Investigating the detonation reaction zone structures of high explosives is significant for understanding detonation reaction mechanism.This study employed an integrated approach combining machine learning prediction,...Investigating the detonation reaction zone structures of high explosives is significant for understanding detonation reaction mechanism.This study employed an integrated approach combining machine learning prediction,theoretical calculation,and experimental characterization to determine the detonation reaction zone width of CL-20-based aluminized explosive.In this study,the detonation reaction zone refers to the reaction zone between the von Neumann(VN)peak and sonic point,which usually means the so-called detonation driving zone(DDZ).For the machine learning prediction,an ensemble model integrating Random Forest and Support Vector Regression was developed to predict the reaction zone width using a dataset of 19 publicly available samples.For the theoretical calculation,the Wood-Kirkwood(W-K)detonation theory model was utilized to implement numerical calculation of the reaction zone structures,incorporating chemical reaction kinetics to describe the detonation reaction progress.In experimental characterization,the Photon Doppler Velocimetry(PDV)was applied with LiF as the optical window to measure the particle velocity profile of detonation products and derive the reaction zone width.The results indicate that the reaction zone width values are 0.25 mm,0.28 mm,and 0.26 mm obtained from machine learning prediction,theoretical calculation,and experimental characterization,respectively.The corresponding velocities at the Chapman-Jouguet(CJ)point are 1,938 m/s,2,047 m/s,and 1,982 m/s,respectively.The maximum relative deviation in reaction zone width among three methods is approximately 7.7%,while that for CJ particle velocity is approximately 3.3%.These results from all three methods agree well within engineering error.This validates the effectiveness of integrating machine learning prediction,theoretical calculation and advanced experimental techniques for studying the detonation reaction zone structures of high explosives.This research provides insights into the detonation reaction mechanism and reaction zone characteristics of CL-20-based aluminized explosive.展开更多
文摘Investigating the detonation reaction zone structures of high explosives is significant for understanding detonation reaction mechanism.This study employed an integrated approach combining machine learning prediction,theoretical calculation,and experimental characterization to determine the detonation reaction zone width of CL-20-based aluminized explosive.In this study,the detonation reaction zone refers to the reaction zone between the von Neumann(VN)peak and sonic point,which usually means the so-called detonation driving zone(DDZ).For the machine learning prediction,an ensemble model integrating Random Forest and Support Vector Regression was developed to predict the reaction zone width using a dataset of 19 publicly available samples.For the theoretical calculation,the Wood-Kirkwood(W-K)detonation theory model was utilized to implement numerical calculation of the reaction zone structures,incorporating chemical reaction kinetics to describe the detonation reaction progress.In experimental characterization,the Photon Doppler Velocimetry(PDV)was applied with LiF as the optical window to measure the particle velocity profile of detonation products and derive the reaction zone width.The results indicate that the reaction zone width values are 0.25 mm,0.28 mm,and 0.26 mm obtained from machine learning prediction,theoretical calculation,and experimental characterization,respectively.The corresponding velocities at the Chapman-Jouguet(CJ)point are 1,938 m/s,2,047 m/s,and 1,982 m/s,respectively.The maximum relative deviation in reaction zone width among three methods is approximately 7.7%,while that for CJ particle velocity is approximately 3.3%.These results from all three methods agree well within engineering error.This validates the effectiveness of integrating machine learning prediction,theoretical calculation and advanced experimental techniques for studying the detonation reaction zone structures of high explosives.This research provides insights into the detonation reaction mechanism and reaction zone characteristics of CL-20-based aluminized explosive.
