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曹涛 , 孙浩 , 周游 , 等 . 近地爆炸冲击波传播特性数值模拟与应用 [J ] . 兵器装备工程学报 , 2020 , 41 ( 12 ): 187 - 191 .

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张晓颖 , 李胜杰 , 李志强 . 爆炸载荷作用下夹层玻璃动态响应的数值模拟 [J ] . 兵工学报 , 2018 , 39 ( 7 ): 1379 - 1388 . DOI: 10.3969/j.issn.1000-1093.2018.07.016 http://doi.org/10.3969/j.issn.1000-1093.2018.07.016 为研究爆炸载荷作用下夹层玻璃的动态响应，利用有限元软件LS-DYNA对爆炸载荷作用下夹层玻璃的动态响应进行了数值模拟。通过改变外层玻璃与内层玻璃的厚度，系统地研究不同组合下夹层玻璃的动态响应规律，描述爆炸产物与结构相互作用过程，分析夹层玻璃不同部分的能量吸收效率，观察夹层玻璃的裂纹扩展过程。结果表明：玻璃厚度的改变对结构动态响应有明显影响，随着爆炸距离增加，影响程度逐渐减小；爆炸产物先于空气冲击波对玻璃的冲击有损伤破坏作用；结构外层玻璃的能量吸收效率最大，聚乙烯醇缩丁醛胶层次之，内层玻璃吸收效率最小；爆炸载荷下夹层玻璃的裂纹以环向裂纹为主，径向裂纹相对较少。

ZHANG X Y , LI S J , LI Z Q . Numerical simulation of dynamic response of laminated glass subjected to blast load [J ] . Acta Armamentarii , 2018 , 39 ( 7 ): 1379 - 1388 . (in Chinese)

赵海涛 , 王成 . 空中爆炸问题的高精度数值模拟研究 [J ] . 兵工学报 , 2013 , 34 ( 12 ): 1536 - 1546 . DOI: 10.3969/j.issn.1000-1093.2013.12.008 http://doi.org/10.3969/j.issn.1000-1093.2013.12.008 针对空中爆炸初期强间断和爆炸后期接触间断物理特性，提出了虚拟流体方法（GFM）和真实虚拟流体方法（RGFM）2种界面处理方法相耦合的计算方法。在高密度比、高压力比同时存在的 爆炸初期和压力、密度及速度等物理量相接近的爆炸后期，分别采用RGFM和GFM对物质界面两侧物理量进行处理。采用Local Level Set方法对运动界面进行追踪，并用5阶高精度加权本质非振荡（WENO）格式和3阶TVD Runge-Kutta方法对控制方程进行离散，编制了空中爆炸数值模拟程序，应用该程序对不同高度近地面空中爆炸以及冲击波与挡墙相互作用问题进行数值模拟，模拟结果能够较好地反映空中爆炸中冲击波的产生、传播、反射、绕射及爆炸产物的膨胀等现象，并与经验公式和试验结果吻合较好。证明了该耦合方法能够模拟空中爆炸问题，并且爆炸波在传播过程中具有很好的对称性，为模拟高密度比、高压力比的多物质之间相互作用问题提供了有效的计算方法。

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CHEN Z W , WANG Z Q , ZENG L H . A method for predicting peak pressure in an explosion shock tube based on BP neural network [J ] . Explosion and Shock Waves , 2024 , 44 ( 5 ): 054101 . (in Chinese)

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徐永康 , 薛琨 . 基于人工神经网络算法的多相云雾爆轰毁伤效应预测模型 [J ] . 兵工学报 , 2024 , 45 ( 6 ): 1889 - 1905 . DOI: 10.12382/bgxb.2023.0094 http://doi.org/10.12382/bgxb.2023.0094 云雾爆轰毁伤范围的预测是云爆武器造成大规模毁伤的研究基础,但云雾爆轰后超压场分布规律与燃料浓度的依赖关系未知,制约了对于云爆毁伤范围的预测。因此,针对2种最为常用的多相云雾燃料,采用最小自由能法计算得到了液相燃料完全以液滴或蒸汽形式存在的多相云雾发生理想爆轰的CJ参数,并通过拟合,得到体JWL状态方程参数。在此基础上计算得到了不同浓度和状态的多相云雾理想爆轰造成的超压场,并借助人工神经网络获得了浓度范围在0.03~0.30kg/m³的气固两相和气液固三相云雾场爆轰峰值超压随比例距离衰减规律的代理模型并预测不同毁伤等级对应的毁伤比例半径随燃料浓度的变化,得到毁伤比例半径最大的最优浓度。研究结果表明:云雾区中液相燃料以液滴或蒸汽形式存在对云雾爆轰参数,产物JWL状态方程参数,与云爆爆轰后超压场分布规律的影响都比较微弱(<1.5%);在0.03~0.18kg/m³的燃料浓度范围内,Ⅰ级~Ⅲ级毁伤比例半径的最大和最小值分别相差21%、19%、6%,因此大装药结构形成的云雾场爆轰后,Ⅰ级和Ⅱ级毁伤半径与燃料浓度的依赖性更强。

XU Y K , XUE K . Artificial neural network-based prediction model for damage effect of fuel-air explosive [J ] . Acta Armamentarii , 2024 , 45 ( 6 ): 1889 - 1905 . (in Chinese) DOI: 10.12382/bgxb.2023.0094 http://doi.org/10.12382/bgxb.2023.0094 The prediction of damage range caused by Fuel-air Explosive is fundamental to the study of large-scale damage caused by Fuel-air Explosive weapons. However, the distribution pattern of shock waves after detonation and its dependenc e on fuel concentration are unknown, which limits the prediction accuracy of damage range. In this study, the minimum free energy method is used to calculate the CJ parameters for the ideal detonation of biphasic cloud fog with liquid fuel present in either droplet or vapor form. The JWL equation of state parameters are obtained through fitting. Subsequently, the peak overpressure caused by ideal detonation of biphasic cloud fog with different concentrations and states is calculated. A proxy model is developed by utilizing an artificial neural network. The proposed model is used to predict the decay law of peak overpressure with respect to the scaled distance for biphasic gas-solid and gas-liquid-solid cloud detonations with concentrations ranging from 0.03 to 0.30kg/m 3 . The model is also used to predict the variation of damage proportion radius with fuel concentration for different damage levels, obtaining the optimal concentration with the maximum damage proportion radius. The study reveals that the influences of liquid fuel in droplet or vapor form on cloud detonation parameters, JWL equation of state parameters, and shock wave distribution after cloud detonation are relatively weak ( < 1.5%). Within the fuel concentrations ranging from 0.03 to 0.18kg/m 3 , the maximum and minimum values of damage proportion radius for damage levels Ⅰ-Ⅲ are differed by 21%, 19%, and 6%, respectively. Thus, the dependence of damage radii on fuel concentration is stronger for damage levels Ⅰ and Ⅱ after cloud burst caused by large explosive structures.

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