Browse Topic: Calibration
This paper presents an efficient numerical framework for prediction of broadband noise scattering through time-domain synthesis and propagation. For efficient scattering of broadband noise sources, a time-domain boundary element method is applied to propagate all frequencies together in a single computation. To obtain a time-resolved incident field without high-fidelity aerodynamic simulation, a stochastic broadband noise synthesis method is developed based on a semi-analytical airfoil broadband noise modeling approach. The framework is validated for airfoil trailing edge noise prediction, and the correspondence of the time-domain broadband noise synthesis method to existing semi-analytical broadband noise models is demonstrated. The framework is then applied to predict fuselage scattering of rotor tonal and broadband noise for a full-size urban air mobility concept vehicle. Significant differences are observed between the scattering effects in the tonal and broadband contributions.
Traditional safe-life methodologies for rotorcraft structural components rely on deterministic safety factors to account for uncertainty in loads, material properties, and operational usage. While effective for ensuring safety, these approaches lead to early retirement lives and reduced aircraft availability. This paper presents an updated digital twin-based probabilistic framework for rotorcraft component fatigue life assessment that integrates a probabilistic stress–life (S-N) material model, machine learning-based load estimation from flight data, and Monte Carlo uncertainty propagation. The approach is demonstrated for a critical location on the CH-146 Griffon main rotor yoke. Compared with earlier work, the present study advances the framework through independent validation of the load-estimation model and application to available in-service flight data from multiple mission categories. A probabilistic sensitivity analysis is used to examine the separate and combined effects of
The certification of highly integrated electric Vertical Take-Off and Landing (eVTOL) aircraft requires a rigorous bridge between simulation and flight reality. This paper presents the Joby Disturbance Generator, a high-integrity software framework natively integrated into the aircraft flight control system. The system utilizes a deterministic state machine to inject a library of signals, ranging from standard doublets and chirps to complex waveforms, directly into internal control loops. Applications include frequency sweeps for stability margin extraction and structural mode identification, time-domain inputs for handling qualities assessment, synthetic fault injection for redundancy management verification, and precise loads model validation. The system continuously monitors vehicle health, automatically aborting test points upon detecting genuine failures. For loads validation, it coordinates temporary relaxation of flight envelope protections with precise disturbance injection
Deep learning (DL) models have attained state-of-the-art performance in numerous fields. Nevertheless, for certain real-world applications, existing models encounter diverse challenges, ranging from a lack of generability to new data to issues of scalability and overfitting. In this context, integrating information extracted from different modalities holds promise as a potential solution to alleviate these challenges. This paper introduces MAVEN, a multimodal deep-learning framework for long-range atmospheric visibility estimation. Using multimodal deep learning, MAVEN fuses various modalities to estimate long-range atmospheric visibility. These modalities include RGB imagery, Edge Map, Entropy Map, Depth Map, and Normal Surface Map. Results show that in contrast to single-modality RGB, which achieves only 87.92% accuracy, multimodal deep learning models achieve an accuracy of over 96%. This significant improvement highlights the potential of multimodal approaches to enhance the
In this study, a multifidelity aeroelastic framework is presented for predicting trim conditions in rotary-wing aircraft, with the main focus placed on the DUST implementation and its application to helicopters and quadrotors. The methodology combines aerodynamic and structural solvers of different fidelity, specifically DUST and the multibody dynamics solver MBDyn, through the preCICE coupling interface to enable direct comparison with rigid and coupled aeroelastic solutions. The trim problem is formulated from the six degree of freedom rigid body equilibrium equations in a helical turn reference frame, naturally covering both steady and maneuvering flight. Although the same formulation can be extended to fixed-wing configurations, the present paper is focused on rotorcraft applications. The framework is first applied to the SA330 Puma helicopter, chosen for the availability of validated flight test data. The methodology is then extended to a multirotor derived from a NASA quadrotor
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
Fault detection in autonomous VTOL aircraft is critical because even minor degradations can quickly destabilize multirotor vehicles in safety-critical environments. However, real-flight fault detection remains challenging due to sensor noise, environmental disturbances, and the nonlinear aeromechanics of multirotor platforms. This study proposes a comprehensive machine-learning framework for rotor fault detection, isolation, and severity prediction using real flight data. A convolutional neural network (CNN) architecture is developed to learn spatio-temporal patterns from multivariate flight dynamics, enabling direct inference of both the faulty rotor and its damage level. The framework is first validated using simulated data generated by our in-house flight dynamic model. Next, to verify the framework using real flight data, a hexcopter was designed, fabricated and flight tested for both nominal and faulty cases by introducing controlled blade-tip breakage. The trained model achieves
This paper details comprehensive analysis modeling and analysis supporting the development of the Research Aircraft for eVTOL Enabling techNologies (RAVEN). An isolated rotor model was developed in CAMRAD II, and predictions of rotor performance and rotor aeroelastic stability were generated. The rotor stability predictions are part of assessing airworthiness of the RAVEN vehicle. The performance predictions were used to calibrate the surrogate model for the NASA Design of Rotorcraft (NDARC).
