A quasi-isentropic model of a cylinder driven by aluminized explosives based on characteristic line analysis

A quasi-isentropic study on the process of driving a cylinder with aluminized explosives was carried out to examine the influence of the aluminum (Al) reaction rate on cylinder expansion and the physical parameters of the detonation products. Based on the proposed quasi-isentropic hypothesis and relevant isentropic theories, the characteristic lines of aluminized explosives driving a cylinder were analyzed, and a quasi-isentropic model was established. This model includes the variation of the cylinder wall velocity and the physical parameters of the detonation products with the Al reaction degree. Using previously reported experimental results, the quasi-isentropic model was verified to be applicative and accurate. This model was used to calculate the physical parameters for cylinder experiments with aluminized cyclotrimethylenetrinitramine explosives with 15.0 % and 30.0 % Al content. The results show that this quasi-isentropic model can be used not only to calculate the cylinder expansion rule or Al reaction degree, but also to calculate the physical parameters of the detonation products in the process of cylinder expansion. For explosives with 15.0 % and 30.0 % Al, 24.3 % and 18.5 % of the Al was found to have reacted at 33.9 μs and 34.0 μs, respectively. The difference in Al content results in different reaction intensity, occurrence time, and duration of two forms of reaction (diffusion and kinetic) between the Al powder and the detonation products; the post-detonation burning reaction between the Al powder and the detonation products prolongs the positive pressure action time, resulting in a continuous rise in temperature after detonation.     

The SSA-BP-based potential threat prediction for aerial target considering commander emotion

The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system. However, the traditional threat prediction methods mostly ignore the effect of commander's emotion. They only predict a target's present threat from the target's features itself, which leads to their poor ability in a complex situation. To aerial targets, this paper proposes a method for its potential threat prediction considering commander emotion (PTP-CE) that uses the Bi-directional LSTM (BiLSTM) network and the backpropagation neural network (BP) optimized by the sparrow search algorithm (SSA). Furthermore, we use the BiLSTM to predict the target's future state from real-time series data, and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model. Therefore, the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion. The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction, regardless of commander's emotional effect.     

军用发动机控制系统技术分析及改进研究

控制系统是航空发动机的重要组成部分。文章对国外航空技术强国,特别是美国和俄罗斯的航空发动机控制系统技术的发展历程进行了分析,着重介绍了某发动机控制系统的技术特点。针对该发动机控制系统提出了数字化改进方案,可以有效减轻控制系统重量,挖掘发动机潜力,并提升发动机性能。从航空发动机控制系统的发展历程可以看出,全权限数字电子控制技术(FADEC)是航空发动机控制系统发展的必然趋势,会对航空技术的发展产生巨大的推动作用。     

从线性供应到网络化保障——英军陆地作战后勤保障转型构想及措施

随着联合作战条件下后勤保障复杂性的日益增加,以及历次战争中后勤保障暴露出诸多不足,为赢取陆地作战的胜利,着眼于不同的陆地作战类型和作战区域,英军提出后勤保障从线性供应转型为网络化保障构想,并提出一系列具体措施,完成陆地作战后勤保障网络的构建,实现转型。     

美国网络安全战略管理体系分析

文章从美国政府颁布的政策文件、法律法规和各机构部门的职能三个方面,对美国网络安全战略管理体系进行了介绍和分析。     

基于随机拟合的悬浮式深弹弹阵特征建模

通过分析悬浮式深弹发射后在空中的弹道特性,建立了深弹弹着点坐标的计算模型。采取蒙特卡洛方法,分别对舰艇六自由度状态下,单管和六联装火箭深弹的弹着点坐标进行了仿真计算,对弹着点的分布规律进行了研究,并得出弹着点的联合密度函数。结果表明,在发射参数存在正态扰动下,弹着点散布区域均呈椭圆形分布,弹着点坐标均仍服从正态分布。     

水雾扩散模型仿真

建立水雾红外衰减模型时,通常把水雾空间看作单一均匀整体处理,然而雾滴的扩散现象使水雾的雾滴密度无法保证处处相等,所以仿真结果与实际情况相比普遍存在较大偏差。针对这一问题,基于高斯扩散理论与Mie散射理论,提出了一种新的建模方案。通过将水雾空间划分为多个子空间,并计算每个子空间内水雾消光性能的建模方法,提高建模的准确性。同时结合红外制导导弹来袭的方向变化,计算出不同方向上的水雾消光性能。从仿真结果可以看出,这种建模方式能够很好地克服以往建模方法的缺陷,为探索水雾的精确建模提供了一种新思路。     

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.     

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。