a change law of the interface ultimate shear strength are identified.Studies have shown that the Cr coating/steel substrate interface's ultimate shear strength has a significant weakening effect due to increasing temperature.In this study,the interfacial ultimate shear strength decreases from 2.57 GPa(no erosion)to 1.02 GPa(laser power is 160 W).The data from this experiment is employed to establish a Cr coating/steel substrate interface shear damage model.And this model is used to predict the flaking process of Cr coating by finite element method.The simulation results show that the increase of coating crack spacing and coating thickness will increase the service life of gun barrel.