Browse Topic: Maintenance and Aftermarket
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Traditional safe-life methodologies for rotorcraft structural components often result in overly conservative life estimates, increasing maintenance costs and reducing aircraft availability. This study explores the integration of digital twin concepts with probabilistic modeling and machine learning to enhance structural life assessment, demonstrated through a practical case involving the Royal Canadian Air Force CH-146 Griffon helicopter. A probabilistic fatigue model determines a fatigue life distribution by incorporating material variability and uncertain operational loads inferred directly from flight data. Unlike conventional approaches, this method dynamically estimates load spectra, including uncertainty instead of relying on conservative assumptions. Monte Carlo simulations are used to quantify structural risk and assess the impact of load and material uncertainties. Sensitivity analyses highlight these uncertainties’ contributions to failure probability. The proposed approach
The operation of Urban Air Mobility Vehicles (UAMVs) presents significant technical and operational challenges, particularly in the areas of safety, training, and cost management. This paper explores how advanced simulation models and predictive algorithms can address these challenges. A digital transformation framework is developed and applied in an Urban Air Mobility (UAM) case study to illustrate the effectiveness of these tools. Through the development of simulation models, critical insights are provided on damage detection, impact analysis, and maintenance optimization. The application of predictive algorithms enables quick damage assessment, improving safety by facilitating timely maintenance and repair decisions. To help showcase the benefits of this research, a demonstration was designed and built that allows users to interact with the developed tools and get a better understanding through hands-on training.
Electrification could improve full-size rotorcraft performance by reducing peak turbine power demand, reducing transmission system weight and complexity, and reducing operating costs. Integrating electric machines with mechanical powertrains requires careful consideration of the system-level weight and efficiency impacts. This paper presents an optimization framework for evaluating parallel hybrid powertrain configurations using Geometric Programming (GP). Both retrofit and clean-sheet vehicle designs are considered. The results show that high-speed electric motors integrated into a parallel hybrid configuration using batteries can reduce the sized gas turbine power, enabling more efficient engine operation at lower power levels. For retrofit designs, with a fixed vehicle gross weight, adding batteries and motors reduces usable fuel, decreasing mission capability. Clean-sheet designs offer additional flexibility to re-size the vehicle and rotor, resulting in energy savings for an
As part of a human factors research project aimed at optimizing technical documentation used in helicopter maintenance with multimedia elements, we compared different instruction formats to observe their effects on the performance of an assembly task. This task offers us the opportunity to test procedures that call for similar actions as a maintenance task (e.g., localization, action sequencing, assembly). Static (i.e., image and image with text) and dynamic instruction formats (i.e., video, video with text and video with audio) were compared to determine if dynamic formats allowed a better motor performance of the task for assembly reaction time (time needed to complete the assembly) and accuracy. We were also interested in how the use of the text instructions interacted with both visual dynamic and static instructions. Reaction times were recorded and measured with eye tracking data. Subjective data was collected in questionnaires during and after the experiment. Results showed
Aircraft Certification is a mature and complex bureaucracy that has successfully ensured a very high degree of safety of aircraft design, construction, operation and maintenance. Outside of a very few doing the work, there is a general lack of knowledge of certification details. For novel technologies such as electric power, and innovative configurations such as multi-rotors, the rules are far less mature and still emerging and so also poorly understood. Within the Advanced Air Mobility (AAM) initiative, many new aircraft developments are underway using novel configurations, and the public announcements of regulatory progress toward FAA or EASA Type Certification capitalize on this ignorance by being vague or even misleading. Honeywell conceived the Regulatory Readiness Level (RRL) indicator as an objective measure of certification status to serve the AAM industry and ecosystem, with applicability across aviation. The released RRL Version 1 now enables credible, objective assessment of
The emergence of electric Vertical Takeoff and Landing (eVTOL) air vehicles is transforming how people and freight are moved in short distances. This transformation has a profound impact on surrounding infrastructure necessary to provide Aircraft On Ground support for eVTOLs. The hover capabilities of eVTOLs have similar operating characteristics within terminal and uncontrolled airspace. However, the need to conserve battery energy via rapid approaches and departures affects terminal airspace management. To attract eVTOL operators, existing airports, landing zones, and vertiports are modifying their infrastructure to include fixed electric charging stations, additional taxiways, upgraded fire suppression systems, separate hangers, and capable MRO facilities. Augusta Regional Airport (KAGS) is the base airport for the annual Masters Golf Tournament which experiences five times the normal airport traffic and some 40,000 commuting patrons. eVTOLs can offset land traffic issues associated
This paper presents a meshless large eddy simulation approach for rotorcraft wake prediction, using a vortex particle method accelerated on GPUs. The solver couples a rotor model with a vortex particle wake model, employing the Fast Multipole Method for computational efficiency and implementing viscous diffusion through Particle Strength Exchange and Core Spreading Methods. GPU acceleration achieves speed-ups of up to 10x compared to CPU execution. The solver’s predictions are validated against experimental data, showing excellent agreement. Effects of time step size, numerical integration schemes, viscous models, and particle overlap factors on simulation accuracy and computational cost are systematically analyzed. This GPU-based vortex particle framework provides a fast, accurate, and scalable tool for rotorcraft wake simulations.
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
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