Browse Topic: Advanced air mobility (AAM)
When quadrotor unmanned aerial vehicles (UAVs) operate in urban low-altitude airspace, especially within complex environments, their sensor perception signals are highly susceptible to blockages, deviations, and the inclusion of high-frequency noise. These factors, in turn, induce nonlinear variations in the UAVs’ flight mechanical properties, giving rise to abnormal flight stability issues such as attitude jitter, altitude fluctuations, and trajectory deviations. To address these challenges, this paper puts forward a method aimed at enhancing the positional accuracy of quadrotor UAVs, which is based on Extended Kalman Filter (EKF) multi-sensor fusion. In conjunction with the redundant configuration of sensors, a proportional-integral controller is specifically designed to allow optical flow sensors to compensate for the speed data generated by inertial sensors. Building on the EKF method, a comprehensive data fusion model is established, encompassing both position and speed states. Leveraging the MATLAB platform, trajectory flight simulations are conducted, utilizing multi-sensor data fused via EKF, with the sensor suite including GPS, IMU, Optical Flow sensors, and Barometers. The simulation results demonstrate that this proposed method can effectively mitigate the adverse impacts of environmental interference and sensor noise on the positional accuracy of quadrotors. By continuously correcting position information and accurately estimating position states, it significantly improves the UAVs’ flight position accuracy. This research outcome lays a robust and theoretically sound foundation for in-depth investigations on critical issues related to general aviation applications, such as the safe and efficient autonomous flight, adaptive and reliable intelligent navigation, and ultra-precise and mission-critical operations of quadrotor UAVs, thereby significantly contributing to the sustained and innovative advancement of the field.
Urban Air Mobility (UAM) vertical takeoff and landing (VTOL) air taxis might exhibit flight motions that are unfamiliar to many passengers. Researchers at the NASA Armstrong Flight Research Center performed a study to identify relationships between rotational flight motion and passenger comfort and acceptance. Fifty test subjects each completed a 20-min passenger experience in a virtual air taxi simulation. Subjects evaluated flight maneuvers with varying levels of rotational motion and indicated their comfort level and willingness to take a real flight with the motion they experienced. Study factors included yaw rate, pitch rate, and roll rate. Participants evaluated four levels of each study factor. The study found linear relationships for each study factor showing a decrease in passenger comfort and acceptance with increased motion. Statistical significance of the results, the possible influence of participants’ backgrounds and experiences, and other potential sources of bias in the study population are discussed.
Autonomous Inspection via small Unmanned Aircraft Systems (sUAS) is increasingly utilized across industrial use cases such as inspection of bridges, buildings, construction sites, roadways, transmission lines, pipes, wind turbines and power systems (1). In principle, the system workflow of inspection; identification and characterization of defects; and mapping in space is very similar across industries. Boeing and Proxim (A Near Earth Autonomy Company) have partnered to pursue this technology in the Aerospace and Defense industry for General Visual Inspection (GVI) of airframes predominantly in a maintenance setting. This activity began by deploying Proxim’s Autonomous Aircraft Inspection (AAI) technology and Boeing's Automated Damage Detection Software (ADDS) on Boeing C-17 Globemaster III at Joint Base Pearl Harbor-Hickam. It has expanded to offer U.S. Department of War (DoW) and Commercial customers aircraft-agnostic enhanced exterior GVI capability at point of need by leveraging unique ADDS AI algorithm in support of both home station and deployed operations. This paper gives an overview to industry developments in Autonomous Inspection, and the development AAI/ADDS technologies.
This paper develops an engineering concept and research framework showing how Cherokee MC2 (Mobile Command Center) and MVP (Mobile Vertipad Platform) can individually, and then as a combined system, resolve key operational and infrastructure challenges facing rotorcraft, eVTOL, VTOL, and UAS missions across civil, commercial, and military contexts. The investigation synthesizes current vertiport / vertipad design guidance, UAM and UTM operational architectures, and recent research on rotor downwash and degraded visual environment hazards to derive a deployable "vertiport node" architecture for austere and time-critical operations. MC2 is treated as the digital and procedural core enabling command, control, communications, data fusion, and manned–unmanned teaming, while MVP is treated as the physical landing interface enabling rapid, load-bearing, illuminated vertical-lift operations without fixed infrastructure. The primary contribution is a traceable topic-to-capability mapping supported by standards and research, plus a modeling, simulation, and optimization workflow to validate safety zones, capacity, scheduling, and resilience. Conclusions identify practical deployment pathways and research gaps for certification-aligned operations.
