An investigation of methodologies for stochastic simulation of helicopter accidents under autorotation scenarios

Abstract

Traditionally investigation into helicopter accidents has focused heavily on building complex, deterministic FE models and attempting to validate them with a small number of full size crash tests. One of the major drawbacks of this methodology is that it does not address the dissimilarity of loads generated in nominally identical crashes. To take into account inherent uncertainties in the helicopter crashes, the classic deterministic approach has been replaced in this work by simulation using stochastic algorithms. The chaotic nature, due to complexity in helicopter accidents, requires a much more robust simulation framework to capture a broad spectrum of potential crash scenarios. The thesis aims to investigate helicopter accidents resulting from engine failure by using stochastic simulation methods. An innovative, hierarchical block structure for a stochastic simulation is developed for investigating the variability in a full crash event reduced to two phases: flight emergency simulation and structure crash simulation. Non-parametric analysis is performed on the stochastic injury responses which attempt to predict the severity of the occupant’s injuries caused by helicopter accidents. The modelling framework establishes correlations between the critical parameters prior to helicopter accidents and possible occupant injury metrics. To achieve this aim, various stochastic algorithms are performed throughout the stochastic framework for the typical helicopter accidents. An investigation into helicopter accidents after engine failure is investigated by simulating auto-rotational flight within a range of sampled pre-event flight conditions to establish the correlation between initial flight parameters and the impact response. The structural crash simulation uses impact loads randomly selected from within the boundary of the flight simulation results to determine accelerations at the seat position. The potential injury is predicted by the stochastically generated acceleration peak-values the human body is subjected to and is evaluated by the resulting robust solution based on a computational geometry algorithm. A highly flexible, hierarchical, stochastic simulation framework has been established for investigating helicopter accidents. It has been demonstrated for the case of complete engine failure during flight leading to autorotation. The significance of this approach is the resulting combination of a complete crash event into a single framework which can be robustly analysed to study the limits of occupant harm. This work will lead to strategies for minimizing occupant harm and maximizing survivability for a wide range of helicopter accident scenarios by both structural modification and flight procedural changes. Some techniques supporting the stochastic analysis are successfully used offering the possibility of projecting the methodology into other engineering domains.