Reduced Dimension Analysis on Combustion Dynamics Using Deep Auto-Encoder

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  • 深層自己符号化器を用いた燃焼ダイナミクスの低次元化解析
  • シンソウ ジコ フゴウカキ オ モチイタ ネンショウ ダイナミクス ノ テイジゲンカ カイセキ

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Abstract

<p>A technique using deep neural network (DNN) is introduced for the analysis of combustion dynamics recently. The functionality of the conventional machine learning, proper orthogonal decomposition (POD), technique, is summarized; and how it can be implemented by DNN is explained from mathematical viewpoint. Deep auto-encoder (DAE) consist of an encoder and a decoder networks is validated to be the superposition of POD and its variant, variational auto-encoder (VAE), is found to be suited for projecting the dynamics onto a low-order phase space. The superiority of the DAEs is explained through the flexibility in projection of the dynamic motion on a manifold in high-order space onto a low-dimensional phase space. The results of analysis using VAE is presented for the intrinsic combustion oscillation in a rocket combustor and for the cool flame oscillation of a droplet array. For both cases, VAE could determine the phase appropriately from the spatial distributions of various physical parameters; and it could derive the locus on 2D phase plane from time variation of the distributions. The distinctive attractor of nonlinear limit-cycle oscillations is written on the phase plane and the possibility of near-term prediction is demonstrated.</p>

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