利用轨道瞬时根数预报的离轨制动控制方法

针对再入飞行器离轨制动问题,在考虑地球引力J2项摄动及有限推力影响下,设计了一种航天器自主离轨制动控制算法。该算法根据再入点状态约束,确定了离轨过渡轨道的平均轨道根数及其与离轨待命轨道平均轨道根数的关系,从而得到制动参数初值。通过在线数值递推轨迹,实时预报再入点瞬时轨道根数并计算再入点航迹倾角,当预报的航迹倾角满足约束条件时结束制动,并根据再入点纬度幅角误差修正制动起始点,从而修正制动参数。制动过程中,在考虑了J2项摄动影响下实时预报再入点瞬时轨道根数,依据实际任务需求确定关机时机。最后通过考虑初始状态误差、质量误差、推力误差以及姿态误差情况下的蒙特卡洛打靶仿真,分析了不同关机策略的落点散布特性,检验了该算法的自主决策和高精度再入点控制能力。     

基于SCSO-BP神经网络的卫星姿态控制系统故障预测

近年来,随着人工智能的迅速发展,基于人工神经网络的卫星姿态控制系统故障预测方法得到了越来越多的重视。在反向传播(Back Propagation ,BP)神经网络中,权重和偏置是重要的可调节参数,与神经网络的预测性能密切相关。BP神经网络的初始权重和偏置为随机生成,设置不当容易导致网络在训练过程中陷入局部极值,进而影响预测性能。为了提高BP神经网络的预测性能,提出了一种将沙猫群优化(Sand Cat Swarm Optimization ,SCSO)算法与BP神经网络相结合的预测方法。在训练过程中,首先通过SCSO 算法对BP神经网络权重和偏置进行预训练,在此基础上,利用精调后的BP神经网络对卫星姿态控制系统周期渐变故障数据的未来趋势进行预测。实验结果表明,与原始BP神经网络预测方法相比,SCSO-BP预测方法能够有效减小预测误差,具有更好的预测精度。     

基于BP神经网络X频段调制器故障预测

本文采用BP神经网络对X频段调制器的故障预测进行研究,通过仿真软件编程构建BP神经网络故障预测模型,验证X频段调制器输出功率指标的精确性,根据测试结果表明建立的BP神经网络故障模型预测误差很小,能够较准确对X频段调制器的输出功率故障进行预测。     

An improved prediction model for corner stall in axial compressors with dihedral effect

Corner stall predictions are important and difficult in axial compressors. However, all of the prediction models have proved to be ineffective for advanced compressor blades, which tend to use the combined sweep and dihedral. As for the prediction parameter DL, although it effectively modeled the effects of the adverse pressure gradient and secondary flow, it failed to predict the corner stall of curved blades because the model failed to consider the intersection of the boundary layer at the corner region. In this paper, the shape factor gradient Ψ of the boundary layer at the corner region was investigated by numerically studying specially shaped expansion pipes under different adverse pressure gradients. The improved prediction parameter DJ was presented based on the model of Ψ and the circumferential pressure gradient ξ. A comparison of the critical range of the prediction parameters DL and DJ was investigated using the NACA65 cascade database, which was established by a numerical method. Then, the stall criterion was validated according to the experimental results of various test facilities with different blade geometries and experimental conditions. The results show that the improved prediction parameter is able to predict the corner separation/stall flows and is in good agreement with the experimental results for axial compressors with three-dimensional designed blades.     

Impact point prediction guidance based on iterative process for dual-spin projectile with fixed canards

The fixed canards configuration of a dual-spin projectile makes it difficult to apply the traditional guidance law. In this study, a modified impact point prediction guidance strategy based on an iterative process was developed for a class of dual-spin projectiles with fixed canards, to reduce the impact point dispersion. The guidance strategy is dependent on the modified projectile linear theory to rapidly predict the flight states and the impact point. For projectiles with control applied to the trajectory, the modified projectile linear theory method is known to achieve poor impact point prediction. To improve the prediction accuracy, improvements were made to the modified projectile linear theory by considering the products of the yaw rate and other small quantities. The guidance strategy is based on the iterative process for the continuous adjustment of the expected output of the roll angle of the course correction fuze, to minimize the direction error between the predicted impact point and target location. Studies were conducted on a model dual-spin projectile configuration to demonstrate the guidance details. The numerical simulations indicate that the proposed guidance strategy can effectively reduce the projectile impact point dispersion.     

A prediction model of short-term ionospheric foF2 based on AdaBoost

In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.     

Investigation of the relationship between ionospheric foF2 and earthquakes

Variations of the ionospheric F2 region critical frequency (foF2) have been investigated statistically before earthquakes during 1980–2008 periods in Japan area. Ionosonde data was taken from Kokubunji station which is in the earthquake preparation zone for all earthquakes. Standard Deviations and Inter-Quartile Range methods are applied to the foF2 data. It is observed that there are anomalous variations in foF2 before earthquakes. These variations can be regarded as ionospheric precursors and may be used for earthquake prediction.     

40CrNi2Si2MoVA钢机械加工与喷丸试样旋转弯曲疲劳寿命的预测方法

为了预测机械加工及喷丸强化后40CrNi2Si2MoVA钢试样在室温下的旋转弯曲疲劳寿命,结合商用有限元软件ABAQUS和疲劳寿命分析软件FE-SAFE对不同数值计算方法的适用性及准确性进行比较,提出了喷丸强化40CrNi2Si2MoVA钢的寿命预测经验公式。结果表明:对于机械加工试样,选择"Brown-Miller"算法和表面残余应力用于计算可获得比较准确的预测结果;对于喷丸强化试样,高应力水平下也可选用"Brown-Miller"算法及表面残余应力,而低应力水平下则应改用"Stress-based Brown Miller"算法及最大残余应力。基于上述两种方法提出的经验公式:σmax=–64.378·lgN+1449.268,可改善40CrNi2Si2MoVA钢喷丸强化试样疲劳寿命预测的准确性。