The proliferation of Autonomous Aerial Vehicles (AAVs) necessitates robust solutions for dynamic obstacle avoidance, particularly against non-cooperative intruders whose trajectories are unpredictable. While traditional path-planning algorithms excel in static environments, they struggle with dynamic obstacles due to the inherent difficulty in accurately estimating and registering their real-time depth and velocity into a world model. This paper presents a novel two-stage vision-based framework that leverages deep learning for reactive avoidance of non-cooperative dynamic intruders. Our approach decouples the perception and decision-making processes: an object detection deep neural network first processes monocular camera images to detect and track the 2D pixel coordinates of intruders. This perceptual output is then fed into a deep reinforcement learning agent, which learns a mapping from the intruder's image-space location to a high-level avoidance maneuver. This leads to more efficient learning, as the RL agent focuses solely on the policy without the burden of learning visual features. The advantage of using RL lies in its ability to handle partially observable situations—because reliable depth or full 3-D position information is not always readily available from monocular imagery, the RL agent learns to act based on the observable visual cues. Simulation results confirm that our proposed framework provides an effective solution for vision-based, non-cooperative intruder avoidance.
The aerospace industry is undergoing a profound transformation driven by emerging aviation technologies, including Advanced Air Mobility (AAM), electric vertical takeoff and landing (eVTOL) aircraft, and highly automated flight control systems. These complex systems often feature tightly coupled flight controls and power plants where traditional methods of compliance — relying heavily on physical ground and flight testing — are becoming increasingly impractical due to the vast number of potential interaction cases. To address this challenge, the SAE G-35 Modeling, Simulation, and Training for Emerging Aviation Technologies and Concepts Committee was formed to develop industry consensus standards. This presentation discusses the landmark release of SAE ARP7094, "Recommended Practice for Using Modeling and Simulation for Certification of Aircraft, Products, and Systems" and its role in establishing a standardized, simulation-based path to certification. The SAE G35C group is responsible for developing standards and procedures for using modeling and simulation as a method of compliance for the certification of AAM aircraft similar to the RCbS project conducted in collaboration between EASA, academia and industry in Europe.
Electric vertical takeoff and landing aircraft impose significantly higher electrochemical and thermal demands on Li-ion batteries than conventional electric vehicles, yet publicly available aging datasets for this application remain limited in applicability, cell technology, and statistical robustness. This study experimentally characterizes the degradation behavior of state-of-the-art Molicel P45B 21700 cells under realistic Urban Air Mobility operating conditions involving high power demand, rapid turnaround, and repeated cycling. Eight cells are subjected to over 3,000 cycles using a fast constant-current charging protocol and a multi-segment constant-power discharge profile. The discharge profile is derived from a representative 7000-lb winged eVTOL with a 20-mile range, requiring normalized power rates of 6.4E during takeoff and landing and 1.6E during cruise. Periodic Reference Performance Tests are conducted to track capacity fade, internal resistance evolution, and energy efficiency. The cells retained over 90% of their initial capacity after 3,270 cycles, while total energy efficiency remained stable at 91%, comprising impedance and hysteresis-driven components of approximately 94% and 97%, respectively. Direct current internal resistance exhibited an initial decrease before stabilizing, yet total discharged capacity increased from 1.81 Ah to 1.84 Ah, indicating aging-driven polarization effects not captured by DCIR. A zero-order equivalent circuit model underpredicts discharged capacity by approximately 4% for fresh cells, increasing to nearly 6% at cycle 3,270 due to unmodeled time-dependent polarization effects. These results demonstrate that while modern Li-ion cells exhibit strong durability under repetitive high-power usage, the accuracy of battery performance prediction is strongly dependent on dynamic impedance effects beyond conventional DCIR-based models.