This paper describes the development process of a comprehensive pilot-in-the-loop simulation framework suitable for preliminary feasibility, and on-deck handling qualities assessment of the Leonardo AW609 civil tiltrotor, when operating with the Italian Navy aircraft carrier Cavour. A pilot-in-the-loop engineering simulator was used for simulations in which steady, quasi-unsteady, and fully unsteady ship airwakes were created using Computational Fluid Dynamics (CFD) and experimental data. A dedicated analysis of the simulation environment provided a strong agreement with various pilot inputs and aircraft response parameters when compared with flight data. Snapshot CFD simulations taken from a simulated lateral entry on ship deck allowed a comparison of airframe loads predicted by the aeromechanical model. While there are some good agreements and matched trends, development is ongoing to improve these aspects. Back-to-back piloted simulator approaches found a relatively good
Ever-increasing modeling and simulation capabilities and the desire to use simulations in support of system qualification, regulatory compliance, and other critical decision-making roles, raises the bar on the need for rigorous V&V of all aspects of the models used to create the simulation data. US Department of Defense Directives and Instructions, and emerging regulatory and industry standards on Modeling and Simulation in a Digital Engineering context require rigorous M&S Verification, Validation, and Accreditation (M&S VV&A). These specifications aim to create trusted and credible simulation data that can be used in critical decision-making roles on complex systems. Implementing a well-defined, structured, model-based and standards-based M&S VV&A Process early in the program lifecycle facilitates collaboration and documented buy-in on M&S VV&A for program with customers and/or regulatory agencies. This collaboration increases acceptance throughout the program and product lifecycles
Dufour Aerospace designs and manufactures an automated tilt-wing aircraft for critical cargo delivery missions. Emphasizing operational efficiency, the platform integrates path generation and tracking techniques tailored for the unique dynamics of tilt-wing flight and builds upon the existing lower level control. While there exist a myriad of methods for high-level aircraft automation ranging from PID to MPC, they often require a trade-off between complexity and the capability to handle non-linear dynamics of the system they are controlling. Hence, a lightweight, deterministic geometric path generation approach using clothoid-based transitions between three waypoints and a robust SO(3)- based path tracking controller adapted for tilt-wing dynamics are presented. Additionally, a high-level automation framework is introduced that includes failure mode handling for GNSS loss and communication breakdowns. This system ensures mission continuity and operational safety while supporting
The H-60 Black Hawk remains a cornerstone of U.S. Army Aviation, but its legacy avionics architecture presents modernization challenges. To ensure long-term operational relevance and interoperability with future platforms like the Future Long Range Assault Aircraft (FLRAA), the Army is implementing a Modular Open Systems Approach (MOSA). This strategy facilitates rapid technology integration, enhances sustainment efficiency, and mitigates obsolescence. The Army's MOSA adoption aligns with regulatory mandates such as the National Defense Authorization Act and Department of Defense (DoD) acquisition policies, ensuring modularity, scalability, and interoperability across aviation systems. The application of modern open standards, such as the Future Airborne Capability Environment (FACE®), within the Black Hawk supports software reuse and hardware commonality, reducing lifecycle costs and vendor lock. A phased modernization approach, including a Digital Backbone architecture supported by
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
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
This paper presents an overview of the comprehensive aerodynamic framework developed at ERC for the analysis and simulation of electric vertical takeoff and landing (eVTOL) aircraft. Addressing the challenges inherent to distributed propulsion architectures and the complex transition between hover and forward flight, the methodology integrates multi-fidelity simulation tools ranging from analytical models and low-fidelity simulation to fully-resolved transient CFD. The framework addresses all phases of aircraft design and validation, and includes dedicated insight into aeroacoustics, aeroelasticity, and interactional aerodynamics problems. A modular approach is adopted, where individual phenomena are first studied in isolation before being synthesized into an aircraft model. Experimental validation through wind tunnel testing, full-scale static thrust test stand measurements, and scaled model flight tests is essential to ensuring model accuracy and validity. The paper concludes with an
