A study on the damage evaluation by the analysis of high velocity impact phenomenon and fragment behavior

Abstract

In this dissertation, the high velocity impact phenomenon was analyzed based on the numerical simulation, and the damage generated in structure was studied. The behavior of fragments produced by the impact was analyzed, then the generated damage in structures due to the fragments was estimated and evaluated. An impact simulation method using SPH (smoothed particle hydrodynamics) was validated to describe high velocity impact phenomenon. Using the verified simulation method and a machine learning-based clustering algorithm, a methodology to analysis numerous fragments produced by the impact and dispersed in space was developed. Based on the physical quantities of fragments and the equation for penetration depth, a methodology to quantitatively estimate and evaluate the damage in structure caused by the dispersed fragments was presented. A numerical simulation was conducted to analyze fragments produced from the impact between a projectile and a target. The SPH method which is capable to describe a large deformation, a hydrodynamic behavior and fragmentation phenomenon under high velocity impact condition was applied to the impact simulation, then the simulation method was verified. The effect of factors influencing the reliability and accuracy of the simulation result such as inter-particle spacing, smoothing length, and time step were analyzed. Simulation results for variables of debris cloud were compared with the measured value from the reported impact experiment to verify simulation method using SPH. Consequently, the analytical approaching method to the study of high velocity impact was validated. A methodology to analyze fragments produced by a high velocity impact was developed by applying a machine learning algorithm. A numerical simulation using the verified SPH method was conducted, and the dispersed particles were analyzed to determine the fragment formed by the particles. It was discovered that the particles forming a fragment have similar trajectories by analyzing the physical quantities of each particle. Through a machine learning-based k-means clustering algorithm, the particles having similar trajectories were grouped, and the fragments formed by particles were determined. The physical quantities of each fragment were obtained, then the threat of fragments such as the distribution of fragment number, mass, and kinetic according to dispersion angle was quantitatively evaluated. A method to estimate the damage in structure caused by multiple fragments was developed based on the analyzed physical quantities of each fragment. The impact condition between each fragment and the structure was calculated using the trajectory of each fragment and the geometrical conditions of structure. The formation of perforation hole, namely, the damage in structure was determined through an equation for penetration depth, therefore the generated damage in structure could be quantitatively estimated. As a result, a code to estimate the damage caused by fragments was developed, which has the particle physical quantities and the geometrical conditions of structure as input data. Thus, a methodology for quantitative evaluation of damage in various structures caused by multiple fragments was presented. |본 논문에서는 해석적 기법에 기반하여 고속충돌 현상을 분석하고, 이에 기반하여 구조물에 발생한 손상에 대한 연구를 수행하였다. 충돌로 발생한 파편의 거동을 분석하였고 이로 인한 구조물의 손상을 예측 및 평가하였다. 고속충돌 현상 모사를 위하여 입자완화 유체동역학(SPH, smoothed particle hydrodynamics)을 적용한 고속충돌 해석기법을 검증하였다. 검증된 해석 기법과 머신러닝 기반 클러스터링 알고리즘을 사용하여 충돌에 의해 발생하여 공간상에 비산되는 대량의 파편을 분석하기 위한 방법론을 제시하였다. 파편의 물리량과 관통깊이 예측식에 기반하여 비산된 파편이 구조물에 가하는 손상을 정량적으로 예측 및 평가하는 방법론을 제시하였다. 위협체와 표적간의 충돌로 발생하는 파편을 분석하기 위한 해석을 수행하였다. 고속충돌시 발생하는 대변형, 유체학적 거동 및 파편현상을 모사할 수 있는 SPH 기법을 적용하여 고속충돌 해석을 수행하였고, 이의 타당성을 검증하였다. 해석결과의 신뢰도 및 정확도에 영향을 미치는 입자간 거리, 입자완화 거리 및 시간 간격과 같은 변수의 영향도를 분석하였다. 충돌로 발생한 파편운의 변수에 대한 해석 결과값과 보고된 충돌실험의 측정값을 비교하여 SPH를 적용한 고속충돌 해석기법을 검증하였다. 