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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.
Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning
In most cases, the method is able to timely predict two types of thermoacoustic instabilities on test data not used for training.
Multidisciplinary Design Optimization of Reusable Launch Vehicles for Different Propellants and Objectives
Finally, launch vehicles using a hydrocarbon fuel in the first stage and liquid hydrogen in the upper stage are an appealing alternative, combining both fuels' benefits.
A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines
The open-loop control system cannot provide optimal engine performance due to external disturbances or the degeneration of engine components over time.
Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks
Methane is considered being a good choice as a propellant for future reusable launch systems.
