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Research on simulation of gun muzzle flow field empowered by artificial intelligence
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作者 Mengdi Zhou Linfang Qian +3 位作者 Congyong Cao guangsong chen Jin Kong Ming-hao Tong 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期196-208,共13页
Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow fie... Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal value.Then physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation laws.Through two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and expansion.The proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is divided.It is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous area.Comparison with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation accuracy.The proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions. 展开更多
关键词 Muzzle flow field Artificial intelligence Deep learning Data-physical fusion driven Shock wave
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Uncertainty quantification of mechanism motion based on coupled mechanism—motor dynamic model for ammunition delivery system
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作者 Jinsong Tang Linfang Qian +3 位作者 Longmiao chen guangsong chen Mingming Wang Guangzu Zhou 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第3期125-133,共9页
In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to pro... In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method,in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system.The advantages of this include establishing intuitive coupling between the mechanism and motor,facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism,and enabling dynamic simulation with controller to take the randomness of the electric load into account.Dynamic simulation considering feedback control of ammunition delivery system is carried out,and the feasibility of the model is verified experimentally.Based on probability density evolution theory,we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization.Our work can not only provide guidance for engineering design of ammunition delivery mechanism,but also provide theoretical support for modeling and uncertainty quantification research of mechatronics system. 展开更多
关键词 Ammunition delivery system Electromechanical coupling dynamics Uncertainty quantification Generalized probability density evolution
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High-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer based on probability density evolution method
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作者 Mingming Wang Linfang Qian +3 位作者 guangsong chen Tong Lin Junfei Shi Shijie Zhou 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期209-221,共13页
This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer.Firstly,the dynamic model of projectile motion is establi... This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer.Firstly,the dynamic model of projectile motion is established considering the flexible deformation of the barrel and the interaction between the projectile and the barrel.Subsequently,the accuracy of the dynamic model is verified based on the external ballistic projectile attitude test platform.Furthermore,the probability density evolution method(PDEM)is developed to high-dimensional uncertainty quantification of projectile motion.The engineering example highlights the results of the proposed method are consistent with the results obtained by the Monte Carlo Simulation(MCS).Finally,the influence of parameter uncertainty on the projectile disturbance at muzzle under different working conditions is analyzed.The results show that the disturbance of the pitch angular,pitch angular velocity and pitch angular of velocity decreases with the increase of launching angle,and the random parameter ranges of both the projectile and coupling model have similar influence on the disturbance of projectile angular motion at muzzle. 展开更多
关键词 Truck-mounted howitzer Projectile motion Uncertainty quantification Probability density evolution method
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General design principle of artillery for firing accuracy 被引量:3
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作者 Linfang Qian guangsong chen +1 位作者 Minghao Tong Jinsong Tang 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2022年第12期2125-2140,共16页
