强磁暴期间TIEGCM模式与CHAMP卫星热层大气密度的比较分析

利用NCAR-TIEGCM模式计算了2003年11月20—21日强磁暴期间410km高度上的大气密度,并与CHAMP/STAR加速度计反演数据进行对比和分析. 结果表明,模式结果能够准确反映磁暴期间大气密度的分布和变化情况,与实测结果在变化趋势和量级上具有较好的一致性,但在精细结构和数值大小上仍存在一定差异. 模式低估了磁暴期间大气密度的增幅,实测大气密度增幅高达250%～400%,而模式结果为100%～125%. 模式结果与实测数据的偏差在高纬地区高于低纬地区,日侧高于夜侧. 通过模式和实测数据的分析发现,磁暴期间大气密度扰动具有日夜侧和南北半球不对称性. 此外,模式能够准确反映磁暴期间大气密度扰动从高纬向低纬的传播以及大气密度对SYM-H指数响应的延迟特性.     

TC-1和Geotail对亚暴过程中近地等离子体片磁扰动的联合观测

以2004年9月28日02:53:20 UT的亚暴为例, 通过TC-1在磁尾约12.5 R_e 和Geotail卫星在近地磁尾等离子体片约8～9 R_e的联合观测, 研究亚暴触发过程中近地磁尾等离子体片中等离子体波动特征. 结果表明, 亚暴触发区是近地磁尾中心等离子体片中较小的一个区域, 在亚暴触发区中低混杂不稳定性在近地磁尾等离子体片中存在, 准垂直传播的低混杂波发生在亚暴触发过程中, 而亚暴触发过程中近地磁尾等离子体片外边界区内的磁场偶极化信号和扰动都非常微弱. 在亚暴触发和亚暴膨胀相过程中出现了多次具有不同特征的磁场偶极化现象.     

航天器动力管路热控设计与试验研究

针对某航天器动力系统管路布局分散造成系统温差大、控温难的问题,结合动力管路温度指标要求和边界环境条件,采用以被动热控措施为主、辅以电加热主动热控措施的设计方案。分析确立动力管路的热环境,建立换热模型;通过仿真分析和整器热平衡试验,选取不同工况,验证了动力系统氧化剂管路和燃烧剂管路温度均维持在8～20 ℃范围内的热控设计结果。该方案对各类航天器的动力管路热控设计和分析有一定的指导和借鉴作用。     

Gravity and perceptual stability during translational head movement on earth and in microgravity

We measured the amount of visual movement judged consistent with translational head movement under normal and microgravity conditions. Subjects wore a virtual reality helmet in which the ratio of the movement of the world to the movement of the head (visual gain) was variable. Using the method of adjustment under normal gravity 10 subjects adjusted the visual gain until the visual world appeared stable during head movements that were either parallel or orthogonal to gravity. Using the method of constant stimuli under normal gravity, seven subjects moved their heads and judged whether the virtual world appeared to move “with” or “against” their movement for several visual gains. One subject repeated the constant stimuli judgements in microgravity during parabolic flight. The accuracy of judgements appeared unaffected by the direction or absence of gravity. Only the variability appeared affected by the absence of gravity. These results are discussed in relation to discomfort during head movements in microgravity.     

基于半实物仿真的地磁导航等值线匹配算法评估

影响地磁导航匹配算法性能的因素很多,而现阶段算法的评估完全依靠计算机仿真,其可信性有待进一步验证.以等值线(ICCP)算法为研究对象,首先从理论上分析影响算法性能的因素;然后搭建了地磁匹配导航半实物仿真系统,通过引入磁场仿真环境和磁传感器,提高了仿真的可信度;最后从测量噪声、匹配长度、匹配区域和惯导误差4个方面对ICCP 匹配算法的性能进行半实物仿真试验分析.仿真结果表明,通过半实物仿真试验可以对算法的抗干扰性、算法匹配长度的确定、匹配区域的选择以及惯导误差的影响做出有效评估,从而推动地磁匹配导航及匹配算法的工程化进程.      

