Browse Topic: Maintenance and Aftermarket
A 4-rotor uninhabited air vehicle is described, with a primary mission of supporting personnel fighting wildfires. The paper demonstrates the use of technical design tools for a small Uninhabited Aircraft System (sUAS). A description of the design process is provided, including developing requirements, identifying constraints, the software tools employed, and examination of results. The vehicle is capable of delivering more than 20 kg of supplies to a delivery point 10 nm away while penetrating 30 kt winds. The sized vehicle is transportable in a medium-duty pickup truck and can be picked up and moved for ground handling by one or two individuals. The vehicle information will be publicly released for NDARC software users. Future work will examine other requirements, such as maneuvering and gust rejection.
Urban Air Mobility (UAM) concepts require multidisciplinary analyses across multiple modes of operation and often involve discrete architectural differences such as propulsion type, rotor configuration, and mission context. Existing optimization and workflow frameworks support continuous design variables but provide limited mechanisms for handling discrete variants, multi-modal vehicle definitions, and vehicle management for UAM vehicles. This paper presents uam4x, an open-source Python framework that addresses these challenges through a structured problem definition representation, a plugin-based execution engine, integrated version control, and a function-based branching script mechanism for constructing analysis scenarios. The framework provides integration of existing tools including Open Vehicle Sketch Pad (OpenVSP), NASA Design and Analysis of Rotorcraft (NDARC), M4 Structures Studio (M4SS), and Intelligent Cross Section Generator (IXGEN) through unified plugin interfaces. Parameter sweeps, nested analyses, and optimization via OpenMDAO are supported within the same architecture. This paper also presents demonstrations that were created to illustrate the various capabilities and integration efforts of the framework.
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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.
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 equivalent design mission.
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 significant differences in the time spent on the instructions and the time spent on the assembly, depending on the format of instructions. Overall, assembly time is shorter with video instruction formats, but videos took longer to be consulted than static formats. Results also showed a difference in the number of actions required to do the assembly. Videos facilitated the right path of action sequence in comparison with static formats. With the analysis of both subjective and objective data, the results give us a better idea of the advantages and drawbacks of using dynamic formats in technical documentation.
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 new aircraft progress toward FAA Type Certification, and Operational Approval for Part 135 operations, to allow consistent apples-to-apples comparisons with other aircraft in development. An emerging complementary version of the rubric for EASA Type Certification is ready for publication to enable RRL determination against the European Union criteria. Future releases will consider other Nation's regulatory authorities, supplemental types certifications (STCs), and risk-based airworthiness assessments such as the Specific Operations Risk Assessments (SORA).
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 with commuters and supplies. Since KAGS is centroid to 32,000 square miles of territory void of major highways, basing eVTOLs can offer expedited transit services for people and goods which will have a profound impact on the economic viability and quality of life in the area.
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.
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 provides probabilistic life predictions, supporting risk-based maintenance strategies to potentially optimize operational efficiency. The long-term goal is to develop an adaptive digital twin model that continuously updates with new operational flight data, enhancing predictive accuracy for helicopter fleet management.
The Shake-The-Box technique was applied to experimentally quantify the time-resolved volumetric flow field around a free-flying quadcopter UAV with an overall span of about 0.5 m. State-of-the-art LED illumination and high-speed camera equipment was combined with modern Lagrangian tracer particle tracking and data assimilation techniques, facilitating a measurement volume larger than 1.5m3. The setup allowed for both hover and limited maneuvering of the quadcopter, while resolving even small details of the complex interactional aerodynamics. In hover out of ground effect, the four individual rotor wakes merged into a single jet within a few rotor radii below the rotor planes. Evaluating the mass and momentum fluxes over suitable control volumes yields accurate estimates for the quadcopter's total thrust, the asymmetric thrust distribution between front and back rotors, and the entrainment of external flow through turbulent mixing. Hover in ground effect decreases the power requirement and induces recirculating flow in the center of the four rotors. The outwash pattern is non-uniform with jets developing between the rotors and pointing in radially outward directions. Forward flight cases result in a skewed, rapidly merging wake flanked by the roll-up of two "super-vortices" similar to the wingtip vortices of fixed-wing vehicles.
