Survivability assessment of spacecraft impacted by orbit debris

To help optimize the spacecraft design and reduce the risk of spacecraft mission failure, a new approach to assess the survivability of spacecraft in orbit is presented here, including the following three steps:1) Sensitivity Analysis of spacecraft. A new sensitivity analysis method, a ray method based on virtual outer wall, is presented here. Using rays to simulate the debris cloud can effectively address the component shadowing issues. 2) Component Vulnerability analysis of spacecraft. A function"Component functional reduction degree — Component physical damage degree"is provided here to clearly describe the component functional reduction. 3) System-level Survivability Assessment of spacecraft. A new method based on expert knowledge reasoning, instead of traditional artificial failure tree method, is presented here to greatly improve the efficiency and accuracy of calculation.     

An efficient SPH methodology for modelling mechanical characteristics of particulate composites

Particulate composites are one of the widely used materials in producing numerous state-of-the-art components in biomedical, automobile, aerospace including defence technology. Variety of modelling techniques have been adopted in the past to model mechanical behaviour of particulate composites. Due to their favourable properties, particle-based methods provide a convenient platform to model failure or fracture of these composites. Smooth particle hydrodynamics (SPH) is one of such methods which demonstrate excellent potential for modelling failure or fracture of particulate composites in a Lagrangian setting. One of the major challenges in using SPH method for modelling composite materials depends on accurate and efficient way to treat interface and boundary conditions. In this paper, a master-slave method based multi-freedom constraints is proposed to impose essential boundary conditions and interfacial displacement constraints in modelling mechanical behaviour of composite materials using SPH method. The proposed methodology enforces the above constraints more accurately and requires only smaller condition number for system stiffness matrix than the procedures based on typical penalty function approach. A minimum cut-off value-based error criteria is employed to improve the compu-tational efficiency of the proposed methodology. In addition, the proposed method is further enhanced by adopting a modified numerical interpolation scheme along the boundary to increase the accuracy and computational efficiency. The numerical examples demonstrate that the proposed master-slave approach yields better accuracy in enforcing displacement constraints and requires approximately the same computational time as that of penalty method.     

Adaptive phasefield modelling of crack propagation in orthotropic functionally graded materials

In this work, we extend the recently proposed adaptive phase field method to model fracture in orthotropic functionally graded materials (FGMs). A recovery type error indicator combined with quadtree decomposition is employed for adaptive mesh refinement. The proposed approach is capable of capturing the fracture process with a localized mesh refinement that provides notable gains in computational efficiency. The implementation is validated against experimental data and other nu-merical experiments on orthotropic materials with different material orientations. The results reveal an increase in the stiffness and the maximum force with increasing material orientation angle. The study is then extended to the analysis of orthotropic FGMs. It is observed that, if the gradation in fracture properties is neglected, the material gradient plays a secondary role, with the fracture behaviour being dominated by the orthotropy of the material. However, when the toughness increases along the crack propagation path, a substantial gain in fracture resistance is observed.     

基于改进核极限学习机和集成学习理论的目标机动轨迹预测

为了提高目标轨迹预测的精度以及预测模型的泛化能力,提出基于改进蝙蝠算法优化的核极限学习机(Kernel Extreme Learning Machine,KELM)和集成学习理论目标机动轨迹预测模型。构建KELM模型,并采用改进的蝙蝠算法对KELM的参数进行优化;以优化后的KELM神经网络为弱预测器,结合集成学习算法生成强预测器,通过训练不断优化强预测的结构和参数,得到一种基于集成学习理论的目标机动轨迹预测模型;基于不同规模的样本,将所得预测模型与逆传播神经网络、支持向量机和极限学习机等模型进行对比分析。仿真结果表明:所提目标机动轨迹预测模型具有较好的预测精度和泛化能力。     

基于内角流动的板式表面张力贮箱内推进剂流动过程研究

分析了在微重力环境下,板式表面张力贮箱内推进剂的流动和定位过程.利用VOF方法对贮箱内推进剂的重定位过程进行了仿真计算,验证了贮箱及PMD的推进剂管理性能.用数学方法分析了液体在导流板的内角流动,用解析计算的方法求出流动过程中液面的长度,并和仿真计算结果进行了对比.因为实际的贮箱模型比较复杂,仿真计算结果和理论计算结果存在一定的误差,但其流动趋势保持一致.本文的工作能够为内角流动的研究提供有益的参考.     

Parametric inference for component distributions from lifetimes of systems with dependent components

In system reliability analysis, for an n ‐component system, the estimation of the performance of the components in the system is not straightforward in practice, especially when the components are dependent. Here, by assuming the n components in the system to be identically distributed with a common distribution belonging to a scale‐family and the dependence structure between the components being known, we discuss the estimation of the lifetime distributions of the components in the system based on the lifetimes of systems with the same structure. We develop a general framework for inference on the scale parameter of the component lifetime distribution. Specifically, the method of moments estimator (MME) and the maximum likelihood estimator (MLE) are derived for the scale parameter, and the conditions for the existence of the MLE are also discussed. The asymptotic confidence intervals for the scale parameter are also developed based on the MME and the MLE. General simulation procedures for the system lifetime under this model are described. Finally, some examples of two‐ and three‐component systems are presented to illustrate all the inferential procedures developed here. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

基于子协方差阵加权的三星时差定位数据综合

为提高三星时差定位系统的定位精度,需要将系统在不同时间得到的多个定位估计结果进行统计综合。本文提出了一种基于子协方差阵加权的定位数据综合算法。该算法根据单点定位精度分析结果,先利用定位误差协方差的二维子阵的逆矩阵作为权矩阵,通过加权最小二乘估计目标空间三维坐标位置中的其中两维,再利用WGS-84地球模型求解另一维坐标。仿真实验表明,相比已有算法,所提新算法可以明显提高定位精度。     

基于云重心评估法的装备健康状态评估

装备健康状态评估作为健康管理的重要组成部分,是实施状态维修的关键环节。针对装备健康状态信息的随机性和模糊性。对状态指标体系进行了定性和定量的组合。将装备健康状态划分为5个等级,建立了基于云重心评估法的装备健康状态评估模型,采用加权偏离度来衡量装备的健康状态,构成了定性和定量评价间的相互映射。通过应用,验证了该方法的可行性。     

信息熵和判断矩阵的专家聚类赋权法

为了降低专家聚类赋权过程中由排序向量引起的不确定性和判断矩阵引起的矛盾性,提高权值分配的精确性,提出了基于信息熵和判断矩阵相结合的专家聚类赋权法。该方法采用聚类分析原理,对排序向量进行分类,根据分类结果、信息熵值和一致性比率确定专家权重系数。实例分析表明:基于信息熵和判断矩阵相结合的专家聚类赋权法在具体应用中得到的结果离期望值更近,说明该算法有效可行。     

变后掠翼导弹制导精度研究(英文)

将飞行器变后掠翼技术应用于导弹上,弹翼的运动对弹体动态特性与整个系统的制导精度会直接产生影响。首先基于多刚体动力学原理获得的变后掠翼导弹完整的数学模型,通过小扰动线性化方法得到了弹体相关的传递函数,并利用统计线性化方法计算了制导数学模型的均值与协方差传播方程,最后应用协方差分析描述函数方法分析得到了导弹的弹翼转动速度、弹翼后掠角大小与弹翼转动时刻对制导精度影响的规律。