Browse Topic: Optimization
Emerging technologies in the field of electrified propulsion systems offer a promising solution to reduce the dependence on fossil fuels and improve efficiency. However, the design of high-power density electric machines introduces new challenges, including limited passive cooling potential and the issue of the weight of electric motors. To address these challenges, this paper considers analysis and design methods for high torque-to-weight ratio axial flux motors. A magnetic equivalent circuit model coupled with a lumped parameter thermal network is developed for design space exploration and optimization. This inexpensive analytical model predicts the performance of a single-stator dual-rotor axial flux motor based on geometry, loading condition, and slot and pole pair combination. To enable comparisons against real-world data, the optimization study was demonstrated using the hover mission requirements from the Research Aircraft for eVTOL Enabling techNologies (RAVEN) vehicle to minimize the mass of the motor. In tandem with the analytical model, a higher-fidelity finite element model was also developed, and good agreement between predicted power and efficiency was demonstrated across a range of axial flux motor designs. The lightest weight design that satisfied the hover mission requirements was the 12 pole pair 27 slot (12PP 27S) configuration with a fixed weight of 9.28 kg. The analytic model undersized the output power of the electric motor by approximately 9% across a range of slot and pole pair combinations.
This digital standard is a requirements extract of AS4159 Specification For An Automated Interchange Of Standards Data. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
This digital standard is a requirements extract of AS6500A Manufacturing Management Program. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
This digital standard is a requirements extract of AS861C Minimum General Standards for Oxygen Systems. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
This digital standard is a requirements extract of AS5127D Aerospace Standard Test Methods for Aerospace Sealants Methods for Preparing Aerospace Sealant Test Specimens. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
This digital standard is a requirements extract of AS13001A Delegated Product Release Verification Training Requirements. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
This digital standard is a requirements extract of AS50881H Wiring Aerospace Vehicle. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC. 
This paper presents a reinforcement learning (RL)–based outer-loop controller for quadrotor UAV trajectory tracking and its real-world experimental validation. The proposed approach integrates RL into a standard cascaded flight-control architecture by replacing the conventional PID outer loop while retaining the onboard attitude and body-rate PID controllers. This hierarchical design preserves reliable inner-loop stabilization while leveraging RL to address nonlinear dynamics, coupling effects, and modeling uncertainty in translational motion. The controller is trained entirely in a physics-based simulation using Proximal Policy Optimization (PPO) and transferred directly to a Crazyflie quadrotor without additional tuning. Performance is evaluated through real-world figure-8 trajectory tracking experiments with varying time scales to impose increasing dynamic demands. Compared to a conventional PID outer-loop controller operating under identical conditions, the RL-based controller consistently reduces effective phase delay and achieves lower position and velocity tracking errors, particularly for aggressive trajectories. The results demonstrate robust sim-to-real transfer and highlight the potential of learning-based outer-loop control as a drop-in enhancement to classical quadrotor flight controllers.
This paper presents the design, development, and subscale flight testing of an optionally-autonomous lift-plus-cruise (LPC) eVTOL aircraft for emergency response missions that bridges the gap between existing aerial capabilities and the needs of first responders. A 4+1 LPC configuration consisting of four vertical lift propellers and a single pusher propeller was selected to balance hover performance and cruise efficiency. The vehicle is sized around a 600 lbs gross takeoff weight with a 125 lbs payload capacity. VTOL and Pusher propeller blades were optimized using parametric studies, resulting in a high Figure of Merit and propulsive efficiency. Trim analysis demonstrates efficient hover to cruise transition, lift-to-drag ratios of 10-11 between 70-90 knots, and propulsive efficiency exceeding 0.9 at the cruise speed of 100 knots. The subscale configuration utilized a simulation framework for trim and optimization of flight control laws, which were subsequently implemented on a 1/3-scale subscale demonstrator. Subscale flight tests showed stable hover and robust trajectory tracking under wind disturbances throughout the flight envelope, which demonstrates the feasibility of the proposed LPC architecture.
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.
A comprehensive numerical study was conducted to reduce helicopter rotor hub vibratory loads and fuselage vibrations using the Higher Harmonic Control (HHC) technique. A CAMRAD II model of a medium utility helicopter was developed for aeromechanical simulation, and a linear system model representing both hub vibratory load and fuselage vibration characteristics was identified offline. Optimal control inputs were then computed to minimize vibration responses under different weightings on hub vibratory load and fuselage vibration in the objective function. The predicted performance was verified through CAMRAD II simulations. Additionally, a closed-loop HHC system incorporating actuator amplitude limitations was investigated. A control algorithm regulated actuator amplitudes while maintaining phase consistency, dynamically adjusting control inputs after each iteration. The results demonstrate that the amplitude-limited closed-loop control limits excessive pitch link loads while maintaining effective vibration suppression, thereby maintaining structural safety and ensuring practical feasibility for real-world helicopter applications.
