Spontaneous Longitudinal Combustion Instability in a Continuously-Variable Resonance Combustor

Yu, Yen; Sisco, James C.; Rosen, Stanford; Madhav, Ajay; Anderson, William E.

doi:10.2514/1.59770

Cited by 24 publications

(35 citation statements)

References 13 publications

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“…Experimental Overview The CVRC experiment has been well documented by Yu et al, 13 this section serves to briefly introduce the experiment and overview some relevant results which will help to anchor the computational results in the subsequent section. The experiment uses gaseous methane for the fuel and decomposed hydrogen peroxide for the oxidizer, a summary of the operating conditions is shown in Table 1.…”

Section: Comparison Of Computations and Experimentsmentioning

confidence: 98%

Combustion Instability Mechanisms in a Pressure-coupled Gas-gas Coaxial Rocket Injector

Harvazinski

Huang

Sankaran

et al. 2013

49th AIAA/ASME/SAE/ASEE Joint Propulsion Conference

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An investigation of the instability mechanism present in a laboratory rocket combustor is performed using computational fluid dynamics (CFD) simulations. Three cases are considered which show different levels of instability experimentally. Computations reveal three main aspects to the instability mechanism, the timing of the pressure pulses, increased mixing due to the baroclinic torque, and the presence of unsteady tribrachial flame. The stable configuration shows that fuel is able to flow into the combustor continuously allowing continuous heat release. The unstable configuration shows that a disruption in the fuel flow into the combustor allows the heat release to move downstream and new fuel to accumulate in the combustor without immediately burning. Once the large amounts of fuel in the combustor burn there is rapid rise in pressure which coincides with the timing of the acoustic wave in the combustor. The two unstable cases show different levels of instability and different reignition mechanisms.

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Section: Comparison Of Computations and Experimentsmentioning

confidence: 98%

Combustion Instability Mechanisms in a Pressure-coupled Gas-gas Coaxial Rocket Injector

Harvazinski

Huang

Sankaran

et al. 2013

49th AIAA/ASME/SAE/ASEE Joint Propulsion Conference

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“…[25][26][27] The CVRC is a dump combustor with coaxial injection of decomposed hydrogen peroxide and methane gas. The fluidic oscillator sweeping jet will be used to increase mixing in the recirculation zone after the dump plane and disrupt the location of the tibrachial flame which is thought to have an effect on combustion instability.…”

Section: Discussionmentioning

confidence: 99%

Computational Characterization of the Feedback Free Fluidic Oscillator

Meier¹,

Heister²

2014

50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference

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The feedback free fluidic oscillator uses the unsteady nature of two colliding jets to create a single oscillating outlet jet with a wide sweep angle. These devices have the potential to increase mixing in combustion applications and provide additional combustion control. The work presented in this paper uses two-dimensional computational fluid dynamics, CFD, to analyze the jet oscillation frequency over a range of operating conditions and to determine the effect that geometric changes in the oscillator design have on the frequency. In addition, microphone data gathered from six 3D printed fluidic oscillators is used to validate the CFD and correlate the 2D analysis to several oscillator aspect ratios. Results presented in this paper illustrate the changes in jet oscillation frequency with gas type, gas temperature, operating pressure, pressure ratio across the oscillator, aspect ratio of the oscillator, and the frequency trends with various changes to the oscillator geometry.

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“…In this work, a quasi-1D version of the single element Continuously Variable Resonance Combustor (CVRC) [36,37] is used as the full order model. The geometry of the CVRC is sketched in Fig.…”

Section: A Full Order Modelmentioning

confidence: 99%

Reduced-Order Modeling Framework for Combustor Instabilities Using Truncated Domain Training

Huang

Duraisamy

2020

AIAA Journal

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A multi-fidelity framework is established and demonstrated for prediction of combustion instabilities in rocket engines. The major idea is to adapt appropriate fidelity modeling approaches for different components in a rocket engine to ensure accurate and efficient predictions. Specifically, the proposed framework integrates projection-based Reduced Order Models (ROMs) that are developed using bases generated on truncated domain simulations. The ROM training is performed on truncated domains, and thus does not require full order model solutions on the full rocket geometry, thus demonstrating the potential to greatly reduce training cost. Geometry-specific training is replaced by the response generated by perturbing the characteristics at the boundary of the truncated domain. This training method is shown to enhance predictive capabilities and robustness of the resulting ROMs, including at conditions outside the training range. Numerical tests are conducted on a quasi-1D model of a single-element rocket combustor and the present framework is compared to traditional ROM development approaches. Nomenclature α = amplification factor A = cross sectional area C f /o = stoichiometric fuel-to-oxidizer ratio η = cumulative energy in singular values gr = pressure oscillation growth rate J = characteristic invariant for incoming acoustic wave k = number of modes in proper orthogonal decomposition (POD) basis L c = chamber length l s = starting location of fuel injection l f = ending location of fuel injection

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Spontaneous Longitudinal Combustion Instability in a Continuously-Variable Resonance Combustor

Cited by 24 publications

References 13 publications

Combustion Instability Mechanisms in a Pressure-coupled Gas-gas Coaxial Rocket Injector

Combustion Instability Mechanisms in a Pressure-coupled Gas-gas Coaxial Rocket Injector

Computational Characterization of the Feedback Free Fluidic Oscillator

Reduced-Order Modeling Framework for Combustor Instabilities Using Truncated Domain Training

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