Numerical simulation of the natural fragmentation of explosively loaded thick walled cylinders

The ability to predict the natural fragmentation of an explosively loaded metal casing would represent a significant achievement. Physically-based material models permit the use of small scale laboratory tests to characterise and validate their parameters. The model can then be directly employed to understand and design the system of interest and identify the experiments required for validation of the predictions across a wide area of the performance space. This is fundamentally different to the use of phenomenologically based material algorithms which require a much wider range of characterisation and validation tests to be able to predict a reduced area of the performance space. Eulerians numerical simulation methods are used to describe the fragmentation of thick walled EN24 steel cylinders filled with PBXN-109 explosive. The methodology to characterise the constitutive response of the material using the physically based Armstrong–Zerilli constitutive model and the Goldthorpe path dependent fracture model is described, and the results are presented. The ability of an Eulerian hydrocode to describe the fragmentation process and reproduce the experimentally observed fragment mass and velocity distributions is presented and discussed. Finally the suitability of the current experimental analysis methodology for simulation validation is addressed.     

Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression

An artificial neural network (ANN) constitutive model is developed for high strength armor steel tempered at 500 °C, 600 °C and 650 °C based on high strain rate data generated from split Hopkinson pressure bar (SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnson–Cook (J–C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stress–strain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures (500–650 °C), strains (0.05–0.2) and strain rates (1000–5500/s) are employed to formulate J–C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). R and AARE for the J–C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.     

车载火控系统标定原理及误差分析

巡航导弹的快速发展改变了现代战争的作战模式,为了实现对该目标的有效拦截,将防空火控系统组网是一种有效手段。文中以车载火控系统组网为例,介绍了火控系统两种标定方法的原理,并结合目标态势对误差进行了分析,结果表明系统标定引入的误差与定位定向设备精度、标定系统距离和目标态势都是相关的。     

云模型在雷达干扰资源多目标优化配置中的应用

鉴于干扰效果/效益评估在雷达干扰资源优化分配中的重要地位,提出了一种基于云模型和匈牙利算法的雷达干扰资源优化分配模型。利用一维云模型单条件单规则发生器和逆向云发生器构建了评估指标干扰效果推理器,利用基于风险态度因子的云模型映射函数定义了云模型之间距离的度量方法。提出了一种基于云模型与逼近理想解法的雷达干扰效益矩阵求解方法,最后运用匈牙利算法实现了干扰资源优化分配。将所建模型应用到干扰资源分配中,结果表明该模型能够较好的处理评估中的不确定性知识,是合理、可行的。     

中庭式建筑防火设计探讨

根据消防实际工作经验,以某星级饭店为工程研究对象,就中庭式建筑的火灾危险性以及规范中关于中庭防火设计问题存在的不明确之处进行探讨,并提出了一些建议和解决对策.     

沿海船舶边防治安管理立法问题研究

沿海船舶边防治安管理是维护海上治安秩序稳定的重要手段。为加强海上治安管理,需要通过立法活动赋予公安边防机关特殊的执法权,并以此规范公安边防行政执法活动。分析沿海船舶边防治安管理立法概况及其存在的问题,提出完善沿海船舶边防治安管理立法的建议。     

基于概率图模型的海上多目标跟踪

针对海上复杂环境中多运动目标跟踪问题,提出了一种融合概率图模型和粒子滤波的跟踪方法.该方法采用二元马尔可夫随机场构建多目标模型,利用势函数表示其联合概率分布,最后用变差分法和粒子滤波推理后验概率密度.通过二阶回归模型准确构建状态转移方程,以及Mean-Shift迭代加速粒子采样,提高了算法精确度和运行效率.实验表明:该方法能很好地实现海上复杂环境中的多目标实时跟踪.     

考虑地磁场空间梯度的飞行器磁干扰建模与求解方法

首先,在研究飞行器背景磁干扰模型与小信号模型的基础上,分析了地磁梯度沿纬度与高度的变化规律,分析可知由于飞行器的机动,地磁梯度会对测量结果造成影响;然后,考虑地磁梯度因素,对原有小信号模型求解方法进行了改进：飞行器在四个航向上做小角度的机动飞行,将地磁场沿纬度与高度的梯度作为未知量,与飞行器磁干扰模型中的剩磁参数、感磁参数和涡磁参数一起求解;最后,利用实测试验数据进行了验证,结果表明：考虑地磁梯度的影响与不考虑地磁梯度的影响相比,求解的模型参数可以在细节上进一步提高飞行器背景磁干扰的补偿效果。     

MEO非共振轨道导航星座摄动补偿控制

针对我国新一代全球导航星座长期构型维持控制问题,提出了MEO星座构型状态描述方法,分析了主要摄动力和轨道参数偏差作用下的星座构型演化规律,结合实测数据对稳定性规律进行了验证;研究了参数偏置摄动补偿控制原理,提出了一种改进的解耦控制方案,分析了星座部署时间对摄动补偿控制量的影响规律,得到了一些有益的关键性结论,为我国全球导航系统的星座构型设计和运控策略制定奠定了基础。     

A novel gradient approach for optimal design and sensitivity analysis of EWMA control charts

Conventional control charts are often designed to optimize out‐of‐control average run length (ARL), while constraining in‐control ARL to a desired value. The widely employed grid search approach in statistical process control (SPC) is time‐consuming with unsatisfactory accuracy. Although the simulation‐based ARL gradient estimators proposed by Fu and Hu [Manag Sci 45 (1999), 395–413] can alleviate this issue, it still requires a large number of simulation runs to significantly reduce the variance of gradient estimators. This article proposes a novel ARL gradient estimation approach based on integral equation for efficient analysis and design of control charts. Although this article compares with the results of Fu and Hu [Manag Sci 45 (1999), 395–413] based on the exponentially weighted moving average (EWMA) control chart, the proposed approach has wide applicability as it can generally fit into any control chart with Markovian property under any distributions. It is shown that the proposed method is able to provide a fast, accurate, and easy‐to‐implement algorithm for the design and analysis of EWMA charts, as compared to the simulation‐based gradient estimation method. Moreover, the proposed gradient estimation method facilitates the computation of high‐order derivatives that are valuable in sensitivity analysis. The code is written in Matlab, which is available on request. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 223–237, 2014