@inproceedings{MoehrenBergmannJanseretal.2023,
  author    = {M{\"o}hren, Felix and Bergmann, Ole and Janser, Frank and Braun, Carsten},
  title     = {On the determination of harmonic propeller loads},
  series = {AIAA SCITECH 2023 Forum},
  booktitle = {AIAA SCITECH 2023 Forum},
  publisher = {AIAA},
  doi       = {10.2514/6.2023-2404},
  pages     = {12 Seiten},
  year      = {2023},
  abstract  = {Dynamic loads significantly impact the structural design of propeller blades due to fatigue and static strength. Since propellers are elastic structures, deformations and aerodynamic loads are coupled. In the past, propeller manufacturers established procedures to determine unsteady aerodynamic loads and the structural response with analytical steady-state calculations. According to the approach, aeroelastic coupling primarily consists of torsional deformations. They neglect bending deformations, deformation velocities, and inertia terms. This paper validates the assumptions above for a General Aviation propeller and a lift propeller for urban air mobility or large cargo drones. Fully coupled reduced-order simulations determine the dynamic loads in the time domain. A quasi-steady blade element momentum approach transfers loads to one-dimensional finite beam elements. The simulation results are in relatively good agreement with the analytical method for the General Aviation propeller but show increasing errors for the slender lift propeller. The analytical approach is modified to consider the induced velocities. Still, inertia and velocity proportional terms play a significant role for the lift propeller due to increased elasticity. The assumption that only torsional deformations significantly impact the dynamic loads of propellers is not valid. Adequate determination of dynamic loads of such designs requires coupled aeroelastic simulations or advanced analytical procedures.},
  language  = {en}
}
@inproceedings{ThomaStiemerBraunetal.2023,
  author    = {Thoma, Andreas and Stiemer, Luc and Braun, Carsten and Fisher, Alex and Gardi, Alessandro G.},
  title     = {Potential of hybrid neural network local path planner for small UAV in urban environments},
  series = {AIAA SCITECH 2023 Forum},
  booktitle = {AIAA SCITECH 2023 Forum},
  publisher = {AIAA},
  doi       = {10.2514/6.2023-2359},
  pages     = {13 Seiten},
  year      = {2023},
  abstract  = {This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17\%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.},
  language  = {en}
}