Fast explosive performance prediction via small-dose energetic materials based on time-resolved imaging combined with machine learning,Journal of Materials Chemistry A

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Fast explosive performance prediction via small-dose energetic materials based on time-resolved imaging combined with machine learning
Journal of Materials Chemistry A ( IF 10.7 ) Pub Date : 2022-06-13 , DOI: 10.1039/d2ta02626k
Xianshuang Wang ₁ , Yage He ₁ , Wenli Cao ₂ , Wei Guo ₁ , Tonglai Zhang ₂ , Jianguo Zhang ₂ , Qinghai Shu ₃ , Xueyong Guo ₂ , Ruibin Liu ₁ , Yugui Yao _{1,

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Affiliation

Fast, reproducible, and quantitative performance evaluation of monomolecular energetic materials (EMs) is a significant challenge that limits the tailored applications of EMs and the development of new high-energy-density materials. Here, a small-dose-based alternative method for the detection of the detonation velocity (DV), heat of detonation (HoD), volume of detonation (VoD), detonation pressure (DP), and detonation temperature (DT) from laser-induced shock-wave images combined with a machine learning algorithm is proposed. On the basis of a comprehensive investigation of the time-resolved plume dynamic behavior and spectral emission properties, we concluded that the exothermic chemical reaction associated with detonation occurred at about 25 μs after the laser pulse termination. Moreover, 27 types of explosives were tested to build the prediction model to validate the proposed theory. Our model accurately predicted the explosion parameters with an average relative error of test set (ARETe) of the models for the DV, VoD, and DT of <5%. In particular, the maximum relative error of test set (MRETe) of the quantitative analysis model for the DV and VoD was less than 4%. Our results demonstrate the value of laser-based micro-explosion technology in an explosion testing system and provide an analytic means for estimation of the detonation performance of EMs in a high-speed, less-consumption, low-cost, high-precision, and practical way.

中文翻译：

基于时间分辨成像结合机器学习的小剂量含能材料快速爆炸性能预测

单分子含能材料 (EM) 的快速、可重复和定量性能评估是一项重大挑战，它限制了 EM 的定制应用和新的高能量密度材料的开发。在这里，一种基于小剂量的替代方法，用于检测激光的爆速 (DV)、爆轰热 (HoD)、爆轰体积 (VoD)、爆轰压力 (DP) 和爆轰温度 (DT)。提出了结合机器学习算法的诱发冲击波图像。在对时间分辨羽流动力学行为和光谱发射特性进行全面研究的基础上，我们得出结论，与爆轰相关的放热化学反应发生在激光脉冲终止后约 25 μs 处。而且，对 27 种炸药进行了测试，建立了预测模型，以验证所提出的理论。我们的模型准确地预测了爆炸参数，DV、VoD 和 DT 模型的测试集平均相对误差 (ARETe) <5%。特别是DV和VoD的定量分析模型的测试集最大相对误差（MRETe）小于4%。我们的研究结果证明了基于激光的微爆技术在爆炸试验系统中的价值，并为在高速、低消耗、低成本、高精度和实用的方法。特别是DV和VoD的定量分析模型的测试集最大相对误差（MRETe）小于4%。我们的研究结果证明了基于激光的微爆技术在爆炸试验系统中的价值，并为在高速、低消耗、低成本、高精度和实用的方法。特别是DV和VoD的定量分析模型的测试集最大相对误差（MRETe）小于4%。我们的研究结果证明了基于激光的微爆技术在爆炸试验系统中的价值，并为在高速、低消耗、低成本、高精度和实用的方法。

更新日期：2022-06-13

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