Alexia P. Payan

Helicopter flight simulators offer the potential to train pilots in recognizing, avoiding, and recovering from hazardous flight conditions such as Vortex Ring State (VRS). However, current FAA and EASA simulator qualification standards lack objective fidelity requirements for VRS onset representation, relying primarily on subjective assessments. Thus, this paper presents an objective evaluation method to assess how accurately different simulation models represent VRS onset. Using flight test data, from dedicated flight tests on a Robinson R66, and flight test results from the literature, fidelity criteria were established for key VRS onset aspects: collective trim position, vertical acceleration fluctuations, and sudden increases in descent rate. These criteria were used to evaluate three models: an H125 VR reduced-motion platform simulator, two FLIGHTLAB models with Peters-He inflow (newest and previous releases), and a more advanced but non-real-time FLIGHTLAB VVPM model. Results showed that while the VR simulator and VVPM model captured important VRS characteristics, they both had limitations. The Peters-He model failed to represent VRS onset cues. These findings highlight the need for improved simulation models to enhance the effectiveness of VRS avoidance training in flight simulators.

Approach and landing are among the most challenging and dangerous tasks required of helicopter pilots. Helicopter accident statistics suggest that the majority of helicopter accidents occur during ...

Wire strike accidents are a category of aviation accidents which entail an aircraft or rotorcraft colliding with power lines, communication wires, and any other mounted wires. Currently, there are a limited amount of helicopter equipment options available to protect helicopters from collision with a wire. In particular, there is no commercially available protection system for most lightweight rotorcraft against wire strike events. Indeed, the Wire Strike Protection System (WSPS) developed by Magellan Aerospace is mainly available for medium to heavy weight helicopters. The currently available passive protection system consists of a windshield deflector and sets of two angled blades mounted on the top and under the belly of the helicopter, as well as on the skids when applicable. The system relies on the momentum of the helicopter to successfully sever the wire. Lightweight helicopters may have more difficulty using these protection systems due to having less momentum, but the efficacy of passive protection systems on lightweight helicopters has not been fully defined thus far. The goal of the present study is to develop a modeling and simulation environment enabling the analysis of wire strike events by integrating simulations of helicopter dynamics, wire behavior upon a dynamic impact, and mechanics of wire cutting with shear. The main output of the modeling and simulation environment is the prediction of whether a given type of wire will be severed by a wire cutter mounted on a specified helicopter type. A model combining techniques in cutting mechanics and wire dynamics was implemented, and the results show accordance with experimental studies of the WSPS and accident reports involving the WSPS. The estimated benefits of the tool that was created are both the quantification of the limitations that lightweight helicopters face in using current passive wire strike protection systems, as well the creation of an environment to model and simulate future mechanically enhanced protection system designs.

In recent years, there has been a rapid growth in the application of data science techniques that leverage aviation data collected from commercial airline operations to improve safety. This paper presents the application of machine learning to improve the understanding of risk factors during flight and their causal chains. With increasing complexity and volume of operations, rapid accumulation and analysis of this safety-related data has the potential to maintain and even lower the low global accident rates in aviation. This paper presents the development of an analytical methodology called Safety Analysis of Flight Events (SAFE) that synthesizes data cleaning, correlation analysis, classification-based supervised learning, and data visualization schema to streamline the isolation of critical parameters and the elimination of tangential factors for safety events in aviation. The SAFE methodology outlines a robust and repeatable framework that is applicable across heterogeneous data sets containing multiple aircraft, airport of operations, and phases of flight. It is demonstrated on Flight Operations Quality Assurance (FOQA) data from a commercial airline through use cases related to three safety events, namely Tire Speed Event, Roll Event, and Landing Distance Event. The application of the SAFE methodology yields a ranked list of critical parameters in line with subject-matter expert conceptions of these events for all three use cases. The work concludes by raising important issues about the compatibility levels of machine learning and human conceptualization of incidents and their precursors, and provides initial guidance for their reconciliation.

Despite advances in computational power, simulation‐based experiments of complex problems remain time‐consuming. Significant efforts are required to define an accurate representation of the problem and draw relevant conclusions about relationships between input parameters and output performance measures. To reduce the computational burden without sacrificing accuracy, it is customary to design a set of relevant scenarios. Doing so necessitates that the problem be decomposed into relevant parameters and that the consistency of these parameters be analyzed. To address this challenge, we propose a multilevel probabilistic morphological analysis. Traditionally, morphological analysis documents the decomposition of a system into its main components, and performs binary compatibility assessments to document relational data between component options. However, this approach leads to a rigid definition of the problem space and a static solution space that does not account for uncertain and dynamic behaviors associated with complex systems‐of‐systems. This research incorporates two important improvements. First, it explicitly incorporates a multilevel approach accommodating any successive decomposition steps that may be required when dealing with a complex system‐of‐systems problem. Second, this research introduces probabilistic cross‐consistency assessments instead of traditional binary assessments to account for uncertain future states of the world. This probabilistic scheme is used to describe degrees of likelihood that two elements may coexist in a given scenario. This way, more complex interactions may also be captured and more realistic scenarios may be defined. Finally, the proposed method is applied to a problem investigating the design of architectures of detection systems for the protection of homeland critical assets.