Page 98 - 《爆炸与冲击》2026年第2期
P. 98

第 46 卷           彭江舟,等: 城市建筑外爆威力场与毁伤效应数智仿真模型及应用                                 第 2 期

                    Nanjing University of Science and Technology, 2015.
               [4]   都浩, 李忠献, 郝洪. 建筑物外部爆炸超压荷载的数值模拟 [J]. 解放军理工大学学报                (自然科学版), 2007, 8(5): 413–418.
                    DOI: 10.3969/j.issn.1009-3443.2007.05.002.
                    DU H, LI Z X, HAO H. Numerical simulation on blast overpressure loading outside buildings [J]. Journal of PLA University
                    of Science and Technology, 2007, 8(5): 413–418. DOI: 10.3969/j.issn.1009-3443.2007.05.002.
               [5]   李忠献, 师燕超, 周浩璋, 等. 城市复杂环境中爆炸波的传播规律与超压荷载 [J]. 工程力学, 2009, 26(6): 178–183.
                    LI Z X, SHI Y C, ZHOU H Z, et al. Propagation law and overpressure load of blast wave in urban complex environment [J].
                    Engineering Mechanics, 2009, 26(6): 178–183.
               [6]   王栋. 拱形地下结构抗爆性能试验研究 [D]. 北京: 清华大学, 2022. DOI: 10.27266/d.cnki.gqhau.2022.000098.
                    WANG  D.  Experimental  study  on  anti-explosion  performance  of  arched  underground  structure  [D].  Beijing:  Tsinghua
                    University, 2022. DOI: 10.27266/d.cnki.gqhau.2022.000098.
               [7]   ZHOU X S, ZHANG X M, REN T H, et al. Waveform characteristics of tunnel blast waves and a wave-blocking method [J].
                    Shock and Vibration, 2022, 2022: 3013130. DOI: 10.1155/2022/3013130.
               [8]   KRÁLIK E J, BARAN M. Numerical analysis of the exterior explosion effects on the buildings with barriers [J]. Applied
                    Mechanics and Materials, 2013, 390: 230–234. DOI: 10.4028/www.scientific.net/AMM.390.230.
               [9]   ZHANG M T, PEI Y, YAO X, et al. Damage assessment of aircraft wing subjected to blast wave with finite element method
                    and artificial neural network tool [J]. Defence Technology, 2023, 25: 203–219. DOI: 10.1016/j.dt.2022.05.010.
               [10]   严国建, 周明安, 余轮, 等. 空气中爆炸冲击波超压峰值的预测 [J]. 采矿技术, 2011, 11(5): 89–90,112. DOI: 10.3969/j.issn.
                    1671-2900.2011.05.035.
                    YAN G J, ZHOU M A, YU L, et al. Prediction of peak overpressure of explosion shock wave in air [J]. Mining Technology,
                    2011, 11(5): 89–90,112. DOI: 10.3969/j.issn.1671-2900.2011.05.035.
               [11]   ZHANG K, ZHANG K, BAO R. Prediction of gas explosion pressures: a machine learning algorithm based on KPCA and an
                    optimized  LSSVM  [J].  Journal  of  Loss  Prevention  in  the  Process  Industries,  2023,  83:  105082.  DOI:  10.1016/j.jlp.2023.
                    105082.
               [12]   潘美霖, 彭卫文, 冷春江, 等. 基于贝叶斯深度学习的复杂结构爆炸载荷的快速估计 [J/OL]. 爆炸与冲击, [2024-12-02].
                    https://www.bzycj.cn/cn/article/doi/10.11883/bzycj-2024-0191. DOI: 10.11883/bzycj-2024-0191.
                    PAN M L, PENG W W, LENG C J, et al. Fast estimation of blast loading in complex structures based on Bayesian deep
                    learning [J/OL]. Explosion and Shock Waves, [2024-12-02]. https://www.bzycj.cn/cn/article/doi/10.11883/bzycj-2024-0191.
                    DOI: 10.11883/bzycj-2024-0191.
               [13]   陈皓, 郭明明, 田野, 等. 卷积神经网络在流场重构研究中的进展 [J]. 力学学报, 2022, 54(9): 2343–2360. DOI: 10.6052/
                    0459-1879-22-130.
                    CHEN H, GUO M M, TIAN Y, et al. Progress of convolution neural networks in flow field reconstruction [J]. Chinese Journal
                    of Theoretical and Applied Mechanics, 2022, 54(9): 2343–2360. DOI: 10.6052/0459-1879-22-130.
               [14]   WANG Z Q, HUA Y, AUBRY N, et al. Fast optimization of multichip modules using deep learning coupled with Bayesian
                    method [J]. International Communications in Heat and Mass Transfer, 2023, 141: 106592. DOI: 10.1016/j.icheatmasstransfer.
                    2022.106592.
               [15]   HUA  Y,  WANG  Z  Q,  YUAN  X  Y,  et  al.  Estimation  of  steady-state  temperature  field  in  multichip  modules  using  deep
                    convolutional neural network [J]. Thermal Science and Engineering Progress, 2023, 40: 101755. DOI: 10.1016/j.tsep.2023.
                    101755.
               [16]   闫盼盼, 牛青林, 高文强, 等. 基于卷积神经网络的地面尾喷焰流场预测 [J]. 力学学报, 2024, 56(4): 980–990. DOI: 10.6052/
                    0459-1879-23-412.
                    YAN P P, NIU Q L, GAO W Q, et al. Prediction of ground rocket exhaust plume flow field based on convolutional neural
                    network [J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(4): 980–990. DOI: 10.6052/0459-1879-23-412.
               [17]   吴昊恺, 陈耀然, 周岱, 等. 基于  CNN  与  GAN  深度学习模型近壁面湍流场超分辨率重构的精细化研究 [J]. 力学学报,
                    2024, 56(8): 2231–2242. DOI: 10.6052/0459-1879-24-019.
                    WU H K, CHEN Y R, ZHOU D, et al. Refined study of super-resolution reconstruction of near-wall turbulence field based on
                    CNN and GAN deep learning model [J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(8): 2231–2242.
                    DOI: 10.6052/0459-1879-24-019.


                                                         022202-15
   93   94   95   96   97   98   99   100   101   102   103