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

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.

[1]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[2]  Jan Deeken,et al.  Numerically Efficient Fatigue Life Prediction of Rocket Combustion Chambers using Artificial Neural Networks , 2019 .

[3]  Asok Ray,et al.  Life extending controller design for reusable rocket engines , 2001 .

[4]  Christoph Räth,et al.  Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing. , 2019, Chaos.

[5]  Vigor Yang,et al.  Liquid rocket engine combustion instability , 1995 .

[6]  Paris Perdikaris,et al.  Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..

[7]  Günther Waxenegger-Wilfing.  Hardware-In-The-Loop Tests of Complex Control Software for Rocket Propulsion Systems , 2020 .

[8]  LUMEN – DESIGN OF THE REGENERATIVE COOLING SYSTEM FOR AN EXPANDER BLEED CYCLE ENGINE USING METHANE , 2021 .

[9]  Julien Marzat,et al.  A survey of automatic control methods for liquid-propellant rocket engines , 2019, Progress in Aerospace Sciences.

[10]  Michael Oschwald,et al.  A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines , 2021, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Jianjun Wu,et al.  Liquid-propellant rocket engines health-monitoring—a survey , 2005 .

[12]  Dimitris Drikakis,et al.  Machine-Learning Methods for Computational Science and Engineering , 2020, Comput..

[13]  M. Oschwald,et al.  Nonlinear Control of an Expander-Bleed Rocket Engine using Reinforcement Learning Virtual Conference 2021 , 2021 .

[14]  Günther Waxenegger-Wilfing,et al.  Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks , 2019, Journal of Thermophysics and Heat Transfer.

[16]  Ushnish Sengupta,et al.  Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning , 2020, Chaos.

[17]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[18]  H. Piet-Lahanier,et al.  Model-based active fault-tolerant control for a cryogenic combustion test bench , 2020, Acta Astronautica.

[19]  D. Bertsekas.  Reinforcement Learning and Optimal ControlA Selective Overview , 2018 .

[20]  P. Roncioni,et al.  Heat transfer modeling for supercritical methane flowing in rocket engine cooling channels , 2015 .

[21]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[22]  M. Oschwald,et al.  Experimental Study of Methane Heat Transfer Deterioration in a Subscale Combustion Chamber , 2019, Journal of Propulsion and Power.

[23]  Li Liu,et al.  A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges , 2020, Inf. Fusion.