Browse Topic: Crashes
This study provides a comprehensive framework for establishing land use compatibility guidelines specific to vertiports serving electric Vertical Takeoff and Landing (eVTOL) aircraft within urban settings. Recognizing critical gaps in current regulatory standards, the research systematically integrates analyses of accident risk, noise propagation, and aerodynamic impacts—including downwash and outwash—to delineate compatibility zones around vertiports. Employing an artificial intelligence (AI) augmented system, the study conducted safety and hazard assessments, various quantitative analyses, and simulations to identify spatial constructs of operational risks and environmental impacts. Results indicated significant discrepancies between existing aviation infrastructure guidelines and the unique operational characteristics of eVTOLs, necessitating revised zoning parameters. The proposed multi-tiered safety zoning framework provides precise, evidencebased recommendations for urban planners, enhancing safety, minimizing noise impacts, and ensuring environmental sustainability. Ultimately, this research offers policymakers and stakeholders practical tools for integrating advanced air mobility infrastructure safely into complex urban landscapes.
Rotorcraft continue to experience higher fatal accident rates compared to fixed-wing aircraft, primarily due to low altitude flight operations and reduced situational awareness in complex environments. A critical factor is the limited availability of accurate, up-to-date information on helipads and surrounding obstacles - such as trees, poles, and buildings - that pose significant risks during takeoff and landing. Existing resources, including the Federal Aviation Administration's heliport registry, are often outdated and incomplete, particularly for private or state-operated sites, and fail to report nearby obstacles. This lack of up-to-date data is largely due to privacy restrictions at certain locations and the high cost associated with comprehensive obstacle surveys. To address this challenge, we develop a deep learning (DL) framework that automatically detects helipads and nearby obstacles from high-resolution satellite imagery. Our approach combines Mask R-CNN for precise pixel-level helipad segmentation with Grounding DINO, a zero-shot vision-language model that identifies obstacles using flexible text prompts (e.g., "Pole", "Tree") without task-specific training. This text-guided, scalable detection method adapts to diverse and evolving operational settings. We validate our framework across helipads in the United States, and demonstrate strong performance in both helipad localization and obstacle detection. In addition, we build a web-based application that automates image processing, updates incorrect heliport coordinates, and provides obstacle reports. This work aims to enhance aviation safety, modernize infrastructure records, and deliver scalable tools to the aviation and machine learning communities.
This paper outlines observations from an FAA-sponsored research project that examined aviation Fly-By-Wire (FBW) accidents. The goal was to identify risk areas that will help guide a focus for FAA certification testing. Part of this study specifically focused on current powered-lift tiltrotors, identifying six general categories of causal factors for accidents, which will be discussed in detail regarding how they influenced flight control designs. The results of this survey, along with extrapolation to current designs, will be discussed and will illustrate why manufacturers are moving toward state-based flight control designs. In a state-based flight control scheme, the pilot does not have direct control over aircraft attitudes and motor tilt angles. Instead, the pilot requests a speed and or flight path with inceptor input, and the commanded attitudes and motor tilts are scheduled by the flight control computer. Additionally, recent lessons learned from electric Vertical Takeoff and Landing (eVTOL) aircraft accidents will be discussed, along with a comparison of powered-lift causal factors to accidents in the transport category FBW fixed-wing aircraft. From this analysis, broad observations will be offered about the trend of how accident-causal factors may evolve with greater maturity in aircraft design. This accident survey will be detailed further as part of an upcoming FAA Research Report.
We present our ongoing efforts towards the development of crash-tolerant rotorcraft airframe structures through topology optimization, with the goal of enhancing energy absorption and occupant survival during vertical impact events. A high strain rate explicit dynamics solver has been developed, fully accelerated on GPUs, to enable rapid and accurate simulation of impact events critical to crashworthiness evaluation. In parallel, we have built a scalable three-dimensional topology optimization framework that enforces stiffness, weight, and frequency constraints simultaneously, driving structurally efficient and vibration-resistant designs. Benchmarking results demonstrate significant GPU-enabled speedups, facilitating high-fidelity crash simulations and large-scale optimization at practical turnaround times. This work establishes a computational foundation for future integration of crash-centric objectives and constraints into the optimization framework.
The Crashworthy and Escape Systems Branch at NAWCAD has been developing an integrated restraint harness concept for several years, with the intent of developing a novel method of providing improved occupant protection in a crash scenario. A series of tests was conducted on the Horizontal Accelerator at NAS Patuxent River to evaluate the performance of the prototype integrated-restraint system under MIL-STD-58095 conditions with the 50th percentile male Hybrid III Anthropomorphic Test Device (ATD). While occupant flail was the primary metric being analyzed in this effort, ATD instrumentation was also captured, showing that the integrated restraint system demonstrated a significant reduction in head flail compared to five-point restraints while maintaining injury criteria within acceptable levels.
Dynamic rollovers represent a major hazard for helicopters during near-ground operations, often resulting in significant aircraft damage and passenger injuries. To improve safety in operations, recent studies have focused on developing a Helicopter Flight Data Monitoring framework to provide data-driven insights on operational safety. This work contributes to that effort by proposing an approach to identify precursors to dynamic rollovers. According to NTSB reports, approximately 60% of such incidents occur during in-flight phases like hover, hover-taxi, or landing. To capture the complex non-linear dynamics of helicopters, physics-based simulations were conducted to estimate a first hitting time metric, defined as the time until blade-ground contact, across a wide range of initial conditions for an inflight initial state of the helicopter. Eight parameters were identified as driving the first hitting time, and a probabilistic model was created to predict the distribution of that metric for different values of those parameters. Based on the predicted distributions, a risk-based metric was derived to robustly assess the risk of dynamic rollover and identify safer operational boundaries.
