Mixture fraction enhanced physics-informed neural networks for flow reconstruction in pool fires

This thesis explores the use of physics-informed neural networks (PINNs) to reconstruct the flow fields in a pool fire flame, a canonical configuration in non-premixed combustion. Due to the difficulty in obtaining adequate experimental characterizations of such flows, reacting flows like pool fires stand in need of novel methods capable of...

Impact of Exhaust Gas Recirculation on NOx emissions in non-premixed combusted Gas Turbines using CRN

The major challenges in the world today is to reduce pollutant emissions from gas turbines while still being able to retain the maximum efficiency in the gas turbine. Especially due to stringent norms on emissions, the research on gas turbines have increased to reduce NOx levels. Most of the gas turbines have been using non-premixed combustion...