The paper presents recent and ongoing activities of the German Aerospace Center (DLR) focusing on experimental icing investigations within the nationally funded project InTEnt-H (2018-2022) and progressive activities in continuing internal DLR projects. The aim of InTEnt-H was to investigate innovative de-icing and anti-icing technologies for small and medium-weight helicopters, for which no rotor de-icing technologies exist to date, and to demonstrate the effectiveness of these systems in a suitable test facility. For this purpose, the whirl tower test facility of the DLR in Braunschweig has been converted into an icing test facility that is unique in Europe and will allow for the generation of atmospheric icing conditions. In this facility, de-icing and anti-icing systems for rotor blades can be tested under centrifugal loads and various icing conditions. The paper starts with a short presentation of the retrofitting works at the DLR whirl tower test facility and its major components. Then, the progress of the first test campaigns of the projects are reported. The main focus is on the design and test of the de-icing rotor system, carrying different antiicing/ de-icing technologies. The paper closes with an outlook on the upcoming activities planned to satisfy and verify EASA CS-29 Appendix C icing conditions in the frame of the DLR internal project SAFER2.
Wear debris monitoring and analysis is a common practice for the condition assessment of engine and transmission health. Oil debris monitoring (ODM) and electronic chip detectors (ECD) are two common methods deployed for continuous monitoring of oil wetted component health in-flight. This study evaluates the diagnostic performance of the two sensing technologies within controlled rolling element bearing (REB) fault experiments. Progressive visual inspection of the REB spall progression through failure provided a ground truth against which both systems could be compared. Quantifiable metrics of reliability, diagnostic accuracy, provided maintenance interval were defined to create a framework for condition-based maintenance (CBM) program decision making. In summary, it was found that the ODM sensor system provided earlier fault notice, but more so, vastly outperformed the ECD in reliability and avoidance of false positives.
ABSTRACT The impact of hover download on rotorcraft design has long been recognized, though analytical focus on the issue has been intermittent, for both technical and programmatic reasons. Advanced models employed on high performance computer systems have shown impressive ability to capture observed behavior, though physics-based tools better suited to routine early stage design analysis are highly desirable. Prior papers conducted an initial assessment the ability of several contemporary "mid-fidelity" analysis tools to compute download and rotor/airframe interaction on helicopters and compound rotorcraft in hover, with initial studies focusing on single rotor aircraft operating out of ground effect. This paper extends this work to the consideration of other rotor configurations (e.g., tiltrotor/side-by-side and coaxial cases); given the development and prospective use of multiple new vehicles featuring these design elements an assessment of this capability is judged timely. In addition, this paper will address the application of these models to the impact of ground proximity on download, an important operational consideration. The major features of airframe download and rotor performance are captured for several representative cases, and observations on differences in predicted and measured performance are discussed.
ABSTRACT Accurate prediction of aeroelastic coupling between rotor wake and structure remains a key challenge to the development of advanced rotorcraft. Limitations of existing analysis tools to predict such aeroelastic interactions, notably empennage buffeting effects, have resulted in costly late-cycle design changes in multiple rotorcraft development programs, including the UH-60A and AH-64A. Aeromechanical phenomena involving interactions of the fuselage and rotor wake are complex, interdisciplinary, and three-dimensional in nature. For this reason, full vehicle CFD/CSD coupled analysis is essential to accurately capture the mutually dependent interactions between the aerodynamic loads and the aeroelastic response associated with these phenomena. The current state-of-the-art in rotorcraft analysis involves CFD/CSD coupled analysis of aeroelastic rotors and wings, but rigid representations of the fuselage and empennage structures (Ref. 1). To address this limitation, an elastic fuselage coupling capability was developed using RCAS and FUN3D. The elastic fuselage CFD/CSD interface has been successfully tested for several demonstration cases and one validation case.