Prior to 1950, use of the helicopter for evacuation was extremely limited, as military top brass often considered it a worthless contraption; thus, rescue was uncertain at best for downed pilots and wounded soldiers stranded behind enemy lines. However, this all changed in Korea, where twelve U.S. Army helicopters from three detachments, working in tandem with seven, newly created Mobile Army Surgical Hospital (MASH) units, would fundamentally change the Army's medical-evacuation doctrine forever. Using several models of the Bell H-13, the Hiller H-23, and the Sikorsky H-5 and H-19, this small band of courageous pilots pushed themselves and their aircraft to their limits, transporting 21,212 critically wounded soldiers for life-saving surgery to various MASH units, cutting the fatality rate from World War II in half. Adopting the 3rd Air Rescue Squadron's motto, "That Others May Live," these pilots and their helicopters were affectionately known to the wounded as "Angels of Mercy."
ABSTRACT For many rotorcraft platforms, incorrect timing of the autorotation flare and deceleration maneuvers may result in significant aircraft damage and injury to the crew, or worse. There is a clear need for new pilot cueing and control augmentation technologies that lead to a higher probability of a successful autorotation landing. This paper describes a recent effort to develop two different Tau (time-to-contact)-based autorotation controllers that can be used to drive visual aids to help guide a pilot to apply the required control inputs to complete a safe autorotative landing. Such controllers may also be useful for fully autonomous autorotation landing for unmanned vehicles.
Air safety investigators must seek out all available sources of evidence from an aircraft crash incident in order to make informed root cause determinations. This is especially important when the incident aircraft was not equipped with flight and voice data recorders. Previous investigations have utilized trajectory analysis methods as a technique to determine where debris items may be found on the ground after an in-flight breakup. Alternatively, if the ground placement of debris items is known, then the airspeed and heading of the aircraft may be back-calculated. In this paper, a probabilistic trajectory analysis is developed to infer crash debris initial conditions at time of impact. Digital methods including computational fluid dynamics have been employed to calculate debris item aerodynamic coefficients and probabilistic sampling techniques to determine a likely range of initial angles and initial velocities. Finally, two example cases are presented to illustrate application of the technique and its utility for design making with respect to crash investigation.
Maintenance of spatial orientation (SO) is achieved primarily through visual information where the horizon and celestial reference cues or flight instruments are used by pilots to infer aircraft orientation. However, cross checking the instruments in degraded visual environments can be complicated by factors such as workload, distraction, and situations where the vestibular and proprioceptive systems may provide false and competing orientation information. We describe experiments measuring pilot performance using a flight simulator under challenging conditions where the sensory information was controlled. Reducing available visual instruments increased the task difficulty. A wearable vibrotactile array could provide concurrent, additional orientation information. Increasing the flying task segment difficulty increased the perceived workload and also corresponded to an increase in accidents. Adding tactile orientation information reduced the accident rate.
From 2008 to 2021, 48 helicopter accidents have involved Vortex Ring State (VRS) encounters in the United States. For dangerous situations such as VRS encounters and recoveries, Scenario-Based Training (SBT) in flight simulators could supplement flight training, allowing pilots to practice in a safe environment. In this study, we created and tested a proof of concept for scenario-based training in flight simulators, dedicated to VRS-related accident prevention. The goal is to evaluate pilots' awareness, avoidance, detection, and recovery skills during VRS-inducing scenarios. Moreover, this study intends to provide a holistic analysis of VRS encounters and recoveries by examining the situations and factors leading to VRS-related crashes, and to lay out the pilot's decision-making process during the event. For that purpose, a comprehensive set of scenarios was developed based on an analysis of VRS-related accidents from the National Transportation Safety Board (NTSB) database. Overall, the scenarios successfully triggered VRS encounters as well as avoidance actions, providing insight into pilots' behavior in and around VRS conditions.
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Rotorcrafts are generally subject to a higher fatal accident rate than other segments of aviation, including commercial and general aviation. The safety improvement for rotorcrafts would directly improve the efficiency of air traffic control, since rotorcrafts operate primarily within low-level airspace; an area that is becoming increasingly complex with new entrants, such as unmanned aircraft systems and urban air mobility. The recent impact of artificial intelligence and deep learning algorithms on various aspects of our lives has led to the investigation of the application of these algorithms in the aviation domain; as it may offer a prime opportunity to enhance safety within the aviation community. In this research, we explore the efficacy, reliability, and, more importantly, the explainability of modern deep learning algorithms. We use machine learning models to predict the attitude (pitch and yaw) of rotorcrafts using video data recorded with ordinary cameras. The cameras were mounted inside the helicopter cockpit and recorded outside view through windshield continually during the flight. We train four different architectures of convolutional neural networks (CNNs), i.e., VGG16, VGG19, ResNet50, and Xception. The models achieved 90%, 91%, 88%, and 88%, respectively, average attitude prediction accuracy on the test video dataset. Furthermore, we use gradient class activation maps (grad-CAM) to ascertain the features and regions of the image that influenced the model to make a specific prediction. We show that CNNs learn to focus on similar features as human operators (pilots), i.e., the natural horizon curve. Our findings demonstrate the feasibility of using deep learning models for attitude prediction from f light videos recorded using ordinary inexpensive cameras. The proposed video analytics framework provides a cost-effective means to supplement traditional Flight Data Recorders (FDR); a technology that is often beyond the financial reach of most general aviation rotorcraft operators.
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