Combustion noise in the laboratory scale PRECCINSTA (prediction and control of combustion instabilities in industrial gas turbines) burner is simulated with a new, robust, and highly efficient approach for combustion noise prediction. The applied hybrid method FRPM-CN (fast-random particle method for combustion noise prediction) relies on a stochastic, particle-based sound source reconstruction approach. Turbulence statistics from reacting CFD-RANS (computational fluid dynamics–Reynolds-Averaged Navier–Stokes) simulations are used as input for the stochastic method, where turbulence is synthesized based on a first-order Langevin ansatz. Sound propagation is modeled in the time domain with a modified set of linearized Euler equations and monopole sound sources are incorporated as right-hand side forcing of the pressure equation at every timestep of the acoustics simulations. First, the reacting steady-state CFD simulations are compared to experimental data, showing very good agreement. Subsequently, the computational combustion acoustics (CCA) setup is introduced, followed by comparisons of numerical with experimental pressure spectra. It is shown that FRPM-CN accurately captures absolute combustion noise levels without any artificial correction. Benchmark runs show that the computational costs of FRPM-CN are much lower than that of direct simulation approaches. The robustness and reliability of the method is demonstrated with parametric studies regarding source grid refinement, the choice of either RANS or URANS statistics, and the employment of different global reaction mechanisms.

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