火控系统故障预测与健康管理技术

故障预测和健康管理(PHM)技术具有故障诊断、故障预测以及健康管理的功能,对降低火控装备维修成本、增强火控系统的完备性以及提高火控系统管理效率具有重要意义。根据通用火控系统发展现状及应用需求,将PHM技术引入到火控系统中。首先介绍了PHM技术原理和国内外发展现状,重点讨论了PHM关键技术和通用火控系统PHM体系结构。最后展望了火控系统PHM技术的发展趋势。     

新型优化同心筒自力发射流场机理与降温效果

针对同心筒自力发射结构优化与热环境改善的问题,基于雷诺平均方程及轴对称N-S方程,依托弹性变形和网格再生成方法结合的动网格技术,对3种不同结构形式同心筒发射装置的二维轴对称流场进行了非定常数值计算,得到了不同结构条件下动态的流场机理及导弹热环境特性。计算结果显示:导流器方案能实现燃气流迅速顺利排导;筒底收缩段设计能有效遮挡反溅燃气流;筒口导流板结构较好抑制了内筒的"倒吸效应",确保导弹和内筒的热安全;揭示了优化同心筒的流场结构与降温机理,可为相关研究提供参考。     

不同形状喷嘴内流场模拟分析

建立了前混合磨料水射流喷嘴内固液两相流动的数学模型,运用Fluent软件对圆柱型、圆锥收敛型、流线型和圆锥带圆柱段收敛型喷嘴内流场进行了数值模拟,分析了喷嘴形状对喷嘴内流场和磨料颗粒加速性能的影响,并通过切割实验验证了仿真结果的可靠性。结果表明:圆柱型喷嘴能量损耗较大,圆锥收敛型喷嘴对磨料颗粒的加速性能差,流线型喷嘴性能良好但不易加工,只有圆锥带圆柱段收敛型喷嘴是用作前混合式磨料水射流喷嘴的最优选择。     

基于差分进化算法的铁磁物体磁化率优化方法

针对工程中铁磁物体磁化率难以确定的问题,以单元表面积分铁磁物体感应磁场模型为基础,利用磁场测量值建立了多维磁化率数学优化模型,并运用具有全局多维寻优能力的进化差分算法对多维磁化率数学模型进行了求解,进而确定了铁磁物体的感应磁场分布。最后,设计了空心圆筒磁测实验,实验结果表明:该方法能有效获取铁磁物质多维磁化率,还能精确计算铁磁物体的感应磁场分布。     

基于流程仿真的物资接收系统可靠性预计方法

针对船舶物资接收时间计算的问题,新增加了对设备发生故障的考虑,通过引入接收时间可靠性的概念,将其转换为船舶物资接收系统的可靠性预计问题。为了解决上述可靠性预计问题,提出了基于流程仿真的系统可靠性预计方法。分析了物资接收系统的工作流程,依据工作流程实现了仿真程序,用蒙特卡洛法对物资接收系统的可靠性进行了求解,并分析了设备不同的可靠性水平对物资接收系统可靠性的影响。仿真结果表明,增加了对设备故障的考虑后,对物资接收时间的估计更加接近实际;基于流程仿真的系统可靠性预计方法的模型更容易理解,所得结论更加具体。     

日常交互中朋友关系强度度量方法

针对如何度量日常生活中人们之间的关系强度问题展开研究,提出一个从日常轨迹、语义位置以及语义标签三个层次度量朋友之间关系强度的层级模型FRSHV。采用动态时间规整模型通过计算朋友之间的空间距离来度量其日常轨迹之间的相似度,进而使用轨迹序列熵值对用户每天轨迹的相似度进行加权处理,将其作为朋友之间的关系强度;采用主题模型隐含狄利克雷分布分别计算朋友之间的基于语义位置和语义标签的行为模式的相似性,将其作为朋友之间的关系强度;采用集成学习的思想对三个层次的度量结果进行投票,以投票结果作为最终的朋友之间的关系强度。在公开数据集上对FRSHV模型的有效性进行实验验证,结果表明该模型能够有效地度量朋友之间的关系强度。     

电传动装甲车辆负载功率预测实时能量管理策略

针对电传动装甲车辆负载功率预测功能缺失导致控制作用滞后的问题,提出一种具有较高负载功率预测精度的实时能量管理策略。在分析整车结构的基础上,采用理论分析和数据拟合方法,建立各动力源数学模型。将差分自回归移动平均模型和自适应马尔可夫链两种预测方法相结合,设计非平稳趋势性负载功率组合预测方法。在非线性模型预测控制框架下,构建多目标优化函数,采用序列二次规划法在有限时域内实时求解最优控制指令,优化多动力源协调控制过程。依托硬件在环仿真平台进行多路面行驶实验,对比有无功率预测的能量管理控制效果。结果表明,改进的实时能量管理策略对未来负载功率具有较好的预见性,能够显著优化多动力源协调控制过程,提升车辆燃油经济性,稳定母线电压和电池荷电状态,对传统模型预测控制下的工程应用场景具有一定借鉴意义。     

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.     

天然气在土壤中泄漏的稳态温度场分析

埋地高压天然气经小孔泄漏会导致土壤冻结形成冻土,应用一元气体动力学、流体力学和传热学知识建立了天然气小孔泄漏吸热率模型和土壤稳态温度场模型,并进行了实例计算。结果表明：当天然气输送压力小于临界压力,小孔泄漏吸热率随压力的增大而增大;当天然气输送压力大于等于临界压力,小孔泄漏吸热率达到最大值;冻土厚度随土壤热导率或空气对流传热系数的增大而减小,但土壤热导率对其影响较小,空气对流传热系数对其影响较大。     

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.