使用深度神经网络进行圆环火焰振荡器圆环阵列的降维和动态模式识别
Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network
AI总结：
本文提出Bi-LSTM-VAE-WDC方法，利用深度神经网络对圆环火焰振荡器阵列的动态模式进行有效识别和分类。
摘要：
航空发动机和现代燃气轮机中的振荡燃烧往往对运行产生显著的不良影响，准确识别各种振荡模式是理解并控制燃烧不稳定性的重要前提。然而，复杂燃烧系统的高维空间-时间数据通常对动态模式识别构成相当大的挑战。基于双层双向长短期记忆变分自编码器（Bi-LSTM-VAE）降维模型和基于二维Wasserstein距离的分类器（WDC），本研究提出了一种有前途的方法（Bi-LSTM-VAE-WDC）用于识别振荡燃烧系统中的动态模式。具体而言，Bi-LSTM-VAE降维模型被引入以将燃烧系统的高维空间-时间数据降至低维相空间；基于网格中相点分布计算高斯核密度估计（GKDE）；从GKDE地图中计算二维WD值以识别振荡模式。本研究中所用的时间序列数据来自圆环阵列层流火焰振荡器的数值模拟。结果表明，新颖的Bi-LSTM-VAE方法能够产生非重叠的相点分布，表明了有效的无监督模式识别和分类。此外，本方法在区分复杂火焰系统中的动态模式方面表现出比VAE和PCA（主成分分析）更突出的性能，暗示了其在研究湍流燃烧中的潜力。
英文摘要：
Oscillatory combustion in aero engines and modern gas turbines often has significant adverse effects on their operation, and accurately recognizing various oscillation modes is the prerequisite for understanding and controlling combustion instability. However, the high-dimensional spatial-temporal data of a complex combustion system typically poses considerable challenges to the dynamical mode recognition. Based on a two-layer bidirectional long short-term memory variational autoencoder (Bi-LSTM-VAE) dimensionality reduction model and a two-dimensional Wasserstein distance-based classifier (WDC), this study proposes a promising method (Bi-LSTM-VAE-WDC) for recognizing dynamical modes in oscillatory combustion systems. Specifically, the Bi-LSTM-VAE dimension reduction model was introduced to reduce the high-dimensional spatial-temporal data of the combustion system to a low-dimensional phase space; Gaussian kernel density estimates (GKDE) were computed based on the distribution of phase points in a grid; two-dimensional WD values were calculated from the GKDE maps to recognize the oscillation modes. The time-series data used in this study were obtained from numerical simulations of circular arrays of laminar flame oscillators. The results show that the novel Bi-LSTM-VAE method can produce a non-overlapping distribution of phase points, indicating an effective unsupervised mode recognition and classification. Furthermore, the present method exhibits a more prominent performance than VAE and PCA (principal component analysis) for distinguishing dynamical modes in complex flame systems, implying its potential in studying turbulent combustion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
