Browse Topic: Data acquisition and handling
The paper discusses the design and high-fidelity flight dynamics modeling of a 13-lb lift-plus-cruise unmanned aerial vehicle (UAV) using Rotorcraft Comprehensive Analysis System (RCAS) in order to (1) better understand its physics of flight during a wide range of maneuvers, and (2) provide insight into the fidelity needed to achieve quantitative accuracy when compared to flight test data. Wind tunnel tests of the full aircraft were performed at a 65% scale to provide lookup tables for the flight dynamics model. Flight test data was collected while providing high control inputs to excite a variety of dynamic states in hovering and cruising modes to systematically validate the physics model. Near quantitative agreement was observed between the model predictions and test data during hover; however, the predictions began to disagree at higher forward cruising speeds. To address the discrepancy between the prediction and experiment, the flight dynamics model was improved by learning a correction from flight test data using a neural network. This hybrid physics plus data-driven approach reduced the error between the physics model and experiment by 74% and only needing 12 minutes of flight data for training. This hybrid methodology presents an alternate approach to high fidelity modeling which only needs a relatively small amount of flight test data.
This study aims to develop Control Equivalent Gust Input (CEGI) and Rotor Control Equivalent Gust Input (RCEGI) profiles that accurately reproduce vehicle response to deterministic gusts. This involves creating an inverse model using adaptive neural networks in order to map vehicle response to pilot and rotor control inputs. The accuracy of the CEGI and RCEGI models are then quantified using the Time Domain Integrated Cost Function (Ref. 1) to determine trends within the CEGI and RCEGI models for gusts of varying shape, magnitude, and duration as well as at varying flight conditions. Analysis using the cost function shows that the CEGI and RCEGI models follow similar trends. Both models are more accurate for gusts of short duration and small amplitude, and both models are more accurate for sinusoidal gusts than top hat gusts.
This paper presents updates to The Rotorcraft Optimization Tools (RCOTools) package to streamline iterative rotorcraft comprehensive design. The work is presented in three parts. Part I. a brief introduction to our simplified API is shown, in addition to a new mission profile dashboard. Part II. demonstrates high-throughput using the embarrassingly parallel paradigm to produce large-scale datasets structured by simple design of experiments (DOE) as shown by our discussion on urban air mobility (UAM) emission minimization. Such datasets provide a necessary component for rapid database generation and supervised machine learning. Part III. the API is used to couple rotor performance and sizing optimization. A simple technique for ultra-fast hover calibration is given, as well as possible applications for neural network modeling in comprehensive design. These enhancements accelerate design workflows and enable data-driven approaches for next-generation urban air mobility and planetary rotorcraft concepts.
This paper discusses the development of a quantitatively-accurate non-linear hybrid flight dynamics model of a hover-capable Air-Launched Tailsitter Unmanned Aerial System (ALUAS) in order to 1) understand its dynamics during complicated maneuvers, and 2) provide a high-fidelity framework to develop novel control laws. Wind tunnel tests were conducted on a 1:1 scale model of the full aircraft to measure the airloads, which were used in the simulation as a lookup table. Flight tests of the ALUAS were performed in hover, transition, and cruise to collect a large amount of unique state measurements by providing large excitations to induce highly transient motion. The flight dynamics predictions using Rotorcraft Comprehensive Analysis System (RCAS) software were then compared with experimental flight test data. To correct any discrepancies in the RCAS physics-based predictions, a correction was learned from the experimental measurements, making use of the large amount of collected flight test data. Using a neural network to learn this correction, the end result was a quantitatively accurate neural network assisted flight dynamics model. The accuracy of current simulations in complex flight states successfully demonstrates the applicability of the proposed methodology for correcting the dynamics model of novel out-of-the-box aircraft configurations.
