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基于爆速的状态方程的炸药爆轰参数预测模型

Predictive model of explosive detonation parameters from an equation of state based on detonation velocity.

作者信息

G Bastante Fernando, Araújo María, Giráldez Eduardo

机构信息

CINTECX, GESSMin Group, Department of Natural Resources and Environmental Engineering, University of Vigo, Campus as Lagoas, Vigo, Pontevedra, 36310, Spain.

出版信息

Phys Chem Chem Phys. 2022 Apr 6;24(14):8189-8195. doi: 10.1039/d2cp00085g.

Abstract

This article describes a predictive model of explosive detonation velocity and pressure based on first-order approximation of the detonation velocity equation. Detonation pressure was calculated from equations derived from the ideal detonation theory since that pressure is functionally related to detonation velocity. In the model calibration process, several product formation hierarchies were explored, with the best results yielded by the Kamlet and Jacobs (KJ) hierarchy. The predictive capacity of our model (labelled DEoS) was tested using different experimental databases, and was compared with predictions by thermochemical models (BKW-RR, JCZ3-J and JCZS) and by the empirical KJ method. The prediction values obtained using an experimental database of 238 explosive substances (75 singles and 163 composites), for a range of densities (1 g cc to 2 g cc), were excellent in terms of both velocity and pressure, with root mean square error values of 1.7% (519 data items) and 6.0% (263 data items), respectively. We analysed results, broken down by explosive type, in detail, finding that the model residuals did not correlate with the predictor variables and also that the model predicts reasonable values for other parameters in the detonation state, such as density, the Jones parameter, and the Grüneisen parameter.

摘要

本文描述了一种基于爆速方程一阶近似的炸药爆速和压力预测模型。爆轰压力是根据理想爆轰理论推导的方程计算得出的,因为该压力与爆轰速度存在函数关系。在模型校准过程中,探索了几种产物生成层次结构,其中Kamlet和Jacobs(KJ)层次结构产生的结果最佳。我们的模型(标记为DEoS)的预测能力使用不同的实验数据库进行了测试,并与热化学模型(BKW-RR、JCZ3-J和JCZS)以及经验KJ方法的预测结果进行了比较。使用包含238种爆炸物(75种单质和163种混合物)的实验数据库,在一系列密度(1 g/cc至2 g/cc)范围内获得的预测值,在速度和压力方面都非常出色,均方根误差值分别为1.7%(519个数据项)和6.0%(263个数据项)。我们详细分析了按炸药类型分类的结果,发现模型残差与预测变量不相关,并且该模型还能预测爆轰状态下其他参数的合理值,如密度、琼斯参数和格林爱森参数。

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