Injector-coupled thermoacoustic instabilities in an experimental LOX-methane rocket combustor during start-up

Klein, Sebastian; Börner, Michael; Hardi, Justin; Suslov, Dmitry; Oschwald, Michael

doi:10.1007/s12567-019-00294-4

Cited by 11 publications

(6 citation statements)

References 11 publications

(17 reference statements)

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“…Future work will focus on exploring the effectiveness of these tools on DLR’s other experimental datasets, such as the LOX-methane tests, which also displayed injection-coupled acoustic instabilities. 37 We will also explore the generalizability of data-driven diagnostics across multiple datasets and combustors, using approaches that utilise transfer learning or domain invariance. Hardware upgrades are currently being undertaken at the test facility that will allow the implementation of these methods into the engine control algorithm.…”

Section: Discussionmentioning

confidence: 99%

Forecasting thermoacoustic instabilities in liquid propellant rocket engines using multimodal Bayesian deep learning

Sengupta

Waxenegger-Wilfing

Martin

et al. 2022

International Journal of Spray and Combustion Dynamics

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We present a method that combines multiple sensory modalities in a rocket thrust chamber to predict impending thermoacoustic instabilities with uncertainties. This is accomplished by training an autoregressive Bayesian neural network model that forecasts the future amplitude of the dynamic pressure time series, using multiple sensor measurements (injector pressure/ temperature measurements, static chamber pressure, high-frequency dynamic pressure measurements, high-frequency OH* chemiluminescence measurements) and future flow rate control signals as input. The method is validated using experimental data from a representative cryogenic research thrust chamber. The Bayesian nature of our algorithms allows us to work with a dataset whose size is restricted by the expense of each experimental run, without making overconfident extrapolations. We find that the networks are able to accurately forecast the evolution of the pressure amplitude and anticipate instability events on unseen experimental runs 500 milliseconds in advance. We compare the predictive accuracy of multiple models using different combinations of sensor inputs. We find that the high-frequency dynamic pressure signal is particularly informative. We also use the technique of integrated gradients to interpret the influence of different sensor inputs on the model prediction. The negative log-likelihood of data points in the test dataset indicates that prediction uncertainties are well-characterized by our model and simulating a sensor failure event results in a dramatic increase in the epistemic component of the uncertainty, as would be expected when a Bayesian method encounters unfamiliar, out-of-distribution inputs.

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Section: Discussionmentioning

confidence: 99%

Forecasting thermoacoustic instabilities in liquid propellant rocket engines using multimodal Bayesian deep learning

Sengupta

Waxenegger-Wilfing

Martin

et al. 2022

International Journal of Spray and Combustion Dynamics

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“…Cases of coupling between the liquid oxidizer (LOX) tube and the combustion chamber, have been reported by Jensen et al 5 , for the J-2S engine, 4,6 by Klein et al 7 , Martin et al 8 and others. [9][10][11] This shows that LOX injection coupling is a common excitation source of thermoacoustic instabilities in cryogenic rocket engines.…”

Section: Introductionmentioning

confidence: 91%

“…Klein et al 7 described that the injection-coupled combustion instability in a LOX/LNG combustor was related to the two-phase flow of the injected LOX. Another recent studiy also reported a connection between low-frequency combustion instabilities in a rocket engine with two-phase flow effects in the injection system.…”

Section: Analysis Of the Internal Excitation Mechanism Of The Lox Pos...mentioning

confidence: 99%

Impact of shear-coaxial injector hydrodynamics on high-frequency combustion instabilities in a representative cryogenic rocket engine

Armbruster¹,

Hardi²,

Oschwald

2022

International Journal of Spray and Combustion Dynamics

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The excitation mechanism of a thermoacoustic instability in a 42-element research rocket thrust chamber with representative operating conditions with respect to European cryogenic rocket engines is investigated in detail. From previous research it was known that the chamber 1T mode can be excited by persistent heat release rate oscillations which are modulated by the resonant modes of the liquid oxygen injectors. The excitation source of the longitudinal injector eigenmodes is investigated in this study. Fibre-optical probes measuring the OH* dynamics from the recess volume of two injectors showed additional frequency content which could neither be explained by the chamber acoustics, nor the acoustics of the injection system. Instead, the temporal evolution of these frequencies correlate with the oxidizer flow velocity. In this work we show that the additional flame modulation originates from a hydrodynamic effect in the injection system. Even though the exact process cannot be precisely identified, an effect designated orifice whistling at the injector inlet orifice seems to be a likely candidate. Combining the new results with previous publications about this combustor, it is now possible to explain past and present observations in terms of the hydrodynamic and thermoacoustic conditions which are necessary for the combustion instability to appear. The conditions, which lead to an injection-driven excitation of the 1T mode are matching frequencies of the 2L mode of the injectors and the chamber 1T mode as well as a Strouhal number between 0.2 and 0.4 based on the length and flow velocity of the injector inlet orifice.

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“…Future work will focus on exploring the effectiveness of these tools on DLR's other experimental datasets, such as the LOX-methane tests, which also displayed injection-coupled acoustic instabilities [39]. We will also explore the generalizability of data-driven diagnostics across multiple datasets and combustors, using approaches that utilise transfer learning or domain invariance.…”

Section: Acknowledgementsmentioning

confidence: 99%

Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning

Sengupta¹,

Waxenegger-Wilfing²,

Martin³

et al. 2021

Preprint

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The 100 MW cryogenic liquid oxygen/hydrogen multi-injector combustor BKD operated by the DLR Institute of Space Propulsion is a research platform that allows the study of thermoacoustic instabilities under realistic conditions, representative of small upper stage rocket engines. We use data from BKD experimental campaigns in which the static chamber pressure and fueloxidizer ratio are varied such that the first tangential mode of the combustor is excited under some conditions. We train an autoregressive Bayesian neural network model to forecast the amplitude of the dynamic pressure time series, inputting multiple sensor measurements (injector pressure/ temperature measurements, static chamber pressure, high-frequency dynamic pressure measurements, high-frequency OH* chemiluminescence measurements) and future flow rate control signals. The Bayesian nature of our algorithms allows us to work with a dataset whose size is restricted by the expense of each experimental run, without making overconfident extrapolations. We find that the networks are able to accurately forecast the evolution of the pressure amplitude and anticipate instability events on unseen experimental runs 500 milliseconds in advance. We compare the predictive accuracy of multiple models using different combinations of sensor inputs. We find that the high-frequency dynamic pressure signal is particularly informative. We

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Injector-coupled thermoacoustic instabilities in an experimental LOX-methane rocket combustor during start-up

Cited by 11 publications

References 11 publications

Forecasting thermoacoustic instabilities in liquid propellant rocket engines using multimodal Bayesian deep learning

Forecasting thermoacoustic instabilities in liquid propellant rocket engines using multimodal Bayesian deep learning

Impact of shear-coaxial injector hydrodynamics on high-frequency combustion instabilities in a representative cryogenic rocket engine

Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning

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