Výsledky vyhledávání - "Oschwald, Michael"

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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

Autor: Waxenegger-Wilfing, Günther, Dresia, Kai, Deeken, Jan, Oschwald, Michael

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

Externí odkaz: http://arxiv.org/abs/2102.07109

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

Autor: Waxenegger-Wilfing, Günther, Sengupta, Ushnish, Martin, Jan, Armbruster, Wolfgang, Hardi, Justin, Juniper, Matthew, Oschwald, Michael

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 c

Externí odkaz: http://arxiv.org/abs/2011.14985

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Multidisciplinary Design Optimization of Reusable Launch Vehicles for Different Propellants and Objectives

Autor: Dresia, Kai, Jentzsch, Simon, Waxenegger-Wilfing, Günther, Hahn, Robson, Deeken, Jan, Oschwald, Michael, Mota, Fabio

Identifying the optimal design of a new launch vehicle is most important since design decisions made in the early development phase limit the vehicles' later performance and determines the associated costs. Reusing the first stage via retro-propulsiv

Externí odkaz: http://arxiv.org/abs/2009.01664

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A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines

Autor: Waxenegger-Wilfing, Günther, Dresia, Kai, Deeken, Jan Christian, Oschwald, Michael

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, i

Externí odkaz: http://arxiv.org/abs/2006.11108

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

Autor: Waxenegger-Wilfing, Günther, Dresia, Kai, Deeken, Jan Christian, Oschwald, Michael

Methane is considered being a good choice as a propellant for future reusable launch systems. However, the heat transfer prediction for supercritical methane flowing in cooling channels of a regeneratively cooled combustion chamber is challenging. Be

Externí odkaz: http://arxiv.org/abs/1907.11281

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