人体变形中局部坐标系旋转失真问题

分析了人体变形中选取3个基轴构建坐标系所存在的缺陷,提出了一种位置-四元数的方法,解决了曲线骨架驱动的旋转失真现象.采用四元数球面线形插值和B样条四元数插值的方法构建人体变形中曲线骨架的局部坐标系,采用位置-四元数-缩放的方式代替传统的转化矩阵,通过人体表面顶点在该坐标系下的绑定,实现人体皮肤、肌肉的变形.试验结果显示:该方法有效地消除了局部坐标系的旋转失真现象,适用限制少,易于实现.     

地磁扰动期间低轨目标轨道预报的误差修正

针对地磁扰动期间大气密度变化造成的低轨目标较大的轨道预报误差,提出一种根据POES卫星观测的极光能量注入数据改进短期轨道预报的方法。分析表明CHAMP卫星的沿迹大气密度及轨道衰减与极光能量注入具有较好的相关性。通过线性回归方法,建立轨道半长轴衰减及阻力调制系数的修正公式,并使用修正后的阻力调制系数取代两行元(TLE)中的该系数带入SGP4模型进行位置预报。该方案考虑了外推过程中地磁扰动引起的大气密度响应,能更准确地反映外推过程中大气阻力对轨道的影响。将其应用到2008年CHAMP卫星和国际空间站的轨道预报中,结果表明,半长轴和位置的预报误差可分别降低50%和30%左右。进一步对不同年份、不同轨道高度的目标进行了预报误差修正的分析,验证了该方法的普适性。     

一种目标识别中基于关键点的仿射不变矩

针对常用仿射不变矩对轮廓边缘信息敏感的特点,提出了一种基于关键点的局部仿射不变矩,以分割出来的物体灰度图像为基础,首先计算出物体的质心,然后以质心为扩展点向周围引伸出多条射线,寻找每条射线方向上的最近灰度极值点,将所有灰度极值点当作关键点集合,并按照文中提出的计算仿射不变矩的方法提取出多阶不变矩,将其当作神经网络的输入向量,放入已经训练好的神经网络来达到识别物体的目的,将此方法应用在了飞机图像的识别上,实验结果证明此方法在物体轮廓分割不完整和有噪点污染的情况下能保持很好的稳健性,简单有效并具有较广的应用范围.     

Mid-latitude thermospheric zonal winds during the equinoxes

The diurnal variation of the mid-latitude upper thermosphere zonal winds during equinoxes has been studied using data recently generated from CHAMP measurements from 2002 to 2004 using an iterative algorithm. The wind data was separated into two geomagnetic activity levels, representing high geomagnetic activity level (Ap > 8) and low geomagnetic activity level (Ap ? 8). The data were further separated into two solar flux levels; with F10.7 > 140 for high and F10.7 ? 140 for low. Geomagnetic activity is a correlator just as significant as solar activity. The response of mid-latitude thermospheric zonal winds to increases in geomagnetic disturbances and solar flux is evident. With increase in geomagnetic activity, midday to midnight winds are generally less eastward and generally more westward after the about midnight transitions. The results show that east west transitions generally occurred about midnight hours for all the situations analyzed. The west to east transition occurs from 1400–1500 MLT. Enhanced westward averaged zonal wind speeds going above 150 ms⁻¹ are observed in the north hemisphere mid-latitude about sunrise hours (∼0700–1100 MLT). Nighttime winds in the north hemisphere are in good agreement with previous single station ground observations over Millstone Hill. Improved ground observations and multi satellite observations from space will greatly improve temporal coverage of the Earth’s thermosphere.     

Storm-time atmospheric density modeling using neural networks and its application in orbit propagation

Upper atmospheric densities during geomagnetic storms are usually poorly estimated due to a lack of clear understanding of coupling mechanisms between the thermosphere and magnetosphere. Consequently, the orbit determination and propagation for low-Earth-orbit objects during geomagnetic storms have large uncertainties. Artificial neural networks are often used to identify nonlinear systems in the absence of rigorous theory. In the present study, an attempt has been made to model the storm-time atmospheric density using neural networks. Considering the debate over the representative of geomagnetic storm effect, i.e. the geomagnetic indices ap and Dst, three neural network models (NNM) are developed with ap, Dst and a combination of ap and Dst respectively. The density data used for training the NNMs are derived from the measurements of the satellites CHAMP and GRACE. The NNMs are evaluated by looking at: (a) the mean residuals and the standard deviations with respect to the density data that are not used in training process, and (b) the accuracy of reconstructing the orbits of selected objects during storms employing each model. This empirical modeling technique and the comparisons with the models NRLMSIS-00 and Jacchia-Bowman 2008 reveal (1) the capability of neural networks to model the relationship between solar and geomagnetic activities, and density variations; and (2) the merits and demerits of ap and Dst when it comes to characterizing density variations during storms.     

