Abstract Gaseous detonation has complicated cellular surface, whose comprehensive investigation is critical not only to the detonation physics but also the detonation engine development. Because measuring the high-resolution dynamic surface is beyond the present experimental technical skills, we propose a reconstruction method of detonation wave surface based on post-surface flow field. This method combines two technologies, the proper orthogonal decomposition (POD) in fluid research and the artificial neural network (ANN) in machine learning research. POD is employed to extract the main features of flow fields, and the pre-trained ANN builds up the connection between the reduced coefficients of full flow fields and post-surface flow fields. The reconstruction is tested through the numerical results from one-step irreversible heat release model, displaying a good performance in both cellular normal detonations and unstable oblique detonations. The method may provide a universal frame for the detonation research, and has the potential to be employed in other numerical and experimental results.

中文翻译：

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基于人工神经网络和适当正交分解的气爆细胞表面重建

摘要 气态爆震具有复杂的细胞表面，其综合研究不仅对爆震物理而且对爆震发动机的发展至关重要。由于测量高分辨率动态表面超出了目前的实验技术水平，我们提出了一种基于后表面流场的爆轰波面重建方法。该方法结合了流体研究中的适当正交分解（POD）和机器学习研究中的人工神经网络（ANN）两种技术。POD用于提取流场的主要特征，预训练的人工神经网络建立全流场和后表面流场的减少系数之间的联系。重构通过一步不可逆放热模型的数值结果进行检验，在细胞正常爆炸和不稳定的斜向爆炸中都表现出良好的性能。该方法可为爆轰研究提供一个通用的框架，并具有应用于其他数值和实验结果的潜力。