A multifidelity, multipoint aerodynamic blade shape optimization was conducted to design a realistic, full-sized proprotor, representative of recent industry tiltrotor and lift+cruise UAM vehicle designs. The proprotor was designed to achieve a disk loading of 8 psf in hover at sea level standard day and 1.9 psf in cruise at an altitude of 4000 ft above ground level with a multipoint efficiency optimization target. A low-fidelity optimization was first conducted using a differential evolution algorithm with CAMRAD II's uniform inflow model, followed by a mid-fidelity trim using CAMRAD II's nonuniform inflow and free-wake models, a high-fidelity verification using a hybrid RANS/LES approach in FUN3D, and finally a high-fidelity optimization on the low-fidelity optimized blade shape with a gradient-based method using a uRANS approach in FUN3D. The low-fidelity optimization resulted in a proprotor that achieved a hover figure of merit of 0.830 and a propulsive efficiency in cruise of 0.904. Results from the low-fidelity optimization, mid-fidelity trim, and high-fidelity verification were compared to highlight differences in predicted blade span loading and aerodynamic efficiency between the different aerodynamic solvers. It was shown that the low-fidelity solver compared least favorably with the high-fidelity aerodynamic solver. Lastly, the high-fidelity optimization further reduced the torque in cruise by approximately 75 ft-lb, with negligible changes in hover figure of merit and propulsive efficiency in cruise. Negligible blade shape differences were observed between the low-fidelity optimized design and the results from the sequential high-fidelity optimization with the largest difference being collective pitch in both hover and cruise, respectively.
The paper describes a method for optimal design of a helicopter tail shaft that considers rotordynamic effects from long shaft assembly. The tail shaft transmits power from the main gearbox (MGB) to the tail rotor of the helicopter and operates at high speeds that may exceed 6000 rpm. While higher speeds allow for weight reduction, they also pose risks associated with supercritical operation, necessitating careful design optimization. The objective of the optimization is to maximize the first three transverse natural frequencies with the constraint of the safety parameter (avoidance of the resonance/critical zone) while minimizing the weight of the system. A Non-Dominated Sorting Genetic Algorithm (NSGA-II) is used to obtain the solution to this multiobjective optimization problem, which involves shaft design variables such as length, outer diameter, and wall thickness. In addition, the optimization framework also incorporates system related design variables, including the stiffness of tail shaft bearing supports, the location of bearings, and coupling characteristics, to comprehensively evaluate and enhance the system performance. A rotordynamics model utilizing Timoshenko beam finite elements is deployed to predict natural frequencies.
Hybrid additive manufacturing (AM) and subtractive manufacturing (SM) processes utilize the combination of AM (e.g., LPBF and DED) and SM (e.g., milling and turning operations) to produce the final part. Due to the poor surface roughness resulting from the uneven melting of powders in AM, the subtractive process is a necessary finishing operation to improve the surface roughness of the AM part. The hybrid AM/SM technology combines the benefits of AM and SM processes to create complex geometry while introducing good surface finish and compressive stress to prevent crack initiation. However, the relationship between large process parameter space and the residual stress/distortion in the part is not well understood, which impedes the adoption of hybrid AM/SM to minimize the residual stress in the final product. To expedite the process optimization, we establish a pipeline for the sequential modeling of additive manufacturing (AM) and subtractive manufacturing (SM) processes. Key accomplishments achieved under this study include (1) development of thermal abstraction technique for the AM process to speed up the macroscale level heat transfer analysis based on the manufacturing factors including scanning vector, laser power, dwelling time, etc.; (2) development of the sequentially coupled thermal-mechanical model to predict the residual stress and distortion after AM process by passing the temperature history obtained from heat transfer analysis to the mechanical analysis at each time point; (3) validation of the thermal-mechanical model for AM using thin-wall structure from literature and cantilever beam structure from UNT’s experiments data; (4) conduction of the parametric study on the chamber temperature and part design in the AM process to demonstrate how the temperature gradient and supporting structure affect the residual stress and distortion; (5) exploration of macro and micro scale models to predict the bulk and surface residual stress after cutting; (6) applying the developed modeling framework to tailoring the hybrid AM/SM process. To support model verification and demonstration, we print cantilever beam structure with different supporting structure designs and cutting strategies to study how these factors affect the final part residual stress and distortion. The data collected in the printing and cutting process is used to examine the applicability of the developed simulation tool.
