A predictive physics-aware hybrid reduced order model for reacting flows
arXiv preprint arXiv:2301.09860, 2023•arxiv.org
In this work, a new hybrid predictive Reduced Order Model (ROM) is proposed to solve
reacting flow problems. This algorithm is based on a dimensionality reduction using Proper
Orthogonal Decomposition (POD) combined with deep learning architectures. The number
of degrees of freedom is reduced from thousands of temporal points to a few POD modes
with their corresponding temporal coefficients. Two different deep learning architectures
have been tested to predict the temporal coefficients, based on recursive (RNN) and …
reacting flow problems. This algorithm is based on a dimensionality reduction using Proper
Orthogonal Decomposition (POD) combined with deep learning architectures. The number
of degrees of freedom is reduced from thousands of temporal points to a few POD modes
with their corresponding temporal coefficients. Two different deep learning architectures
have been tested to predict the temporal coefficients, based on recursive (RNN) and …
In this work, a new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. This algorithm is based on a dimensionality reduction using Proper Orthogonal Decomposition (POD) combined with deep learning architectures. The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients. Two different deep learning architectures have been tested to predict the temporal coefficients, based on recursive (RNN) and convolutional (CNN) neural networks. From each architecture, different models have been created to understand the behavior of each parameter of the neural network. Results show that these architectures are able to predict the temporal coefficients of the POD modes, as well as the whole snapshots. The RNN shows lower prediction error for all the variables analyzed. The model was also found capable of predicting more complex simulations showing transfer learning capabilities.
arxiv.org
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