Renewable fuels have been successfully used in gas turbine combustion chambers and the layout of the chamber does not require major interventions if the composition is known. However, the variation in the composition in renewable fuels is higher than in fossil ones and it is stochastic. In principle, this variation affects the stability of the combustion, the emissions and the temperature distribution.

The combustion chamber tested in this work has been designed to reproduce the temperature distribution of MT1 test case and modelled using reactive CFD simulations. The fuel is an ideal natural gas with a random mix of methane and hydrogen.

In order to account the stochastic variation of the fuel composition, a probabilistic analysis is carried out with two sampling methods: a Monte Carlo simulation with meta-models and a Probabilistic Collocation Method. The two methodologies show similar results in terms of mean value and standard deviation.

The paper proves that is possible to predict the mean value of temperature and emissions in a modern chamber and their associated standard deviation by applying an uncertainty quantification methodology. One of the major drawbacks of the composition change is the maximum temperature variation at the exit that can reduce the life of the downstream turbine. The variation in the emissions seems less important and all the major differences in the composition are mixed out before the combustion chamber exit.

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