航空发动机飞行载荷谱的预测

提出了基于飞机的设计飞行任务剖面、飞行力学及发动机原理的发动机飞行载荷的预测方法,即首先将飞机的设计飞行任务剖面通过飞行力学的基本原理转化为发动机的推力（或油门）剖面,然后通过发动机性能计算获得发动机的其它工作状态参数,从而获得发动机的飞行载荷谱。      

MPEG-4视频DCT量化模块的FPGA实现

介绍了一种采用FPGA技术实现MPEG-4 ASP级视频DCT量化模块的设计方案。该模块包括二维DCT／IDCT、量化／反量化和帧内直流／交流(DC／AC)预测。用VHDL进行描述并通过模拟试验表明,该模块可在880个时钟周期内处理完一个宏块的数据,工作频率达到40MHZ。文章采用全硬件实现方法,提出了各模块的硬件电路结构设计,减少了电路规模。     

用遥测数据预报导弹落点的误差分析

针对利用遥测数据进行导弹落点预测这一方法 ,分析了误差源 ,在此基础上建立并编写了干扰弹道模型 ,研究讨论了这种方法在进行落点预测时所造成的预测误差。计算结果表明 ,仅仅利用遥测数据进行导弹落点预测有很大的误差 ,必须结合外测手段才能做出较为准确的落点预测     

Solar activity prediction studies and services in NAOC

Solar activity prediction services started in 1960’s in National Astronomical Observatories, Chinese Academy of Sciences (NAOC). As one of the members of the International Space Environment Service (ISES), Regional Warning Center of China (RWC-China) was set up in 1990’s. Solar Activity Prediction Center (SAPC), as one of the four sub-centers of RWC-China, is located in NAOC. Solar activity prediction studies and services in NAOC cover short-term, medium-term, and long-term forecast of solar activities. Nowadays, certain prediction models, such as solar X-ray flare model, solar proton event model, solar 10 cm radio flux model, have been established for the practical prediction services. Recently, more and more physical analyses are introduced in the studies of solar activity prediction, such as the magnetic properties of solar active regions and magnetic structure of solar atmosphere. Besides traditional statistics algorithms, Machine Learning and Artificial Intelligence techniques, such as Support Vector Machine (SVM) method, are employed in the establishment of forecast models. A Web-based integrated platform for solar activity data sharing and forecast distribution is under construction.     

航空结构件铣削变形及其控制研究进展

随着机床加工性能和刀具切削性能的提升,航空结构件的高效高精加工成为可能.航空结构件薄壁加工特征多,在铣削过程中易发生变形,因此预测与控制航空结构件的加工变形是切削加工领域内亟待解决的难题.通过总结了航空结构件的特点及加工难点,对加工变形形成机理进行深入分析;对加工变形影响最为关键的铣削力模型进行归纳;阐述了航空结构件残...     

A tip clearance prediction model for multistage rotors and stators in aero-engines

Tip clearances of multistage rotors and stators greatly affect aero-engines’ aerodynamic efficiency, stability and safety. The inevitable machining and assembly errors, as well as the complicated error propagation mechanism, cause overproof or non-uniform tip clearances. However, it is generally accepted that tip clearances are difficult to predict, even under assembly state. In this paper, a tip clearance prediction model is proposed based on measured error data. Some 3D error propagation sub-models, regarding rotors, supports and casings, are built and combined. The complex error coupling relationship is uncovered using mathematical methods. Rotor and stator tip clearances are predicted and analyzed in different phase angles. The maximum, minimum and average tip clearances can be calculated. The proposed model is implemented by a computer program, and a case study illustrates its performance and verifies its feasibility. The results can be referred by engineers in assembly quality judgement and decision-making.     

航天器火工冲击技术研究进展

系统总结了国内外航天器火工冲击环境预示、地面试验和减冲击技术三个方面的研究进展,并分析了国内在以上三个方面与国外航天强国的差距。在此基础上,从我国航天工程实际需求出发,分析指出了今后航天器火工冲击领域应重点开展的研究方向。     

Study of Solar Corona in China

Considerable progress for the study of solar corona physics has been achieved by China's space physics community. It involves the theoretical study of coronal process of solar active phenomena, solar wind origin, acceleration of solar wind and coronal mass ejections, observational and numerical study of these problems and prediction methods of solar eruptive activities (such as flares/CMEs). Here is a brief summary of the progress in this area. Main progress is put upon the following three topics: corona and solar wind, numerical method, prediction method.     

近地轨道卫星的地影预报算法

针对传统地影预报算法计算量大,不适合星载计算机进行地影时刻自主预报的问题,提出一种地影预报星上算法。通过构造降维坐标系将卫星进出地影过程转换为“星-地-日”平面内的几何问题;基于常数变易法推导出阴影条件的隐式解析公式,其方程系数可由实时轨道参数确定。为简化计算,将星上算法设计为双层结构：构造迭代算法,依据实时轨道参数可精确预报卫星在每个节点上的地影时刻;而在任意两个节点之间采用解析算法进行近似预报。仿真结果表明,星上算法的预报精度高且计算量小,适合星载计算机进行地影时刻自主预报。     

Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting

The support vector machine (SVM) combined with K-nearest neighbors (KNN), called the SVM-KNN method, is new classing algorithm that take the advantages of the SVM and KNN. This method is applied to the forecasting models for solar flares and proton events. For the solar flare forecasting model, the sunspot area, the sunspot magnetic class, and the McIntosh class of sunspot group and 10 cm solar radio flux are chosen as inputs; for the solar proton event forecasting model, the inputs include the longitude of active regions, the flux of soft X-ray, and those for the solar flare forecasting model. Detailed tests are implemented for both of the proposed forecasting models, in which the SVM-KNN and the SVM methods are compared. The testing results demonstrate that the SVM-KNN method provide a higher forecasting accuracy in contrast to the SVM. It also gives an increased rate of ‘Low’ prediction at the same time. The ‘Low’ prediction means occurrence of solar flares or proton events with predictions of non-occurrence. This method show promise for forecasting models of solar flare and proton events.