Research on vacuum plume and its effects

In vacuum environment, the exhaust flow of attitude control thrusters would expand freely and produce the plume, which possibly causes undesirable contamination, aerodynamic force and heating effects to the spacecraft. Plume work station (PWS) is developed by Beihang University (BUAA) for numerically simulating the vacuum plume and its effects. An approach which combines the direct simulation Monte Carlo (DSMC) method and difference solution of Navier-Stokes (N-S) equations is applied. The internal flows in nozzles are simulated by solving the NS equations. The flow parameters at nozzle exit are used as the inlet boundary condition for the DSMC calculation. Experimental studies are carried out in a supersonic low density wind tunnel which could simulate the 60-80 km altitude environment to investigate the plume and its effects. To demonstrate the capability of PWS, numerical simulations are performed for the vacuum plume of several typical attitude control thrusters. The research results are of great help for the engineering design.     

Space-radiation-induced photon luminescence of the Moon

We report on the results of a continuing study of the photon luminescence of the Moon induced by Galactic Cosmic Rays (GCRs) and space radiation from the Sun, using the Monte Carlo program FLUKA. Understanding the space radiation environment is critical to future exploration of the Moon, and this includes photons. The model of the lunar surface is taken to be the chemical composition of soils found at various landing sites during the Apollo and Luna programs, averaged over all such sites to define a generic regolith for the present analysis. This surface model then becomes the target that is bombarded by Galactic Cosmic Rays (GCRs) and Solar Energetic Particles (SEPs) or Solar Particle Events (SPEs) above 1 keV in FLUKA to determine the photon fluence albedo produced by the Moon’s surface when there is no sunlight and Earthshine. The result is to be distinguished from the gamma-ray spectrum produced by the radioactive decay of radiogenic constituents lying in the surface and interior of the Moon. From the photon fluence we derive the spectrum which can be utilized to examine existing lunar spectral data and to aid future orbiting instrumentation in the measurement of various components of the space-radiation-induced photon luminescence present on the Moon.     

某型航空发动机封严篦齿盘异型螺栓孔公差设计分析

为了有效降低孔边应力水平,以某型航空发动机封严篦齿盘5参量单轴对称异型孔为研究对象。针对此类异型孔的公差设计问题,利用蒙特卡洛模拟技术,通过构造异型孔孔边应力状态的响应面函数以对设计参数进行灵敏度分析,确定了需要给定公差的设计变量。讨论了异型孔的重要设计尺寸公差带对孔边应力分布及配合的影响,并参照美制紧固件螺栓通孔设计标准,确定了此类异型孔尺寸公差选取应满足的强度要求及装配等级。     

基于自适应重要抽样的可靠性分析方法

基于自适应重要抽样(AIS,Adaptive Importance Sampling)的可靠性分析方法,能够克服基于蒙特卡洛方法分析小概率事件时存在的效率低、精度差问题.为解决显性失效方程不存在时寻找失效点困难问题,首先利用条件递归寻找失效点,并使之尽可能在失效面附近;以该失效点为采样中心,抽取失效样本,并不断调整采样中心,使失效样本不断靠近设计点;然后利用这组失效样本估计重要抽样函数的参数,再执行自适应迭代过程,直至失效概率的误差缩小到允许误差限内.最后通过两个典型案例对方法进行应用验证,仿真结果表明在没有失效方程的情况下,能够通过仿真方法很快找到失效点,表明对于不存在显性失效方程的系统,该方法同样适用.与蒙特卡洛方法的对比结果表明该方法在仿真效率上具有较大优越性,且失效概率越小,这种优越性越明显.      

航天器热模型蒙特卡罗法参数分析及健壮度评估研究

针对传统航天器热模型的参数分析及温度余量选择存在的缺陷,提出了基于蒙特卡罗法的参数分析及健壮度评估方法。文章首先以航天器设备温度为因变量,以各不确定参数为自变量,应用蒙特卡罗法模拟取样,得到动态参数样本;通过样本的统计式推导,给出了参数分析及健壮度评估的求解方法。结合某星热模型进行了仿真应用,结果表明:与传统方法相比,蒙特卡罗法不但可以提高参数分析的效率,而且量化了各不确定参数敏感性以及设备温度分布概率,为设计人员判断设备温度余量提供数值依据。     

Von Karman模型三维大气紊流仿真理论与方法

基于三维紊流场相关函数矩阵,用蒙特卡罗法仿真生成Von Karman模型三维空间大气紊流场,实现了对复杂的Von Karman模型的精确推导和应用,仿真流场的相关特性在理论上可以任意逼近Von Karman理论模型.通过提高白噪声序列的白化程度及优化蒙特卡罗算法的方法提高了仿真精度和效率.数值仿真结果证明:所生成的空间大气紊流场符合模型特性,具有较好的统计特性和仿真精度,满足实时飞行仿真对紊流场数据的要求.     

任意形状凹面和相互可视表面φ2,φ3的分析计算

推导了空间任意形状凹面和相互可视表面的地球红外角系数φ３和地球反照角系数φ２的计算公式；并运用ＭｏｎｔｅＣａｒｌｏ法对半球凹地、抛物凹面、面相互可视的直角平板、平板表面外凸出圆柱表面的地球反照角系数和地球红外角系数进行了计算。运用这种方法，可以计算任意形状凹面和相互可视表面的φ２、φ３。     

弹道导弹基于仿真的落点精度修正

介绍了利用计算机仿真技术预测、修正弹道导弹落点 ,从而提高其命中精度的原理和方法 ,给出了导弹射前初始状态的测量方法以及导弹仿真的程序模型 ,并对一枚具体的导弹作了落点的仿真预测与落点修正 ,仿真的结果与实际情况相吻合。此方法在弹道导弹的实弹演习与作战中具有十分重要的价值 ,可在相关单位广泛使用。     

高可靠性分析中分布函数的研究

结构可靠性设计与分析方法要求用概率分布函数描述某些设计变量，这时，有可能选择了非真实的概率模型。利用蒙特卡洛数值仿真方法，就同此而产生的可靠性设计与分析的影响进行了初步研究，并给出了工程上有意义的结论。     

A Bayesian approach for integrating multilevel priors and data for aerospace system reliability assessment

This paper investigates Bayesian methods for aerospace system reliability analysis using various sources of test data and expert knowledge at both subsystem and system levels. Four scenarios based on available information for the priors and test data of a system and/or subsystems are studied using specific Bayesian inference techniques. This paper proposes the Bayesian melding method for integrating subsystem-level priors with system-level priors for both system- and subsystem-level reliability analysis. System and subsystem reliability outcomes are compared under different scenarios. Computational challenges for posterior inferences using the sophisticated Bayesian melding method are addressed using Markov Chain Monte Carlo (MCMC) and adaptive Sampling Importance Re-sampling (SIR) methods. A case study with simulation results illustrates the applications of the proposed methods and provides insights for aerospace system reliability analysis using available multilevel information.