Neonatal patients in need of specialized care may require transport by rotary-wing air ambulances. These patients are subjected to environmental stressors during transport, including elevated levels of mechanical vibration. Aircraft vibration is transmitted through the transport system and incubator to the patient. The unique vibration profile is dependent on vehicle model and phase of flight. To improve safety for these patients, we aim to evaluate the vibration exposure across this complex system. The purpose of this paper is to present and evaluate the methods used for aircraft data collection and replication of aircraft vibration profiles in a laboratory setting. Our current focus is on neonatal transportation in Ontario, Canada, where Leonardo AW139 helicopters are used for patient transport. AW139 field data were collected and processed to generate excitation profiles for discrete phases of flight. The vehicle data were used to drive a series of laboratory shaker-table experiments, in three axes, to evaluate the response of different configurations of the transport system. We present the methods used to simulate transport conditions, from vehicle data collection to laboratory shaker experimentation, and evaluate the behavior of the test apparatus. The simulated motion has been verified against the aircraft data to identify sources of error in the experimental setup. Some limitations in the shaker and control system present inherent differences in the input and response; however, it was found that the greatest spectral error occurred outside the frequency range of interest (>80 Hz), and that the shaker controller successfully replicates the energy levels recorded in the aircraft. The shaker experiment results, such as the response of the transport system and incubator, will be analysed in future work to identify equipment configurations and/or modifications which can reduce neonatal patient vibration exposure during rotary-wing transportation.
A quantitative understanding of the perceptual elements of handling qualities rating brings us to the heart of pilot control. In previous work it was shown that pilot induced oscillation ratings (PIORs) were a strong linear function of the closed loop dominant mode decay rate of the modeled pilot-vehicle system. While PIORs are based solely on the degree that oscillation degrades the task, the handling qualities rating (HQR) scale employs aggregate performance criteria and three apparently distinct sensations: workload, compensation, and controllability. However, in practice the pilot must modulate control in real time based on an instantaneous sense of performance. It is incumbent to model these four perceptions if the objective is to reproduce the manner and resolution with which the pilot assigns HQRs. The current work examines the same offset landing task that was conducted in two separate piloted studies: 1) Flight, using the Calspan variable stability NT-33A aircraft, and 2) Fixed and motion-based simulation, using the NASA Vertical Motion Simulator (VMS). The substantial difference in actual pilot ratings between the inflight and simulation studies was unexpected and hitherto could not be accounted for. The perception-based theory developed herein predicted pilot ratings that matched well the two studies’ ratings. It is demonstrated how acceleration washout (employed in most motion simulators) could impede the vestibular system from sensing decay rate and require the visual system to be used for primary control. A biomechanical feedback model is developed that computes dynamic limb tension from the neuromuscular system which is then integrated with the feel system to produce a stick force disturbance in response to aircraft motion. This model is flight-data-validated, and the neuromuscular mode’s decay rate was used as a metric to successfully predict the occurrence of roll ratchet (a higher frequency PIO phenomenon).
In this paper the time accurate coupling between the high fidelity CFD code FLOWer and the multi-body dynamics code SIMPACK is presented. To facilitate this coupling a socket-based data exchange was developed and used to exchange aerodynamic forces and kinematic data. Two flight states were investigated: a hover and a forward flight. To obtain a reasonable initial flight state a previously obtained, trimmed solution was taken as the base. This study shows the feasibility of the strong coupling approach with the direct influence of the helicopter motion on the flow field and vice-versa. As expected, the factor limiting the overall performance is the runtime of the CFD simulation. The effort of running the flight mechanics simulation and the data exchange necessary for the strong coupling is negligible compared to this runtime.
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 Main Gearbox of a helicopter is a crucial component that delivers the desired performance and ensures the highest possible level of safety of the aircraft; it includes several gears and bearings, which require to be continuously lubricated by a pressurized oil flow. Undesired circumstances may cause the oil to leak from the main circuit, hence reducing its pressure and consequently the oil flow rate targeted towards the rotating components; this modifies their friction coefficient, and subsequently leads to an overheating of the parts with the risk of degenerating in a catastrophic failure. During the design of a helicopter drive system, engineers need to take proper precautions and make sure that the MGB is fully equipped with the proper features to cope with a loss of lubrication event; specifically, the drive system is supposed to be able to run at least 30 minutes after the oil pressure drops to zero. A lot of effort has been put over the years at Leonardo Helicopters to find robust solutions to attain the longest performance of the drive system in no-oil conditions: the most important result is the certification of the AW189 for a 50-minutes “run dry” capability. Nevertheless, the dynamic environment typical of the rotorcraft industry pushes towards continuous innovation, and in the last few years the Transmissions Systems Design department of LH has been asked to investigate suitable ways to further augment the no-oil capabilities of the MGB: the main steps followed and entailed results are presented in this paper. The first part of the manuscript discusses the “state of the art” auxiliary lubrication system, currently flying on the AW189 drive system. The second part tackles the approach adopted to meet the novel requirements, unveiling both the methodology and the final design choice: the latter includes a metering element, able to tune the oil flow rate headed towards the component deemed the most critical in order to satisfy the requirement of longer no-oil performance. Numerical and experimental tools are exploited as complementary tools to properly crystallize the obtained results and corroborate the solution.
