A Systematic Review and Analysis of Machine Learning models in Liquid Rocket Engine Control
DOI:
https://doi.org/10.47611/jsrhs.v13i4.7649Keywords:
Liquid Rocket Engine, Neural Network, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Anomaly DetectionAbstract
This paper explores machine learning (ML) methods that enhance performance, design, health, and operation of liquid rocket engines. Various ML approaches, including reinforcement learning (RL), supervised, and unsupervised learning can potentially transform rocket propulsion technologies, essential for critical interplanetary missions. Specifically, this study reviews neural network-based models for health monitoring of rocket engines, RL for control of engine ignition and operation, and ML techniques for anomaly detection. The application of these algorithms leads to significant advances in analyzing and predicting rocket engine system performance. While such techniques enhance efficiency, they also present challenges, which are discussed herein. Subsequently, this study provides a comprehensive synthesis of ML techniques as applied to rocket engine diagnostics and prognostics. This approach not only addresses the existing research gap but also lays the groundwork for future explorations into the predictive maintenance and optimization of space propulsion systems.
Downloads
References or Bibliography
Jianjun Wu. Liquid-propellant rocket engines health-monitoring—a survey. Acta Astronautica, 56(3):347–356, 2005.
Zhigang Feng and Qi Wang. Research on health evaluation system of liquid-propellant rocket engine ground-testing bed based on fuzzy theory. Acta Astronautica, 61(10):840–853, 2007.
Richard Strunz and Jeffrey W Herrmann. Reliability as an independent variable applied to liquid rocket engine hot fire test plans. Journal of propulsion and power, 27(5):1032–1044, 2011.
Günther Waxenegger-Wilfing, Kai Dresia, Jan Deeken, and Michael Oschwald. Machine learning methods for the design and operation of liquid rocket engines–research activities at the dlr institute of space propulsion. arXiv preprint arXiv:2102.07109, 2021.
Hao Li and Cor-Paul Bezemer. Studying popular open source machine learning libraries and their cross-ecosystem bindings. arXiv preprint arXiv:2201.07201, 2022.
Dheeraj Yadav. Machine learning: Trends, perspective, and prospects. Science, 349:255–260, 2020.
Nobendu Sen, Gautham M Nair, and A Immanuel Selvakumart. Machine learning in combustion: Optimization of fuel combustion of rockets. Acceleron Aerospace Journal, 2(4):241–250, 2024.
Steven L Brunton, J Nathan Kutz, Krithika Manohar, Aleksandr Y Aravkin, Kristi Morgansen, Jennifer Klemisch, Nicholas Goebel, James Buttrick, Jeffrey Poskin, Adriana W Blom-Schieber, et al. Data-driven aerospace engineering: reframing the industry with machine learning. AIAA Journal, 59(8):2820–2847, 2021.
Mark Schwabacher, Nikunj Oza, and Bryan Matthews. Unsupervised anomaly detection for liquid-fueled rocket propulsion health monitoring. Journal of aerospace computing, information, and communication, 6(7):464–482, 2009.
Rodney A Martin. Unsupervised anomaly detection and diagnosis for liquid rocket engine propulsion. In 2007 IEEE Aerospace Conference, pages 1–15. IEEE, 2007.
Mark Schwabacher. Machine learning for rocket propulsion health monitoring. SAE transactions, pages 1192 1197, 2005.
Oskar J Haidn. Advanced rocket engines. Advances on propulsion technology for high-speed aircraft, 1:6–1, 2008.
Kai Dresia, Günther Waxenegger-Wilfing, Robson Henrique Dos Santos Hahn, Jan C Deeken, and Michael Oschwald. Nonlinear control of an expander-bleed rocket engine using reinforcement learning. 2021.
Daniel Klinger, Alex Casey, Tim Manship, Steven Son, and Alejandro Strachan. Prediction of solid propel- lant burning rate characteristics using machine learning techniques. Propellants, Explosives, Pyrotechnics, 48(4):e202200267, 2023.
Vladimir Nasteski. An overview of the supervised machine learning methods. Horizons. b, 4(51-62):56, 2017.
Nur Amalina Diyana Suhaimi and Hafiza Abas. A systematic literature review on supervised machine learning algorithms. Perintis Ejournal, 10(1):1–24, 2020.
Batta Mahesh. Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9(1):381–386, 2020.
Leo Breiman. Random forests. Machine learning, 45:5–32, 2001.
