银河Ⅱ仿真机在导弹型号研制中的应用

本文叙述了使用银河Ⅱ仿真机进行导弹仿真原理及过程，介绍了银河Ⅱ仿真机及其在导弹型号研制中的作用，同时分析了某仿真试验中出现的问题，给出解决方法，总结了通过仿真试验获得的经验。     

考虑冲击因素的间歇工作系统可靠性分析

间歇工作模式广泛存在于工业生产和军事应用场景中,通过对间歇工作系统转换点退化状态随机性的定量分析,基于维纳过程建立了多阶段随机退化间歇工作系统可靠性分析模型,在此基础上,考虑随机冲击因素对间歇工作系统退化过程的影响,分别分析了环境应力冲击到达强度和冲击幅度随机特性,通过非齐次泊松过程对随机环境应力冲击进行了建模,得到了考虑冲击因素影响条件下的间歇工作系统可靠性分析模型。通过蒙特卡罗仿真实验证明了模型对状态转换点随机性和冲击随机性描述的准确性,对不同参数条件下冲击因素对间歇工作系统可靠性影响进行了灵敏度分析,实现了考虑冲击因素的间歇工作系统可靠性定量描述。     

遗传算法在计算机仿真技术中的应用

通过对随机性问题进行计算机仿真，从而得出待解问题的解。提出了基于遗传算法进行计算机仿真的基本模型。通过圆周率的计算，实践了该模型的应用过程。实践证明，该模型在进行计算机仿真时准确度比较高。     

在仿真行业建立和实施ISO9000质量体系的实践

文章介绍了珠海亚洲仿真公司在仿真界成为国内首家通过ＩＳＯ９０００质量体系认证企业所取得的独特经验之后，将质量保证的理论与仿真机制造业的具体实际相结合，为仿真机和软件开发商提供了可借鉴的质量管理思路，并提出了国内仿真机制造业如何与国际市场接轨的途径。     

导弹武器系统开发中的仿真机系统综述

该文从仿真计算机开发的角度，介绍了仿真机研制中需要注意的几方面问题以及预计的发展方向，在系统体系结构、仿真语言、仿真网络等方面进行了讨论，为相关开发者提供了参考，尤其是通用计算机平台仿真应用中的软硬件配合和实时网络应用方面的分析对今后仿真机系统的开发具有很好的借鉴作用。     

基于BLINK的DEH控制系统仿真

火电机组仿真模型按生产流程划分为锅炉设备模型、汽机设备模型、发电机设备模型和控制系统模型。仿真支撑软件对仿真机的开发、维护是至关重要的。本文以某300MW机组的DEH控制系统为例，介绍了基于Blink仿真支撑软件控制系统仿真模型的建立、调试方法及步骤，同时简要介绍了仿真支撑软件Blink的建模思想及特点，应用本文介绍的方法，在开发多台仿真机的具体实践中取得了良好的效果，缩短了仿真机的开发时间，有利于提高仿真机运行的可靠性。     

系统可靠性的计算机仿真方法

随着各种新型复杂系统的建立和工程项目的实施，常规可靠性设计理论与工程实践的矛盾日益突出。当进行可靠性计算时，由随机应力和随机强度的概率密度函数来获取功能函数的概率密度函数较困难，该文就如何将常规可靠性仿真方法推广进行计算机可靠性仿真，提出了将模糊可靠性设计问题转化为常规可靠性设计问题求解的思路，给出了转化的基本原理和方法，由此推出了一系列的计算公式及其仿真方法，从而使得可靠性设计方法能同时处理设计中的随机性和模糊性。     

系统仿真的过去,现在和未来

系统仿真的过去、现在和未来徐庚保（北京航天自动控制研究所，１００８５４）中国计算机用户协会仿真机分会（当时叫仿真机协会）于１９８３年７月１日成立，至今已十五周年，今天来自全国各地的仿真界朋友们再次欢聚在一起，庆贺仿真机分会成立十五周年！十五年来，仿真...     

