Deep Learning Algorithms for Detecting Combustion Instabilities
Deep Learning Algorithms for Detecting Combustion Instabilities
复制标题
用于检测燃烧不稳定性的深度学习算法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. Sarkar
中科院分区：
文献类型：
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作者：
Tryambak Gangopadhyay;A. Locurto;J. Michael;S. Sarkar
燃烧不稳定性普遍存在于包括燃气轮机在内的各种系统中。在这方面，主动控制的引入为燃烧室设计和优化提供了新的范例。然而，有限的能力来检测不稳定的开始可能导致实施主动控制方法的困难。机器学习--特别是深度学习工具--可用于从与燃烧过程相关的各种测量和传感器数据中检测不稳定性。深度学习模型最近显示出了从数据中提取有意义的特征而不需要手工操作的巨大潜力。作为深度学习用于燃烧不稳定性检测的早期研究之一，我们从表现出不同程度燃烧不稳定性的预混钝体稳定火焰的高速图像中提取序列图像帧。使用有效的检测框架(基于2-D卷积神经网络)来检测不稳定模式的增长可以导致有效的控制方案。此外，我们应用第二个深度学习框架来捕捉数据中的时间相关性和相应的学习空间特征。
Combustion instabilities are prevalent in a variety of systems including gas turbine engines. In this regard, the introduction of active control opens the potential for new paradigms in combustor design and optimization. However, the limited ability to detect the onset of instabilities can lead to difficulty in implementing active control approaches. Machine learning—specifically deep learning tools—may be employed to detect instabilities from various measurement and sensor data related to the combustion process. Deep learning models have recently shown remarkable potential for extraction of meaningful features from data without the need to hand-craft. As one of the early studies of deep learning for combustion instability detection, we extract sequential image frames from high-speed images of a premixed, bluff-body stabilized flame which exhibits varying levels of combustion instability. Using an efficient detection framework (based on 2-D convolutional neural networks) to detect the growth of an unstable mode can lead to effective control schemes. In addition, we apply a second deep learning framework to capture the temporal correlations in the data with corresponding learned spatial features.