Blade–wake interaction (BWI) is a significant source of broadband noise and is often dominant in rotors with high blade counts. Accurately capturing the resulting unsteady blade loading is computationally expensive and, therefore, drives the cost of BWI noise calculation. To address this challenge, a low-fidelity BWI noise prediction tool was developed using aerodynamic data from the blade element momentum theory (BEMT) and the lattice Boltzmann method (LBM) for a series of rotor configurations with medium to high solidity. Starting from a six-bladed baseline rotor, 13 additional configurations were generated by varying blade twist, taper, root collective, solidity, and blade count. The relationship between vortex miss distance and blade loading unsteadiness was quantified to construct a semi-empirical BWI noise model. The model predicted BWI noise with a root mean square error of 3.9 dBA and a mean absolute percentage error of 1%. It was subsequently integrated into a BEMT framework to produce aerodynamic and acoustic data for training a tandem neural network (TNN) that was employed to optimize two rotor geometries. The optimized designs achieved up to a 7% reduction in BWI noise and a 7% improvement in performance. Additional geometric modifications—including blade tip anhedral, forward sweep, and a mixed configuration—were also assessed using LBM, each demonstrating notable noise reduction.
This paper discusses the development of a flight dynamics model (or digital twin) of a compact and re-configurable coaxial-propeller-based micro air vehicle (MAV) in hover, edgewise, and maneuvering flight using a hybrid physics-based plus data-driven approach. The MAV has a mass of 366 grams (0.81 lb), and features a 52 mm (2.05 in) diameter cylindrical fuselage, foldable propellers, and a two-axis gimbal thrust vectoring mechanism for pitch and roll control. The aircraft has been successfully launched from a pneumatic cannon and has achieved stable and controlled flight. A physics-based flight dynamics model of this novel MAV has been developed using Rotorcraft Comprehensive Analysis System (RCAS). RCAS is able to predict the translational dynamics near hover reasonably well; however, the accuracy decreases for rotational dynamics in edgewise flight resulting in significant differences between predicted dynamics and flight test data, known as residual dynamics. The current hybrid model utilizes the residual dynamics via a data-driven approach to correct the physics-based model. Using the measured vehicle states and control inputs, a deep neural network (DNNs) was trained to learn the residual forces and torques. The resulting hybrid model reduced prediction errors by 55% on average compared to the RCAS model based on pure physics.
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.
Enhancing rotor efficiency has been a persistent challenge in the development of micro aerial vehicles (MAV) especially for surveillance and covert operations. This study introduces a new Hybrid Flapping Wing Rotor (Hybrid FWR) configuration inspired by insect's wing flapping mechanics to address the efficiency limitation of traditional rotor designs. Unlike traditional rotary systems that rely solely on rotational motion, the Hybrid FWR combines rotational and flapping motions to significantly enhance lift generation. A comprehensive mathematical model was developed to analyze and predict the optimal aerodynamic performance, demonstrating that the Hybrid FWR configuration achieves a substantial improvement, with a power efficiency increase of up to 2.148-fold compared to conventional micro rotorcraft. Experimental validation was conducted to confirm the theoretical predictions, identifying an optimal hybrid ratio of approximately 0.7, which effectively minimizes aerodynamic resistance during the upstroke phase while maximizing lift during the downstroke. This bio-inspired hybrid approach addresses critical limitations of existing MAV rotors, such as limited operational endurance and range. The findings of this research contribute significantly to the advancement of micro rotorcraft technology, presenting a promising direction for future MAV developments with enhanced flight performance and energy efficiency.
Big Data technologies have become quite ubiquitous in the last years, allowing for the storage of substantial amounts of data, typically flight test data as recorded by the flight test installation. On recent helicopter prototypes, we generate in excess of 50 GB of raw data per flight hour, usually in a format not adequate for efficient large-scale processing. With some specific optimizations and the setup of a specialized infrastructure, there are now practicable means to store timeseries in ways that allow for requests spanning hundreds or thousands of flights to complete within minutes, opening the way to some substantial savings and new insights. However, to make the most of these data and make informed decisions it is often quite important to store contextual data that go beyond the pure timeseries data, typically on helicopters where optional installations can have a significant impact on aircraft performance or behavior. This paper explores the various kinds of data and metadata related with flight tests, how to collect them and relate them with one another in order to maximize raw data value and avoid some common pitfalls. We define more accurately the data of interest, where to collect them and some ideas to further improve data collection in the future.
