A novel recursive unsupervised clustering MoE to represent flamelet tables

Flamelet-based modeling has been extensively used to reduce the computational costs of combustion simulations. However, this method requires a large amount of storage that substantially increases the costs of combustion simulations in 3D. A novel recursive unsupervised-learning based clustering method is introduced to predict the flamelet table. The proposed technique uses a Mixture of Experts (MoE) architecture involving specialized Deep Neural Networks (DNNs) trained on separate parts of the input space, which is subdivided using an unsupervised clustering algorithm. The models were trained and evaluated on a 4-dimensional flamelet table and are tested a-priori and a-posteriori through 3D simulation of Sandia Flame D. Comparing the proposed method with the predictions of a Single Neural Network (SNN) model and standard interpolation method demonstrates significant improvement in predicting OH and NO mass fractions in different spatial locations.