翼身组合体流场测量和分析

本文给出了翼身组合体空间流场测量结果,揭示了机身涡、机翼涡随迎角的变化及相互干扰情况。当迎角足够大时,翼面上方流态从破裂涡发展成完全分离流,其尺度可达5～8倍机身直径。     

选择发动机最佳循环参数的一体化方法研究

 航空发动机是飞机的动力装置,它的性能、重量、尺寸都对飞机性能有显著影响。本文从系统工程的观点出发,综合考虑飞机、发动机性能的相互影响和匹配,提出了以最好地满足飞机飞行任务为目标,选择发动机最佳循环参数和调节计划的一体化方法。 本文的应用实例是歼击机动力改型时优选加力发动机的最佳设计方案。计算结果表明:飞机飞行任务不同、改型要求不同、约束条件不同,发动机最佳方案的类型、循环参数和调节计划也完全不同。因此,一体化选择方法具有重要实际意义。     

国外二茂铁类燃速催化剂研究的新进展

本文综述二茂铁类燃速催化剂研究的最新进展,着重介绍几种引人注目的常用和新发展的燃速催化剂;同时,也涉及这类催化剂的挥发性和对AP及推进剂热分解的催化作用.最后分析了这类催化剂今后的发展趋向.     

三维机身外形快速生成及表面分块

 本文提出了纵向放样线作锥线链的最小二乘法拟合,剖面外廓线作圆弧、椭圆弧及直线混合融合计算的三维机身外形生成方法。在SIEMENS 7.760机上建立了包含计算机绘图在内的FORTRAN程序。可以方便而快速地生成构形复杂的三维机身外形及气动力面元法分析所需的表面小块。几个算例证实了本法的通用性、快速性及简便性。程序发展后可能作为CAD中的图象主尺寸系统。     

高层建筑风载的数值模拟

本文对高层基不同风向下的流场进行模拟，并将所得的结果与ＣＡＡＲＣ的标准模型实验进行比较，这些比较表明，ＮＳＴＲ程序包是对建筑物的风载进行预测的有效工具。     

偶联剂对推进剂填料界面影响研究

本文用不同界面性质填料的推进剂,经单轴拉伸试验,研究偶联剂对不同填料提高推进剂力学性能的偶联效应.结果表明:MAPO对包AP、未包AP起偶联作用,T313-B、T603、5527对A1起偶联作用;T313组合偶联剂兼顾了单元偶联剂各自的效应,且对包AP有显著的正协同效应.本文还解释了T313在配方中提高力学性能的作用;提出了选择推进剂偶联剂的方向.     

横向风影响下空冷塔内外流场的数值研究

用有限体积方法对有横向自然风环境下空冷塔的三维湍流流场及温度场进行了数值模拟，计算中采用了标准ｋ－ε湍流模式；对结果的分析揭示出刮风时空冷塔散热能力下降的主要原因，可为进一步提出防风措施提供理论依据。     

飞行控制律评估与确认的μ分析方法

针对目前应用的网格法在实际使用中的不足,将μ分析方法应用到飞行控制律 的评估与确认中.以某型战斗机及为其设计的鲁棒飞行控制律为研究对象,采用相应的稳定 性准则,利用线性分式变换形式,考虑各种已知的不确定参数摄动,用μ分析方法实现了对 该准则的评估与确认.与传统网格法相比,基于μ分析的方法具有两个明显的优点,一是网 格间的点可以得到评估,二是不存在网格法的"维数灾"问题,从而提高了评估的可靠性和 效率.     

REMS: The Environmental Sensor Suite for the Mars Science Laboratory Rover

The Rover Environmental Monitoring Station (REMS) will investigate environmental factors directly tied to current habitability at the Martian surface during the Mars Science Laboratory (MSL) mission. Three major habitability factors are addressed by REMS: the thermal environment, ultraviolet irradiation, and water cycling. The thermal environment is determined by a mixture of processes, chief amongst these being the meteorological. Accordingly, the REMS sensors have been designed to record air and ground temperatures, pressure, relative humidity, wind speed in the horizontal and vertical directions, as well as ultraviolet radiation in different bands. These sensors are distributed over the rover in four places: two booms located on the MSL Remote Sensing Mast, the ultraviolet sensor on the rover deck, and the pressure sensor inside the rover body. Typical daily REMS observations will collect 180 minutes of data from all sensors simultaneously (arranged in 5 minute hourly samples plus 60 additional minutes taken at times to be decided during the course of the mission). REMS will add significantly to the environmental record collected by prior missions through the range of simultaneous observations including water vapor; the ability to take measurements routinely through the night; the intended minimum of one Martian year of observations; and the first measurement of surface UV irradiation. In this paper, we describe the scientific potential of REMS measurements and describe in detail the sensors that constitute REMS and the calibration procedures.     

Static Frame Model Validation with Small Samples Solution Using Improved Kernel Density Estimation and Confidence Level Method

An improved method using kernel density estimation (KDE) and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.