The advent of electric propulsion technology has led to a paradigm shift in aircraft design over the past few decades. This shift has expanded the possibilities for design and optimization processes more than at any previous time. To support these advancements, efficient flight dynamics simulation models that can be employed in iterative optimization and design processes are essential. Among the modules of a typical flight dynamics framework—namely, control, flight dynamics, and aerodynamics—the aerodynamics module, which includes the rotor performance model, generally demands the most computational effort, thereby limiting simulation efficiency. In this study, a novel machine learning (ML)-assisted flight dynamics framework is developed, incorporating a Neural Network Blade Element Theory (NN-BET) model as the rotor performance module. The results show a 7- to 8-fold reduction in computational time compared to fast, physics-based frameworks utilizing efficient Blade Element Momentum Theory (BEMT) models, without compromising predictive accuracy. Furthermore, the modular architecture of the framework allows for easy adaptation to a wide range of practical applications by replacing modules with functionally equivalent alternatives. The demonstrated accuracy and computational efficiency of the proposed flight dynamics framework make it a highly promising candidate for optimization and design applications.
Air data measurement and calibration are fundamental components in the pursuit of accurate and reliable aerodynamic assessments. The systematic collection of essential data regarding air properties are important for evaluating aircraft performance under various conditions and configurations. The scope is to achieve a comprehensive understanding of airflow characteristics, which is fundamental for design improvements and operational strategies, contributing to safer and more efficient flight operations in a several range of scenarios. This type of data measurement is even more challenging for the AW609 Tiltrotor which combines vertical take-off technology capabilities with the fixed-wing flight efficiency. The activity starts from known pitot-static system calibration methodologies for conventional applications and shows what were the difficulties encountered in a non-conventional Tiltrotor approach. The paper goes through the presentation of the original Pitot-Static and Air Data system and all the problematics that driven to a design change. After the presentation of the new architecture and the new data collection activity, it will be discussed the optimization of the data calibration strategy, also related to some peculiarities of the Tiltrotor, and how it drives to infer the calibration curves for the Air Data Computers (ADCs).
This study investigates the application of neural network architectures to predict control inputs required to replicate rotorcraft responses under vertical gust disturbances. Two modeling approaches are developed: the Control Equivalent Gust Input (CEGI) model, using body-axis inputs and the Rotor Control Equivalent Gust Input (RCEGI) model using rotor-specific inputs. Initial models employed single-input single-output (SISO) LSTM networks, which demonstrated limitations in capturing transient behavior and exhibited delay in predicted control inputs. By incorporating multiple vehicle response features and increasing the number of hidden neurons, multiple-input single-output (MISO) architectures significantly improved accuracy and reduced Root Mean Square Error (RMSE). Further enhancement was achieved by implementing bidirectional LSTM (BiLSTM) layers, which reduced both delay and transient error. Comparisons with inverted linear time-invariant (LTI) approximations showed that neural networks provided superior performance, particularly in modeling nonlinear dynamics. The results highlight the potential of deep learning approaches to improve the accuracy of control input mapping and inform real-time control strategies in unsteady flight environments.
This paper will present the use of a licensed open-source software application based on commercially available off-the-shelf hardware for the control and data acquisition of aerospace system integration test rigs. System integration test rigs are complex systems requiring real-time deterministic control and high-speed data acquisition. Various aircraft flight systems and subsystems can be tested to see if they interact as they would on the aircraft without an airframe. These systems are critical to ensure interoperability during the development phase and facilitate the interchangeability of actual flight hardware, prototypes, and simulation models throughout the development cycle. Deploying open, flexible, and highly configurable real-time control and data acquisition systems ensures that development milestones will be achieved cost-effectively, whether using actual flight hardware or working with a simulation. This is because, as the prototype hardware is developed, the remaining aircraft systems can still be tested by interacting with the model.
