相似系统间调整因子的量化方法

探讨一种便于操作、系统而量化的方法 ,以预计新研系统的可靠性参数。在相似设备法的基础上 ,提出了利用可量化的调整因子对相似系统的可靠性参数进行转换的方法 ,有效地提高了相似设备法的可操作性和分析精度 ,对研究相似设备法在工程技术领域的应用也具有一定的参考价值     

如何估计软件的MTBF

软件剩余初始故障个数No、平均无故障时间MTBF、故障暴露率λ和软件运行的成功率θ等是衡量软件可靠性的重要参数.其中对高可靠性软件(包括军用软件等)MTBF是最重要的.传统的MTBF计算方法有2种一是计算发生故障的平均时间间隔二是通过软件可靠性模型进行估计.前者代价较大,后者不太准确.本文从软件的失败率入手研究软件的MTBF,这是一个比较贴近实际的方法.     

后方油库整体生存概率分析计算

针对我军后方油库特点,探讨了油库整体生存概率计算的基本思路和方法,分析了各类分项目标生存概率的计算方法,采用层次分析对后方油库各分项目标权值进行了详细分析计算,可为后方油库伪装防护效能评估提供依据。     

Optimal (τ, T) opportunistic maintenance of a k‐out‐of‐n:G system with imperfect PM and partial failure

The opportunistic maintenance of a k‐out‐of‐n:G system with imperfect preventive maintenance (PM) is studied in this paper, where partial failure is allowed. In many applications, the optimal maintenance actions for one component often depend on the states of the other components and system reliability requirements. Two new (τ, T) opportunistic maintenance models with the consideration of reliability requirements are proposed. In these two models, only minimal repairs are performed on failed components before time τ and the corrective maintenance (CM) of all failed components are combined with PM of all functioning but deteriorated components after τ; if the system survives to time T without perfect maintenance, it will be subject to PM at time T. Considering maintenance time, asymptotic system cost rate and availability are derived. The results obtained generalize and unify some previous research in this area. Application to aircraft engine maintenance is presented. © 2000 John Wiley & Sons;, Inc. Naval Research Logistics 47: 223–239, 2000     

线导鱼雷导引弹道的动力学分析

通过线导鱼雷常用导引方法—三点法、前置角法导引弹道的动力学分析 ,推导出不同方法下鱼雷旋回速率的解析表达式 ,并通过数字仿真对鱼雷线导导引弹道动力学特性进行探讨 ,明确了线导鱼雷导引弹道特性与导引方法之间的关系 .     

气体化学反应速率的 DSMC 方法

为预报化学反应过程,应用ＤＳＭＣ方法研究了双原子分子的离解反应,讨论了高温情况下（Ｔ～Ｅａ／ｋ）采用由实验测定得到的反应速率与气体温度间Ａｒｒｈｅｎｉｕｓ经验关系式的可靠性问题,分析了振动热非平衡效应对于离解反应速率的影响,给出了由ＤＳＭＣ方法得到的离解反应速率与气体温度的关系曲线。     

防空武器火控系统对空中目标参数估计方法仿真

针对防空武器火控系统的特点,提出了一种防空武器火控系统对空中目标参数估计的方法,可以在不知道目标距离的条件下得到目标的航向角与俯仰角,实现对目标的被动式跟踪,对估计误差的统计特性进行了分析,仿真结果表明了该方法比传统的估计方法更加可行.     

The logistic–exponential survival distribution

For various parameter combinations, the logistic–exponential survival distribution belongs to four common classes of survival distributions: increasing failure rate, decreasing failure rate, bathtub‐shaped failure rate, and upside‐down bathtub‐shaped failure rate. Graphical comparison of this new distribution with other common survival distributions is seen in a plot of the skewness versus the coefficient of variation. The distribution can be used as a survival model or as a device to determine the distribution class from which a particular data set is drawn. As the three‐parameter version is less mathematically tractable, our major results concern the two‐parameter version. Boundaries for the maximum likelihood estimators of the parameters are derived in this article. Also, a fixed‐point method to find the maximum likelihood estimators for complete and censored data sets has been developed. The two‐parameter and the three‐parameter versions of the logistic–exponential distribution are applied to two real‐life data sets. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

What you should know about simulation and derivatives

Derivatives (or gradients) are important for both sensitivity analysis and optimization, and in simulation models, these can often be estimated efficiently using various methods other than brute‐force finite differences. This article briefly summarizes the main approaches and discusses areas in which the approaches can most fruitfully be applied: queueing, inventory, and finance. In finance, the focus is on derivatives of another sort. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Phenomenological modelling of flow behaviour of 20MnMoNi55 reactor pressure vessel steel at cryogenic temperature with different strain rates

In the present study a phenomenological constitutive model is developed to describe the flow behaviour of 20MnMoNi55 low carbon reactor pressure vessel (RPV) steel at sub-zero temperature under different strain rates. A set of uniaxial tensile tests is done with the variation of strain rates and temperature ranging from 10⁻⁴ s⁻¹ to 10⁻¹ s⁻¹ and -80 °C to −140 °C respectively. From the experimental data, family of flow curves at different temperatures and strain rates are generated and fitted exponentially. The strain rate and temperature dependence of the coefficients of the exponential flow curves are extracted from these curves and characterised through a general phenomenological constitutive coupled equation. The coefficients of this coupled equation are optimised using genetic algorithm. Finite element simulation of tensile tests at different strain rates and temperatures are done using this coupled equation in material model of Abaqus FEA software and validated with experimental results. The novelties of proposed model are: (a) it can predict precisely the flow behaviour of tensile tests (b) it is a simple form of equation where fitting parameters are both function of strain rate ratio and temperature ratio, (c) it has ability to characterize flow behaviour with decreasing subzero temperatures and increasing strain rates.