@inproceedings{oai:jaxa.repo.nii.ac.jp:00003402,
 author = {玉山, 雅人 and 齊藤, 健一 and 吉本, 周生 and 有薗, 仁 and Tamayama, Masato and Saitoh, Kenichi and Yoshimoto, Norio and Arizono, Hitoshi},
 book = {宇宙航空研究開発機構特別資料, JAXA Special Publication: Proceedings of the First International Symposium on Flutter and its Application},
 month = {Mar},
 note = {First International Symposium on Flutter and its Application (May 15-17, 2016. Mielparque-tokyo), Minato-ku, Tokyo, Japan, Although airplane's model certification must be proved by showing enough damping margin at every flight condition, the damping is not a reliable index to conduct flight tests safely, i.e. it might change drastically against the flight condition. Flight tests should stand on much more reliable index rather than damping. In this study, the Discrete Flutter Margin is used. Whichever index is used during the flight test, the accuracy of index might be influenced by the original vibration data of structures. For this purpose, two methods are taken in this study: the Random Decrement (RDD) method and the Natural Excitation Technique (NExT), each of which can effectively reduce the structural response caused by a random noise. By applying each of the RDD and the NExT processing methods to the original data, the resultant signal becomes the structural quasi-step or quasi-impulse responses. For the method to identify the system model from step and impulse responses, the Eigen-system Realization Algorithm (ERA) suits well. In this study, two sets of system identification procedures are applied to the wind tunnel experimental data: one is the combination of the RDD and the ERA, and another is the combination of the NExT and the ERA. The wind tunnel model is the half-spanned wing model of Super Sonic Transport (SST) Airplane. The test data are acquired in the JAXA's 0.6m×0.6m Transonic Flutter Wind Tunnel. The resultant Discrete Flutter Margin values acquired from both sets of procedures are compared., 形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: AA1630046009, レポート番号: JAXA-SP-16-008E},
 pages = {97--104},
 publisher = {宇宙航空研究開発機構(JAXA), Japan Aerospace Exploration Agency (JAXA)},
 title = {Effect of vibration data preprocessing for flutter margin prediction},
 volume = {JAXA-SP-16-008E},
 year = {2017}
}