For Urban Air Mobility taxis, passengers will experience different levels of heave motion during flight. Researchers at NASA Armstrong Flight Research Center conducted two studies in which passengers were exposed to varying levels of heave motion in the Armstrong Virtual Reality Passenger Ride Quality Laboratory. In the first study, twenty-three volunteers from the Armstrong workforce evaluated the motions on a five-point rating scale and a binary comfort scale; in the second study, fifty volunteers evaluated a flight experience with varying levels of stimuli using a five-point comfort scale and a five-point passenger acceptance scale. This paper combines the results of these two studies to observe the relationship between heave motion and passenger rider quality and acceptance. Both passenger comfort and acceptance were found to decrease with increasing heave acceleration The statistically significant relationship between the magnitude of heave acceleration and passenger comfort for individual studies and combined results are discussed.
Electric Vertical Take-Off and Landing (eVTOL) vehicles are emerging as solutions for urban air mobility, but their operation can encounter hazardous aerodynamic conditions such as the Vortex Ring State (VRS), which causes thrust loss and intense vibrations. This study investigates VRS for the Archer Maker tilter propeller by combining numerical simulations using the mid-fidelity solver DUST and the high-fidelity solver OVERFLOW with prior experimental observations. Propeller performance is evaluated through thrust and torque evolution under various descent conditions, while flow fields in the propeller wake at different descent ratios around VRS conditions are evaluated and compared via 2D visualizations. A comparison of results reveals that both numerical approaches are capable of evaluating the performance degradation in correlation with vortex ring formation within specific descent regimes, showing slight discrepancies particularly regarding the descent ratio regime where VRS occurs. A flow field comparison with experimental data validates the multi-fidelity numerical approach, showing the capabilities of both numerical approaches to capture the flow physics mechanisms that lead to the generation of the vortex ring around the propeller disk.
Urban air mobility with electric Vertical Take-Off and Landing (eVTOL) aircraft faces critical micro-weather and infrastructure readiness challenges. This paper proposes a novel socio-technical solution: a tokenized gamification platform that crowdsources hyper-local wind and weather data to enhance operational resilience. We outline the safety gap left by traditional aviation weather systems (METAR, AWOS, ASOS) in urban environments, and leverage community engagement to fill it. The proposed system integrates with Unmanned Traffic Management (UTM) and Safety Management Systems (SMS) to validate user-contributed micro-weather observations, incentivize accurate reporting through tokens and skill-level progression, and feed data into AI-driven forecasts. Early proof-of-concept results indicate improved wind hazard detection and robust user participation. By aligning with emerging regulations (FAA, EASA, DGCA) and test frameworks, this crowdsourced micro-weather ecosystem shows potential to uplift eVTOL safety, build public trust, and support city-scale planning for advanced air mobility.
Rainwater accumulation and management are critical to the safety and reliability of drones and emerging eVTOL aircraft. Current industry practice relies on physical rain testing, such as RTCA DO-160, which defines rainfall conditions for environmental qualification but is costly and difficult to apply during early design stages. This work presents a virtual rainwater assessment framework using Smoothed Particle Hydrodynamics (SPH) simulation in PreonLab. Using an early-stage APELEON cargo drone as a reference case, the method predicts rain impingement, surface runoff, pooling, and ingress under representative rainfall conditions. The meshless SPH approach enables direct simulation of complex geometries and transient interactions without mesh generation, while also supporting rotating components and arbitrary orientations. Results identify key mechanisms governing water transport, including geometry-driven runoff, hinge-related ingress, and droplet deflection from nearby structures. While the total water accumulated on the aircraft can reach on the order of several hundred grams, the amount entering the cargo space remain small. Localized moisture exposure highlights potential durability risks. The framework enables early design evaluation, parametric studies, and rapid assessment of mitigation strategies, supporting simulation-driven development and certification preparation for advanced air mobility systems.
Advanced air mobility (AAM) seeks to develop a large-scale transportation system to revolutionize how people live and work, with electric vertical take-off and landing (eVTOL) aircraft serving a central role due to reduced emissions and noise impact. An important aspect for eVTOL aircraft certification is safe urban operations, which require understanding of the response due to aerodynamic disturbances. Experimental data are required to support eVTOL aircraft development with respect to flight dynamics and controllability, as well as design specification development. While flight testing of the full-sized air vehicle will be necessary as part of the certification process, subscale testing offers many advantages with respect to cost and flexibility, in addition to examining operational conditions that one would be reluctant to test in flight at full scale such as emergency conditions. These advantages only may be seen if the underlying scaling principles of flight dynamics / control, aerodynamic interactions, and propulsion-airframe integration are understood. This paper describes initial work towards development of a general subscale testing methodology for eVTOL aircraft flight dynamics and disturbance response characteristics including limited degree of freedom (DOF) and free flight testing. An overview of the initial development work is provided, including discussion of scaling relationships, subscale air vehicle model development, and testing activities focusing on flying qualities and stability / control characteristics.
