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

Noise-Induced Dynamics in the Subthreshold Region in Thermoacoustic Systems

[+] Author and Article Information
Aditya Saurabh

Chair of Fluid Dynamics
Technische Universität Berlin,
Berlin 10623, Germany
e-mail: aditya.saurabh@tu-berlin.de

Lipika Kabiraj

Chair of Fluid Dynamics
Technische Universität Berlin,
Berlin 10623, Germany
e-mail: lipika.kabiraj@gmail.com

Richard Steinert, Christian Oliver Paschereit

Chair of Fluid Dynamics
Technische Universität Berlin,
Berlin 10623, Germany

1Corresponding authors.

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 5, 2016; final manuscript received July 30, 2016; published online October 11, 2016. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(3), 031508 (Oct 11, 2016) (6 pages) Paper No: GTP-16-1305; doi: 10.1115/1.4034544 History: Received July 05, 2016; Revised July 30, 2016

This article is a report of experiments conducted in order to investigate the role of noise on thermoacoustic systems. In contrast to most studies in this direction, in the present work, the role of noise in the subthreshold region, prior to the (subcritical) Hopf bifurcation and the associated saddle-node bifurcation, is considered. In this regime, a thermoacoustic system is stable and does not undergo transition to self-excited thermoacoustic oscillations. However, the system can feature dynamics, which arise due to the proximity of the system to the approaching Hopf bifurcation, in response to noise. Experiments were performed on a model thermoacoustic system featuring a laminar flat flame. Noise was introduced in a controlled manner, and the effect of increasing levels of noise intensity was studied. Results presented here show that noise addition induces coherent oscillations. The induced coherence is observed to depend on the noise amplitude and the proximity to the Hopf bifurcation. Furthermore, this noise-induced behavior is characterized by a well-defined “resonance-like” response of the system: An optimum level of coherence is induced for an intermediate level of noise. These results can be of importance in practical thermoacoustic systems (e.g., combustors), which are inherently noisy due to factors such as flow turbulence and combustion noise.

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Fig. 1

Schematic of the premixed (quasi-) flat flame combustor under investigation. The flame stabilizes on the perforated plate at the interface of the upstream duct and the quartz glass duct.

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Fig. 2

Details of the subcritical Hopf bifurcation: values of the parameter, ϕ, are mentioned as labels. The stable (thin line), unstable (thick line), and the bistable (hatched) regions are as indicated.

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Fig. 3

The shapes of the amplitude distribution of (a) pressure amplitude fluctuations and (b) the heat release rate fluctuations corresponding to three noise levels (D = 5.7 Pa (), 9.9 Pa (○), and 18.4 Pa (▾)) for varying equivalence ratios. The variation of the shapes from unimodal to bimodal can be seen except for the parameter case ϕ=0.714, where a unimodal shape exists only for low noise amplitudes.

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Fig. 4

The response observed in the amplitude of p’ fluctuations to various noise levels in the subthreshold region of the system. Noise levels and the limit cycle amplitude are indicated.

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Fig. 5

The response observed in the amplitude of q’ fluctuations to various noise levels in the subthreshold region of the system. Noise level is the same as in Fig. 4, and the limit cycle amplitude is indicated.

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Fig. 6

Pressure spectra for three parameters values are shown together with the isothermal case to the modification in the features of the spectral density close to the peak frequency, fp, in presence of increasing noise levels, at three different values of ϕ. Darker shades indicate larger noise intensity.

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Fig. 7

Illustration for β calculation from spectral density plots: the solid curve is a Lorentzian fit to the spectra (points). The light gray curves are examples of inappropriate fit.

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Fig. 8

Variation of coherence factor, β, with noise levels. The solid curves drawn through each data are fits to emphasize the trend followed by β for the corresponding parameter values.

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Fig. 9

Autocorrelation for acoustic pressure for parameter value, ϕ=0.708, at three noise levels: low (top), intermediate (middle), and high (bottom). The curve for the intermediate noise level plot has the slowest decay, indicating that noise induced a greater coherence for this noise level compared to low and high levels.




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