Solar flares,CMEs and solar energetic particle events during solar cycle 24

We present here a study of Solar Energetic Particle Events (SEPs) associated with solar flares during 2010–2014 in solar cycle 24. We have selected the flare events (≥GOES M-class), which produced SEPs. The SEPs are classified into three categories i.e. weak (proton intensity?≤?1?pfu), minor (1?pfu?<?proton intensity?<?10?pfu) and major (proton intensity?≥?10?pfu). We used the GOES data for the SEP events which have intensity greater than one pfu and SOHO/ERNE data for the SEP event less than one pfu intensity. In addition to the flare and SEP properties, we have also discussed different properties of associated CMEs.     

数字化干涉仪测角技术

数字化干涉仪测角采用的是高精度数字鉴相技术，该技术与传统的模拟鉴相技术相比可以大幅度提高鉴相精度，从而满足高精度干涉仪测角的要求。基于数字干涉仪鉴相算法，通过两路正交本振信号分别与两路待测相信号相乘，使用CIC 滤波器除乘积中的高频信号，最后通过CORDIC 算法计算出相位差。经过干涉仪测角模拟器上测试结果表明，其数字鉴相误差小于0.2°。     

Classification of solar wind structures and intense geomagnetic storm alarms with self-organizing maps

Intense geomagnetic storms (D_st < −100 nT) usually occur when a large interplanetary duskward-electric field (with Ey > 5 mV m⁻¹) lasts for more than 3 h. In this article, a self-organizing map (SOM) neural network is used to recognize different patterns in the temporal variation of hourly averaged Ey data and to predict intense storms. The input parameters of SOM are the hourly averaged Ey data over 3 h. The output layer of the SOM has a total of 400 neurons. The hourly Ey data are calculated from solar wind data, which are provided by NSSDC OMNIWeb and ACE spacecraft and contain information on 143 intense storms and a fair number of moderate storms, weak storms and quiet periods between September 3, 1966 and June 30, 2002. Our results show that SOM is able to classify solar wind structures and therefore to give timely intense storm alarms. In our SOM, 21 neurons out of 400 are identified to be closely associated with the intense storms and they successfully predict 134 intense storms out of the 143 ones selected. In particular, there are 14 neurons for which, if one or more of them are present, the occurrence probability of intense storms is about 90%. In addition, several of these 14 neurons can predict big magnetic storm (D_st  −180 nT). In summary, our method achieves high accuracy in predicting intense geomagnetic storms and could be applied in space environment prediction.     

三维非线性撕裂模耦合和快速磁场重联

基于三维不可压缩电阻性ＭＨＤ方程，在长柱形位型下，数值研究了电阻撕裂模不稳定性所引起的磁场重联过程。研究结果表明，撕裂模的非线性相互作用和耦合，将产生许多模式的强裂解稳和导致快速的磁场重联。这一过程的进一步发展，导致具有螺旋形结构，不同模式的磁岛磁力线重叠，从而引起较大区域内的磁场各态经历和磁力线随机走向，形成撕裂模端动。     

Structure and development of quiet loops in the solar corona

X-ray emission from solar coronal loops changes on two different timescales: a) flare loops and transient active region brightenings show a rapid variability, b) quiet region loops are quasi-steady and change only slowly with time. This different time behavior has been analyzed on the basis of Yohkoh SXT observations and we report here on the results from our analysis, mainly focussing on quiet loop variability.     

1982年12月30日日浪

介绍了１９８２年１２月３０日太阳视面上Ｓ１０Ｗ２０处伴随耀斑产生的日浪形态，并给出其速度场随时间演变的特征．光谱观测结果表明，日浪上升过程中有旋转运动，速度为２０—３０ｋｍ／ｓ，旋转速度随时间而减小．     

有翼航天飞行器高速动态气动特性试验研究

在CARDC0.6m×0.6m高速风洞中进行了航天飞机类模型的动态失速试验。在M数为0.4～1.2,迎角为0°～75°范围测量了模型的动态气动特性,研究了各种运动参数对动态气动特性的影响。结果表明,在试验范围内,俯仰振荡引起了不同程度的气动迟滞现象,各运动参数对模型的动态气动特性都有重要影响,仅在迎角约为20°～40°时,非定常法向力增量存在,相应的非定常效应较明显。     

浅析飞机的大攻角气动问题及其研究方法

本文分析了现代战斗机的发展趋势,阐明了进行大攻角/过失速机动的必要性,指出解决至今威胁着飞行安全的失速/尾旋问题仍为当代空气动力学家的重任;论述了各种研究大攻角气动问题的方法之优劣;对我国开展大攻角气动问题研究的技术途径提出了一些建议。     

高速风洞超大迎角试验技术初步研究

大迎角风洞试验技术是先进高机动飞行器研制必需的关键技术。气动中心发展的高速风洞超大迎角试验技术 ,包括大迎角机构、模型、天平等。 1 .2m风洞超大迎角试验结果与 2 .4m量级的大风洞试验数据具有较好的一致性 ,试验精度基本达到了××标准对小迎角试验精度的要求 ,表明 1 .2m风洞超大迎角试验技术研究获得了成功。