Super-resolution reconstruction of scalar fields from the pyrolysis of pulverised biomass using deep learning

Abstract

Recently, advanced deep-learning techniques have been successfully applied as deconvolution operators to super-resolve the low-resolution data in large-eddy simulation (LES). The super-resolution (SR) operator provides an approximate inverse to the filter operators in LES such that the under-resolved and un-resolved sub-grid information can be reconstructed from the resolved scales. In this work, a particle-aware attention-based conditional super-resolution generative adversarial network (PACASRGAN) is proposed for the fourfold SR of gas field scalars which are generated by the pyrolysis process in a hot turbulent flow laden with pulverised biomass particles. Multiple carrier-phase direct numerical simulations (DNS) of two-way coupled particle-laden flows with heat and mass transfer, that mimic the near-burner field of pulverised biomass combustion (PBC) systems, are carried out to build the training/testing datasets. The model performance is assessed in an a priori manner by investigating statistical quantities of interest for the modelling in LES of PBC. The results show that the proposed model can super-resolve the temperature and mixture fraction fields to a good accuracy and outperforms unconditional GAN models. Particles create localised sources/sinks via two-way coupling which sharpen scalar gradients in the subgrid. The particle mask and feature vector encode this localisation to improve the predictions of the generator. The scalar spectra, the conditional average of unresolved scalar variances, the probability density function (PDF), and the conditional average of the square of the mixture fraction gradient from the reconstructed fields follow the DNS values well. Slight deviations are observed at rich conditions in conditional statistics and at the tail of the PDFs. Nonetheless, the results demonstrate that SR is applicable to two-way coupled particle-laden flows with heat and mass transfer, providing reasonably accurate high-resolution information for both the entire gas field and particle positions.