波音737-800飞机起飞性能随襟翼放出量的变化特点

波音737飞机与其他民用机型的一个不同之处是前缘缝翼在起飞时可以使用开缝状态。分析了波音737-800飞机下列四个起飞性能参数与襟翼放出量的关系:场地长度限制的最大起飞重量;爬升梯度限制的最大起飞重量;障碍物限制的最大起飞重量;起飞决断速度。结果显示这四个参数在襟翼放出量为10时出现异常,原因是前缘缝翼状态发生了变化。在襟翼放出量由5增大到10的过程中,前缘缝翼由不开缝的放出状态变成开缝的完全放出状态,降低了升力系数。     

现代民机新型灭火剂性能研究

对新型灭火剂研究背景和种类进行了简要介绍,对具有开发潜力的新型灭火剂的理化性能、毒性、腐蚀性、灭火性能、环保性能进行深入分析与研究。通过对新型灭火剂性能的研究,得出每种灭火剂的优缺点,筛选出适用于现代民机灭火系统的灭火剂。     

Materials selection for electronic enclosures in space environment considering electromagnetic interference effect

Using low power electronic devices for space applications to reduce the mass and energy consumption has lead to electromagnetic interference (EMI) problem. Electronic enclosures are used to shield electronic devices against EMI. In the past, electromagnetic shielding has been mainly the only criteria considered in electronic enclosure design. However, there are several structural and thermal requirements for selection of shielding materials which should also be taken into account. In this research work, three quantitative materials selection methods, i.e. Digital Logic (DL), Modified Digital Logic (MDL), and Z-transformation, are employed to select the best material from among a list of candidate materials. Composite and metallic electronic enclosures are explored and the best material is selected. Z-transformation method is applicable to both of the considered case studies while DL and MDL can only be used for solving one of them. Z-transformation method ranks aluminum as the first choice among various metallic materials. The wide range of Z-transformation application and its practical results confirm the superiority of Z-transformation method over DL and MDL methods.     

Al—Mg系合金工艺性能试验及结果

本文介绍了几种常用的铝镁合金，通过若干工艺性能的对比试验，以实际的检测数据分析说明各自的特点及其共性，以供产品设计和工程应用时选材参考。     

碳/环氧复合材料两次表面镜基板研制

文章介绍了碳/环氧复合材料两次表面镜基板的结构与铺层、选材与成型工艺,性能测试等内容。     

SPOT-5卫星系统性能概述

概要介绍SPOT - 5卫星的主要成像传感器 ,精确定位技术及数据格式。介绍分析卫星数据的应用前景     

Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)

In the last 20?years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2?m.     

ADS-B监视报文参数统计及其所需性能

基于广播式自动相关监视（ADS-B）报文的位置导航不确定性指标（NUC_P）的监视质量备受关注。针对位置导航不确定性参数好、监视质量却不高的问题，深入研究广播式自动相关监视报文数据项及其与基于性能的通信与监视的相关性。在充分考虑通信导航监视相关性及全球卫星导航系统（GNSS）广域增强等因素基础上，推导、构建了监视所需性能估计模型，并给予合理简化。在测试空域利用2个具有运行资质的生产商ADS-B接收站，连续采集近2亿条ADS-B报文，统计分析报文各信息项的状态参数，并应用到所需性能估计模型评估计算这两个ADS-B站的所需监视性能。此外还将参考文献报文参数代入模型评估所需性能。结果显示，当考虑报文各信息项统计状态及具备广域增强服务支撑时，ADS-B所需监视性能参数估计值未达到99.9%要求。若缺乏广域增强服务及相关性因素等，系统所需性能与国际民航组织（ICAO）监视要求的差距则更大。     

One-dimensional numerical investigation on multi-cylinder gasoline engine fueled by micro-emulsions,CNG, and hydrogen in dual fuel mode

This research work is the novel state-of-the-art technology performed on multi-cylinder SI engine fueled compressed natural gas, emulsified fuel, and hydrogen as dual fuel. This work predicts the overall features of performance, combustion, and exhaust emissions of individual fuels based on AVL Boost simulation technology. Three types of alternative fuels have been compared and analyzed. The results show that hydrogen produces 20% more brake power than CNG and 25% more power than micro-emulsion fuel at 1500 r/min, which further increases the brake power of hydrogen, CNG, and micro-emulsions in the range of 25%, 20%, and 15% at higher engine speeds of 2500–4000 r/min, respectively. In addition, the brake-specific fuel consumption is the lowest for 100% hydrogen, followed by CNG 100% and then micro-emulsions at 1500 r/min. At 2500–5000 r/min, there is a significant drop in brake-specific fuel consumption due to a lean mixture at higher engine speeds. The CO, HC, and NO_x emissions significantly improve for hydrogen, CNG, and micro-emulsion fuel. Hydrogen fuel shows zero CO and HC emissions and is the main objective of this research to produce 0% carbon-based emissions with a slight increase in NO_x emissions, and CNG shows 30% lower CO emissions than micro-emulsions and 21.5% less hydrocarbon emissions than micro-emulsion fuel at stoichiometric air/fuel ratio.     

Collaborative image compression and classification with multi-task learning for visual Internet of Things

Widespread deployment of the Internet of Things(Io T) has changed the way that network services are developed, deployed, and operated. Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T. Typically, the sender would compress images, and then through the communication network, the receiver would decode images, and then analyze the images for applications. However, image compression and semantic inference are generally conducted separately, and t...