@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}
}