Abstract
In this work, the convolutional autoencoder is applied to the reduced order model for a turbulent methane jet flame. Autoencoder is a machine learning algorithm, which reduces the problem dimension by non-linear projection. It has an advantage in reconstruction of data with significant non-linearity. Additionally, with a convolutional layer the characteristics of original data can be trained with a relatively small number of hyper-parameters. To check accuracy of the reduced order model using the convolutional autoencoder, we applied it to surrogate model and sparse reconstruction problem, and compared it with other dimension reduction algorithms. For model training, five parameters are selected as the model training parameters and 20 and 40 sensor data are extracted for the sparse reconstruction problem. The proposed convolutional autoencoder shows better accuracy than the linear projection-based dimension reduction algorithm.
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