基于三元组的扩频码构造及其性能分析

为提高扩频通信的可靠性和安全性,提出一类基于三元组密钥流的扩频码构造方法。针对传统扩频码周期短和混沌扩频码算法随机性弱等不足,将周期不重复、初值敏感和算法随机的三元组随机序列进行同态均匀映射或正交变换,以获取同态三元组扩频码或正交三元组扩频码;通过实验验证了该类扩频码具有良好的平衡度、自相关和互相关等特性指标;将该类三元组扩频码应用于蒙特卡罗直接扩频通信模型和DS-CDMA多用户通信系统模型进行仿真测试,仿真结果表明,该类扩频码相比传统扩频码、混沌扩频码更适合扩频通信需要。     

非平稳随机激励下太阳能帆板随机振动分析

研究非平稳随机激励下航天器太阳能帆板结构的随机振动优化问题,由于太阳风、火箭发动机、空间碎片等因素影响,造成太阳能帆板所受激励带有了随机性,即非平稳随机激励.为太阳能帆板结构建立有限元模型,给出帆板模型的动力方程.仿真航天器发动机运行情况,给出太阳能帆板所受非平稳随机激励,结合虚拟激励法,将构造成虚拟激励,引入结构动力方程.采用改进随机振动分析方法,计算太阳能帆板结构随机动力响应.通过与蒙特卡罗法计算结果对比,验证了本文方法的高效性和精确性.计算结果表明,太阳能帆板在随机激励作用结束后,结构振动仍持续较长时间,且振动幅值较大,通过加强帆板之间铰链的刚度,能有效抑制太阳能帆板振动,为太阳能帆板结构分析和设计提供参考.     

Monte Carlo simulation-compatible stochastic field for application to expansion-based stochastic finite element method

In order to endow the expansion-based stochastic formulation with the capability of representing the characteristic behavior of stochastic systems, i.e., the non-linear dependence of the response variability on the coefficient of variation of the stochastic field, a Monte Carlo simulation-compatible stochastic field is suggested. Through a theoretical comparison of displacement vectors in the Monte Carlo method and an expansion-based scheme, it is found that the stochastic field adopted in the expansion-based scheme is not compatible with that appearing in the Monte Carlo simulation. The Monte Carlo simulation-compatible stochastic field is established by means of enforcing the compatibility between the stochastic fields in the expansion-based scheme and the Monte Carlo simulation. Employing the stochastic field suggested in this study, the response variability is reproduced with high precision even for uncertain fields with a moderately large coefficient of variation. Furthermore, the formulation proposed here can be used as an indirect Monte Carlo scheme by directly substituting the numerically simulated random fields into the covariance formula. This yields a pronounced reduction in the computation cost while resulting in virtually the same response variability as the Monte Carlo technique.     

Multilevel Monte Carlo methods and applications to elliptic PDEs with random coefficients

We consider the numerical solution of elliptic partial differential equations with random coefficients. Such problems arise, for example, in uncertainty quantification for groundwater flow. We describe a novel variance reduction technique for the standard Monte Carlo method, called the multilevel Monte Carlo method, and demonstrate numerically its superiority. The asymptotic cost of solving the stochastic problem with the multilevel method is always significantly lower than that of the standard method and grows only proportionally to the cost of solving the deterministic problem in certain circumstances. Numerical calculations demonstrating the effectiveness of the method for one- and two-dimensional model problems arising in groundwater flow are presented.     

Integrating subset simulation with probabilistic re-analysis to estimate reliability of dynamic systems

It is often expensive to estimate the failure probability of highly reliable systems by Monte Carlo simulation. Subset Simulation breaks the original problem of estimating a small probability into the estimation of a sequence of large conditional probabilities, which is more efficient. The conditional probabilities are estimated by Markov Chain simulation. Uncertainty in the power spectral density of the excitation makes it necessary to re-evaluate the reliability for many power spectral densities that are consistent with the evidence about the system excitation. Subset Simulation is more efficient than Monte Carlo simulation, but still requires a new simulation for each admissible power spectral density. This paper presents an efficient method to re-evaluate the reliability of a dynamic system under stationary Gaussian stochastic excitation for different load spectra. We accomplish that by re-weighting the results of a single Subset Simulation. This method is applicable to both linear and nonlinear systems provided that all of the spectra contain the same amount of energy. The authors are currently working on an extension of the method to nonlinear systems, even when the sampling and true power spectral density functions contain different amounts of energy.     

