Prediction of Detonation Velocity and N-O Composition of High Energy C-H-N-O Explosives by Means of Artificial Neural Networks
Prediction of Detonation Velocity and N-O Composition of High Energy C-H-N-O Explosives by Means of Artificial Neural Networks
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DOI：
10.1002/prep.201800325
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发表时间：
2019-05-01
影响因子：
1.8
通讯作者：
Anufrieva, Darya A.
中科院分区：
文献类型：
--
作者：
Chandrasekaran, Nichith;Oommen, Charlie;Anufrieva, Darya A.
数据科学方法在预测某些宏观性质的应用的可能性已经在高能化合物中进行了研究。人工神经网络是数据科学中最有前途的方法之一，已被用于基于训练集预测爆速，训练集由包含从65种具有不同特征和性质的爆炸化合物和组合物中提取的104个数据点的大型数据集组成。该方法的实用性已被证明，通过验证超过37爆炸性化合物再次与不同的特点构成了一个数据集的74个数据点。该方法的实用性和多功能性是显而易见的，因为它具有类似的预测精度与来自其他两个著名的经验模型的类似数据进行比较。这种预测能力将是工程师和科学家使用高能炸药的一个很好的工具，可以快速简单地预测给定化学成分的爆炸速度，反之亦然。
The possibilities of the application of Data Science Methods in predicting certain macroscopic properties have been examined in energetic compounds. Artificial neural networks, one of the most promising methods of Data Science, has been used for predicting detonation velocity based on a trained set comprising of a large data set containing 104 data points extracted from over 65 explosive compounds and compositions with diverse characteristics and properties. The utility of the method has been demonstrated through validation for over 37 explosive compounds again with diverse characteristics constituting to a data set of 74 data points. The usefulness and versatility of the method is clear as it exhibits similar predictive accuracy on comparison with the similar data derived from two other well-known empirical models. Such predictive capabilities will be a great tool for engineers and scientists working with high energetic explosives for quick and simple prediction of detonation velocity given the chemical composition and vice versa.