Quenching is the most critical step in the sequence of heat-treating operations, aiming to preserve the solid solution formed at the solution heat-treating temperature by rapidly cooling the material to near room temperature. Currently, there is no reliable, performance-informed quenching process that can consistently reduce the high scrap rate of airframe aluminum forging parts, which often suffer from significant residual stress and distortion. This limitation stems from the complex interactions between temperature, phase transformations, and stress/strain behavior—each influenced by the evolving temperature distribution and microstructural state of the workpiece. Conventional modeling techniques for quenching processes typically lump these multiscale, multi-physics phenomena into a simplified heat transfer coefficient (HTC). However, determining the spatial and temporal variations of HTC through experiments is both prohibitively time-consuming and costly. To address this challenge and enable rapid process tailoring for reduced distortion, we have developed and validated a digital twin-based Quenching Laboratory Software (QLAB) tool. QLAB integrates a thermal multi-phase computational fluid dynamics (CFD) model, sequentially coupled with a Mechanical Threshold Stress (MTS) model and a precipitation model. The thermal CFD component captures turbulent flow, multi-phase transformations, and the complex heat transfer stages of quenching including vapor blanket formation, nucleate boiling, and convection—to accurately predict temperature evolution. The MTS-precipitation model quantifies the effects of microstructural precipitates on the material's mechanical response under thermal loading. QLAB has been thoroughly validated using representative aluminum airframe components, including aluminum bars with pockets and Lcorner parts. We demonstrate the tool's predictive accuracy by comparing its output against experimentally measured temperature and distortion fields. Finally, we apply the validated QLAB to conduct a virtual quenching test on a simplified aluminum airframe structure, showcasing its potential for performance-informed process optimization.
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.
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.
Advanced structural analysis methods, known as progressive damage and failure analysis tools, are being developed to predict initiation and propagation of damage under repeated loading based on capturing individual and interacting damage modes. This work develops structural fatigue life prediction capability in state-of-the-art emerging progressive damage failure analysis tool CDMat developed at the University of Texas Arlington Advanced Materials and Structures Lab. While JIntegral, implemented in CDMat, appears as the most objective and rigorous approach to predict delamination growth-based fatigue life of composite structures, the key material properties of the J-Integral fatigue model have not been measured with the adequate accuracy. This work addressees a fundamental challenge of eliminating the established and routine assumptions and developed a methodology to determine the key material properties meeting the material input data requirements for the JIntegral based structural fatigue life prediction models. This work generated input data for fatigue crack growth propagation, including optimized input data parameters for the in-situ cohesive law and modeling as-manufactured specimen conditions. In addition, a methodology to account for and characterize the effects of fiber bridging in static tests is presented. It uses a simple standard unidirectional panel for testing, before a component is manufactured, fabricated from the same batch of prepreg to qualify the effects of fiber bridging. Also, this work attempted to determine a minimum conservative initial crack size to streamline the fatigue crack propagation prediction. Fatigue predictions have been demonstrated on a representative composite skin–hat stiffener sub-component section and compared with tests.
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.
Whirl testing of a full-scale rotor with positive flap-bending/twist composite coupled blades was performed to evaluate the dynamic and performance effects of the coupling. A positive flap-bending/twist coupling, in which a flap up deformation induces a nose down elastic twist, was introduced in the blades through tailoring of the laminate layups; the magnitude of the coupling was maximized through an optimization of the layup, with the intent of maximizing the potential impact of the coupling for correlation purposes. An uncoupled version of the blade using the same geometry and materials was also fabricated to provide a baseline set of measurements for comparison, with the coupled blade optimized to also minimize changes in bending and axial stiffness properties in an effort to isolate the effect of coupling by itself. Rap testing was conducted to measure blade modal frequencies and shapes in a free-free environment. Whirl testing was performed for both the coupled and baseline rotors to measure frequencies and damping for stability, load distributions, and performance characteristics. Rotorcraft Comprehensive Analysis System (RCAS) models were run for all cases and are presented along with the measurements for correlation purposes; generally good agreement was observed in trends between measurements and predictions.
This study presents an integrated optimization framework for rotor blade design that combines aerodynamic shape optimization and internal structural design within a unified multidisciplinary process. A variable fidelity modeling (VFM) approach is employed to efficiently optimize the blade outer geometry for improved figure of merit (FM) in hover and lift-to-drag ratio (L/Dq) in forward flight. Based on the optimized aerodynamic shapes, internal structural optimization is subsequently performed using a surrogate model for predicting cross-sectional properties, ensuring dynamic feasibility while minimizing blade vibration and weight. Final aeroelastic performance is evaluated through high-fidelity CFD/CSD loose coupling simulations. Optimization results show that individual designs achieve up to 6.5% improvement in FM or up to 6.6% improvement in L/Dq compared to the baseline HART II rotor. Furthermore, cross-validation comparing blades independently optimized by Seoul National University (SNU) and DLR reveals similar aerodynamic trends and performance, demonstrating the robustness and general applicability of the proposed framework across different simulation setups.
This paper explores a significant step forward, regarding the further detailed understanding of the Fenestron®. Since its patent in 1968 – for the Gazelle helicopter –, the shrouded tail rotor has been resized, inclined, modulated, etc. and has thus been continuously enhanced on different rotorcraft. Half a century after its invention, Airbus is once again exploring in more detail the magic of the Fenestron®, with the objective of optimizing it even further, for future helicopter applications. To grasp and observe properly some specific phenomena, a model (scaled to one third) capable of both unprecedented functions and modularities, was developed. The present paper will describe in detail the novel model and the related challenges and solutions. This model is capable of high rotor speed and dynamic pitch inputs, delivering power levels high enough to reach stall effects, while allowing the measurement of propulsive efficiency and to differentiate rotor vs fairing thrust. Furthermore, the model had to provide aerodynamic-shape modularity, both in the shroud, on the covers and on the tail gearbox supports. The first test campaign performed on this model allowed us to define design drivers and aerodynamic preferences. And since one idea often leads to another, another challenge is being addressed on this scaled mock-up: Reduction of the tail rotor noise level.
