LES Modeling and Analysis of Combustion Instabilities with Carbon Neutral Fuels

Carbon neutral fuels will play an increasingly large role on the future energy market. For aircraft engines, sustainable aviation fuels can serve as a drop-in solution for decarbonization of existing airplanes while hydrogen may serve as a full carbon neutral energy carrier in future engines. For electricity,  due to their high load flexibility, hydrogen and ammonia are candidates for use in gas turbine power plants during times of peak demand or during times where solar and wind yield too little output. 

However, the additional fuel flexibility is not without its challenges. One major hurdle is the emergence of thermoacoustic combustion instabilities, which can damage the equipment, or severely limit the operability of the engine, and hence they have to be avoided at all times. Predicting these instabilities using a multi-fidelity approach based on computational fluid dynamics (CFD) and low-order network models (LOM) is a cost effective design strategy for their mitigation and analysis.

These CFD-based approaches have been predominantly developed and investigated for classical hydrocarbon fuels, such as kerosene or natural gas. The vastly different properties of hydrogen and ammonia - such as flame speed and thermodiffusive effects -  pose significant difficulties for existing models and lead to unreliable predictions. For these purposes, we develop and investigate state of the art models that enable quantitatively accurate prediction of combustion dynamics. We couple high-fidelity large eddy simulation (LES) with advanced combustion modeling approaches to facilitate accurate prediction of flame dynamics. These reacting flow simulations use optimized global chemical schemes to preserve the relevant physical effects while dramatically reducing computational demand.

The primary goal of these simulations is to predict thermoacoustic instabilities and combustion noise, which in principle requires fully compressible flow modeling. To reduce computational cost, however, we can first run a much cheaper low-Mach LES to generate representative flame data. We then apply system-identification methods to extract low-order models that capture the flame or entropy response to acoustic perturbations. These reduced models can be coupled to acoustic solvers, enabling instability prediction, parametric studies and the exploration of complex configurations without repeating expensive simulations, enabling faster turn-around times for the implementation of carbon neutral fuels.

Nicolas M. Garcia has been funded by the German Federal Ministry for Economics and Climate Action under the Federal Aeronautical Research Program (LuFo VI, Call 3) under grant 20M2264C within the OptTuGen project, in collaboration with Rolls-Royce Deutschland Ltd & Co KG, Blankenfelde-Mahlow, Germany, whose cooperation is highly appreciated.