결과적으로 고속충돌 연구에 대한 해석적 접근방법의 타당성을 확인하였다. 머신러닝 알고리즘을 적용하여 고속충돌로 발생한 파편을 분석하는 방법론을 개발하였다. 검증된 SPH 기법을 적용한 충돌 해석을 수행하였고, 비산된 다수의 입자를 분석하여 이들이 형성하는 파편을 판별하였다. 각 입자의 물리량을 분석하여, 파편을 이루는 입자들은 유사한 궤적을 가지고 있음을 밝혀내었다. 머신러닝 기반의 k-means 클러스터링 알고리즘을 적용하여 유사한 궤적을 가지는 입자들을 그룹화 하였고 입자들이 구성하는 파편을 판별 하였다. 이를 토대로 개별 파편의 물리량을 획득하였고, 비산 각도에 따른 파편 개수, 질량 및 운동에너지 분포와 같은 위협을 정량적으로 평가할 수 있었다. 분석된 파편의 물리량에 기반하여 다수의 파편이 임의의 구조물에 유발하는 손상을 예측하기위한 방법론을 개발하였다. 각 파편의 궤적과 구조물의 기하학적 조건을 통하여 파편과 구조물간의 충돌 조건을 계산하였다. 관통깊이 예측식을 적용하여 각 충돌 조건에서 관통구의 형성 여부, 즉 손상 발생여부를 판별하였고, 따라서 구조물에 발생한 손상을 정량적으로 예측 할 수 있었다. 결과적으로 SPH 입자의 물리량과 구조물의 기하학적 조건을 입력값으로 하여 파편으로 인한 구조물의 손상을 예측하는 코드를 개발하였다. 이를 통하여 다수의 파편으로 인한 다양한 구조물의 손상을 정량적으로 예측 및 평가할 수 있는 방법론을 제시하였다.; In this dissertation, the high velocity impact phenomenon was analyzed based on the numerical simulation, and the damage generated in structure was studied. The behavior of fragments produced by the impact was analyzed, then the generated damage in structures due to the fragments was estimated and evaluated. An impact simulation method using SPH (smoothed particle hydrodynamics) was validated to describe high velocity impact phenomenon. Using the verified simulation method and a machine learning-based clustering algorithm, a methodology to analysis numerous fragments produced by the impact and dispersed in space was developed. Based on the physical quantities of fragments and the equation for penetration depth, a methodology to quantitatively estimate and evaluate the damage in structure caused by the dispersed fragments was presented. A numerical simulation was conducted to analyze fragments produced from the impact between a projectile and a target. The SPH method which is capable to describe a large deformation, a hydrodynamic behavior and fragmentation phenomenon under high velocity impact condition was applied to the impact simulation, then the simulation method was verified. The effect of factors influencing the reliability and accuracy of the simulation result such as inter-particle spacing, smoothing length, and time step were analyzed. Simulation results for variables of debris cloud were compared with the measured value from the reported impact experiment to verify simulation method using SPH. Consequently, the analytical approaching method to the study of high velocity impact was validated. A methodology to analyze fragments produced by a high velocity impact was developed by applying a machine learning algorithm. A numerical simulation using the verified SPH method was conducted, and the dispersed particles were analyzed to determine the fragment formed by the particles. It was discovered that the particles forming a fragment have similar trajectories by analyzing the physical quantities of each particle. Through a machine learning-based k-means clustering algorithm, the particles having similar trajectories were grouped, and the fragments formed by particles were determined. The physical quantities of each fragment were obtained, then the threat of fragments such as the distribution of fragment number, mass, and kinetic according to dispersion angle was quantitatively evaluated. A method to estimate the damage in structure caused by multiple fragments was developed based on the analyzed physical quantities of each fragment. The impact condition between each fragment and the structure was calculated using the trajectory of each fragment and the geometrical conditions of structure. The formation of perforation hole, namely, the damage in structure was determined through an equation for penetration depth, therefore the generated damage in structure could be quantitatively estimated. As a result, a code to estimate the damage caused by fragments was developed, which has the particle physical quantities and the geometrical conditions of structure as input data. Thus, a methodology for quantitative evaluation of damage in various structures caused by multiple fragments was presented.