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.
This paper presents a distributed algorithm to track a desired target while fostering the emergence of a swarm formation and providing obstacle avoidance capability to deal with unknown scenarios. The proposed approach is based on the merge between a Flight Management System for global path planning and the definition of virtual forces through a custom Artificial Potential Field to prevent drones collisions between each other, with external objects and to provide cohesion of the swarm configuration. Each drone independently computes its global route and adjusts its path based on an optimal control action to minimize a potential energy function induced by its neighbors and obstacles. This approach results in a high cost-effective strategy to enhance UAVs autonomy level by managing a large group of drones, guaranteeing a low cost per unit thanks to the low computational effort and low-budget sensor suit while providing all the capabilities to accomplish the desired mission.
We extend the previously developed integrated VABS (iVABS) framework for rotor blade structural optimization with an enhanced cross-section template for practical manufacture considerations; these include the introduction of curved spar corners, a continuous wrap-around skin, trailing-edge tabs and a conformal non-structural mass. The added fidelity is exercised on a UH-60A-based outer mold line through three multi-objective optimization case studies, including a case where the cross-sections are optimized independent of each other, and two cases where all the cross-sections are optimized simultaneously with manufacture considerations. It was found that the latter cases produce straight spars that are relatively more practical to manufacture when compared to the first case, while achieving significant reduction of up to 80% in the mismatch of stiffness values, inertia properties, and shear center locations, when compared to the prior work. A subsequent sensitivity analysis of the Pareto set isolates five critical combinations of design variables, out of the original nearly 100 variables, such as root/mid-span skin-ply count and spar-web placement, that can be adjusted to refine the Pareto solution. The study demonstrates that manufacturability-aware parameterization and data-driven variable reduction can deliver practical composite-blade designs within a scalable optimization loop.
Rotor performance in a Martian environment was analyzed with an objective of increasing thrust with minimal impact on efficiency. The Sample Recovery Helicopter (SRH) and Rotorcraft Optimization for the Advancement of Mars Exploration (ROAMX) rotors were studied by varying solidity, blade count, and chord distribution to determine which configuration delivered the most desirable performance. For all configurations, the ROAMX rotor displayed better performance than the SRH rotor. It was observed that increasing solidity reduced the blade loading required to achieve the peak figure of merit, and beyond a solidity ratio of 0.3 the figure of merit was negatively impacted. For both rotors a 6-bladed configuration with a solidity ratio of 0.3 delivered the optimal figure of merit.
This paper explores novel airfoils for rotorcraft applications using a gradient-free, multi-objective genetic algorithm with 2D URANS simulations. The study considers dynamic kinematics at a Reynolds number of 5×105 and a mean Mach number of 0.35. Two optimization scenarios are analyzed: 1) pre-stall kinematics (0° ≤α ≤10°) and 2) dynamic stall kinematics (0° ≤ α ≤ 20°). The paper compares two objective functions: f1, based on the cycle averaged lift, and ˜ f1, which modifies f1 by penalizing hysteresis in the lift coefficient. The effects of uniform vs. fluctuating freestream velocity and reduced frequency on optimal airfoils are also discussed. The proposed optimization approach has resulted in novel airfoil shapes that are characterized by a drooped nose, with a convex surface on the aft upper surface similar to a reflex camber in pre-stall kinematics and less unsteadiness in the air loads for the optimized airfoils under the dynamic stall kinematics.
This paper demonstrates methods of aircraft sizing, flight dynamics modeling, and performance analysis using a lift+cruise concept vehicle with an electric powertrain and variable-speed rotors. The central focus is the development of methods to relate the aircraft design sizing constraints to achievable maneuverability and predicted handling qualities. A toolchain is demonstrated that performs aircraft sizing, mass moment of inertia estimation, powertrain modeling, trim optimization, dynamics linearization, handling qualities prediction, and quantification of achievable maneuverability under both nominal conditions and control effector failures. A convex optimization problem framework is introduced to compute agility bound estimates without requiring control system design or control allocation, potentially supporting rapid design iteration as well as early detection of deficiencies and undesirable operating conditions. This analysis is supplemented with more conventional methods of analysis to provide additional perspective and observations. Overall, the results suggest that each modeling and analysis element in the demonstrated toolchain adds significant value, with the combined approach supporting a more efficient and comprehensive exploration of trade-offs within the design space.
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.
