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Estimation of Autoignition Propensity in Aeroderivative Gas Turbine Premixers Using Incompletely Stirred Reactor Network Modelling

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Jella, Sandeep 
Iavarone, Salvatore 
Versailles, Philippe 
Mastorakos, Epaminondas  ORCID logo


The study of autoignition propensity in premixers for gas turbines is critical for their safe operation and design. Although premixers can be analysed using reacting Computational Fluid Dynamics (CFD) coupled with detailed autoignition chemical kinetics, it is essential to also develop methods with lower computational cost to be able to explore more geometries and operating conditions during the design process. This paper presents such an approach based on Incompletely Stirred Reactor Network (ISRN) modelling. This method uses a CFD solution of a non-reacting flow and subsequently estimates the spatial evolution of reacting scalars such as autoignition precursors and temperature conditioned on the mixture fraction, which are used to quantify autoignition propensity. The approach is intended as a "post-processing'" step, enabling the use of very complex chemical mechanisms and the study of many operating conditions. For a representative premixer of an aeroderivative gas turbine, results show that autoignition propensity can be reproduced with ISRN at highly reactive operating conditions featuring multi-stage autoignition of a dual fuel mixture. The ISRN computations are consequently analysed to explore the evolution of reacting scalars and propose some autoignition metrics that combine mixing and chemical reaction to assist the design of premixers.



Journal Title

Proceedings of the ASME Turbo Expo

Conference Name

ASME Turbo Expo 2022

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Siemens Energy Canada Limited