This paper introduces a novel concept for an AI-powered system designed to manage vertiport takeoffs and landings by proactively addressing the safety-critical issues of downwash and outwash. The proposed system utilizes a stream of live feedback from on-site sensors, combined with a robust predictive modeling engine, to generate optimal, aircraft-specific approach and landing trajectories in real-time. By leveraging a comprehensive database of pre-computed downwash/outwash scenarios for a multitude of UAM aircraft configurations, the AI can accurately predict the unique outwash operational footprint for each individual landing operation, based on the approaching aircraft and under the prevailing conditions. This powerful predictive capability allows the system to calculate and assign an optimal approach vector that actively minimizes risk by directing hazardous airflows away from personnel, active walkways, and other sensitive ground assets. This represents a paradigm shift from static, reactive safety measures to a proactive, intelligent, and performance-based operational model; thereby paving the way for the safe, efficient, and large-scale implementation of UAM aircraft and air taxi operations.
This paper presents an adaptive model predictive control (MPC) framework for nonlinear urban air mobility (UAM) vehicles operating across the full flight envelope. The proposed approach leverages a linear parameter-varying (LPV) representation to update the predictive model online, enabling accurate capture of strongly nonlinear and time-varying dynamics associated with distributed electric propulsion (DEP) eVTOL aircraft. To systematically address the highdimensional and coupled nature of MPC tuning, a multi-objective evolutionary optimization strategy based on NSGAII is employed, incorporating proper normalization of states and control inputs to ensure balanced weighting and meaningful exploration of the design space. The resulting controller explicitly accounts for actuator constraints and enables reconfigurable control allocation for fault-tolerant operation. The framework is evaluated in nonlinear simulations using NASA's Generic Urban Air Mobility (GUAM) model and benchmarked against a robust servomechanism linear quadratic regulator (RSLQR). Results demonstrate that the proposed adaptive MPC achieves improved trajectory tracking and enhanced robustness under both nominal conditions and actuator degradation scenarios, including partial motor failure, while maintaining constraint satisfaction throughout all flight regimes.
Electric Vertical Take-Off and Landing (eVTOL) aircraft are poised to transform urban and regional mobility by offering zero-emission, congestion-free transportation. As regulatory frameworks evolve and advanced air mobility (AAM) gains traction, manufacturers are exploring propulsion strategies that improve range, power delivery, and overall system efficiency. A key challenge in eVTOL development is balancing range with payload capacity. While larger battery packs can extend range, they also increase system weight, reduce payload, and prolong charging times, limiting operational flexibility and turnaround time. Hydrogen fuel cells, supported by liquid hydrogen (LH₂) present a promising alternative for eVTOL propulsion. This study proposes a methodology for optimizing fuel cell propulsion systems tailored to eVTOL applications. A multi-physics modeling framework for eVTOL flight dynamics and propulsion system was developed, representing the target eVTOL configuration. For a defined flight path including vertical takeoff, hover, cruise, and landing, a Genetic Algorithm (GA) based optimization was conducted on propulsion system. The algorithm down-selected battery size, fuel cell stack specifications, and hydrogen tank capacity to meet mission requirements while minimizing propulsion system weight. The modeling framework was also used to evaluate trade-offs between payload and performance as functions of component sizing, battery chemistry and energy distribution strategy.