This paper investigates an output-based approach for predicting limit-cycle oscillations caused by freeplay, which can affect actuated structures of vertical lift vehicles. The proposed approach uses pre-critical time-history data to estimate the recovery rate to equilibrium following perturbations as a function of amplitude and a varying parameter. Recovery rate data points in the parameter-amplitude plane are fitted and extrapolated to predict limit-cycle oscillation solutions, corresponding to a recovery rate of zero. While previous work demonstrated this approach for systems with geometrical or polynomial stiffness nonlinearities, this study investigates its applicability to freeplay for the first time. The study uses time-history data from simulations of an analytical model of an idealized, elastically mounted tilting propeller in airplane mode, with freeplay in the tilting mechanism. The results highlight the promise of the proposed approach, paving the way for addressing more complex configurations.
ABSTRACT Northrop Grumman has developed a software and hardware solution to provide enhanced 360 degree local situational awareness (LSA) to enable the warfighter with an overmatch capability on today’s modern battlefield. The architecture exploits technological gains in cameras, video processing, and video compression. The approach allows rapid comprehension of local and remote situational views presented with operational relevance for a ground combat platform or tactical wheeled platform crew. The 360 Degree LSA approach provides direct visualization of relative positioning of targets, threats, and lines of fire; and additionally offers common situational understanding / operational picture from the dismounted soldier to higher echelon commands. The approach provides prioritized information through LSA software to provide an enhanced view to the warfighter whereas the squad leader becomes an integral part of the crew with a view of the common operating picture (mounted) and additional sensors on tablet or handheld device (dismounted via wireless). The approach uses a platform agnostic form factor with components that can be selected and applied to legacy or new platforms based on their size, weight, power, and mission constraints.
This paper investigates an output-based approach for tiltrotor whirl flutter bifurcation analysis. The approach uses free decay output data for a quantity of interest at various forward speeds to estimate the system's recovery rate to equilibrium while capturing its variation with amplitude. The recovery rate is then extrapolated to predict the bifurcation diagram, which gives the limit-cycle oscillation amplitude for the quantity of interest as a function of the forward speed. The approach is demonstrated using output data from transient simulations of a notional tiltrotor model with polynomial structural nonlinearities. The approach accurately predicts the tiltrotor whirl flutter speed and limitcycle oscillation amplitudes while only requiring two free decays. This approach can facilitate whirl flutter bifurcation analyses of tiltrotor systems exhibiting nonlinear dynamics.
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."
As part of maintenance improvement on helicopters, Airbus Helicopters has made available a proactive analysis service based on Health and Usage Monitoring System data generated during the flight. The present paper describes the new approach used to detect and classify any changes in time series behavior thanks to A.I. (Artificial Intelligence) especially computer vision. This new approach is more efficient and relevant than the classical approach based one statistical law [Ref 1]; in fact, it is acting, as the human eye, which is able to identify easily any abrupt change on the time series, and classifies it, whether Machine learning or Deep Neural Networks both have shown excellent results in term of classification accuracy. First part of this paper highlights how the learning data were prepared, then the second and the third parts give more details about how the time series are transformed into image presentation and how the different Artificial Intelligence models were selected and feed, ultimately the obtained results.
This article addresses the critical need for enhanced weather observation and prediction systems for rotary-wing aircraft. Current weather systems lack granularity in low-altitude airspace, posing safety risks. The application of the ASTM F3673 - 23 Weather Standard Specification is proposed to standardize weather data collection and transition towards a weather sensor performance-based approach rather than instrument certifications, facilitating the deployment of advanced weather sensors. Today, heliports have a binary weather measurement system choice, expensive certified surface weather stations or a windsock. The standard has the potential to change this paradigm, by allowing the deployment of cost-effective digital sensor technology to reduce uncertainty about what is happening at a heliport or vertiport/vertiplex destination. Operationalizing this specification requires rigorous testing and collaboration through public-private partnerships. Bridging the weather educational gap is essential for enhancing safety in low-altitude aviation. Additionally, the integration of Digital Flight Rules (DFR) alongside the ASTM F3673 - 23 Weather Standard presents opportunities for modernizing air traffic management.
