对称螺杆流量计转子热变形的综合分析

利用有限元理论对８０ｍｍ口径的对称螺杆流量计转子的热变形进行了综合分析．整个计算程序分为平衡和非平衡两套系统．通过计算转子的热变形,推导出热变形对转子泄漏和运转等方面的影响．最后得出对生产有指导意义的具体数值和具体方法．     

迟滞非线性弹性元件动力特性数学模型

描述了迟滞非线性弹性元件动态特性数学模型的发展状况．详细介绍了理想干摩檫模型、双线性模型、Bouc模型和迹法模型．提出了评判数学模型优劣的标准．     

弹炮结合防空武器系统机动生存能力模型研究

从弹炮结合防空武器系统通过机动作战提高生存能力的角度出发,结合防空武器系统机动作战过程中的战术背景,提取了该过程中决定防空武器系统生存能力的决定性要素,然后分析了该过程中影响防空武器系统生存能力的总机动时间、被发现概率、抗击成功率、被毁伤概率等各项指标的特点,在此基础上建立了用于研究该过程的数学模型,最后给出实例进行了仿真计算,通过对计算结果的分析,证明了该模型的有效性,从而为防空兵机动作战模拟提供了一定的参考.     

实施精品战略 锻造品牌期刊

期刊的传统风格特色是创办品牌、名牌期刊的基础,是其构成要素的一部分。要将期刊办成品牌期刊、名牌期刊,就必须把它过去的风格特色继承下来、传承下去,这是实施精品战略,锻造品牌期刊的基础和前提。论述了品牌期刊的价值构成及要素,并对如何培育锻造品牌期刊作了一些有益的探讨。     

基于RS近似算法的医疗诊断知识挖掘

针对医学领域诊断规则挖掘算法中时间和空间复杂性问题,提出一种基于邻域系统的决策表近似算法,用于医疗诊断数据挖掘预处理阶段的数据压缩.该方法以代表元素代替若干相近元素,有效地压缩了原始决策表的对象个数,同时保证决策表本身的判断能力基本不变.实例仿真表明,该算法具有比传统聚类算法更优的性能.     

超空泡航行体双层壳结构动力稳定性分析

针对超空泡航行体轴向受力特点,建立超空泡航行体双层壳结构有限元模型,对结构动力稳定性进行分析.数值计算表明,当壳间实肋板厚度增大或环向肋骨数量和尺寸增加时,动力失稳区域整体下移,不稳定激发频率下降,失稳区域宽度减小幅度较小;当壳间间距减小或纵向肋骨数量和尺寸增加时,双层壳结构动力不稳定激发频率上升,动力失稳区域宽度较大...     

临近空间对地观测平台的矢量化建模及稳定性分析

研究了临近空间对地观测平台的矢量化建模和稳定性问题.定义了描述观测平台空间运动的坐标系及运动参数;在受力分析的基础上,根据Newton-Euler方程建立了矢量形式的六自由度非线性数学模型;基于此模型,应用Lyapunov稳定性理论对观测平台的稳定性进行了分析.研究结果表明:对于给定的未扰运动,观测平台是渐近稳定的;纵...     

弹药集装单元储存静力学有限元分析

以简单的六面体结构为基础,以“铝板-聚氨酯泡沫材料-铝板”复合板材为弹药集装单元的主体材料,结合弹药集装单元的承载能力要求,建立弹药集装单元的基本模型。通过其在储存状态下的静力学有限元分析,了解3种不同堆垛情况下,弹药集装单元的应力与应变情况,为进一步研究弹药集装单元结构强度提供理论依据。     

运动激光辐照圆柱薄壳温度场的数值模拟

对圆柱薄壳在四种不同运动模式激光源辐照下的温度场分布进行了分析研究。运用傅立叶级数展开法推导了在不同运动模式激光源作用下圆柱薄壳温度场的解析表达式,并对该解析表达式进行了编程计算,其计算结果与有限元解吻合良好。详细分析了旋转角速度与平动速度对圆柱壳温升分布的影响,发现在往复运动加热模式下光斑区域的温升变化最为剧烈。计算结果为导弹的抗激光辐射提供了理论依据。     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.