Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks

Waxenegger-Wilfing, Günther; Dresia, Kai; Deeken, Jan C.; Oschwald, Michael

doi:10.2514/1.t5865

Cited by 41 publications

(17 citation statements)

References 27 publications

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“…Hence, we can directly use Stable-Baselines for training and testing. A big advantage of the RL approach is that it works regardless of whether one uses a lumped parameter model, continuous state-space models, surrogate models employing artificial neural networks [40], [41], or a combination of the above.…”

Section: Simulation Environment and Rl Implementationmentioning

confidence: 99%

A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines

Waxenegger-Wilfing¹,

Dresia²,

Deeken³

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

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Nowadays, liquid rocket engines use closed-loop control at most near steady operating conditions. The control of the transient phases is traditionally performed in open-loop due to highly nonlinear system dynamics. This situation is unsatisfactory, in particular for reusable engines. The open-loop control system cannot provide optimal engine performance due to external disturbances or the degeneration of engine components over time. In this paper, we study a deep reinforcement learning approach for optimal control of a generic gas-generator engine's continuous start-up phase. It is shown that the learned policy can reach different steady-state operating points and convincingly adapt to changing system parameters. Compared to carefully tuned open-loop sequences and PID controllers, the deep reinforcement learning controller achieves the highest performance. In addition, it requires only minimal computational effort to calculate the control action, which is a big advantage over approaches that require online optimization, such as model predictive control.

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Section: Simulation Environment and Rl Implementationmentioning

confidence: 99%

A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines

Waxenegger-Wilfing¹,

Dresia²,

Deeken³

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

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“…An accurate NN-based surrogate model for the maximum wall temperature along the cooling channel is developed by Waxenegger-Wilfing et al [9]. The model is extended for different channel curvatures and rib thicknesses and used to study the cooling channel performance of the LUMEN engine [10].…”

Section: Nn-based Surrogate Model For the Maximum Wall Temperaturementioning

confidence: 99%

“…Modern ML methods offer a potent possibility to reduce the numerical effort. Similar to the method used in [9] NNs are trained by Dresia et al [12] using samples of the computationally expensive calculation. The training data is generated by a FEM calculation of the first loading cycle followed by a fatigue life estimation during post-processing that includes Coffin-Manson theory and ductile failure.…”

Section: Nn-based Surrogate Model For the Fatigue Life Estimationmentioning

confidence: 99%

Machine Learning Methods for the Design and Operation of Liquid Rocket Engines -- Research Activities at the DLR Institute of Space Propulsion

Waxenegger-Wilfing,

Dresia,

Deeken

et al. 2021

Preprint

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The last years have witnessed an enormous interest in the use of artificial intelligence methods, especially machine learning algorithms. This also has a major impact on aerospace engineering in general, and the design and operation of liquid rocket engines in particular, and research in this area is growing rapidly. The paper describes current machine learning applications at the DLR Institute of Space Propulsion. Not only applications in the field of modeling are presented, but also convincing results that prove the capabilities of machine learning methods for control and condition monitoring are described in detail. Furthermore, the advantages and disadvantages of the presented methods as well as current and future research directions are discussed.Recent research and development activities at the DLR Institute of Space Propulsion prove the feasibility of such methods for supporting the design and operation of liquid rocket engines. So far, NNs are used for the prediction of heat transfer in rocket engine cooling channels and fatigue life estimation. Other applications include the automatic discovery of suitable precursors to combustion instabilities and optimal control of the engines.

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“…Related work by McCartney et al 20 uses the detrended fluctuation analysis (DFA) spectrum of the pressure signal as input to a random forest and finds that this approach compares favorably to precursors from the literature. Recent works have investigated machine learning methods for the design and operation of cryogenic rocket engines [21][22][23][24] .…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning

Waxenegger-Wilfing,

Sengupta,

Martin

et al. 2020

Preprint

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Combustion instabilities are particularly problematic for rocket thrust chambers because of their high energy release rates and their operation close to the structural limits. In the last decades, progress has been made in predicting high amplitude combustion instabilities but still, no reliable prediction ability is given. Reliable early warning signals are the main requirement for active combustion control systems. In this paper, we present a data-driven method for the early detection of thermoacoustic instabilities. Recurrence quantification analysis is used to calculate characteristic combustion features from short-length time series of dynamic pressure sensor data. Features like the recurrence rate are used to train support vector machines to detect the onset of an instability a few hundred milliseconds in advance. The performance of the proposed method is investigated on experimental data from a representative LOX/H 2 research thrust chamber. In most cases, the method is able to timely predict two types of thermoacoustic instabilities on test data not used for training. The results are compared with state-of-the-art early warning indicators.High-amplitude pressure oscillations known as combustion instability are an issue for all chemical propulsion systems. Combustion instabilities are particularly problematic for rocket thrust chambers because of their high energy release rates. Especially thermoacoustic oscillations are a major hazard, but difficult to predict. An important question is whether features of combustion noise can be used to construct reliable early warning signals for representative rocket thrust chambers. Among other things, instability precursors are needed for active combustion control systems. In this study, we use recurrence quantification analysis and well-known machine learning models to construct an early warning signal that should be able to predict high-amplitude instabilities a few hundred milliseconds in advance. The performance of the proposed method is investigated on experimental data from a representative LOX/H 2 research thrust chamber. We describe the test case in detail and also evaluate other measures like the Hurst exponent.

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Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks

Cited by 41 publications

References 27 publications

A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines

A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines

Machine Learning Methods for the Design and Operation of Liquid Rocket Engines -- Research Activities at the DLR Institute of Space Propulsion

Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning

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