This study evaluates the impact of range extension on gross takeoff weight (GTOW) and energy cost for the NASA Lift+Cruise eVTOL configuration under present and near-term battery technology limitations. A baseline 8,210 lb, 6-passenger vehicle, originally designed for a 75-mile mission at 400 Wh/kg battery energy density, is shown to achieve only 15 miles at a more realistic 200 Wh/kg, largely due to the 20-minute SFAR reserve, which accounts for 64% of total onboard energy. To quantify the penalties of range extension, three sizing strategies are examined: fixed GTOW with payload trade-offs, fixed-geometry overloading, and fully co-scaled vehicle resizing. The co-scaled configurations reveal a strong nonlinear GTOW growth driven by an "adding battery to carry battery" effect, in which increases in GTOW necessitate heavier structure and propulsion, leading to a practical feasibility ceiling near 45 miles. Energy cost per payload-mile is found to be non-monotonic, reaching a minimum near 20-25 miles before increasing due to compounding weight penalties, contrary to trends predicted by fixed-weight analyses. Significant off-design penalties are also observed; operating a 40-mile aircraft on a 10-mile mission incurs a 72% energy cost increase relative to a co-scaled vehicle for 10 miles. Increasing battery energy density by 22.5% (to 245 Wh/kg) reduces the 6-passenger 40-mile design GTOW by 33% (from 13,712 lb to 9,187 lb) and shifts the optimal energy cost point outward to 30 miles. Ultimately, battery energy density is identified as the dominant parameter dictating the vehicle size, weight, performance, and the fundamental operational feasibility of UAM eVTOLs.
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.
A high-fidelity computational study investigates the aerodynamic behavior, flight response, and control effectiveness of a multirotor electric Vertical Take-Off and Landing (eVTOL) configuration. The investigation is organized into two parts. Part I employs an unsteady computational fluid dynamics (CFD) framework coupled with a six-degree-of-freedom (6-DoF) rigid-body dynamics module. Simulations for isolated coaxial rotors and a complete eVTOL isolate rotor aerodynamics and rotor–airframe interactions under constrained kinematics, quantifying lift capability, fuselage download, and a residual nose-up pitching moment arising from fore-aft rotor lift imbalance. Fully coupled 6-DoF free-flight simulations capture the transient vehicle response to a motor failure and recovery sequence during hover. Part II assesses flight control response through a cascade Proportional-Derivative (PD) controller implemented in MATLAB/Simulink across two maneuver cases: hover stabilization and climb rate tracking, which are parameterized using aerodynamic data extracted from the isolated rotor CFD simulation. This decoupled approach enables systematic gain tuning and controller assessment without the computational overhead of fully coupled closed-loop CFD simulations. The results confirm that the CFD–6-DOF framework effectively resolves tightly coupled aerodynamic-dynamic interactions inherent to distributed electric propulsion configurations, and that the cascade PD architecture provides initial control authority assessment across the primary flight axes. These findings establish a foundation for trim strategy development, advanced control law design, and future integration toward robust flight control for urban air mobility operations.
Urban Air Mobility (UAM) vehicles will operate in complex atmospheric environments where unsteady inflow conditions can significantly influence rotor performance and acoustic emissions. This study investigates the impact of an axially oscillating time-harmonic gust (i.e., a single mode of an impinging turbulence spectrum) on the aerodynamics and acoustics of a subscale UAM rotor in edgewise flight. Both mid-fidelity and high-fidelity analysis methods are employed to capture the unsteady rotor response and provide cross-validation of predictions. The results highlight a frequency modulation mechanism, wherein the gust excitation introduces sideband content around the blade-passing frequency (BPF), located at BPF ± the gust frequency and higher harmonics. This modulation alters both the tonal structure and overall acoustic signature of the rotor, with corresponding implications for performance metrics. A novel analytical method, employing a spatio-temporal modal representation of the gust field, is used to predict the unsteady rotor response.
A plethora of electric vertical takeoff and landing (eVTOL) aircraft development projects aim at developing passenger-carrying aerial vehicles for the purpose of providing point-to-point mobility services within and between metropolitan areas. The nascent passenger-carrying advanced air mobility (AAM) industry promises more affordable fares and seamless experience through innovation in aircraft design with the roll-out of these novel eVTOL within five years, new concepts of operations involving more automation, and higher utilization than with today's helicopter charter operations. AAM operators plan to leverage existing heliport facilities as well as trigger the development of new vertiports-some of them could be located on existing city real estate (e.g., high-rise buildings and parking garage rooftops). Moving vertical flight toward higher-intensity passenger operations and enabling a broad adoption as outlined during the dawn of AAM raise the question of accessibility. This includes access to the VTOL door sill for passengers with reduced mobility (PRMs), the VTOL boarding and deplaning process, and the feasibility of carrying their equipment onboard (including wheelchairs). This context includes the intermodal aspects of the first and last miles to the vertiport, the availability of equipment for facilitating PRM boarding, and the broad variety of aviation facilities that are served by VTOL aircraft-including small helipads and vertipads that are not all PRM-accessible. The study identifies key design and operational criteria for reducing barriers to an unhindered boarding and deboarding of VTOL aircraft-including helicopters and eVTOLs. It suggests mitigation in aircraft design and vertiport design. The analysis emphasizes the importance of incorporating accessibility considerations in the development of the different components of AAM in order to enhance operational safety, efficiency, and customer experience and to prevent adverse impacts on dignity, fairness, and mobility for all.