Recursive mode star identification algorithms

Star identification can be accomplished by several different available algorithms that identify the stars observed by a star tracker. However, efficiency and reliability remain key issues and the availability of new active pixel cameras requires new approaches. Two novel algorithms for recursive mode star identification are presented here. The first approach is derived by the spherical polygon search (SP-search) algorithm, it was used to access all the cataloged stars observed by the sensor field-of-view (FOV) and recursively add/remove candidate cataloged stars according to the predicted image motion induced by camera attitude dynamics. Star identification is then accomplished by a star pattern matching technique which identifies the observed stars in the reference catalog. The second method uses star neighborhood information and a catalog neighborhood pointer matrix to access the star catalog. In the recursive star identification process, and under the assumption of "slow" attitude dynamics, only the stars in the neighborhood of previously identified stars are considered for star identification in the succeeding frames. Numerical tests are performed to validate the absolute and relative efficiency of the proposed methods.     

Robust wavelet neuro control for linear brushless motors

Design, simulation and experimental implementation of a wavelet basis function network learning controller for linear brushless dc motors (LBDCM) are considered. Stability robustness with position tracking is the primary concern. The proposed controller deals mainly with external disturbances, e.g. nonlinear friction force and payload variation in motion control of linear motors. It consists of two parts, one is a state feedback component, and the other one is a learning feedback component. The state feedback controller is designed on the basis of a simple linear model, and the learning feedback component is a wavelet neural controller. The attenuation effect of wavelet neural networks on friction force is first verified by the numerical method. The learning effect of wavelet neural networks on friction force is also shown in the numerical results. Then, a wavelet neural network is applied on a real LBDCM to on-line suppress the friction force, which may be variable due to the different lubrication. The effectiveness of the proposed control schemes is demonstrated by simulated and experimental results.     

Application of high-order accurate methods to solve the computational gas dynamics and aeroacoustics problems

The application of the low dissipative high-order accurate scheme for numerical solution of the Euler and Navier-Stokes equations in the two-dimensional statement is considered. The scheme of calculating space derivatives is the seven-point central-difference approximation of the fourth order with the coefficients optimized to reduce the dispersion errors. The optimized six-step Runge-Kutta method is used for evaluating the time derivatives. By means of filtration, spurious pulsations are suppressed and shocks are processed. The results of verifying the calculation scheme realized on the stationary problem of flow around the model turbine blades are presented.     

Systems for the indication and display of secondary information in avionics and autobasing complexes

We consider the peculiarities of application for the cabin indicating systems of secondary information display, formulate and systematize the compositional and technical requirements for display systems and their elements. The tendencies and lines in the development of head-up displays for avionics and autobasing complexes were determined. The technique of calculating optics of such display systems with the use of electronic distortion compensation is presented.     

A new global version of M(3000)F2 prediction model based on artificial neural networks

A new version of global empirical model for the ionospheric propagation factor, M(3000)F2 prediction is presented. Artificial neural network (ANN) technique was employed by considering the relevant geophysical input parameters which are known to influence the M(3000)F2 parameter. This new version is an update to the previous neural network based M(3000)F2 global model developed by Oyeyemi et al. (2007), and aims to address the inadequacy of the International Reference Ionosphere (IRI) M(3000)F2 model (the International Radio Consultative Committee (CCIR) M(3000)F2 model). The M(3000)F2 has been found to be relatively inaccurate in representing the diurnal structure of the low latitude region and the equatorial ionosphere. In particular, the existing hmF2 IRI model is unable to reproduce the sharp post-sunset drop in M(3000)F2 values, which correspond to a sharp post-sunset peak in the peak height of the F2 layer, hmF2. Data from 80 ionospheric stations globally, including a good number of stations in the low latitude region were considered for this work. M(3000)F2 hourly values from 1987 to 2008, spanning all periods of low and high solar activity were used for model development and verification process. The ability of the new model to predict the M(3000)F2 parameter especially in the low latitude and equatorial regions, which is known to be problematic for the existing IRI model is demonstrated.