There is a shift in the industry driving avionics manufactures to provide more interactive connectivity than they have had to in the past. The increasing threat of cyber security attacks in our communication systems is an increasing problem in our society and the avionics industry cannot ignore the fact that the threats are real and they must protect the systems from these attacks. Another element driving these concerns is the implementation of the FAA's NextGen or EASA's SESAR technologies which will require avionics vendors to replace their proprietary, relatively isolated embedded computer systems with information systems that interoperate and share data throughout FAA's/EASA's operations. In order to make the National Airspace (NAS) operate in the most efficient way all aircraft and ground systems will need to share information. The FAA and EASA have released standards to address these systems. This paper is only going to address aspects of security from a software perspective; more specifically, what an operating system and its environment should provide as a foundation for the security requirements. It is important to note that a generic solution to any security problem does not exist. Providing a complete security solution is the result of going through and addressing the airworthiness security process (AWSP).
A state-of-the-art emerging progressive damage failure analysis tool CDMat has been successfully applied to multiple material systems on open-hole tension and compression, and double shear bearing laminate coupons under static and fatigue loading including simulation to ultimate failure. CDMat also successfully demonstrated component-level strength/fatigue analysis under the Air Force Composite Airframe Life Extension (CALE) and the Fail-Safe Technologies for Bonded and Unitized Composite Structures (FASTBUCs) Programs. Building on the success of CDMat an integrated software solution for certification and sustainment of rotorcraft primary composite structures is being developed. A method and an algorithm for fatigue crack growth simulation in laminated structures are proposed to improve the accuracy of CDMat fatigue predictions. The method is based on using cohesive material model, tracking material points at the crack front, and calculating the pointwise energy release rate employing the J-integral. The algorithm was implemented as a set of user material subroutines developed within the framework of explicit finite element formulation for ABAQUS. The effectiveness of the method is demonstrated on several examples of Mode I and II fatigue crack growth.
The airframe digital twin analysis framework developed at the National Research of Canada is being transposed to safe life applications for rotorcraft components. A probabilistic safe life prediction approach, consisting of uncertain material property data and uncertain load spectra is used to calculate risk assessment metrics, such as the cumulative probability of failure, the hazard rate, and the average hazard rate as a function of time. A demonstration of this approach is presented for a CH-146 Griffon component, for which the uncertain loads are estimated from a model developed through machine learning. This preliminary assessment shows the feasibility of using digital twin concepts as a viable alternative to traditional deterministic life predictions, with the potential to reduce maintenance costs and increase aircraft availability.
This paper investigates a sliding-window matrix pencil method for predicting flutter points and limit-cycle oscillation amplitudes of nonlinear aeroelastic systems that experience whirl flutter. The approach applies the matrix pencil method to a short time window that slides along the free decay of a quantity of interest, quantifying the variation in the system's recovery rate to equilibrium with amplitude. The recovery rates at each amplitude and various forward speeds are extrapolated to predict the critical forward speed of zero recovery rate at those amplitudes. This process yields a set of limit-cycle oscillation solutions that can be visualized as a bifurcation diagram. The approach is demonstrated using output data from transient simulations of a propeller-nacelle test case with hardening structural nonlinearities. The impact of each parameter in the sliding-window matrix pencil method is first characterized via sensitivity analyses. Next, the bifurcation diagram is predicted using the recovery rates for the optimal parameter values. The results are compared with direct time marching and with the extrapolation of recovery rates estimated from envelope functions. The proposed method accurately captures the bifurcation diagram using two pre-flutter transient simulations with no need for envelope functions. This approach shows promise for output-based bifurcation analysis of nonlinear aeroelastic systems exhibiting limit-cycle oscillations associated with whirl flutter.
The rotorcraft community faces significantly higher accident rates compared to fixed-wing commercial aircraft, underscoring the critical need for enhanced safety measures. While Helicopter Flight Data Monitoring programs hold promise in improving safety, their widespread adoption remains limited, partly due to challenges associated with the acquisition and analysis of flight data. This paper proposes a Deep Learning (DL) solution to address safety concerns within the rotorcraft community by efficiently acquiring and analyzing flight data for a more automated and comprehensive safety assessment. Specifically, we leverage data obtained with cost-effective off-the-shelf cameras, and process it through Convolutional Neural Networks for automated detection and classification of gauges from several helicopters' cockpits. Our DL pipeline integrates a classifier for helicopter identification, an object detector for cockpit gauges detection and classification, and a network to infer the reading of each detected gauge. The contribution of this work is two-fold: (1) enhance rotorcraft safety by developing a DL framework capable of detecting, classifying, and inferring gauge readings for different helicopter types, and (2) boost research in the field by constructing a curated dataset valuable for aviation and machine learning communities.
