A novel temperature-compensation method based on correlation analysis for multi-FOG INS

Aimed at improving the bias stability of Fiber-Optic Gyroscope (FOG)-based inertial navigation systems in environments of various ambient temperatures, a novel temperature-compensation method based on a correlation analysis of the same batch of FOGs is proposed. The empirical mode decomposition method was employed to filter the high-frequency noises of the FOGs. Then, the correlation information of the multiple FOGs was used to analyze the feasibility of the method. Eventually, the same residual error of the FOGs was compensated via the simple piecewise linear models. The experimental results indicate that excellent compensation effects for both high- and low-accuracy FOGs are achieved using the proposed method. Specifically, the accuracies of high-accuracy FOGs are improved by approximately 33.9%, 20%, and 31.2%, while those of low-accuracy FOGs are improved by approximately 39.1%, 20.8%, and 26.1%. The method exhibits the merits of simplicity, validity, and stability, and thus is expected to be widely used in engineering applications.     

Effects of temperature on drag reduction in a subsonic turbulent boundary layer via micro-blowing array

A comparative study of two micro-blowing temperature cases has been performed to investigate the characteristics of drag reduction in a subsonic flat-plate flow (where the freestream Mach number is 0.7) by means of Direct Numerical Simulation (DNS). With minute amount of blowing gas injected from a 32 × 32 array of micro-holes arranged in a staggered pattern, the porosity of micro-holes is 23% and the blowing coefficient is 0.125%. The simulation results show that a drag reduction is achieved by micro-blowing, and a lower wall-friction drag can be obtained at a higher blowing temperature. The role of micro-blowing is to redistribute the total kinetic energy in the boundary layer, and the proportion of stream-wise kinetic energy decreases, resulting in the thickened boundary layer. Increasing micro-blowing temperature can accelerate this process and obtain an enhanced drag reduction. Moreover, an explanation of drag reduction by micro-blowing related to the micro-jet vortex clusters is proposed that these micro-jet vortex clusters firmly attached to the wall constitute a stable barrier, which is to prevent the direct contact between the stream-wise vortex and the wall. By Dynamic Mode Decomposition (DMD) from temporal/spatial aspects, it is revealed that small structures in the near-wall region play vital role in the change of turbulent scales. The high-frequency patterns are clearly strengthened, and the low-frequency patterns just maintain but are lifted up.     

Hypersonic shock wave and turbulent boundary layer interaction in a sharp cone/flare model

A direct numerical simulation of hypersonic Shock wave and Turbulent Boundary Layer Interaction(STBLI) at Mach 6.0 on a sharp 7° half-angle circular cone/flare configuration at zero angle of attack is performed. The flare angle is 34° and the momentum thickness Reynolds number based on the incoming turbulent boundary layer on the sharp circular cone is Re_θ = 2506. It is found that the mean flow is separated and the separation bubble occurring near the corner exhibits unsteadiness. The Reynolds analogy factor changes dramatically across the interaction, and varies between 1.06 and 1.27 in the downstream region, while the QP85 scaling factor has a nearly constant value of 0.5 across the interaction. The evolution of the reattached boundary layer is characterized in terms of the mean profiles, the Reynolds stress components, the anisotropy tensor and the turbulence kinetic energy. It is argued that the recovery is incomplete and the near-wall asymptotic behavior does not occur for the hypersonic interaction. In addition, mean skin friction decomposition in an axisymmetric turbulent boundary layer is carried out for the first time. Downstream of the interaction, the contributions of transverse curvature and body divergence are negligible, whereas the positive contribution associated with the turbulence kinetic energy production and the negative spatial-growth contribution are dominant. Based on scale decomposition, the positive contribution is further divided into terms with different spanwise length scales. The negative contribution is analyzed by comparing the convective term, the streamwise-heterogeneity term and the pressure gradient term.     

基于奇异值分解的接收机自主完好性监测算法

基于最小二乘法残差的接收机自主完好性监测（receiver autonomous integrity monitoring，RAIM）算法本质是一种基于伪距残差矢量的一致性监测算法，但由于残差矢量中各分量具有一定的关联性，掩饰了某些重要的不一致性信息。为了消除这种关联性，提出了一种基于奇异值分解的接收机自主完好性监测方法。在方法中利用奇异值分解对伪距观测矩阵中的观测系数矩阵进行分解，获得奇异值空间矢量和奇异值空间矩阵。基于奇异值空间矢量构造能够直接反映故障卫星偏差信息的检验统计量，从而可以简便地进行粗差监测，更好地满足完好性监测需求。鉴于实际中完好性故障包含运控系统故障、导航系统故障、信号传播异常以及地面接收处理故障等多类因素，以脉冲型和阶跃型两种故障方式进行基于奇异值分解的RAIM故障检测与识别，并开展仿真分析研究。结果表明，提出的方法能够正确检测、识别故障卫星，在特定参数下能够达到很好的故障识别率，即当误警概率设置为1×10-5/h、引入阶跃故障误差为25 m时，算法能够实现98.8%的故障识别率。     

Unsteady analysis of jet impingement under vibration conditions

The vibration of thermodynamic machinery will affect its cooling system. In this research, a high-resolution simulation of jet impingement was performed to quantify the unsteady turbulent convection under vibration conditions. A newly developed Self-Adaptive Turbulence Eddy Simulation(SATES) method was used. The Reynolds number was Re = 23000, the jet-towall distance was fixed at H/D = 2, and the vibrating frequency of the impinging wall f varied from 0 to 200 Hz. Compared with the static wall c...     

