A Predictive Physics-Aware Hybrid Reduced Order Model for Reacting Flows

Abstract

This article introduces an innovative methodology that merges modal decomposition, extracting physical patterns, with deep learning networks (DLNs) for forecasting reacting flows. The model is generalizable and capable of predicting complex simulations with just one training of the model, showing transfer learning capabilities. The primary objective is to optimize computational resources while maintaining accuracy on the predictions. With the combination of proper orthogonal decomposition (POD) and DLNs, our approach offers an efficient and effective solution for flow dynamics prediction. The new hybrid (POD/DLN) predictive model is designed for solving reacting flow problems. The POD block segregates temporal and spatial information, while the DLN block operates solely on the temporal domain with significantly reduced dimensionality. The POD modes contain the main flow characteristics, turning the model into a physics-based model. This architecture leads to substantial enhancements in computational cost and memory requirements, while maintaining the precision in the predictions. Such advancements are particularly crucial for addressing the challenges posed by high-dimensional multivariate and complex time-series forecasting tasks. Two different deep learning architectures have been tested to predict the temporal coefficients, based on recursive (RNN) and convolutional (CNN) neural networks, introducing a novel physics-aware loss function. From each architecture, different models have been created to understand the behavior of each parameter of the neural network. The results show that these architectures are able to predict the temporal evolution of the reactive flow. To the authors' knowledge, this is the first time this type of hybrid models is used to temporal prediction in reactive flows. The generalization capabilities and robustness of this physics-aware ROM shed light on new development of predictive models for this research field.