This paper explores the dynamics of rotating Tuned Vibration Absorbers (TVAs), focusing on the phenomena arising from gyroscopic effects. Some products from Leonardo Helicopters (LH) can have a TVA fitted in the rotor mast, counteracting the in-plane vibratory loads of the rotor directly at their source, implying that the absorber rotates with the rotor itself. Although gyroscopic effects are negligible for most of the LH TVAs, specific design choices may have notable impacts on tuning and performance. An analytical model is implemented, demonstrating that the gyroscopic terms influence the dynamics causing a frequency displacement of the anti-resonance evaluated without considering this effect. Additionally, a regression analysis investigates the interplay between this phenomenon and the physics of the system, revealing how to optimize the design to mitigate gyroscopic effects. Finally, the performance of the TVA is analyzed as a coupled problem, showing that the anti-resonance effectiveness is reduced by the orthogonal contribution introduced by the gyroscopic matrix.
ABSTRACT Today’s combat vehicle designs are largely constrained by traditional manufacturing processes, such as machining, welding, casting, and forging. Recent advancements in 3D-Printing technology offer tremendous potential to provide economical, optimized components by eliminating fundamental process limitations. The ability to re-design suitable components for 3D-printing has potential to significantly reduce cost, weight, and lead-time in a variety of Defense & Aerospace applications. 3D-printing will not completely replace traditional processes, but instead represents a new tool in our toolbox - from both a design and a manufacturing standpoint.
ABSTRACT The present study considers two notional rotorcraft models: a conventional utility helicopter, representative of an H-60, and a wing-only compound utility rotorcraft, representative of an H-60 with with a wing similar to the X-49A wing. An Explicit Model Following (EMF) control scheme is designed to achieve stability and desired Rate Command / Attitude Hold (RCAH) response around the roll, pitch and yaw axes, while alleviating vibratory loads through both feed-forward and feedback compensation. The harmonic decomposition methodology is extended to enable optimization of primary flight control laws that mitigate vibratory loads. Specifically, Linear Time Periodic (LTP) systems representative of the periodic rotorcraft dynamics are approximated by Linear Time Invariant (LTI) models, which are then reduced and used in LQR design to constrain the harmonics of the vibratory loads. The LQR gains are incorporated in the EMF scheme for feedback compensation. One innovative approach is the addition of rotor state feedback to standard rigid body state feedback. A Pseudo Inverse (PI) strategy is incorporated into the EMF scheme for redundant control allocation. Finally, simulation results with and without load alleviation are compared and the impact of PI feed-forward and rotor state feedback compensation on handling qualities is assessed in terms of ADS-33E specifications.
Airfoil optimization for rotor blades is a critical endeavor aimed at enhancing aerodynamic performance and reducing noise. This paper employs a Kriging surrogate model combined with a multi-objective genetic algorithm to optimize thrust, power, and broadband noise. Three airfoil parameterization methods including ParFoil, PARSEC, and CST are compared when used to generate various airfoil shapes for the surrogate model and optimization process. We utilize low-fidelity aerodynamic tools such as XFOIL and blade element momentum theory for aerodynamics. In addition, acoustic modeling is conducted using Lee's wall pressure spectrum model alongside Amiet's trailing-edge noise model. The paper focuses on small-scale rotor configurations, specifically an ideally twisted rotor using the NACA 0012 airfoil and a modified XV-15 blade. Both blades are used as baseline models for hover optimization. The optimization of the ideally twisted rotor across various parameterization methods demonstrates a significant reduction in A-weighted overall sound pressure level by approximately 4.0 dBA. The primary contributors to this noise reduction are identified as a decrease in the chordwise pressure gradient and wall shear stress, which are key factors that contribute to the wall pressure spectrum and resulting trailing-edge noise. Similarly, the optimization of the modified XV-15 blade using the ParFoil method achieves a notable decrease in noise of 3.5 dBA. Further analysis is extended to axial flight conditions, with axial velocities of 5.0, 10.0, and 20.0 m/s, to simulate vertical climb. The results show that these hover-optimized blades are capable of sustaining noise decreases of 3.0-4.0 dBA compared to the baseline, provided that the thrust coefficient is maintained through adjustments in collective pitch.
In over actuated aircrafts a simple relationship between control inputs and forces/moments generated does not exist, however they have become very attractive for their wide range of applications. Control allocation aims at finding a unique surface control distribution as function of flight condition to perform the desired maneuver. The goal of this paper is to present a control allocation methodology applied on a generic over actuated aircraft aimed to determine the surface gearing matrix weights in different operative conditions to minimize the total power consumption. First, the non-linear model of the control forces and moments is derived for an over actuated aircraft. Then, two different optimization problems are introduced: the first to compute the trim equilibrium for any flight condition to minimize power consumption by the aircraft; the second to minimize the surface deflections required to produce desired control forces/moments starting from the trim point previously found. Finally, the optimized solution is subjected to engineering judgement to neglect the ineffective surfaces that do not provide a significant contribution to the required maneuver.
This paper deals with the influence of engine failure during hover on the wiring harness mass of electrical Vertical Take-Off and Landing (eVTOL) aircraft. It starts by presenting possible strategies which can be used to distribute the additional thrust needed during an engine failure among the remaining engines. The most efficient strategy is selected and the impact of different single engine failures on the overall thrust share, while using this strategy, is discussed. The paper proceeds by applying the selected thrust compensation strategy to the mission simulation of three common reference models, which are representative of current eVTOL aircraft configurations. This simulation is used to determine the worst flight phase for the One Engine Inoperative (OEI) condition to occur. The main purpose of the simulation is to optimize the wire sizes of the wiring harness of each configuration while satisfying different design objectives. The results of these optimizations are used to discuss the criticality of each engine failure and its influence on the wiring harness design, especially its mass. It concludes with design recommendations for the wiring harness of eVTOL aircraft.
