Large eddy simulations of NH3-H2 jet flame at elevated pressure using PCA with inclusion of NH3/H2 ratio variation

Abstract

Ammonia-hydrogen blends are receiving significant attention as a viable alternative to hydrocarbon fuels, and improved fundamental understanding of their characteristics is essential for their application. To investigate the fundamental characteristics of turbulent non-premixed ammonia-hydrogen flames at practically-relevant pressure conditions, large eddy simulation (LES) computations are conducted using the PC-transport model, which is based on Principal Component Analysis (PCA). To enhance the size-reduction potential of PCA, it is coupled with nonlinear regression that employs deep neural networks (DNN). This work aims to advance the PC-transport approach by extending the training data set to include variations in local NH3/H2 ratios due to chemical and transport effects. LES results from the PC-DNN approach with a training data set based on fixed (baseline manifold) and varied (extended manifold) NH3/H2 ratios are compared with the recent experimental measurements obtained at the KAUST high-pressure combustion duct (HPCD). The results show that the PC-DNN approach with the extended manifold provides improved predictions, compared to the baseline manifold, and is able to capture key flame characteristics with reasonable accuracy using two principal components only.

Hasan Abed Al Kader Hammoud

PhD Student

Hasan is an Electrical and Computer Engineering Ph.D. student in Image and Video Understanding Lab (IVUL) Group in the Artificial Intelligence Initiative (AII) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem.