The principle of sonic wave measurement was introduced, and cumulative damage effects of underground engineering rock mass under blasting load were studied by in situ test, using RSM-SY5 intelligent sonic wave apparat...The principle of sonic wave measurement was introduced, and cumulative damage effects of underground engineering rock mass under blasting load were studied by in situ test, using RSM-SY5 intelligent sonic wave apparatus. The blasting test was carried out for ten times at some tunnels of Changba Lead-Zinc Mine. The damage depth of surrounding rock caused by old blasting excavation （0.8-1.2 m） was confirmed. The relation between the cumulative damage degree and blast times was obtained. The results show that the sonic velocity decreases gradually with increasing blast times, hut the damage degree （D） increases. The damage cumulative law is non-linear. The damage degree caused by blast decreases with increasing distance, and damage effects become indistinct. The blasting damage of rock mass is anisotropic. The damage degree of rock mass within charging range is maximal. And the more the charge is, the more severe the damage degree of rock mass is. The test results provide references for researches of mechanical parameters of rock mass and dynamic stability analysis of underground chambers.展开更多
Liquid-filled containers(LFC)are widely used to store and transport petroleum,chemical reagents,and other resources.As an important target of military strikes and terrorist bombings,LFC are vulnerable to blast waves a...Liquid-filled containers(LFC)are widely used to store and transport petroleum,chemical reagents,and other resources.As an important target of military strikes and terrorist bombings,LFC are vulnerable to blast waves and fragments.To explore the protective effect of polyurea elastomer on LFC,the damage characteristics of polyurea coated liquid-filled container(PLFC)under the combined loading of blast shock wave and fragments were studied experimentally.The microstructure of the polyurea layer was observed by scanning electron microscopy,and the fracture and self-healing phenomena were analyzed.The simulation approach was used to explain the combined blast-and fragments-induced on the PLFC in detail.Finally,the effects of shock wave and fragment alone and in combination on the damage of PLFC were comprehensively compared.Results showed that the polyurea reduces the perforation rate of the fragment to the LFC,and the self-healing phenomenon could also reduce the liquid loss rate inside the container.The polyurea reduces the degree of depression in the center of the LFC,resulting in a decrease in the distance between adjacent fragments penetrating the LFC,and an increase in the probability of transfixion and fracture between holes.Under the close-in blast,the detonation shock wave reached the LFC before the fragment.Polyurea does not all have an enhanced effect on the protection of LFC.The presence of internal water enhances the anti-blast performance of the container,and the hydrodynamic ram(HRAM)formed by the fragment impacting the water aggravated the plastic deformation of the container.The combined action has an enhancement effect on the deformation of the LFC.The depth of the container depression was 27%higher than that of the blast shock wave alone;thus,it cannot be simply summarized as linear superposition.展开更多
A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,wh...A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.展开更多
基金Project (50490272) supported by the National Natural Science Foundation of ChinaProject(040109) supported by the Doctor Degree Paper Innovation Engineering of Central South University
文摘The principle of sonic wave measurement was introduced, and cumulative damage effects of underground engineering rock mass under blasting load were studied by in situ test, using RSM-SY5 intelligent sonic wave apparatus. The blasting test was carried out for ten times at some tunnels of Changba Lead-Zinc Mine. The damage depth of surrounding rock caused by old blasting excavation （0.8-1.2 m） was confirmed. The relation between the cumulative damage degree and blast times was obtained. The results show that the sonic velocity decreases gradually with increasing blast times, hut the damage degree （D） increases. The damage cumulative law is non-linear. The damage degree caused by blast decreases with increasing distance, and damage effects become indistinct. The blasting damage of rock mass is anisotropic. The damage degree of rock mass within charging range is maximal. And the more the charge is, the more severe the damage degree of rock mass is. The test results provide references for researches of mechanical parameters of rock mass and dynamic stability analysis of underground chambers.
基金supported by the National Natural Science Foundation of China(Grant Nos.12102480,52278543 and 51978660)Natural Science Foundation of Jiangsu Province(Grant No.BK20231489)。
文摘Liquid-filled containers(LFC)are widely used to store and transport petroleum,chemical reagents,and other resources.As an important target of military strikes and terrorist bombings,LFC are vulnerable to blast waves and fragments.To explore the protective effect of polyurea elastomer on LFC,the damage characteristics of polyurea coated liquid-filled container(PLFC)under the combined loading of blast shock wave and fragments were studied experimentally.The microstructure of the polyurea layer was observed by scanning electron microscopy,and the fracture and self-healing phenomena were analyzed.The simulation approach was used to explain the combined blast-and fragments-induced on the PLFC in detail.Finally,the effects of shock wave and fragment alone and in combination on the damage of PLFC were comprehensively compared.Results showed that the polyurea reduces the perforation rate of the fragment to the LFC,and the self-healing phenomenon could also reduce the liquid loss rate inside the container.The polyurea reduces the degree of depression in the center of the LFC,resulting in a decrease in the distance between adjacent fragments penetrating the LFC,and an increase in the probability of transfixion and fracture between holes.Under the close-in blast,the detonation shock wave reached the LFC before the fragment.Polyurea does not all have an enhanced effect on the protection of LFC.The presence of internal water enhances the anti-blast performance of the container,and the hydrodynamic ram(HRAM)formed by the fragment impacting the water aggravated the plastic deformation of the container.The combined action has an enhancement effect on the deformation of the LFC.The depth of the container depression was 27%higher than that of the blast shock wave alone;thus,it cannot be simply summarized as linear superposition.
基金supported by National Natural Science Foundation of China(Grant No.12432018,12372346)the Innovative Research Groups of the National Natural Science Foundation of China(Grant No.12221002).
文摘A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.