Over the past decade, due in large part to heavy investment in the field of Advanced Air Mobility (AAM), significant progress in rotorcraft-focused modeling tools has been made. Such progress has notably increased AAM rotorcraft modeling capabilities in the topics of conceptual design, preliminary design, and more recently flight dynamics. Yet, due to recent and persistent increases in extreme weather events, an emerging interest has been raised in utilizing such modeling capabilities for aiding in emergency relief efforts and other public good missions. This paper uses wildfire fighting as a representative public good mission and demonstrates the relevance of the NASA Revolutionary Vertical Lift Technology (RVLT) rotorcraft toolchain to such missions. An emphasis is placed on flight dynamics modeling and control because of the hazards and challenges associated with the atmospheric environment of wildfires. In this work, the NASA FlightCODE tool was used to analyze both a UH-60 and the NASA six-passenger quadrotor reference model hovering in an experimentally informed wildfire turbulent environment. Preliminary results of this study estimate actuator usage exceedances and disturbance rejection capabilities of the vehicles' translational rate command systems. Leveraging the RVLT toolchain, refinement and expansion of this work could lead to handling qualities envelope estimation and design optimization for wildfire turbulent environments. This would provide pilots with additional information to make real-time decisions in high-risk scenarios and begins preparations for simulating these dangerous environments for pilot training and experimentation.
NASA's 4th New Frontiers Mission is the Titan Dragonfly relocatable lander. This coaxial quadrotor vehicle will be launched on a rocket to Titan in 2028. Following a gravity assisted Earth flyby and an approximate 6-year transit, Dragonfly will enter the Titan atmosphere around 2034 with the goal of exploring Titan's pre-biotic chemistry and habitability. The multirotor design for this unique application has continually evolved since 2016 with constraints such as Titan's cryogenic atmosphere at 95 Kelvin (-288 F), gravity 14% that of Earth's, atmospheric density 440% of standard sea-level air, and the inability to test the entire system together under all these conditions until the first flight on Titan. This paper focuses on rotor design aspects of the Dragonfly lander and introduces a novel framework for multirotor design optimization considering multiple flight conditions. The methodology leverages machine learning methods and is demonstrated in the context of Dragonfly. A new OVERFLOW Machine Learning Airfoil Performance (PALMO) database is first presented. PALMO is then wrapped inside a Bayesian optimization framework and applied to a 4-rotor system (one side of the Dragonfly lander). Training data is generated on each iteration of the optimization using the CAMRAD-II comprehensive analysis software to evaluate successive rotor designs in multiple relevant flight conditions. An optimal design for the 4-rotor system was found with approximately 900 rotor designs analyzed in CAMRAD-II, which required 9 million queries of the PALMO surrogate models. This demonstration case evaluated 10,000,000 potential candidate rotor designs in 5.5 hours on 114 CPU cores using uniform inflow, and in 27.8 hours using the prescribed wake model. This work thus enables mid-fidelity rotor design optimization without requiring access to high-performance computing.
Hybrid-electric propulsion could provide numerous benefits for full-size rotorcraft, including reduced peak turbine power demand, reduced transmission system weight and complexity, and reduced operating costs. Variable speed electric motors, furthermore, could be configured to enable continuously variable rotor speed. Achieving these benefits requires accounting for coupling between the hybrid-electric drivetrain and vehicle performance within a large, unexplored design space. This paper presents a framework for simultaneous optimization of vehicle and electrified powertrain conceptual design using Geometric Programming (GP) methods. Four hybrid-electric powertrain architectures are evaluated relative to a baseline non-electrified powertrain for single main rotor, compound coaxial-rotor, and tiltrotor configurations. For designs with an upper limit on turbine power, electrification increases the maximum cruise speed for the compound coaxial-rotor configuration. Variation of the rotor speed by 15% allows the vehicle to carry 8% more fuel, relative to the non-electrified baseline, and 1,246 lb of battery. Operating the rotor at optimal speeds across the mission results in increased off-design mission performance, most notably a 43% increase in transport radius relative to a baseline powertrain. The results demonstrate the utility of the design optimization framework for exploration of novel hybrid-electric concepts as well as the challenges associated with incorporating electrical components into the drivetrain.
