The reduction of pollution and noise emissions of modern aero engines represents a key concept to meet the requirements of the future air traffic. This requires an improvement in the understanding of combustion noise and its sources, as well as the development of accurate predictive tools. This is the major goal of the current study where the LOTAN network solver and a hybrid CFD/CAA approach are applied on a generic pre-mixed and pressurized combustor to evaluate their capabilities for combustion noise predictions. LOTAN solves the linearized Euler equations (LEE) whereas the hybrid approach consists of RANS mean flow and frequency-domain simulations based on linearized Navier-Stokes equations (LNSE). Both solvers are fed in turn by three different combustion noise source terms which are obtained from the application of a statistical noise model on the RANS simulations and a postprocessing of an incompressible and compressible LES. In this way the influence of the source model and acoustic solver is identified. The numerical results are compared with experimental data. In general good agreement with the experiment is found for both the LOTAN and LNSE solvers. The LES source models deliver better results than the statistical noise model with respect to the amplitude and shape of the heat release spectrum. Beyond this it is demonstrated that the phase relation of the source term does not affect the noise spectrum. Finally, a second simulation based on the inhomogeneous Helmholtz equation indicates the minor importance of the aerodynamic mean flow on the broadband noise spectrum.
Prediction of Combustion Noise in a Model Combustor Using a Network Model and a LNSE Approach
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Ullrich, WC, Hirsch, C, Sattelmayer, T, Mahmoudi, Y, Dowling, AP, Swaminathan, N, Lackhove, K, Sadiki, A, Fischer, A, & Staufer, M. "Prediction of Combustion Noise in a Model Combustor Using a Network Model and a LNSE Approach." Proceedings of the ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. Volume 4B: Combustion, Fuels and Emissions. Charlotte, North Carolina, USA. June 26–30, 2017. V04BT04A009. ASME. https://doi.org/10.1115/GT2017-64300
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