Modeling and simulation of JWL equation of state for reactive Al/PTFE mixture

An analytical method is presented to fit parameters of Jones-Wilkins-Lee （JWL） equation of state （EOS） for the chemical process of aluminum-polytetrafluoroethylene （ AI/PTFE ） mixture. Subroutine codes for both strength model and EOS were developed in explicit-FE code AUTODYN. Firstly, the shock Hugoniot data of reactive A1/PTFE mixture was analytically derived by implemen- ting this methodology. The JWL EOS was verified to fit shock Hugoniot data of both reacted and un- reacted A1/PTFE mixture, which gives reasonable results. Furthermore, to numerically ascertain the reaction phases of ignition and growth and quasi detonation of A1/PTFE mixture, characterized ex- periment was setup to validate the reaction phases and coefficients of JWL EOS for A1/PTFE mix- ture. From the test, a promising example of reactive mixture A1/PTFE is capable to enhance lethality of weapons, the status computation in clude quasi-detonation pressure and temperature of A1/PTFE mixture in different chemical reaction phases is validated.

Study on the prediction and inverse prediction of detonation properties based on deep learning

The accurate and efficient prediction of explosive detonation properties has important engineering significance for weapon design.Traditional methods for predicting detonation performance include empirical formulas,equations of state,and quantum chemical calculation methods.In recent years,with the development of computer performance and deep learning methods,researchers have begun to apply deep learning methods to the prediction of explosive detonation performance.The deep learning method has the advantage of simple and rapid prediction of explosive detonation properties.However,some problems remain in the study of detonation properties based on deep learning.For example,there are few studies on the prediction of mixed explosives,on the prediction of the parameters of the equation of state of explosives,and on the application of explosive properties to predict the formulation of explosives.Based on an artificial neural network model and a one-dimensional convolutional neural network model,three improved deep learning models were established in this work with the aim of solving these problems.The training data for these models,called the detonation parameters prediction model,JWL equation of state(EOS)prediction model,and inverse prediction model,was obtained through the KHT thermochemical code.After training,the model was tested for overfitting using the validation-set test.Through the model-accuracy test,the prediction accuracy of the model for real explosive formulations was tested by comparing the predicted value with the reference value.The results show that the model errors were within 10%and 3%for the prediction of detonation pressure and detonation velocity,respectively.The accuracy refers to the prediction of tested explosive formulations which consist of TNT,RDX and HMX.For the prediction of the equation of state for explosives,the correlation coefficient between the prediction and the reference curves was above 0.99.For the prediction of the inverse prediction model,the prediction error of the explosive equation was within 9%.This indicates that the models have utility in engineering.