Crashworthiness Assessment of the Flying-V Under Complex Crash Scenarios with Partially Detailed Structures

Abstract

This research delves into aircraft crashworthiness, focusing on the innovative Flying-V configuration, aiming to improve safety in unconventional designs. Challenges arise due to the Flying-V's unique V-shaped fuselage, complicating traditional crashworthiness assessments. To address this, the study proposes modelling approaches, particularly for the central part of the fuselage lacking detailed structural information.
Various modelling approaches are explored, building on a finite element model of the Flying-V developed in previous work. Drop tests validate optimal section designs, emphasizing a minimum vertical impact velocity. Spatial variations in Dynamic Response Index (DRI) and Severity Index (SEV) prompt nuanced studies on impact scenarios and potential passenger side loads.
As the analysis progresses, extending the computational domain becomes crucial for reliability. Insights into weight distribution imbalances and challenges with corrective measures emerge from analyses of extended fuselage sections. Spatial fluctuations in DRIs and SEVs underscore the need for a balanced approach between computational efficiency and result realism.
A newly introduced modelling technique leveraging moments of inertia is implemented, yielding realistic results for straightforward scenarios and reducing simulation time significantly. Further analysis explores intricate landing scenarios, highlighting differences between full and reduced models, particularly at elevated pitch angles.
Recognizing the limitations of simplified methodologies, a submodelling technique is proposed for extreme crash scenarios, effectively capturing engine section dynamics with reduced computational time.
While reduced modelling techniques show promise, the study underscores the need for a comprehensive finite element method representation of the Flying-V, recommending successive simulations with a coarse overall mesh followed by submodelling for detailed assessment of critical regions.