Clustering-Enhanced Deep Learning Method for Computation of Full Detailed Thermochemical States via Solver-Based Adaptive Sampling
Résumé
Detailed chemistry computations are indispensable in numerous complex simulation tasks, which focus on accurately capturing the ignition process or predicting pollutant levels. The machine learning method is a modern data-driven approach for predicting a full detailed thermochemical state-to-state behavior in reacting flow simulations. By combining unsupervised clustering algorithms to subdivide the composition space, the complexity of adaptive regression models for temporal dynamics can be significantly reduced. In this article, a more compact dataset is generated, which is essential for the clustering algorithm, by leveraging the adaptive CVODE solver time steps for data augmentation for stiff reactive states. A learning workflow that utilizes a deep residual network model (ResNet) in conjunction with an adaptive clustering algorithm is proposed. This approach aims to replace the stiff ordinary differential equation direct integration solver traditionally used for computing thermochemical species’ state-to-state temporal evolution for detailed chemistry simulations. The learning models are adaptively trained using the K-means clustering algorithm in the non-linear transformation space for different subspaces of dynamic systems. Three test cases, H2 (9 species), C2H4 (32 species), and CH4 (53 species), are investigated, each exhibiting varying complexities. The study demonstrates that the iterative predictions of thermochemical states align well with the results obtained from direct numerical integration. Additionally, employing multiple adaptive regression models in subdomains yields superior performance compared to a single regression model prediction case.