@inproceedings{StarkRiepingEsch2023,
  author    = {Stark, Ralf and Rieping, Carla and Esch, Thomas},
  title     = {The impact of guide tubes on flow separation in rocket nozzles},
  series = {Aerospace Europe Conference 2023 - 10th EUCASS - 9th CEAS},
  booktitle = {Aerospace Europe Conference 2023 - 10th EUCASS - 9th CEAS},
  pages     = {8 Seiten},
  year      = {2023},
  abstract  = {Rocket engine test facilities and launch pads are typically equipped with a guide tube. Its purpose is to ensure the controlled and safe routing of the hot exhaust gases. In addition, the guide tube induces a suction that effects the nozzle flow, namely the flow separation during transient start-up and shut-down of the engine. A cold flow subscale nozzle in combination with a set of guide tubes was studied experimentally to determine the main influencing parameters.},
  language  = {en}
}
@inproceedings{StarkBartelDitscheetal.2023,
  author    = {Stark, Ralf and Bartel, Sebastian and Ditsche, Florian and Esch, Thomas},
  title     = {Design study of a 30kN LOX/LCH4 aerospike rocket engine for lunar lander application},
  series = {Aerospace Europe Conference 2023 - 10th EUCASS - 9th CEAS},
  booktitle = {Aerospace Europe Conference 2023 - 10th EUCASS - 9th CEAS},
  pages     = {9 Seiten},
  year      = {2023},
  abstract  = {Based on lunar lander concept EL3, various LOX/CH4 aerospike engines were studied. A distinction was made between single and cluster configurations as well as ideal and non-ideal contour concepts. It could be shown that non-ideal aerospike engines promise a significant payload gain.},
  language  = {en}
}
@article{FayyaziSardarThomasetal.2023,
  author    = {Fayyazi, Mojgan and Sardar, Paramjotsingh and Thomas, Sumit Infent and Daghigh, Roonak and Jamali, Ali and Esch, Thomas and Kemper, Hans and Langari, Reza and Khayyam, Hamid},
  title     = {Artificial intelligence/machine learning in energy management systems, control, and optimization of hydrogen fuel cell vehicles},
  volume    = {15},
  number    = {6},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/su15065249},
  pages     = {38},
  year      = {2023},
  abstract  = {Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.},
  language  = {en}
}