Estimation of Autoignition Propensity in Aeroderivative Gas Turbine Premixers Using Incompletely Stirred Reactor Network Modelling
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Authors
Jella, Sandeep
Iavarone, Salvatore
Versailles, Philippe
Bourque, Gilles
Journal Title
Proceedings of the ASME Turbo Expo
Conference Name
ASME Turbo Expo 2022
Type
Conference Object
This Version
AM
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Gkantonas, S., Jella, S., Iavarone, S., Versailles, P., Mastorakos, E., & Bourque, G. Estimation of Autoignition Propensity in Aeroderivative Gas Turbine Premixers Using Incompletely Stirred Reactor Network Modelling. Proceedings of the ASME Turbo Expo https://doi.org/10.17863/CAM.83133
Abstract
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.
Sponsorship
Siemens Energy Canada Limited
Embargo Lift Date
2023-04-01
Identifiers
External DOI: https://doi.org/10.17863/CAM.83133
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335697
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