Safety professionals receive data from internal and external sources, then manually determine whether the issue constitutes a safety hazard. Many reports are received, and each report is reviewed, then investigated further, using a tedious, labor intensive, and possibly error prone process. In the course of reaching a decision, human bias is inevitable - any two humans could reach different conclusions, and the same individual human could draw different conclusions on different days. As technology has advanced, numerous approaches have been pursued, attempting to reduce human bias and improve both efficiency and effectiveness of the process. In recent years, moderate success was achieved, which provided accuracy rates near 85% but continued refinement did not achieve acceptable results. In early 2023, the challenge was given to a new team, and within a few months, state-of-the-art Artificial Intelligence/Machine Learning data analytics techniques were utilized to aid in safety data analysis efforts, which resulted in high accuracy and efficiency, with reduced human bias.
A piloted simulation experiment was conducted in the NASA Ames Vertical Motion Simulator to investigate the effects of bandwidth, phase delay, attitude quickness, and maximum achievable rate on yaw-axis handling qualities in hover and forward flight. Two different aircraft were tested, representative of advanced scout-class rotorcraft. Five target acquisition and tracking Mission Task Elements were used in the study. Two of the tasks were modified versions of tasks used to determine the ADS-33E target acquisition and tracking yaw attitude quickness boundaries. Two of the tasks were modified versions of attitude capture and hold and sum-of-sines tracking previously used to evaluate pitch and roll axis handling qualities. The final task was a forward flight target acquisition task developed for this study based on a ground attack or strafing maneuver. Eight Army pilots participated in the study and evaluated 60 yaw-axis configurations. The results of the study suggest that the current yaw-axis hover/low-speed and forward flight bandwidth and hover/low-speed attitude quickness requirements for target acquisition and tracking are too low. Updated boundaries for these requirements are recommended.
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.
This study investigates the use of machine learning (ML) models to estimate the gross weight (GW), the longitudinal position of the center of gravity (CGx), and 1/rev cyclic flapping angles (Δ1c and Δ1s) of a compound helicopter with three redundant controls - main rotor RPM, collective propeller thrust, and stabilator angle. Neural Network (NN), Gaussian Process for Regression (GPR), and Support Vector Machine (SVM) algorithms are employed to develop estimation models using supervised training. The airspeed, redundant controls, main rotor controls, aircraft attitudes, and main rotor torque are selected as input variables (predictors) to the models due to their accessibility through the aircraft Health and Usage Monitoring System (HUMS). The dataset is split into low-speed and high-speed regimes to compare the prediction accuracy and training cost of separate regime models against a combined full-regime model. Separate airspeed regime GPR models showed superior performance in GW estimation, with higher accuracy and cost-effectiveness compared to a single full-regime model. For CG estimation, GPR again outperformed NN and SVM, although the maximum outlier errors increase significantly if a 95% confidence interval is considered. Finally, for 1/rev cyclic flapping angle predictions, SVM estimations, though not superior to GPR or NN, were acceptable and had a significantly lower computational cost. The study also examined the importance of predictors, highlighting that, on average, certain predictors like rotor RPM and rotor torque are less influential, but their removal degraded performance and had no cost benefit.
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.
This paper investigates the feasibility of using machine learning to predict whirl flutter bifurcation diagrams. The machine learning techniques selected for the study are XGBoost and the long short-term memory neural network. These techniques are selected for their suitability for sequential and nonlinear data. The techniques are investigated for a propeller-nacelle test case with polynomial structural nonlinearities resulting in supercritical or subcritical whirl limit-cycle oscillations. The techniques are trained to learn the bifurcation diagram for the amplitude variation of pitch angle limit-cycle oscillations of the propeller-nacelle system as a function of the forward speed for various levels of cubic structural nonlinearity. Bifurcation diagram learning and testing data are generated using the bifurcation forecasting method. XGBoost is computationally faster to train but less accurate for low amounts of learning data, especially for the most weakly and strongly nonlinear cases. The long short-term memory neural network is more computationally expensive to train but shows a less scattered error pattern for sparse learning data. However, it is sensitive to the amplitude resolution of the bifurcation diagrams. The approach to sample the cubic nonlinearity range does not significantly impact the results once the techniques have a sufficient amount of data to learn from. The data requirements observed in the study suggest that, for these techniques, direct learning of bifurcation diagrams may not scale beyond a handful of input parameters.
