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Research Papers: Gas Turbines: Combustion, Fuels, and Emissions

Broadband Combustion Noise Simulation of the PRECCINSTA Burner Based on Stochastic Sound Sources

[+] Author and Article Information
Felix Grimm

Institute of Combustion Technology,
German Aerospace Center (DLR),
Pfaffenwaldring 38-40,
Stuttgart 70569, Germany
e-mail: felix.grimm@dlr.de

Duncan Ohno, Berthold Noll, Manfred Aigner

Institute of Combustion Technology,
German Aerospace Center (DLR),
Pfaffenwaldring 38-40,
Stuttgart 70569, Germany

Roland Ewert, Jürgen Dierke

Institute of Aerodynamics and Flow Technology,
German Aerospace Center (DLR),
Lilienthalplatz 7,
Braunschweig 38108, Germany

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received June 21, 2016; final manuscript received June 30, 2016; published online September 8, 2016. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(1), 011505 (Sep 08, 2016) (10 pages) Paper No: GTP-16-1254; doi: 10.1115/1.4034236 History: Received June 21, 2016; Revised June 30, 2016

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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Figures

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Fig. 2

Dimensions of PIV, Raman, and acoustic pressure measurements for validation studies in the PRECCINSTA burner

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Fig. 1

Schematic drawing of the PRECCINSTA burner with exemplary flame structure and basic dimensions [37]

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Fig. 3

CFD computational domain of the PRECCINSTA burner with local cell volume on intersecting planes. Highlighted are swirler and air–fuel mixing region. Bright color indicates Vcell=1×10−10m3 and dark color Vcell=3.5×10−11m3.

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Fig. 4

Block-structured mesh for the CCA simulations with employed boundary conditions

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Fig. 10

CFD computational domain with midplane distribution of temperature, lateral surfaces with instantaneous acoustic pressure fluctuation, and isosurfaces of the combustion noise source term. Quantities of acoustics simulations referenced to air plenum atmospheric conditions, qp=qp/(fρc2) and p′=p′/(ρc2).

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Fig. 11

Power spectral density of experimental sound pressure and FRPM-CN parametric studies. Simulation cases according to Table 1. TP: Technically premixed and PP: Perfectly premixed.

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Fig. 5

Discrete realization of effective source standard deviation from CFD-RANS quantities with chosen source field extensions, depicted on the CFD computational domain

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Fig. 6

Comparison of axial velocity of different simulation configurations with experimental data on lateral profile lines: (1) PIV measurements; (2) RANS, N5-DLR reaction mechanism; (3) URANS, N5-DLR reaction mechanism; (4) RANS, WB1 reaction mechanism; and (5) URANS, WB1 reaction mechanism

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Fig. 7

Comparison of radial velocity of different simulation configurations with experimental data on lateral profile lines: (1) PIV measurements; (2) RANS, N5-DLR reaction mechanism; (3) URANS, N5-DLR reaction mechanism; (4) RANS, WB1 reaction mechanism; and (5) URANS, WB1 reaction mechanism

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Fig. 8

Comparison of temperature of different simulation configurations with experimental data on lateral profile lines: (1) Raman measurements; (2) RANS, N5-DLR reaction mechanism; (3) URANS, N5-DLR reaction mechanism; (4) RANS, WB1 reaction mechanism; and (5) URANS, WB1 reaction mechanism

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Fig. 9

Comparison of temperature RMS of different simulation configurations with experimental data on lateral profile lines: (1) Raman measurements; (2) RANS, N5-DLR reaction mechanism; (3) URANS, N5-DLR reaction mechanism; (4) RANS, WB1 reaction mechanism; and (5) URANS, WB1 reaction mechanism

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