双门式炮闩膨胀波火炮内弹道建模与仿真

根据膨胀波火炮的发射原理,针对两种不同开闩方式分别进行讨论,着重对最新设计研制的双门式炮闩膨胀波火炮进行内弹道建模与仿真,并将仿真结果与同型号常规封闭火炮的内弹道性能进行对比。结果表明,在相同装填条件下,膨胀波火炮在不影响弹丸初速的前提下能大幅度减小火炮后坐冲量和身管温度。     

火箭弹道学发动机推力模拟方法

发动机工作性能对火箭飞行性能的严重影响已引起众多研究人员的重视,尤其是火箭发动机推力总冲及其工作时间等重要参数.这里提出了一种在静态测定推力时间历程、总冲的基础上,通过实际飞行测试结果确定其扰动量来最终模拟推力时间历程的仿真方法.该方法简洁方便,易于编制程序及模拟仿真,便于工程人员掌握,可用于火箭发射动力学、外弹道学及射表编制等方面研究,具有一定应用价值.     

无伞末敏子弹大攻角非线性空气动力和力矩模型

以无伞末敏子弹为背景,分析了描述大攻角飞行弹丸横向运动和角运动的运动变量,建立了大攻角情况下弹丸所受非线性空气动力和空气动力矩的模型,推导出了复数形式的弹丸横向运动与角运动方程。数值积分计算表明,弹丸在特定非线性空气动力和力矩作用下,能够形成所需形式的扫描运动,证明所建力学模型正确合理,可以用于无伞末敏子弹动力学特性的分析与计算。     

Ballistic response of armour plates using Generative Adversarial Networks

It is important to understand how ballistic materials respond to impact from projectiles such that informed decisions can be made in the design process of protective armour systems. Ballistic testing is a standards-based process where materials are tested to determine whether they meet protection, safety and performance criteria. For the V₅₀ ballistic test, projectiles are fired at different velocities to determine a key design parameter known as the ballistic limit velocity (BLV), the velocity above which projectiles perforate the target. These tests, however, are destructive by nature and as such there can be considerable associated costs, especially when studying complex armour materials and systems. This study proposes a unique solution to the problem using a recent class of machine learning system known as the Generative Adversarial Network (GAN). The GAN can be used to generate new ballistic samples as opposed to performing additional destructive experiments. A GAN network architecture is tested and trained on three different ballistic data sets, and their performance is compared. The trained networks were able to successfully produce ballistic curves with an overall RMSE of between 10 and 20 % and predicted the V₅₀ BLV in each case with an error of less than 5 %. The results demonstrate that it is possible to train generative networks on a limited number of ballistic samples and use the trained network to generate many new samples representative of the data that it was trained on. The paper spotlights the benefits that generative networks can bring to ballistic applications and provides an alternative to expensive testing during the early stages of the design process.