基于GPS残迹的分布式导航算法

全球定位系统(GPS)通信中断等问题经常导致寻路导航系统的失效。为实现寻路导航系统的有效容灾,提出基于GPS残迹的分布式导航算法GVDN。给出算法的主要思想、算法模型、数据结构及运行流程,应用蒙特卡罗方法设计道路可通行程度评估方案,并进行算法效能分析。仿真实验结果证明,该算法具有较好的容灾性能和响应速度。     

A genetic algorithm and the Monte Carlo method for stochastic job-shop scheduling

This paper proposes a method for solving stochastic job‐shop scheduling problems using a hybrid of a genetic algorithm in uncertain environments and the Monte Carlo method. First, the genetic algorithm in uncertain environments is applied to stochastic job‐shop scheduling problems where the processing times are treated as stochastic variables. The Roulette strategy is adopted for selecting the optimum solution having the minimum expected value for makespan. Applying crossover based on Giffler and Thompson's algorithm results in two offspring inheriting the ancestor's characteristics as the operation completion times averaged up to the parent's generation. Individuals having very high frequency through all generations are selected as the good solutions. Second, the Monte Carlo method is effectively used for finding out the approximately optimum solution among these good solutions.     

Monte Carlo methods for matrix computations on the grid

Many scientific and engineering applications involve inverting large matrices or solving systems of linear algebraic equations. Solving these problems with proven algorithms for direct methods can take very long to compute, as they depend on the size of the matrix. The computational complexity of the stochastic Monte Carlo methods depends only on the number of chains and the length of those chains. The computing power needed by inherently parallel Monte Carlo methods can be satisfied very efficiently by distributed computing technologies such as Grid computing. In this paper we show how a load balanced Monte Carlo method for computing the inverse of a dense matrix can be constructed, show how the method can be implemented on the Grid, and demonstrate how efficiently the method scales on multiple processors.     

Exploring pseudo- and chaotic random Monte Carlo simulations

Computer simulations are an increasingly important area of geoscience research and development. At the core of stochastic or Monte Carlo simulations are the random number sequences that are assumed to be distributed with specific characteristics. Computer-generated random numbers, uniformly distributed on (0, 1), can be very different depending on the selection of pseudo-random number (PRN) or chaotic random number (CRN) generators. In the evaluation of some definite integrals, the resulting error variances can even be of different orders of magnitude. Furthermore, practical techniques for variance reduction such as importance sampling and stratified sampling can be applied in most Monte Carlo simulations and significantly improve the results. A comparative analysis of these strategies has been carried out for computational applications in planar and spatial contexts. Based on these experiments, and on some practical examples of geodetic direct and inverse problems, conclusions and recommendations concerning their performance and general applicability are included.     

Uncertainty quantification of MEMS devices with correlated random parameters

The concern about process deviations rises because that the performance uncertainty they cause are strengthened with the miniaturization and complication of Microelectromechanical System (MEMS) devices. To predict the statistic behavior of devices, Monte Carlo method is widely used, but it is limited by the low efficiency. The recently emerged generalized polynomial chaos expansion method, though highly efficient, cannot solve uncertainty quantification problems with correlated deviations, which is common in MEMS applications. In this paper, a Gaussian mixture model (GMM) and Nataf transformation based polynomial chaos method is proposed. The distribution of correlated process deviations is estimated using GMM, and modified Nataf transformation is applied to convert the correlated random vectors of GMM into mutually independent ones. Then polynomial chaos expansion and stochastic collection can be implemented. The effectiveness of our proposed method is demonstrated by the simulation results of V-beam thermal actuator, and its computation speed is faster compared with the Monte Carlo technique without loss of accuracy. This method can be served as an efficient analysis technique for MEMS devices which are sensitive to correlated process deviations.

     

The estimation of probability distribution of SDE by only one sample trajectory

This paper focuses on the method of the simulation of a stochastic system and the main method of our paper is the Monte Carlo computation simulation method. Taking the stochastic Logistic equation as an example, we present the simulation of the sample trajectory by Euler scheme and the invariant probability distribution of stochastic differential equations with the Monte Carlo method. We also compare the simulation result with the analytical result for the autonomous stochastic Logistic model. Moreover, the stochastic Logistic equation with Markovian switching which is described by a Markov chain taking values in a finite state space is considered.     

An Efficient Stochastic Simulation Algorithm for Bayesian Unit Root Testing in Stochastic Volatility Models

In financial times series analysis, unit root test is one of the most important research issues. This paper is aimed to propose a new simple and efficient stochastic simulation algorithm for computing Bayes factor to detect the unit root of stochastic volatility models. The proposed algorithm is based on a classical thermodynamic integration technique named path sampling. Simulation studies show that the test procedure is efficient under moderate sample size. In the end, the performance of the proposed approach is investigated with a Monte Carlo simulation study and illustrated with a time series of S&P500 return data.