基于柔性基础的双层隔振系统概率灵敏度分析

对基于柔性基础的双层隔振系统的功率流传递特性进行了分析,利用有限元分析软件ANSYS计算了传递到基础的功率流,并用概率灵敏度分析方法研究了功率流对隔振系统给定参数的灵敏度,为双层隔振系统的优化设计奠定了基础.     

美国全球作战支持系统

美国的全球作战支持系统(GCSS)是 "武士C4I"的一个组成部分,它将人员、后勤、采办、医疗等支持功能集成到一个跨功能的环境中.GCSS将实现作战支持功能之间以及作战支持与指挥控制功能之间的端对端的信息互操作,为联合作战人员提供融合、实时的多维战场视图.主要介绍了全球作战支持系统的功能、系统组成及其未来作战能力.     

大机动格斗及近距空战的若干问题

较系统地综述了大机动格斗的一些基本战术和技术概念,引入了超机动性的一些度量,然后提出了使用具有刚体性质的质点飞机模型的可行性.最后提出在大机动格斗实战环境中,火力控制需要解决的若干问题.     

采用时序多属性评价方法的空战威胁排序

鉴于空战中威胁目标的多属性及动态变化特点,提出采用时序多属性评价方法来解决空战目标动态威胁排序问题。在建立空战威胁指标体系基础上,采用熵权法确定目标属性权重,并通过指数分布法确定各时刻权重,最后综合依据威胁目标多时刻的属性信息,实现了空战动态威胁排序。仿真结果说明该方法的合理性。     

未来中远距协同空战多目标攻击决策研究

根据先进战斗机的技术特点和未来中远距协同空战发展方向,提出一种基于先敌发现、先敌发射、先敌摧毁能力的空战态势分析模型。同时,采用一种将并行遗传算法与分布估计算法相结合的并行分布遗传算法并将其用于多目标攻击决策。最后利用具体算例进行仿真。结果表明空战态势分析模型能够较为准确地描述未来中远距多机协同空战,采用的算法具有较好的可行性和实时性,为中远距多机协同空战的多目标攻击决策提供了新的思路和方法。     

基于空间约束的舰载机出库调度

舰载机在航母上的调度问题是制约舰载机出动和回收的重要因素,以往研究主要集中在飞行甲板调度方面。针对舰载机在航母上的出入库问题进行研究,着力解决舰载机在航母上二、三波次的出动能力。首先对舰载机出库的流程进行分析,梳理制约因素。其次,应用柔性流水车间调度理论对空间约束条件下的舰载机出库调度问题进行建模。再次,对智能粒子群算法应用于舰载机出库调度问题进行分析,对编码进行设计,并给出算法流程。最后,对出库流程进行仿真,计算出最优出库方案及时间,验证了模型的准确性。     

S弯进气道合成双射流流场控制特性分析

针对飞翼布局无人飞行器中S弯进气道明显流动分离和出口总压畸变等问题,提出了基于合成双射流的主动流动控制方法,建立了合成双射流的S弯进气道数值仿真模型。结果表明,在S弯进气道分离点附近施加合成双射流控制,在整个射流周期内通过“吹”“吸”接力可以有效抑制边界层流动分离,有效提升总压恢复系数。对比研究了合成双射流不同射流角度、射流峰值速度和激励频率对S弯进气道流场控制特性的影响规律。结果表明合成双射流与主流的角度越小,流动分离控制效果越好,较大射流峰值速度会对主流形成“阻挡”致使控制效果下降,激励频率与流场特征频率越接近控制效果越明显。     

低温跨声速风洞设计中的真实气体效应研究

风洞以低温气体为介质运行时,气体会表现出热力和热值的不完全,风洞回路气体流动参数计算需要考虑低温真实气体效应。计算给出了氮气介质在温度100～323 K、压力100～450 kPa范围压缩性因子和比热比的变化规律,并通过将等熵膨胀系数引入一维完全气体流动方程,发展了低温跨声速风洞气流流动参数计算分析模型,获得了跨声速风洞高速运行时气流液化温度和压力的组合边界包络线。对比分析结果表明：在低温跨声速风洞的运行压力(115～450 kPa)和温度(110～323 K)范围内,基于等熵膨胀系数计算得到的气体流动状态参数的理论计算值与气体真实物理解的偏差小于1%,完全可满足低温跨声速风洞工程设计需求。     

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.     

Fitting Lanchester equations to the battles of Kursk and Ardennes

Lanchester equations and their extensions are widely used to calculate attrition in models of warfare. This paper examines how Lanchester models fit detailed daily data on the battles of Kursk and Ardennes. The data on Kursk, often called the greatest tank battle in history, was only recently made available. A new approach is used to find the optimal parameter values and gain an understanding of how well various parameter combinations explain the battles. It turns out that a variety of Lanchester models fit the data about as well. This explains why previous studies on Ardennes, using different minimization techniques and data formulations, have found disparate optimal fits. We also find that none of the basic Lanchester laws (i.e., square, linear, and logarithmic) fit the data particularly well or consistently perform better than the others. This means that it does not matter which of these laws you use, for with the right coefficients you will get about the same result. Furthermore, no constant attrition coefficient Lanchester law fits very well. The failure to find a good‐fitting Lanchester model suggests that it may be beneficial to look for new ways to model highly aggregated attrition. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.