This paper presents a real-time closed-loop rotorcraft simulation framework using HeliUM-A, a high-fidelity flight dynamics analysis, and a Simulink®-based flight control system model. Serial optimization and parallel computing techniques are introduced in HeliUM-A to achieve real-time speeds. A customized ordinary differential equation solver with parallel load balancing enables accelerated time marching simulations. Software interfaces are introduced to encapsulate HeliUM-A into a Level-2 S-function Simulink® block. Using standardized Simulink® ports, control inputs, rotor/body states and their time derivatives as well as relevant output quantities are communicated in-memory between Simulink® and HeliUM-A for closed-loop execution. This encapsulation retains the parallel computing improvements in HeliUM-A when executed through MATLAB, Simulink® or through the compiled executable automatically generated by the Simulink Coder. The framework is demonstrated on a coaxial compound scout
This work proposes an experimental and numerical activity aimed at developing methods to evaluate the strength and toughness of Kevlar/Epoxy composite fastened joints used in aeronautical structures and exposed to high energy impacts. Experiments were conducted using an Arcan rig that allowed applying various loading conditions, ranging from pull-through to bearing. A non-linear model of the material based on a bi-phasic decomposition and hybrid meshing technique was built and calibrated. The material model was used to develop a high-fidelity model of the junction to simulate the pull-through test with the Abaqus/Explicit finite element solver. The results of the analysis point out that the implemented progressive damage laws are capable of achieving an appreciable experimental-numerical correlation, both from the qualitative and the quantitative standpoint. Therefore, the combined experimental-numerical approach is promising for developing a validated numerical tool capable of
The purpose of this paper is to present the technical details of the US Army Program Executive Office - Aviation (PEO AVN) Enterprise Architecture Framework (EAF) while describing its development status as of January 2024. The Future Vertical Lift (FVL) Program developed and continues to improve the FVL Architecture Framework (FAF) (Ref. 1) for use by FVL programs. The EAF is intended to be compatible with the FAF and to provide specific guidance to enduring programs. The EAF contains modeled content from the FAF and employs a technical approach that contains different perspectives for enterprise (i.e., PEO AVN) and program users depending on how the EAF will be used. As an architecture framework, the structure of the EAF follows International Standard ISO/IEC/IEEE 42020 (Ref. 2). The EAF prescribes the conventions, principles, and practices for the development of the Enterprise Architecture (EA). The EA communicates the technical, business, and organizational architecture of the
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
Rapid advances in high fidelity modeling and high performance computing capabilities have enabled their routine utilization in support of aircraft design. Analysts are able to generate orders of magnitude more data that must then be turned into actionable intelligence to guide design. Enabling effective application of advanced analysis to design requires a robust end-to-end digital transformation to make the simulation processes reusable, repeatable, traceable, scalable and minimize setup errors. This is achieved through the development of a Computational Fluid Dynamic (CFD) modeling framework where streamlining and automation are inserted within the current CFD workflow that involves model setup, simulation and post processing. Workflow automation techniques have been implemented in simulation pre and post processing that reduce the overall process time or enhance the fidelity of the simulation. To conduct CFD evaluations through flight envelope efficiently, space filling methods that
A highly extensible coupling framework for analysis of complex systems called Hermes is introduced. An overview of how the coupling framework functions is provided along with a description of a selection of relevant solver modules available for use within Hermes. The application of Hermes to electric vertical takeoff and landing (eVTOL) aircraft vibration analysis is performed using notional models combining a linear structural dynamic fuselage response with rotor dynamics obtained from a comprehensive solver. The rotor blades are assumed sufficiently stiff as to be rigid. The effect stiffening a wing on which rotors are mounted is examined and found to substantially reduce vibrations across the majority of the vibration spectrum.
An approach for redesigning the tip region of helicopter rotors to achieve a desired target pressure distribution is described. In this approach, the difference between the realized target pressure distribution and the target pressure values are used to drive the changes to the blade profiles. Because the design process is independent of the analysis that generates the surface pressure distribution, this approach may be used with a variety of analyses. Sample 2-D applications on the design of low drag rotorcraft airfoil sections are presented to demonstrate the ability of the design process to change the blade section profile iteratively and rapidly. The process is subsequently applied to the S-76 rotor to explore the redesign of the rotor tip region for improved hover performance.
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