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Data-driven analysis and new findings on the loss of tail rotor effectiveness in helicopter accidents.
Saleh, Joseph Homer; Xu, Zhaoyi; Guvir, Anca Ioana; Margousian, Arega; Zhang, Weiqing; Ma, Martin.
Afiliação
  • Saleh JH; School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA. jsaleh@gatech.edu.
  • Xu Z; School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
  • Guvir AI; School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
  • Margousian A; School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
  • Zhang W; School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
  • Ma M; School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Sci Rep ; 12(1): 2575, 2022 02 16.
Article em En | MEDLINE | ID: mdl-35173247
ABSTRACT
Loss of tail rotor effectiveness (LTE) is an unstable dynamic phenomenon that affects single-rotor helicopters and frequently leads to accidents. LTE accidents recur with troubling regularity and show no sign of abatement. This work uncovers new data-driven findings pertaining to LTE and risk factors. First, a scorecard is developed covering a broad range of results to better understand LTE accidents. Second, the risk of LTE is derived for current helicopters. Third, a Deep Learning model is developed that captures the dependence between LTE risk and helicopter features. A danger zone is discovered in the design space for short tail rotor arm and high tail rotor RPM. The results challenge the prevailing narrative of LTE accidents as mere pilot errors and demonstrate an intrinsic propensity to these accidents is embedded in part in the helicopter design. The findings open the door to new, more effective safety interventions for LTE accident prevention.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article