Moderate geomagnetic storms of January 22–25, 2012 and their influences on the wave components in ionosphere and upper stratosphere-mesosphere regions

Moderate geomagnetic storms occurred during January 22–25, 2012 period. The geomagnetic storms are characterized by different indices and parameters. The SYM-H value on January 22 increased abruptly to 67 nT at sudden storm commencement (SSC), followed by a sharp decrease to −87 nT. A second SSC on January 24 followed by a shock on January 25 was also observed. These SSCs before the main storms and the short recovery periods imply the geomagnetic storms are CME  -driven. The sudden jump of solar wind dynamic pressure and IMF _Bz$B_z$ are also consistent with occurrence of CMEs. This is also reflected in the change in total electron content (TEC) during the storm relative to quiet days globally. The response of the ionospheric to geomagnetic storms can also be detected from wave components that account for the majority of TEC variance during the period. The dominant coherent modes of TEC variability are diurnal and semidiurnal signals which account upto 83% and 30% of the total TEC variance over fairly exclusive ionospheric regions respectively. Comparison of TEC anomalies attributed to diurnal (DW1) and semidiurnal (SW2) tides, as well as stationary planetary waves (SPW1) at 12 UTC shows enhancement in the positive anomalies following the storm. Moreover, the impact of the geomagnetic storms are distinctly marked in the daily time series of amplitudes of DW1, SW2 and SPW1. The abrupt changes in amplitudes of DW1 (5 TECU) and SW2 (2 TECU) are observed within 20°S–20°N latitude band and along 20°N respectively while that of SPW1 is about 3 TECU. Coherent oscillation with a period of 2.4 days between interplanetary magnetic field and TEC was detected during the storm. This oscillation is also detected in the amplitudes of DW1 over EIA regions in both hemispheres. Eventhough upward coupling of quasi two day wave (QTDWs) of the same periodicity, known to have caused such oscillation, are detected in both ionosphere and upper stratosphere, this one can likely be attributed to the geomagnetic storm as it happens after the storm commencement. Moreover, further analysis has indicated that QTDWs in the ionosphere are strengthened as a result of coherent oscillation of interplanetary magnetic field with the same frequency as QTDWs. On the otherhand, occurrences of minor SSW and geomagnetic storms in quick succession complicated clear demarcation of attribution of the respective events to variability of QTDWs amplitudes over upper stratosphere.     

“仿生学”沟槽减阻仿真分析及机理研究

"仿生学"沟槽结构在航空发动机叶片减阻和燃滑油系统减阻等领域有潜在的应用前景。为获得该结构构型参数和雷诺数对减阻效果的影响规律,通过对鲨鱼及海豚表皮特征进行仿生设计了横向和纵向沟槽,利用数值模拟方法对不同沟槽构型在不同雷诺数下的减阻效果开展了对比研究和机理分析。结果表明:横向和纵向沟槽构型减阻率均随雷诺数增大而减小,并且雷诺数影响占主导地位。对于横向沟槽构型,沟槽间距较大的构型沿程阻力占主导,在相对高雷诺数下减阻效果较好;沟槽数量较多的构型局部阻力占主导,在相对低雷诺数下减阻效果较好;无间距构型在相对低雷诺数下减阻效果最好,减阻率约为4.14%。对于纵向沟槽构型,减阻率比横向沟槽的高约1个百分点,雷诺数对减阻效果影响不大。     

基于局部屈曲、压损载荷的帽型长桁截面优化设计

由于帽型长桁的截面尺寸较大,其两边突缘与蒙皮相连形成一个闭合截面,具有很高的受压稳定性,因此被广泛应用于复合材料机身壁板。在局部屈曲和压损载荷下,用MATLAB线性优化帽型长桁截面尺寸,并得出帽型长桁局部屈曲载荷与帽底宽度,帽腰和帽底夹角的关系曲线;压损载荷与帽底宽度,帽腰和帽底夹角的关系曲线,为设计师在初步尺寸设计时提供参考。     

管式减涡器入口局部压降和流阻特性

使用CFD仿真分析的方法,研究了带有导流管式减涡器的盘腔内空气流动和损失特性,重点阐明了导流管的减阻机理,特别是不同结构下管口处的流动与局部损失特性,并提出了一个数学模型来预测导流管盘腔内的压力。研究表明:管口处的流动及损失特性与管口之前的流动状态密切相关,且管口处发生的静压损失不容忽视,约占具整个盘腔损失的11%;合适的导流管长度可以有效的改善管口处的损失,在当前研究工况下具有最佳导流管长度的结构可以降低约25%的管入口处局部压降,约10%的整体静压降;建立了可以精确计算盘腔内压力分布的数学模型,模型计算结果与CFD结果相比平均误差约为53%。      

Seasonal variations of storm-time TEC at European middle latitudes

Global Navigation Satellite System (GNSS) measurements of the Total Electron Content (TEC) from local (Dourbes, 50.1°N, 04.6°E) and European IGS (International GNSS Service) stations were used to obtain the TEC changes during the geomagnetic storms of the latest solar activity cycle. A common epoch analysis, with respect to geomagnetic storm intensity, season, and latitude, was performed on data representing nearly 300 storm events. In general, the storm-time behaviour of TEC shows clear positive and negative phases, relative to the non-storm (median) behaviour, with amplitudes that tend to increase during more intense storms. The most pronounced positive phase is observed during winter, while the strongest and yet shortest negative phase is detected during equinox. Average storm-time patterns in the TEC behaviour are deduced for potential use in ionosphere prediction services.