热漏对热机功率效率特性的影响

本文研究热漏对热机最优性能的影响,导出存在热阻和热漏损失的定常态流不可逆热机的功率、效率关系。所得结果不同于仅存在热阻损失时的内可逆热机的功率效率特性,且与实际热机特性相一致。     

静磁场Dirichlet问题Galerkin法和牛顿法

作者在［１］,［２］中给出了静磁场Ｄｉｒｉｃｈｌｅｔ问题和Ｎｅｕｍａｎｎ问题广义解的存在与唯一性。本文从理论上给出静磁场Ｄｉｒｉｃｈｌｅｔ问题的一种Ｇａｌｅｒｋｉｎ近似解法,并给出了在各向同性或无电流区情形下的误差估计和牛顿选代序列。     

细长壳体水声辐射问题的有限元结合边界元解法

采用三角形面积元,提出了一种计算任意形状的细长壳体水声辐射问题的有限元结合边界元解法,推导了三角形面积坐标下的质量矩阵。在水动力方面,根据单层势的概念,提出了声场速度势的边界积分表示式,给出了三角形面积坐标下的附加质量矩阵和附加阻尼矩阵,从而得以建立流固耦合的有限元动力响应方程。本方法的进一步发展可用于计算具有加强肋骨的双层壳体结构的声辐射问题以及船体在随机激励下的远场声功率谱。     

正反向量子斯特林循环有限时间热力学性能界限和优化准则

研究了正反向量子斯特林循环的最优性能.在经典极限下,导出了循环的有限时间热力学性能界限和优化准则.得到了斯特林热机、制冷机和热泵特性参数之间的优化关系.     

Analysis of some critical aspects in hot machining of Ti-5553 superalloy: Experimental and FE analysis

In this study, a newly developed titanium superalloy, i.e., the Ti-5553 alloy has used for hot machining. This material replaced Ti-grade-5 alloy in the application of aerospace, automobile, and biomedical sector. However, similar to Ti-grade-5 alloy, the Ti-5553 alloy has a low thermal conductivity which makes it difficult-to-cut material categories hence, high tool wear, cutting force and bad surface finish. Hot machining of Ti-5553 has been studied at different machining condition (room and hot) using Deform-2D finite element analysis. The result from the simulation test was compared with the experimental value and reduction of cutting and thrust forces was observed. The experiment was carried out with the same input parameters as simulation, and good coherence between them observed. Additionally, cutting zone temperature, effective stress, etc. for both room and elevated the temperature are also discussed.     

基于状态变换的数字电路冗余确认研究

以状态跳变图为基础,深入分析了冗余变换与非法变换的特征,提出结构冗余和功能冗余的概念,并讨论了可测故障、不可测故障和冗余之间的联系.最后结合验证和测试生成,提出状态冗余的隐含遍历确认策略.     

质漏对联合循环化学发动机性能的影响

分析存在有限速率传质和质漏损失的联合循环化学发动机性能,导出功率、效率最佳关系,最大功率及其相应效率,以及最大效率及其相应功率     

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．     

重型装备空投系统的匹配设计与评估方法

为解决重型武器装备空投系统的匹配设计问题,采用非线性有限元方法,结合气体热力学理论,建立重型装备空投系统的仿真模型,结合装备的试验数据进行了模型的验证,在分析重装空投落地缓冲过程的响应特性的基础上,研究了空投系统的匹配设计与评估方法,并以高海拔条件下空投过程中缓冲气囊的缓冲特性为例进行了分析,研究结果对空投系统的优化设计、匹配与评估具有一定的参考价值。     

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.