Paul R Gradl and Christopher S Protz. Channel wall nozzle manufacturing technology advancements for liquid rocket engines. In International Astronautical Congress (IAC), 2019, number M19-7683, 2019.
Hao Ma, Yu-xuan Zhang, Oskar J Haidn, Nils Thuerey, and Xiang-yu Hu. Supervised learning mixing characteris- tics of film cooling in a rocket combustor using convolutional neural networks. Acta Astronautica, 175:11–18, 2020.
John A Halchak, James L Cannon, and Corey Brown. Materials for liquid propulsion systems. Technical report, American Institute of Aeronautics and Astronautics, 2018.
R Amri and T Rezoug. Numerical study of liquid propellants combustion for space applications. Acta Astronautica, 69(7-8):485–498, 2011.
Kai Dresia, Eldin Kurudzija, Jan Deeken, and Günther Waxenegger-Wilfing. Improved wall temperature prediction for the lumen rocket combustion chamber with neural networks. Aerospace, 10(5):450, 2023.
Mingming Guo, Xue Deng, Yue Ma, Ye Tian, Jialing Le, and Hua Zhang. Hypersonic inlet flow field reconstruction dominated by shock wave and boundary layer based on small sample physics-informed neural networks. Aerospace Science and Technology, 150:109205, 2024.
David L Iverson, Rodney Martin, Mark Schwabacher, Lilly Spirkovska, William Taylor, Ryan Mackey, J Patrick Castle, and Vijayakumar Baskaran. General purpose data-driven monitoring for space operations. Journal of Aerospace Computing, Information, and Communication, 9(2):26–44, 2012.
Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore. Reinforcement learning: A survey. Journal of artificial intelligence research, 4:237–285, 1996.
Tobias Kaiser. Optimal control of liquid propellant rocket engines for landing of reusable stages using deep reinforcement learning. PhD thesis, Master’s thesis, Universitat Wurzburg, 2021.
Martin Q Ma, Yue Zhao, Xiaorong Zhang, and Leman Akoglu. The need for unsupervised outlier model selection: A review and evaluation of internal evaluation strategies. ACM SIGKDD Explorations Newsletter, 25(1):19–35, 2023
Zhiping Mao, Ameya D Jagtap, and George Em Karniadakis. Physics-informed neural networks for high-speed flows. Computer Methods in Applied Mechanics and Engineering, 360:112789, 2020.
Sathesh Mariappan, Kamaljyoti Nath, and George Em Karniadakis. Learning thermoacoustic interactions in combustors using a physics-informed neural network. arXiv preprint arXiv:2401.00061, 2023.
Rodney A Martin. An investigation of state-space model fidelity for ssme data. In 2008 International Conference on Prognostics and Health Management, pages 1–12. IEEE, 2008.
BRYAN MATTHEWS and ASHOK N SRIVASTAVA. Comparative analysis of data-driven anomaly detection methods on solid rocket motor faults.
Maziar Raissi, Paris Perdikaris, and George E Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378:686–707, 2019.
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis. Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations. arXiv preprint arXiv:1711.10561, 2017.
Mark Schwabacher. A survey of data-driven prognostics. Infotech@ Aerospace, page 7002, 2005.
Wei Shyy, P Kevin Tucker, and Rajkumar Vaidyanathan. Response surface and neural network techniques for rocket engine injector optimization. Journal of Propulsion and Power, 17(2):391–401, 2001.
Günther Waxenegger-Wilfing, Kai Dresia, Jan Deeken, and Michael Oschwald. A reinforcement learning approach for transient control of liquid rocket engines. IEEE Transactions on Aerospace and Electronic Systems, 57(5):2938–2952, 2021.
Günther Waxenegger-Wilfing, Ushnish Sengupta, Jan Martin, Wolfgang Armbruster, Justin Hardi, Matthew Juniper, and Michael Oschwald. Early detection of thermoacoustic instabilities in a cryogenic rocket thrust chamber using combustion noise features and machine learning. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(6), 2021.
Huahuang Yu and Tao Wang. A method for real-time fault detection of liquid rocket engine based on adaptive genetic algorithm optimizing back propagation neural network. Sensors, 21(15):5026, 2021.
Lodewijk Kallenberg. Markov decision processes. Lecture Notes. University of Leiden, 428, 2011.
Published
How to Cite
Issue
Section
Copyright (c) 2024 Malhar Gandhe
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