The DoD enterprise requires a blueprint for each service and industry base to develop, integrate, and connect crewed and uncrewed platforms across the aviation and ground domains to satisfy the goals of programs such as Replicator, Human Machine Integrated Formation (HMIF) and Joint All Domain Command and Control (JADC2) as a whole. Thanks to years of architecture work by Program Executive Office (PEO) Aviation, PEO Ground Control System (GCS), and the Ground Vehicle Services Center (GVSC), the necessary open standards-based reference architectures, objective architectures, Major System Components (MSCs), and Major System Interfaces (MSIs) can be leveraged to create an executable plan for the Army, the DoD, and the international community as a whole. This paper proposes how to leverage government-owned elements across multiple Army offices to provide a Modular Open Systems Approach (MOSA) that achieves the speed, portability, and interoperability of capabilities needed for the conflicts of tomorrow.
Coupling numerator models are a well-established tool for predicting the closed-loop dynamics of multiple-input multiple-output systems. They describe the input-output dynamics of the open-loop path in a partially controlled system given tight controls and thus may serve as an approximation of the actual dynamics "seen" by the single-axis controller in a controlled multiple-input multiple-output plant. Such models can therefore be employed for a decentralized initial design of multivariable controllers. This paper presents a new, unifying approach for the computation of such constrained input-output dynamics. Connections to existing methods in the literature are established and a general procedure for state-space calculations is given. The use of coupling numerator models for controller design is motivated and different decoupling structures are discussed. The controller design is illustrated using a coupled high-order helicopter model.
This paper documents the re-evaluation and updates to the previous Partial Regime Recognition Spectrum effort for the MH-47G using Structural Usage Monitoring System (SUMS). Further validation of the SUMS algorithm allowed for additions to the spectrum. These additions include more refined categorization of turn and partial power descent regimes based on angle of bank and descent rates, respectively; high load prorates for turns, partial power descents, level flight, and climbs based on the Cruise Guide Indicator; exceedances of maximum density altitude; and use of occurrences for Landing and Run-On Landing regimes. Additional years of flight data from 2013 to 2019 were included in this effort. The updated usage spectrum for the Army MH-47G aircraft has been delivered to the OEM (Original Equipment Manufacturer). The OEM calculated new fatigue lives and updated the "Fatigue Substantiation Report", which will soon be fielded.
A Common Open Data Exchange format for rotorcraft Health and Usage Monitoring Systems (CODEX-HUMS) would offer a more affordable, capable and effective Integrated Vehicle Health Management System. The Society of Automotive Engineers (SAE) HM-1R committee is developing a standard definition for the CODEX-HUMS open data format produced or used by an on-board or off-board system, SAE Aerospace Standard AS7140. The standard format benefits end users (e.g., operators, developers, suppliers, integrators, and maintainers) with the capability to more rapidly operationalize HUMS data. This HUMS open data format meets the intent of a Modular Open System Approach (MOSA) and provides a foundation for rapid realization of operational benefits from the point of maintenance and from the exchange of HUMS data with external enterprise systems.
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.
In this work, a unified framework integrating global and local SHM methods for structural health monitoring (SHM) of rotorcraft structures is proposed. This framework integrates both "local" ultrasonic-guided wave-based and "global" vibration-based SHM schemes for tackling damage detection, identification, and quantification under uncertainty. The local SHM is completed by training a variation of variational auto-encoder (MMD-VAE) along with feed-forward neural networks (FFNN). The compressed latent space vector obtained during the training process is applied to achieve both signal reconstruction and state prediction. In terms of the global model, functionally pooled auto-regressive models with exogenous excitation (VFP-ARX) models are applied including to capture low-frequency vibrations. The complete experimental evaluation and assessment of the proposed framework are presented for an Airbus H125 helicopter blade under both low-frequency vibrations and ultrasonic guided waves for SHM.
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