A key objective of this work was to develop a quantitative rationale to explains some aspects of pilot rating variability, as this would point to the fundamental principles driving pilot response that may not be observable if averaged ratings are used as a handling qualities metric. This paper hypothesizes that the factors affecting a pilot's ability to stabilize and control an aircraft following abrupt control motion is neither the damping nor the frequency of the ensuing oscillation, but rather the length of time that the oscillation remains large enough to interfere with the task (i.e., the product of damping and frequency). A handling qualities metric is introduced called the decay rate parameter that reflects the decay rate of the closed loop dominant mode. Closed loop pilot-vehicle oscillation decay rates were generated by a pilot model employing pitch (visual channel) and pitch rate (vestibular channel) tracking strategies. These decay rates were used to predict minimum and maximum handling qualities ratings and pilot induced oscillation (PIO) ratings which closely matched actual pilot ratings from an inflight PIO study using a variable stability NT-33A aircraft. PIO frequency prediction results were excellent. Predicted handling qualities and PIO ratings from a piloted NASA Vertical Motion Simulator study also closely matched the actual ratings. The results indicate that even with the highest fidelity motion simulation, pilot control relies primarily on the visual channel and is constrained by its inherent limitations. Conversely, the dominant control strategy appears to be the vestibular channel when pilots conduct a visual task that is anchored in the physical, out-the-window environment. The vestibular channel is shown to incur effectively no time delay.
In the field of aerodynamics, there is a growing need for rapid load prediction in engineering applications. Surrogate modeling offers a promising solution, providing faster results compared to high-fidelity computational models. This study focuses on a Machine Learning (ML) framework tailored for surrogate modeling, specifically for integrated aerodynamic load predictions in aircraft design. Central to this framework is a Deep Neural Network (DNN) component capable of handling both steady-state and fluctuating aerodynamics. A key challenge for surrogate models lies in maintaining prediction accuracy, especially in scenarios involving nonlinear flow phenomena like flow separation and transonic shifts. To address these challenges, we introduce a two-step physics-state predictor that integrates an intermediate Convolutional Neural Network (CNN) component. This approach enhances the surrogate model's capability to accurately represent dynamic separated flows and other nonlinear patterns without relying on unrealistic user inputs. Results are presented for NACA0015 dynamic stall predictions for two different physics-state inputs.
Tailsitter configurations that operate in both fixed and rotary wing flight modes are typically capable of generating large control forces and moments, making them inherently capable of rapid transitions and aggressive maneuvers. However, harnessing these capabilities requires feedback control strategies that can effectively estimate the non-linear aerodynamics loads involved to successfully exploit them. This paper describes initial steps in combining an onboard flow sensing strategy with a data-driven approach to estimating inflight air loads. A neural network is trained to use measurements from a multi-hole probe to predict the output from a set of pressure sensors embedded in a wing section undergoing a series of pitch motions in a wind tunnel. We hypothesize that this limited context of emulating a sensor network represents a focused and compartmentalized approach to applying emerging data-driven techniques to challenging aeronautical problems. We compare estimation results from a set of neural networks with varying input configurations to assess the feasibility of our approach and the significance of different sensing modalities on overall performance. Current results show that a gated recurrent network (GRU) trained with unsteady pressure measurements was able to predict the chordwise pressure distribution on a pitching NACA 2412 airfoil using probe measurements, reproducing the transient and non-linear effects observed in our dataset.
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.
Rotor blade optimization presents a multifaceted challenge as traditional design methodologies rely on computationally exhaustive high-fidelity computational fluid dynamics (CFD). Conversely, low-fidelity techniques such as potential flow based codes are inaccurate, especially in the regions of flow separation. This paper proposes leveraging artificial neural networks (ANNs) to predict the performance polar of a given airfoil geometry, and to facilitate the inverse design of airfoil, a modified form of ANNs (known as Tandem Neural Networks (T-NNs)) is implemented. The airfoil inverse design is a multi-point optimization problem (at multiple angles of attack) and therefore, the T-NNs are trained on the vectors of performance polar instead of individual angles of attack. The paper also delves into a comprehensive analysis of data wrangling, airfoil parametrization and design of experiments to cover a wide range of rotorcraft airfoils. A novel way of including practical design constraints for airfoil geometry is also included. Finally, this work demonstrates the application of the proposed methodology for airfoil inverse design, statistical analysis for generating a family of airfoils and optimization of HART-II rotor using T-NNs and Genetic Algorithm (GA).
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