Assessment of electrostatic discharge sensitivity of nitrogen-rich heterocyclic energetic compounds and their salts as high energy-density dangerous compounds: A study of structural variables

IF 5 Q1 ENGINEERING, MULTIDISCIPLINARY Defence Technology(防务技术) Pub Date : 2024-09-01 DOI:10.1016/j.dt.2024.04.001
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Abstract

Nitrogen-rich heterocyclic energetic compounds (NRHECs) and their salts have witnessed widespread synthesis in recent years. The substantial energy-density content within these compounds can lead to potentially dangerous explosive reactions when subjected to external stimuli such as electrical discharge. Therefore, developing a reliable model for predicting their electrostatic discharge sensitivity (ESD) becomes imperative. This study proposes a novel and straightforward model based on the presence of specific groups (–NH2 or -NH-, N=N+O and –NNO2, -ONO2 or -NO2) under certain conditions to assess the ESD of NRHECs and their salts, employing interpretable structural parameters. Utilizing a comprehensive dataset comprising 54 ESD measurements of NRHECs and their salts, divided into 49/5 training/test sets, the model achieves promising results. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Maximum Error for the training set are reported as 0.16 J, 0.12 J, and 0.5 J, respectively. Notably, the ratios RMSE(training)/RMSE(test), MAE(training)/MAE(test), and Max Error(training)/Max Error(test) are all greater than 1.0, indicating the robust predictive capabilities of the model. The presented model demonstrates its efficacy in providing a reliable assessment of ESD for the targeted NRHECs and their salts, without the need for intricate computer codes or expert involvement.

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富氮杂环高能化合物及其盐类作为高能量密度危险化合物的静电放电敏感性评估:结构变量研究
近年来,富氮杂环高能化合物(NRHECs)及其盐类被广泛合成。这些化合物中含有大量能量密度,在受到放电等外部刺激时可能会导致潜在危险的爆炸反应。因此,开发一个可靠的模型来预测这些化合物的静电放电敏感性(ESD)已成为当务之急。本研究根据特定基团(-NH2 或 -NH-、-N=N+-O- 和 -NNO2、-ONO2 或 -NO2)在特定条件下的存在情况,提出了一种新颖而直接的模型,利用可解释的结构参数来评估 NRHECs 及其盐类的 ESD。该模型利用由 54 个 NRHECs 及其盐类的 ESD 测量数据组成的综合数据集(分为 49/5 个训练/测试集),取得了令人满意的结果。据报告,训练集的均方根误差(RMSE)、平均绝对误差(MAE)和最大误差分别为 0.16 J、0.12 J 和 0.5 J。值得注意的是,RMSE(训练)/RMSE(测试)、MAE(训练)/MAE(测试)和 Max Error(训练)/Max Error(测试)之比都大于 1.0,表明该模型具有强大的预测能力。该模型无需复杂的计算机代码或专家参与,即可为目标 NRHEC 及其盐类提供可靠的 ESD 评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Defence Technology(防务技术)
Defence Technology(防务技术) Mechanical Engineering, Control and Systems Engineering, Industrial and Manufacturing Engineering
CiteScore
8.70
自引率
0.00%
发文量
728
审稿时长
25 days
期刊介绍: Defence Technology, a peer reviewed journal, is published monthly and aims to become the best international academic exchange platform for the research related to defence technology. It publishes original research papers having direct bearing on defence, with a balanced coverage on analytical, experimental, numerical simulation and applied investigations. It covers various disciplines of science, technology and engineering.
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