基于大气预估的再入飞行器机动减速制导方法

针对高超声速再入飞行器精确制导与减速控制一体化的需求,提出一种基于大气预估的再入飞行器机动减速制导方法。基于终端位置及落角约束推导得到制导指令闭合解,在此基础上针对终端速度约束,提出在制导上附加机动减速指令的机动减速制导形式。机动减速制导指令由制导指令和机动减速指令叠加生成,制导指令由包含终端位置及角度约束的最优制导律计算确定;机动减速采用对位置影响最小的正弦指令形式,通过数值预测终端速度偏差确定指令参数大小,并引入参数辨识技术实现预测模型大气偏差修正,提高终端速度预测精度。通过仿真验证了该方法能够在偏差条件下,保证制导精度并有效控制终端飞行速度,实现精确制导与减速控制的一体化设计。

汽油机ECU大气压力预估算法的开发与应用

针对没有大气压力传感器或大气压力传感器有故障的汽油机,为保证汽油机吸入足够气量而得以正常运行,基于可压缩流体理论,提出了汽油机大气压力预估算法,并通过软件在环仿真,对控制算法进行了初步验证。然后,使用Embeded-Coder工具箱对控制算法进行自动代码生成,并将其载入汽油机ECU中。最后,进行最终实车测试,结果表明上述预估算法可准确计算出与大气压力传感器测量值相符的大气压力值。

后G20时期杭州市挥发性有机物和可吸入颗粒物的特征分析

后G20时期(2016年9月至2017年12月),臭氧成为杭州夏季首要大气污染物,可吸入颗粒物(PM10)成为杭州冬季首要大气污染物。利用杭州市环境检测朝晖站和环境监测下沙站空气质量数据观测值分析55种挥发性有机化合物(VOC)的臭氧形成潜力,并使用美国国家环境保护局研发的第3代大气质量预估模型(WRF-CMAQ)对杭州市中心PM10的污染数值进行模拟分析,提出了后G20时期PM10和臭氧的最佳管控方案。结果显示:在后G20时期杭州市中心和城郊,芳香烃所占臭氧形成潜力比例最高,其次为烯烃和烷烃,最后为乙炔。在市区和城郊,乙烯、间/对二甲苯、甲苯、丙烯和乙苯依次为生成臭氧最主要的5种挥发性有机化合物,因此管控这5种挥发性有机化合物能最大程度的减少臭氧质量浓度。2017年杭州市中心春夏秋冬四季的PM10中分别有50%,32%,48%和45%源于杭州市以外污染源的长距离跨地域传输。其中,春秋冬三季通过长距离传输来到杭州市区的PM10主要源于市区的北方,夏季则主要源于市区的西南方。2017年,杭州市区的交通源、工业源、生活源与农业源对本地排放PM10的贡献率分别为62.6%,27.8%,7.3%和2.3%。当杭州本地的工业源和交通源在2016年的基础上分别减排15%和5%时,杭州市中心的PM10年均质量浓度可低于70μg·m^-3,达到二类区的标准。

Assessing the Impacts of Eurasian Snow Conditions on Climate Predictability with a Global Climate Model

On the basis of two ensemble experiments conducted by a general atmospheric circulation model(Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model,hereinafter referred to as IAP9L_CoLM),the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated.The predictive skill of sea level pressures(SLP)and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model.Furthermore,the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature(SAT)anomalies over China was improved by applying realistic(prescribed)Eurasian snow conditions.The predictive skill of the summer precipitation in China was low;however,when realistic snow conditions were employed,the predictability increased,illustrating the effectiveness of the application of realistic Eurasian snow conditions.Overall,the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China.When Eurasian snow conditions in the global climate model(GCM)can be more realistically represented,the predictability of summer climate over China increases.

Estimation of atmospheric predictability for multivariable system using information theory in nonlinear error growth dynamics

To estimate atmospheric predictability for multivariable system, based on information theory in nonlinear error growth dynamics, a quantitative method is introduced in this paper using multivariable joint predictability limit(MJPL) and corresponding single variable predictability limit(SVPL). The predictability limit, obtained from the evolutions of nonlinear error entropy and climatological state entropy, is not only used to measure the predictability of dynamical system with the constant climatological state entropy, but also appropriate to the case of climatological state entropy changed with time. With the help of daily NCEP-NCAR reanalysis data, by using a method of local dynamical analog, the nonlinear error entropy, climatological state entropy, and predictability limit are obtained, and the SVPLs and MJPL of the winter 500-hPa temperature field, zonal wind field and meridional wind field are also investigated. The results show that atmospheric predictability is well associated with the analytical variable. For single variable predictability, there exists a big difference for the three variables, with the higher predictability found for the temperature field and zonal wind field and the lower predictability for the meridional wind field. As seen from their spatial distributions, the SVPLs of the three variables appear to have a property of zonal distribution, especially for the meridional wind field, which has three zonal belts with low predictability and four zonal belts with high predictability. For multivariable joint predictability, the MJPL of multivariable system with the three variables is not a simple mean or linear combination of its SVPLs. It presents an obvious regional difference characteristic. Different regions have different results. In some regions, the MJPL is among its SVPLs. However, in other regions, the MJPL is less than its all SVPLs.