This paper introduces a Multidisciplinary Design and Optimization (MDO) approach for the design of a tiltrotor wing, utilizing as test case a semi-wing with integrated nacelle and rotor. Structural integrity is assessed via stress analysis on a GFEM, which also forms the basis for a coupled wing-rotor aeroelastic model to ensure whirlflutter stability. Aerodynamic performance is assessed through CFD analysis of two-dimensional wing's airfoil shape. The MDO workflow leverages three levels of design space control that can influence the structural response of the wing: other than controlling the structural properties of composite materials, the internal wing-box architecture and external airfoil shape are modified acting directly on the FEM by means of a mesh morphing technique. This methodology allows for the use of mid-fidelity finite element models, bypassing CAD reshaping and remeshing. Validation tests confirm the approach's effectiveness in producing optimized designs. Additionally, the study explores surrogate models as efficient alternatives to CFD simulations, yielding sufficiently accurate results while reducing computational costs and the MDO simulation time.
As part of the design process, structural assessment represents an important aspect in the development of new airand rotorcraft. It plays a critical role in supporting the weight of the aircraft, transmitting loads from the rotors to the airframe, and ensuring the overall safety and integrity of the vehicle. The conceptual design phase is characterized by exploration and evaluation of broad design concepts, with minimal detail regarding structural design. In contrast, the preliminary design phase involves refining the chosen design concept and conducting more detailed structural analysis and optimization to prepare for the subsequent detailed design phase. In order to evaluate the airframe, the opensource based design environment PANDORA has been developed at DLR. This paper presents an overview of model generation, topology optimization, sizing, and crashworthiness aspects in PANDORA using validation examples and generic rotorcraft models.
ABSTRACT In this work, a genetic algorithm was implemented to perform an airfoil shape optimization with constraints applied to the airfoil cross-sectional area and pitching-moment coefficient. Constraints are enforced through the use of an augmented Lagrange penalty function. The design variables are formed through a class shape transformation approach with orthogonal, polynomial basis modes. The use of an orthogonal basis provides decreased levels of multicollinearity in higher-order design spaces, while still maintaining the completeness of lower-order spaces. The optimization methodology is demonstrated on the tip airfoil of a UH-60A baseline rotor. The design trade-offs of a new tip airfoil are investigated where the optimized tip section shows improvements in forward-flight performance in exchange for a small reduction in the rotor's stall margin.
This paper presents the design and development of a swashplateless micro helicopter with a target endurance of more than 30 minutes using an optimized direct drive rotor connected to a unique rotor hub that has blades with a flap hinge and proprietary skewed-lag hinge with pitch-lag kinematic coupling. This obviates the need for conventional swashplate based cyclic pitch control, as the cyclic variation in control angle is achieved by cyclically varying the motor RPM. UP12 underactuated propulsion system developed by VertiQ is used for the baseline design. The blades in this propulsion system are optimized using Blade Element Momentum Theory (BEMT) analysis with lookup table to enhance its performance. BEMT is validated using experimental measurements and then used to optimize the geometry of the rotor. The optimized blades offer better performance and are 30% lighter than the original 3D-printed plastic blades. The prototyping of the Micro Aerial Vehicle (MAV) is completed by fabricating the airframe and using off-the-shelf electronic components. Tabletop tests are carried out using a yawing test stand to tune the yaw attitude gains for the MAV. A coupled flap-lag rotor dynamics is combined with the motor angular speed dynamics to simulate the dynamics of the system to carry out fundamental understanding of the control power being generated by the swashplateless system. The swashplateless rotor design is able to generate significant propulsive and side forces, however it is unable to generate control moments which may be inadequate for aggressive maneuvering warranted by small agile MAV.
As military organizations internationally assess life extension and replacement actions for current legacy helicopter fleets and next generation rotorcraft are under development, novel rotor system technologies are required to fulfill challenging low-speed and high-speed flight envelopes and mission requirements. Proposed by the Department of National Defense (DND) and in collaboration with the National Research Council of Canada (NRC), a TTCP AER CP13A.1 Collaborative Project (CP) has been initiated supporting multi-nation development of numerical methods for optimizing and designing next generation main rotor blades. Four NRC laboratories collaborated to assemble a data set comprising design, performance, aerodynamics, structures, dynamics, and flight sciences elements. Acquired through research and testing, this information provides reference, technical, and engineering knowledge to support aero-structural model definition, model output validation, and the numerical optimization process development.
This paper demonstrates a framework for using high-fidelity methods to design rotor blades. An initial rectangular blade is optimised with respect to a generalised blade surface parameterisation, which includes variations in twist, chord, sweep and anhedral. Particular focus is placed on coupling aeroelasticity with the adjoint optimisation and trimming methods at desired flight conditions. Conclusions are drawn on the new design's effectiveness and the framework's efficiency to obtain it.
This study explores the best vibration reduction using a multicyclic controller through an individual blade control (IBC) actuation scheme for a lift-offset coaxial helicopter in high-speed flight. The rotorcraft dynamics model consists of coaxial, three-bladed counter-rotating rotors and a finite element fuselage stick model constructed based on the measured data of the XH-59A helicopter. The two-way coupled rotor-body vibration analysis results exhibit excellent correlations with the test data for rotor hub loads and airframe vibrations. The best actuation scenarios are sought for the minimum vibration of the vehicle using either open- or closed-loop control scheme. It is shown that the IBC actuation effectively reduces the vibrations at both locations of the rotorcraft. The co-reduction of 3P (per rotor revolution) and 6P vibration of the rotorcraft is achieved using the multicyclic control with offline system identification. A multicyclic harmonic IBC actuation enables to suppress the rotorcraft vibration by 81.4% and 3P pilot seat vibration by 92%, respectively, as compared to the uncontrolled case, leading to a significantly reduced vibration level (below 0.05g) of the rotorcraft. The closed-loop multicyclic control using the identified system and the aircraft model shows a good correlation ensuring the suitability of the present optimal control simulations.
