Spray Flame Characteristics of Bio-Derived Fuels in a Simulated Gas Turbine Burner

Panchasara, Heena and Kolhe, Pankaj and Agrawal, Ajay K (2019) Spray Flame Characteristics of Bio-Derived Fuels in a Simulated Gas Turbine Burner. In: ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition, GT, 17-21 June 2019, Phoenix, United States.

Full text not available from this repository. (Request a copy)

Abstract

Fuel injection plays an important role in liquid fueled gas turbine combustion. The strong interdependence of liquid breakup and atomization, turbulent dispersion of these droplets, droplet evaporation, and fuel-air mixing make the spray modeling an extremely challenging task. The physical processes are even more difficult to predict for alternative fuels with different thermophysical properties. In this study, spray flames of unheated and preheated vegetable oil (VO) produced by an air-blast atomizer in a swirl stabilized combustor are investigated experimentally. Phase Doppler particle analyzer (PDPA) is used to measure the instantaneous diameter and axial velocity of droplets at different axial and radial locations in both flames. Experiments are conducted at an equivalence ratio of 0.79 and atomizing air to liquid ratio (ALR) by mass of 2.5 to obtain stable VO flames. Radial profiles of mean axial velocity and Sauter mean diameter are presented to show the effect of fuel preheating. Joint Probability Density Functions (joint PDF) are presented to show the correlation between droplet diameter and axial velocity. Results are analyzed to show that both sprays exhibit self-similar droplet diameter distributions at different axial and radial locations when normalized properly. Thus, the vast amount of PDPA data in the spray can be reduced to simple distribution functions. A method to reconstruct the joint PDF from experimentally determined distribution functions is presented. We envision that the joint PDF approach outlined in this study could be implemented in high-fidelity computational fluid dynamic models to improve spray predictions in future studies.

[error in script]

IITH Creators: