The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristic...The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.展开更多
To increase launching frequency and decrease drag force of underwater projectiles,a serial multiprojectiles structure based on the principle of supercavitation is proposed in this paper.The drag reduction and supercav...To increase launching frequency and decrease drag force of underwater projectiles,a serial multiprojectiles structure based on the principle of supercavitation is proposed in this paper.The drag reduction and supercavitation characteristics of the underwater serial multi-projectiles are studied with computational fluid dynamics(CFD)and machine learning.Firstly,the numerical simulation model for the underwater supercavitating projectile is established and verified by experimental data.Then the evolution of the supercavitation for the serial multi-projectiles is described.In addition,the effects of different cavitation numbers and different distances between projectiles are investigated to demonstrate the supercavitation and drag reduction performance.Finally,the artificial neural network(ANN)model is established to predict the evolution of drag coefficient based on the data obtained by CFD,and the results predicted by ANN are in good agreement with the data obtained by CFD.The finding provides a useful guidance for the research of drag reduction characteristics of underwater serial projectiles.展开更多
基金been supported by Project of the National Natural Science Foundation of China(No.62073256)the Shaanxi Provincial Science and Technology Department(No.2020GY-125)Xi’an Science and Technology Innovation talent service enterprise project(No.2020KJRC0041)。
文摘The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.
基金supported by the National Natural Science Foun-dation of China(Grant No.11972194,12072160).
文摘To increase launching frequency and decrease drag force of underwater projectiles,a serial multiprojectiles structure based on the principle of supercavitation is proposed in this paper.The drag reduction and supercavitation characteristics of the underwater serial multi-projectiles are studied with computational fluid dynamics(CFD)and machine learning.Firstly,the numerical simulation model for the underwater supercavitating projectile is established and verified by experimental data.Then the evolution of the supercavitation for the serial multi-projectiles is described.In addition,the effects of different cavitation numbers and different distances between projectiles are investigated to demonstrate the supercavitation and drag reduction performance.Finally,the artificial neural network(ANN)model is established to predict the evolution of drag coefficient based on the data obtained by CFD,and the results predicted by ANN are in good agreement with the data obtained by CFD.The finding provides a useful guidance for the research of drag reduction characteristics of underwater serial projectiles.
