Author: Rishi Roy
Date: Monday, November 7th, 2022 at 10 am.
Location: Martin Hall, Room EGR 2164
Committee Members:
Professor Ashwani K. Gupta, Chair
Professor Stanislav I. Stoliarov
Professor Bao Yang
Professor Nikhil Chopra
Professor Kenneth H. Yu, Dean’s Representative
Title of Paper: “Investigation of Swirl Distributed Combustion with Experimental Diagnostics and Artificial Intelligence Approach”.
Abstract:
Swirl Distributed Combustion was fundamentally investigated with experimental diagnostics and predictive analysis using machine learning and computer vision techniques. Ultra-low pollutants emission, stable operation, improved pattern factor, and fuel flexibility make distributed combustion an attractive technology for potential applications in high-intensity stationary gas turbines. Proper mixing of inlet fresh air and hot products for creating a hot and low-oxygen environment results in a distributed thick reaction zone without hotspots found in conventional diffusion flames (thin reaction front) leading to reduced NOx and CO emissions. The focus of this dissertation is to develop a detailed fundamental understanding of distributed combustion in a lab-based swirl combustor mimicking gas turbine can combustor at moderate heat release intensities in the range 5.72- 9.53 MW/m3-atm using various low-carbon gaseous fuels such as methane, propane, hydrogen-enriched fuels. Moderate thermal intensities helped to understand the fundamental aspects such as reduction of flame fluctuation, mitigation of thermo-acoustic instability, flame shape evolution, flow field behavior, turbulence characteristics, variation of Damkӧhler number, vortex propagation, flame blowoff, and pollutant and CO2 emission reduction with gradual mixture preparation. Efforts were made to obtain the volumetric distribution ratio, evolution of flame shape in terms of OH* radical imaging, variation of flame standoff, thermal field uniformity, and NO and CO emissions when the flame transitions to distributed reaction zone. Further investigation was performed to study the mitigation of flame thermo-acoustics and precession vortex core (PVC) instabilities in hydrogen-enriched methane fueled (with H2 = 0, 10, 20, 40%vol.) swirl distributed combustion compared to swirl air combustion using the acoustic pressure and qualitative heat release fluctuation data at different CO2 dilution levels with and without air preheats. Proper orthogonal decomposition (POD) technique was utilized to visualize the appearance of dynamic coherent structures in reactive flow fields and reduction of fluctuation energy. Vortex shedding was found responsible for the fluctuation in swirl air combustion while no significant flame fluctuation was observed in distributed combustion. The study of lean blowoff in distributed combustion showed a higher lean blowoff equivalence ratio with gradual increase in heat release intensity, which was attributed to higher instability due to enhanced inlet turbulence. Extension of lean blowoff (ϕLBO) was observed with gradual %H2 which showed decrease of lean blowoff equivalence ratio in distributed reaction zones. Examination of non-reactive flow fields with particle image velocimetry (PIV) demonstrated higher RMS velocity fluctuation leading to healthy turbulence and higher Reynolds stress found in distributed reaction flow cases signifying enhanced mixing characteristics. Measurement of NO and CO emission at different mixture preparation levels (with CO2/ N2 dilutions) exhibited significant reduction in NO (single digit only) and CO emissions compared to swirl air combustion due to mitigation of spatial hotspots and temperature peaks, and uniform stoichiometry. Finally, the use of machine learning and computer vision techniques was investigated for software-based prediction of combustion parameters (pollutants and flame temperature) and feature-based recognition of distributed combustion regimes. The primary goal of using artificial intelligence is to reduce the time of experimentation and frequent manual interference during experiments in order to enhance overall accuracy by reducing human errors. These results will help in developing data-driven smart-sensing of combustion parameters for advanced gas turbine applications and reduce the dependence on rigorous experimental trials.