锯切圆柱形42CrMoA的声发射监测试验

针对锯切加工中的带锯条磨损状态未知、锯切质量下降等问题，本文研究了新试制的FTCUT双金属带锯条锯切圆柱形42CrMoA合金结构钢过程中锯切行为的变化，探讨了声发射信号与单刀锯切过程、锯齿磨损和锯切表面质量变化的关联。研究表明：锯切圆柱材料时随着锯切深度的变化，参与齿数先增大后减小，带锯条约束条件不断变化，进而影响着声发射信号幅值和锯切表面质量；基于时域分析可知锯齿磨损过程分为磨合磨损、快速磨损、稳定磨损三个阶段；基于FFT频域分析发现当频谱图出现多个主频，且第一主频幅值下降时，表明声发射信号来源比较分散，即可认定带锯条失效；基于小波包频域分析发现第7频段信号占比最高，最能反映工件表面质量的变化，其波动情况与锯切表面的微纹和波纹情况可建立一定的联系。因此，可基于声发射信号时域和频域分析判断锯齿磨损和锯切表面恶化情况，为锯齿磨损和锯切表面质量的在线监测提供一定的思路。     

基于极化神经网络的雷达舰船检测识别方法

相参雷达捕获的全极化海面目标距离-多普勒(RD)回波数据中，目标区域占比小、信噪比低，且海况环境与干扰种类多变，使得经典的深度神经网络在此种条件下检测识别精度较低。为此，本文提出了一种基于极化深度神经网络的全极化相参雷达海面目标检测识别算法。首先，引入极化特征提取模块挖掘目标与干扰的差异化特征;其次，通过特征金字塔网络解决小目标检测识别的问题;最后，使用级联结构进一步提升算法性能。在全极化相参雷达回波数据集上的测试结果表明:基于特征值与特征矢量的极化特征对于数据集中两类舰船目标的平均精度分别达到0.907 9与1.0，相比不采用极化特征有着显著提高。     

考虑区间叠加不确定间隙的空间可展机构动力学研究

本文建立了考虑不确定间隙空间可展机构动力学模型，通过对不确定间隙进行描述，结合传统接触模型提出含不确定区间间隙的接触力模型，并将其嵌入展开单元动力学模型，通过切比雪夫区间求解对含区间系统动力学模型进行求解，针对区间求解中启动阶段估计不准、区间叠加误差过大、区间整合的问题与展开单元叠加问题，提出了相适应的区间扩张、局部均值分解与瞬时叠加方法降低区间求解误差，并给出区间间隙累加下动力学响应边界。结果表明:区间间隙大小主要对系统运动精度产生较大影响，而区间间隙数量对系统运动稳定度影响较为严重。区间叠加方法相比于传统拟合方法可进一步提高空间可展机构预示精度，相较于传统拟合结果可提高7.74%。     

小波与BP神经网络联合反演GNSS IR高精度水库水位变化

GNSS-IR技术作为一种近地表遥感的新兴手段，在水库水位监测方面凭借其成本低、精度高等优势成为了研究热点。为了提高GNSS-IR技术反演水库水位变化的精度，提出了利用GPS、BDS双系统观测量基于小波分解与BP神经网络联合的方法反演水库水位变化。选取位于南水北调山东境内双王城水库GNSS变形观测站2017年10月1日—12月26日共87天的信噪比(SNR）数据为研究对象，分别利用小波分解和二阶多项式拟合两种方法消除其趋势项并生成SNR残差序列，然后进行Lomb-Scargle谱分析得到水库水位高度变化值，并与实测水位结果相比较。结果表明：各频段SNR利用小波分解和二阶多项式反演水位变化的平均均方根误差分别为0.1062m和 0.2245m，说明小波分解去趋势项的方法更优。最后，在小波分解去趋势项的基础上，利用阈值筛选出融合所用的频段(S1C、S2L、S5Q和S7I），分别采用均值算法、中值算法、随机森林算法和BP神经网络算法对GPS、BDS多频多模信号进行水库水位的融合反演。结果表明，对于水面较为平静的环境，以上算法均能实现厘米级的反演精度，其中BP神经网络算法用于水位反演的效果更优。     

基于EMD-TFPF的仿生偏振光罗盘去噪方法

为了提高仿生偏振光罗盘定向精度,降低罗盘在进行航向角测量时存在的高斯白噪声,基于经验模态分解(EMD)和时频峰值滤波(TFPF),设计了一种用于仿生偏振光罗盘的EMD-TFPF联合去噪方法.在去噪过程中,首先将含噪声的航向角信号分解为不同的模态,对不同模态采用不同窗长进行时频峰值滤波,进而改善了单一窗长去噪对有用信号造成的衰减,有效提高了去噪算法的自适应能力.经过机载实验验证,采用该方法去噪后的仿生偏振光罗盘可以实现定向精度0.3259°,比原始信号精度提升了18.4％.