求解航空发动机数学模型的混合智能方法

针对传统求解方法收敛性不强而遗传算法求解效率较低的问题,利用BP神经网络逼近发动机平衡方程的反函数,将求解结果作为Newton-Raphson法的初值,提出了求解模型的混合智能方法。仿真结果表明,该方法可以保证非线性数学模型在整个飞行包线范围内收敛,与遗传算法相比又提高了求解效率。     

常规布局小型飞行器设计参数优化研究

对以螺旋桨为动力的长航时小型飞行器，在概念设计阶段的设计参数进行了敏感性分析。主要包含三个方面：一是飞机飞行状态，包括巡航高度；巡航速度及巡航升力系数。二是飞机机翼几何参数，包括尖梢比、展弦比和翼型。三是发动机的布局形式。飞机概念设计是在明确了设计要求的前提下开展的。在满足设计约束的基础上，通过对升力面气动特性及相互干扰分析，机翼剖面的设计选择以及升阻力特性的分析，典型部件重量构成的统计分析，计算出不同构型状态下的飞机重量和重心，保持纵向静安定度在合理范围内，通过对飞机有无配平情况下的阻力特性比较，寻求特定高度满足长航时设计要求的设计点。     

弹道导弹的捷联惯性/天文组合导航方法

针对传统的捷联惯性/天文(SINS/CNS)组合导航系统不能精确估计加速度计偏置而导致导航误差发散的问题,提出一种基于星光折射间接敏感地平的捷联惯性/天文(SINS/RCNS)组合导航方法。利用星敏感器测量星光折射角,结合大气折射模型得到的折射视高度来抑制位置误差的发散。推导了基于星光折射新的量测方程,分析了折射星数目与导航精度的关系,当使用多颗折射星时能够精确估计加速计偏置,从而能够完全抑制位置误差的发散,并对系统进行可观测性分析。通过卡尔曼滤波实现了状态估计。仿真结果表明:本文方法的导航精度优于传统方法,有效抑制了位置误差的发散,验证了本文方法的有效性。     

低频标准角振动台

低频标准角振动台产生标准角位移、角速度和角加速度,用正弦信号对角位移传感器、角速度传感器(陀螺、转速表等)和角加速度计进行幅频和相频特性校准。简要介绍了低频标准角振动台的工作原理,并应用其对角运动传感器进行校准。校准范围为0.1~100H z;幅值灵敏度不确定度优于1%;相移不确定度小于1°。     

压气机转子叶片动频动应力测试技术和应用研究

通过对某型发动机第二级压气机转子叶片在整机上进行动频，动应力测量的应用实例，介绍了一种较为成熟的测量方法。该方法先通过电动振动台对叶片进行应力分布试验，确定了动测叶片的应变片粘贴位置，然后在试车台架上对试验叶片实测，根据应变片输出信号判断叶片的动频，动应力，试验结果表明，该技术具有实际应用价值，可以为同类测量提供参考。     

小波的尺度离散化与稳定性研究

在使用小波变换重构信号的应用中,为了计算处理方便及克服冗余性,常采用离散化处理。为确定均匀点格动态采样网络下的尺度采样间隔的取值及其与小波稳定性的关系,本文分别用Morlet小波和Marr小波对信号进行均匀离散小波变换。对于Morlet小波,其尺度采样间隔有一个取值区间,在这个区间内取值既能分析数字信号的最高频率及避免频谱混叠,又能保证小波的稳定性;而对于Marr小波,其尺度采样间隔的取值不能同时保证分析数字信号的最高频率和维持小波的稳定性,即稳定性与频率选择性是一对矛盾,但在二进抽取采样下选择适当的尺度伸缩因子可缓解这一矛盾。     

磁流变机床误差分析和加工工艺优化

首先通过实验分析不同工艺参数下对去除函数稳定性的影响,并进行了仿真分析,确定了机床的敏感方向;其次,对磁流变机床运动轴误差进行了测量,分析这些误差引起法线方向误差的大小;最后,实验验证采用较大的压深和补偿法线方向误差可以有效提高机床的加工精度.     

面向空间应用的双核容错微处理器的研究与实现

介绍了用双核微处理器实现的容错微处理器系统SPARC—V8FIS。该系统由两个同构微处理器与支持容错操作的容错管理模块组成。微处理器是基于SPRARCV8规范的32-bit微处理器。容错管理模块提供了错误检测、诊断、从“软故障”中故障恢复，以及当发生“硬故障”时，将系统配置成单一处理器继续执行的机制，以适应空间复杂环境应用。SPARC—V8FIS用较少的硬件实现了所有容错操作，以很低的性能损失达到了很高的系统可靠性。     

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.     

Design and simulation of an innovative cylinder fabricated by selective laser melting

Additive Manufacturing (AM) processing has attracted wide attention due to its advantages, e.g., rapid prototyping and free design. In aeronautic and astronautic engineering, it is especially attractive to apply AM processing, especially Selective Laser Melting (SLM), into primary structures that always occupy most of the whole mass of aircraft or spacecraft. In this paper, an innovative lattice sandwich structure that is suitable to be manufactured with SLM is adopted to design the typical cylinder under axial compression. Firstly, the mechanical properties of pyramidal lattice are investigated, and the bearing capacity of the lattice sandwich cylinder is given analytically for each failure mode. Secondly, a composite cylinder is re-designed in the form of lattice sandwich structure, which sizes are ultimately obtained through optimization. Finally, finite element model of the innovative cylinder is established and several kinds of imperfection are introduced to simulate the defects caused by SLM. Numerical result shows that the lattice sandwich design can obviously enhance the bearing capacity of cylindrical shell even though the original one is designed with composite material. In other words, SLM has great potential in improving the primary structure of spacecraft in the future.