This paper introduces ABC2, an advanced framework for rotor blade design optimization that can effectively consider the airfoil shape variations during optimization process. A major component of this framework is an reduced-order model (ROM) that leverages deep-neural-network techniques both for airfoil parameterization and performance prediction. Utilizing the UIUC airfoil database and a two-dimensional unsteady Reynolds-averaged Navier-Stokes (URANS) solver, the ROM can effectively control the airfoil shapes and predict the resulting aerodynamic performance across the wide range of flow conditions. A comprehensive aerodynamic solver is incorporated for blade design optimization. Enhancement of the fidelity of the comprehensive solver is achieved through the integration of a three-dimensional URANS solver, which also plays a crucial role in analyzing the aerodynamics of the optimized blade and uncovering its underlying physics. The competence of the present framework is demonstrated by a hovering optimization problem, yielding satisfactorily optimized blades and identifying the underlying physics.
Ground effect analytical models in the literature, such as the Pressure Potential Based Finite State Model (PPBFSM) or Velocity Potential Based Finite State Model (VPBFSM) have been developed to study an isolated rotor in full ground effect. These models use the mass source distributions to account for the ground plane. Also, these models consider that the exit pressure of those distributions is equal to the pressure exerted from the rotor at the ground plane. However, PPBFSM and VPBFSM do not satisfy the non-penetration of flow boundary condition at the ground plane. This paper develops a new ground effect model using the VPBFSM that considers the non-penetration of flow boundary condition at the ground plane by varying ground rotor size and using optimization to find the strength of the mass source distributions. Additionally, it captures the flow at any point below the disk which is missing in the previous study of ground effect using VPBFSM. The developed model is applied to the R-50 unmanned helicopter rotor using its geometric and aerodynamic data from the literature. The results show a good correlation compared to the Hayden model. Additionally, the present model exhibits a similar trend of rotor inflow in ground effect at different rotor heights above ground compared to the PPBFSM.
This paper examines the Handling Quality Rating (HQR) of the Model-Based Pilot Controller (MBPC) in failure scenarios within the Automatic Flight Control System (AFCS). The MBPC aims to automate the testing of malfunctions in the AFCS of the T625 Gökbey platform. It is constructed using optimal control and estimation theory, with the cost function representing human characteristics determined by weighting matrices. The optimal values of weighting matrices that minimize the cost function are achieved via Genetic Algorithm. This algorithm utilized to systematically minimize user-defined cost functions tailored to optimize performance for selected maneuvers within the scope of ADS33E-PRF, considering user-defined constraints. Time-domain metric performance is provided for two maneuvers: vertical maneuver and hovering turn. The HQRs of the MBPC evaluated according to Power Frequency and Inceptor Peak Power-Phase (IPPP) metrics. The MBPC satisfies the ADS33 desired performance criteria in case of a failure scenario in AFCS throughout the maneuver, achieving satisfactory handling quality results.
The design of a powertrain for an eVTOL is a complex, multidisciplinary challenge whose solution is best approached as a constrained optimization problem. We use a motor and drive optimization tool based on both first principles and empirical models to examine the design space for a medium-sized eVTOL aicraft. The tool is used to perform optimizations based on continuous power requirements in conjunction with time-dependent mission profiles to achieve an optimal powertrain system weight given efficiency and diameter constraints. Additionally, efficiency and diameter sweeps are performed to illuminate the trades surrounding the "optimal" design point. Access to both detailed system-level optimizations and multi-dimensional sensitivity studies gives eVTOL aircraft designers unique and pivotal insights into the interaction between the propulsion system and the rest of the aircraft. A bespoke, integrated, approach also provides significant weight savings over a commercial off-the-shelf (COTS) solution.
Full flight regime trim strategies are examined for a Lift+Cruise eVTOL aircraft. Control laws are designed for hover, transition, and cruise conditions to satisfy standard flying-qualities requirements based on the characteristic behavior of the vehicle (rotorcraft versus fixed wing) while ensuring realistic motor limits (peak and continuous) are satisfied. CONDUIT® is used to optimize control laws to minimize actuator activity while meeting flying-qualities constraints. Variable-RPM control is shown to be sufficient to satisfy Level 1 flying-qualities requirements in hover and low-speed flight where control surfaces have inadequate control authority. Time domain simulations are presented to verify controller performance and ensure actuator limits are not violated while following step commands. The aircraft is able to follow commands well in all axes and flight regimes. Transition through the full flight regime (hover to cruise) is simulated using a stitched model.
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