Advanced Technologies for High Energetic Atmospheric Flight of Launcher Stages


The DLR project AHTEAt is researching technologies for reusable space transport systems that are highly cost-efficient. The reusability of launch vehicles is to be made more reliable and safety margins reduced with a view to improving economic efficiency. As part of the ATHEAt project, the technologies developed are being tested in practice in two space flights and in ground tests.

In the ATHEAt project, the Institute of Software Technology is responsible for analysing selected ground test and rocket flight data using algorithms from the field of artificial intelligence. The analysis is divided into several detailed tasks.

On the one hand, it involves determining the mass loss of the material of rocket fins in ground facilities and determining the regression rate during fuel burn-up using optical methods. To do this, we apply neural networks based on a U-Net architecture to high-speed image data in order to automatically detect structures such as the test models of the rocket fin or the rocket propellant. We determine the uncertainties of our detection results using statistical methods (Uncertainty Quantification).

We also analyse ground and flight test data for anomalies in order to detect faults and problems at an early stage. For anomaly detection, we use both density-based algorithms and customised neural networks based on autoencoders or vision transformers.

Project runtime:

  • 2021-2024

Scientific participants:


Dr.-Ing. Achim Basermann

Head of Department
German Aerospace Center (DLR)
Institute of Software Technology
High-Performance Computing
Linder Höhe, 51147 Köln