In this paper,based on the topological description method,the kinematic and dynamic equations of the projectile flight and projectile-artillery coupling system during the whole process of firing are constructed.The fa... In this paper,based on the topological description method,the kinematic and dynamic equations of the projectile flight and projectile-artillery coupling system during the whole process of firing are constructed.The factors that can affect the projectile burst points,namely the state parameters of the projectile on the muzzle and state parameters of the barrel muzzle,as well as the factors that affect the barrel muzzle state parameters,are analyzed.On this basis,the design principle of artillery firing accuracy is proposed.The error analysis and the corresponding inverse problem,the extraction method of key parameters affecting artillery implicated motion,the conformal and control method of rotating band are analyzed and presented.Finally,the presented method is verified through a vehicle mounted howitzer case,and the muzzle state parameter interval is obtained meeting the given firing accuracy.In addition,the sensitivity analysis of artillery parameters shows that the less the correlation between the parameters and the barrel,the less the influence on the projectile implicated motion.The analysis of the coupling effect between rifling and the rotating band shows that the uniform rifling is the optimal form for the conformal of the rotating band during firing. 展开更多
关键词 Firing accuracy Projectile-artillery coupling Rotating band conformal Sensitivity analysis Error control
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基于深度学习的车载炮驾驶室表面冲击载荷快速预测方法
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作者 周梦笛 钱林方 +3 位作者 曹从咏 陈光宋 徐亚栋 魏胜程 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2024年第4期99-112,共14页
在车载炮驾驶室拓扑优化设计和刚强度分析计算中,需要明确大量的、不同射击条件下的冲击载荷.如何快速获取驾驶室表面的冲击载荷是车载炮设计中尚未解决的难题之一.本文将深度学习方法引入到驾驶室表面冲击载荷的求解中,基于卷积-多维特... 在车载炮驾驶室拓扑优化设计和刚强度分析计算中,需要明确大量的、不同射击条件下的冲击载荷.如何快速获取驾驶室表面的冲击载荷是车载炮设计中尚未解决的难题之一.本文将深度学习方法引入到驾驶室表面冲击载荷的求解中,基于卷积-多维特征LSTM神经网络,提出了一种驾驶室表面冲击载荷快速预测方法,实现了不同发射条件下驾驶室表面冲击载荷计算,求解速度接近实时级别.算例结果表明,深度学习模型的求解精度与传统CFD仿真精度相当,但求解耗时在毫秒级,大大提高了计算效率,具备离线训练、在线计算的潜力.且当驾驶室形貌特征轻微变化时,本文模型依然适用.本文成果可快速为驾驶室刚强度校核和拓扑优化提供载荷条件,有助于缩短车载炮研发周期,为车载炮系统的数字孪生模型构建奠定了基础. 展开更多
关键词 深度学习 冲击载荷 驾驶室 CFD仿真 拓扑优化 多维特征 刚强度分析 在线计算
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薄壁弹药机械臂的柔性动力学建模与辨识研究
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作者 汤劲松 钱林方 +3 位作者 马佳 陈龙淼 陈光宋 董帅 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2024年第1期226-239,共14页
薄壁结构是机械设计中常用的结构形式,其柔性动力学问题一直是工程研究的前沿。针对具有薄壁结构特性的弹药机械臂,发展了一种基于板壳理论的弹药机械臂柔性多体动力学建模方法.我们在浮动参考坐标框架下,得到了Reissner-Mindlin壳结构... 薄壁结构是机械设计中常用的结构形式,其柔性动力学问题一直是工程研究的前沿。针对具有薄壁结构特性的弹药机械臂,发展了一种基于板壳理论的弹药机械臂柔性多体动力学建模方法.我们在浮动参考坐标框架下,得到了Reissner-Mindlin壳结构的运动学方程,其中结构膜变形和弯曲变形与刚性运动的耦合,这是区别于实体结构的特点。为了克服壳体单元模拟中的膜锁定和剪切锁定问题,引入一种基于边中心的应变平滑DSG(ECSS-DSG)单元对结构离散.该方法可以获得更好的膜变形和弯曲变形行为,并且有效克服剪切自锁问题,在结构模态分析中表现出更好的效果.在此基础上,结合实验对弹药机械臂模型参数进行了辨识,并且对辨识模型在各种工况下的动态响应进行了验证,证明了本文方法具有良好的鲁棒性.本文的工作不仅可以为弹药机械臂的进一步研究提供理论支撑,而且可以为薄壁结构多体系统的动力学研究提供参考. 展开更多
关键词 机械臂 实体结构 柔性动力学 柔性多体动力学 薄壁结构 多体系统 板壳理论 弯曲变形
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An iterative interval analysis method based on Kriging-HDMR for uncertainty problems
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作者 Lei Ji guangsong chen +2 位作者 Linfang Qian Jia Ma Jinsong Tang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2022年第7期164-176,I0004,共14页
In recent years,growing attention has been paid to the interval investigation of uncertainty problems.However,the contradiction between accuracy and efficiency always exists.In this paper,an iterative interval analysi... In recent years,growing attention has been paid to the interval investigation of uncertainty problems.However,the contradiction between accuracy and efficiency always exists.In this paper,an iterative interval analysis method based on Kriging-HDMR(IIAMKH)is proposed to obtain the lower and upper bounds of uncertainty problems considering interval variables.Firstly,Kriging-HDMR method is adopted to establish the meta-model of the response function.Then,the Genetic Algorithm&Sequential Quadratic Programing(GA&SQP)hybrid optimization method is applied to search for the minimum/maximum values of the meta-model,and thus the corresponding uncertain parameters can be obtained.By substituting them into the response function,we can acquire the predicted interval.Finally,an iterative process is developed to improve the accuracy and stability of the proposed method.Several numerical examples are investigated to demonstrate the effectiveness of the proposed method.Simulation results indicate that the presented IIAMKH can obtain more accurate results with fewer samples. 展开更多
关键词 UNCERTAINTY Interval analysis Iterative process Kriging-HDMR
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