Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations

Abstract The integration of Artificial Neural Networks (ANNs) and Feature Extraction (FE) in the context of the Sample- Partitioning Adaptive Reduced Chemistry approach was investigated in this work, to increase the on-the-fly classification accuracy for very large thermochemical states. The proposed methodology was firstly compared with an on-the-fly classifier based on the Principal Component Analysis reconstruction error, as well as with a standard ANN (s-ANN) classifier, operating on the full thermochemical space, for the adaptive simulation of a steady laminar flame fed with a nitrogen-diluted stream of n-heptane in air. The numerical simulations were carried out with a kinetic mechanism accounting for 172 species and 6,067 reactions, which includes the chemistry of Polycyclic Aromatic Hydrocarbons (PAHs) up to C$ {}_{20} $. Among all the aforementioned classifiers, the one exploiting the combination of an FE step with ANN proved to be more efficient for the classification of high-dimensional spaces, leading to a higher speed-up factor and a higher accuracy of the adaptive simulation in the description of the PAH and soot-precursor chemistry. Finally, the investigation of the classifier’s performances was also extended to flames with different boundary conditions with respect to the training one, obtained imposing a higher Reynolds number or time-dependent sinusoidal perturbations. Satisfying results were observed on all the test flames. Impact Statement The existing methodologies for the simulation of multidimensional flames with detailed kinetic mechanisms are time-consuming because of the large number of involved chemical species and reactions. This aspect has prompted the development of approaches to reduce the computational requirements of computational fluid dynamics simulations of reacting flows. Among them, adaptive chemistry is worth mentioning, as it allows to use complex kinetic mechanisms only where needed. In this work, an artificial neural network architecture with a prior encoding step via Principal Component Analysis was integrated in the Sample-Partitioning Adaptive Reduced Chemistry approach, to increase the on-the-fly classification accuracy when high-dimensional spaces are considered. Its performances were compared with others supervised classifiers, operating on the full thermochemical space, in terms of speed-up with respect to the detailed simulation and accuracy in the description of Polycyclic Aromatic Hydrocarbon species.