@inproceedings{GoettenFingerHavermannetal.2019,
  author    = {G{\"o}tten, Falk and Finger, Felix and Havermann, Marc and Braun, Carsten and Marino, Matthew and Bil, Cees},
  title     = {A highly automated method for simulating airfoil characteristics at low Reynolds number using a RANS - transition approach},
  series = {Deutscher Luft- und Raumfahrtkongress - DLRK 2019. Darmstadt, Germany},
  booktitle = {Deutscher Luft- und Raumfahrtkongress - DLRK 2019. Darmstadt, Germany},
  doi       = {10.25967/490026},
  pages     = {1 -- 14},
  year      = {2019},
  language  = {en}
}
@inproceedings{GoettenFingerBraunetal.2019,
  author    = {G{\"o}tten, Falk and Finger, Felix and Braun, Carsten and Havermann, Marc and Bil, C. and Gomez, F.},
  title     = {Empirical Correlations for Geometry Build-Up of Fixed Wing Unmanned Air Vehicles},
  series = {APISAT 2018: The Proceedings of the 2018 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2018)},
  booktitle = {APISAT 2018: The Proceedings of the 2018 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2018)},
  publisher = {Springer},
  address   = {Singapore},
  isbn      = {978-981-13-3305-7},
  doi       = {10.1007/978-981-13-3305-7_109},
  pages     = {1365 -- 1381},
  year      = {2019},
  abstract  = {The results of a statistical investigation of 42 fixed-wing, small to medium sized (20 kg-1000 kg) reconnaissance unmanned air vehicles (UAVs) are presented. Regression analyses are used to identify correlations of the most relevant geometry dimensions with the UAV's maximum take-off mass. The findings allow an empirical based geometry-build up for a complete unmanned aircraft by referring to its take-off mass only. This provides a bridge between very early design stages (initial sizing) and the later determination of shapes and dimensions. The correlations might be integrated into a UAV sizing environment and allow designers to implement more sophisticated drag and weight estimation methods in this process. Additional information on correlation factors for a rough drag estimation methodology indicate how this technique can significantly enhance the accuracy of early design iterations.},
  language  = {en}
}
@inproceedings{GoettenFingerMarinoetal.2019,
  author    = {G{\"o}tten, Falk and Finger, Felix and Marino, Matthew and Bil, Cees and Havermann, Marc and Braun, Carsten},
  title     = {A review of guidelines and best practices for subsonic aerodynamic simulations using RANS CFD},
  series = {Asia-Pacific International Symposium on Aerospace Technology (APISAT), At Gold Coast, Australia, 04. - 06. Dezember 2019},
  booktitle = {Asia-Pacific International Symposium on Aerospace Technology (APISAT), At Gold Coast, Australia, 04. - 06. Dezember 2019},
  isbn      = {978-1-925627-40-4},
  pages     = {19 Seiten},
  year      = {2019},
  language  = {de}
}
@article{GoettenHavermannBraunetal.2019,
  author    = {G{\"o}tten, Falk and Havermann, Marc and Braun, Carsten and Gomez, Francisco and Bil, Cees},
  title     = {RANS Simulation Validation of a Small Sensor Turret for UAVs},
  series = {Journal of Aerospace Engineering},
  volume    = {32},
  journal   = {Journal of Aerospace Engineering},
  number    = {5},
  publisher = {ASCE},
  address   = {New York},
  issn      = {1943-5525},
  doi       = {10.1061/(ASCE)AS.1943-5525.0001055},
  pages     = {Article number 04019060},
  year      = {2019},
  abstract  = {Recent Unmanned Aerial Vehicle (UAV) design procedures rely on full aircraft steady-state Reynolds-Averaged-Navier-Stokes (RANS) analyses in early design stages. Small sensor turrets are included in such simulations, even though their aerodynamic properties show highly unsteady behavior. Very little is known about the effects of this approach on the simulation outcomes of small turrets. Therefore, the flow around a model turret at a Reynolds number of 47,400 is simulated with a steady-state RANS approach and compared to experimental data. Lift, drag, and surface pressure show good agreement with the experiment. The RANS model predicts the separation location too far downstream and shows a larger recirculation region aft of the body. Both characteristic arch and horseshoe vortex structures are visualized and qualitatively match the ones found by the experiment. The Reynolds number dependence of the drag coefficient follows the trend of a sphere within a distinct range. The outcomes indicate that a steady-state RANS model of a small sensor turret is able to give results that are useful for UAV engineering purposes but might not be suited for detailed insight into flow properties.},
  language  = {en}
}