Fragment velocity distribution is an important parameter affecting the terminal effects of warheads.The rarefaction wave,end cap,and its confinement state can significantly affect the fragmentation of the cylindrical ...Fragment velocity distribution is an important parameter affecting the terminal effects of warheads.The rarefaction wave,end cap,and its confinement state can significantly affect the fragmentation of the cylindrical charge casing.Most of the existing studies have performed experiments and simulations considering the rarefaction wave and unfixed end caps;research on fixed end caps and sufficient theoretical explanations are limited.In this work,the effects of rarefaction waves,end caps,and their fixed states,on the fragment velocity distribution,were studied via experimentation and simulation,and reasonable theoretical explanations were provided.The results show that the rarefaction wave and end caps affect the fragment velocity by changing the pressure states of the detonation products.At the initiation end,the fragment velocities of casings with unfixed initiation ends are 33.3%(300 m/s)greater than that of casings without end caps,because of the weakening of the attenuation effect of the rarefaction wave.The fragment velocities of the casings with fixed initiation ends are 8.3%(100 m/s)greater than that of casings with unfixed initiation ends.At the non-initiation end,the fragment velocities are 24.8%(297 m/s)greater than that of a casing without end caps,and the reflecting shock wave generated by the fixed non-initiation end increases the fragment velocity by 7.3%(113 m/s),compared to the theoretical velocity.This work provides a basis for the structural design and analysis of the terminal effects of warheads.展开更多
Amid increasingly frequent military conflicts and explosion events,accurately predicting the dynamic response of reinforced concrete(RC) slabs,key load-bearing components in building structures,is essential for unders...Amid increasingly frequent military conflicts and explosion events,accurately predicting the dynamic response of reinforced concrete(RC) slabs,key load-bearing components in building structures,is essential for understanding blast-induced damage and enhancing structural protection.However,current approaches predominantly rely on experimental tests,finite element(FE) simulations,and conventional machine learning(ML) techniques,which are o ften costly,inefficie nt,narrowly applicable,and insufficiently accurate.To overcome these challenges,this study aims to optimize ML models,refine architectural designs,and improve model interpretability.A comprehensive dataset comprising 489 samples was constructed by integrating experimental and simulation data from existing literature,incorporating 15 input features and one target variable.Based on this dataset,a novel method,termed MOPSO-TXGBoost,was proposed.Building on XGBoost as a baseline,the method employs multiobjective particle swarm optimization(MOPSO) for hyperparameter tuning,introduces a tri-stream stacking architecture to enhance feature representation,and trains three distinct models to improve generalization performance.A weighted fusion strategy is employed to further enhance the accuracy of predictio n.Additio nally,a model comprehensive evaluation(MCE) index is introduced,which integrates error metrics and fitting performance to facilitate systematic model assessment.Experimental results indicate that,compared with the baseline XGBoost model,the proposed approach reduces prediction error by 61.4% and increases the coefficient of determination(R^(2)) by 0.217.Moreover,it outperforms several mainstream machine learning(ML) algorithms.The findings of this study advance ML-based blast damage prediction and provide theoretical support for safety assessment and protection optimization of RC slab structures.展开更多
基金the support of the Youth Scientific Research Projects of the Basic Research Program of Shanxi Province(Grant Nos.202303021222111,202303021222113)the China Postdoctoral Science Foundation(Grant No.2025M770001).
文摘Fragment velocity distribution is an important parameter affecting the terminal effects of warheads.The rarefaction wave,end cap,and its confinement state can significantly affect the fragmentation of the cylindrical charge casing.Most of the existing studies have performed experiments and simulations considering the rarefaction wave and unfixed end caps;research on fixed end caps and sufficient theoretical explanations are limited.In this work,the effects of rarefaction waves,end caps,and their fixed states,on the fragment velocity distribution,were studied via experimentation and simulation,and reasonable theoretical explanations were provided.The results show that the rarefaction wave and end caps affect the fragment velocity by changing the pressure states of the detonation products.At the initiation end,the fragment velocities of casings with unfixed initiation ends are 33.3%(300 m/s)greater than that of casings without end caps,because of the weakening of the attenuation effect of the rarefaction wave.The fragment velocities of the casings with fixed initiation ends are 8.3%(100 m/s)greater than that of casings with unfixed initiation ends.At the non-initiation end,the fragment velocities are 24.8%(297 m/s)greater than that of a casing without end caps,and the reflecting shock wave generated by the fixed non-initiation end increases the fragment velocity by 7.3%(113 m/s),compared to the theoretical velocity.This work provides a basis for the structural design and analysis of the terminal effects of warheads.
文摘Amid increasingly frequent military conflicts and explosion events,accurately predicting the dynamic response of reinforced concrete(RC) slabs,key load-bearing components in building structures,is essential for understanding blast-induced damage and enhancing structural protection.However,current approaches predominantly rely on experimental tests,finite element(FE) simulations,and conventional machine learning(ML) techniques,which are o ften costly,inefficie nt,narrowly applicable,and insufficiently accurate.To overcome these challenges,this study aims to optimize ML models,refine architectural designs,and improve model interpretability.A comprehensive dataset comprising 489 samples was constructed by integrating experimental and simulation data from existing literature,incorporating 15 input features and one target variable.Based on this dataset,a novel method,termed MOPSO-TXGBoost,was proposed.Building on XGBoost as a baseline,the method employs multiobjective particle swarm optimization(MOPSO) for hyperparameter tuning,introduces a tri-stream stacking architecture to enhance feature representation,and trains three distinct models to improve generalization performance.A weighted fusion strategy is employed to further enhance the accuracy of predictio n.Additio nally,a model comprehensive evaluation(MCE) index is introduced,which integrates error metrics and fitting performance to facilitate systematic model assessment.Experimental results indicate that,compared with the baseline XGBoost model,the proposed approach reduces prediction error by 61.4% and increases the coefficient of determination(R^(2)) by 0.217.Moreover,it outperforms several mainstream machine learning(ML) algorithms.The findings of this study advance ML-based blast damage prediction and provide theoretical support for safety assessment and protection optimization of RC slab structures.