Urban Air Mobility (UAM) represents a paradigm shift in metropolitan transportation, introducing electric vertical takeoff and landing (eVTOL) aircraft into dense urban ecosystems. This transformation is driven by advances in electrification, digital infrastructure, and integrated airspace management. According to the U.S. Department of Transportation's Advanced Air Mobility National Strategy 2025, UAM is expected to become a cornerstone of multimodal urban transport, with commercial operations projected in multiple U.S. cities before 2030 [1].
Electric vertical takeoff and landing (eVTOL) aircraft have rapidly emerged as a potential promising solution for sustainable and scalable urban air mobility. These vehicles are at a crucial stage of development, with sub-scale and full-scale flight test campaigns already underway. In forward flight, critical structural components of these variable RPM rotary wing aircraft can experience strong time varying aerodynamic loads. These components are lightweight, stiff, and often have very little intrinsic damping. Thus, from a life-cycle perspective, vibratory strains are of significant concern. This work investigates the use of Impact Dampers (IDs) in attenuating the bending and torsional response of a characteristic eVTOL boom. A computational reduced order model of an ID is developed using the Hunt-Crossley nonlinear contact model and linear beam finite elements. Predicted damper performance is compared with prior experimental measurements. Parametric studies are performed by varying key damper design variables, and predicted trends are shown to be consistent with experimentally observed behavior. The IDs attenuated the system structural response at resonance by 60.7% in bending and 52.1% in torsion, for a 5% mass penalty. Building on works presented at the 80th and 81st Vertical Flight Society Annual Forums, this publication is a next step towards establishing IDs as a viable multi-modal passive vibration control device in variable RPM multirotor vehicles.
This study highlights that rotor-rotor interactions can significantly modify both tonal and broadband noise characteristics. Continued investigation into these mechanisms is vital to developing reliable noise prediction methodologies and establishing design strategies that balance propulsion efficiency with acoustic acceptability for AAM vehicles. This work sought to answer what physical mechanisms contribute to the increased dominance of broadband noise in eVTOL-scale rotors. By characterizing the tip vortex of a single rotor, it was found that rotor-rotor interaction is highly dependent on two factors: Blade-vortex phasing and interaction duration. Characteristic vortex time scales can be correlated with increased noise. Interactions that generate increased noise have a non-linear relationship with rotor positioning. The interaction-generated noise is highly directive. This work aims to elucidate the dominant source noise mechanisms of rotor-rotor interaction noise by characterizing blade tip vortices using PIV.
This historical paper explores the development of vertical lift. Beginning with Leonardo's aerial screw, a profound conceptual leap, which was a helical device intended to compress air beneath it and rise vertically. Though never constructed or flown, it represents the first recorded articulation of rotary lift. The idea of rising directly upward would echo through the dirigible era of the early twentieth century, the helicopter age of the mid-century, and the emerging era of Advanced Air Mobility (AAM). This study traces that intellectual lineage and explores the technological and social forces that shaped the destiny of vertical passenger flight.
As electric vertical takeoff and landing (eVTOL) aircraft move closer to commercial reality, companies and engineers are turning to advanced modeling and simulation tools to address some of their most complex design challenges earlier in development. During a recent interview with Aerospace & Defense Technology, Paul Barnard, Application Engineering Manager, MathWorks, provided insights on how the advanced air mobility (AAM) sector is tackling the complexities of eVTOL systems design, with a focus on batteries, avionics and other critical systems.
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.
Vertical Take-Off and Landing (VTOL) aircraft introduce complex monitoring challenges due to distributed propulsion, lightweight structures, and variable operating conditions. This paper presents advanced Frequency and Orders domain techniques that repurpose existing flight control, propulsion, and structural sensor data to enhance observability without additional instrumentation. By transforming vibration, acoustic, and electrical signals into frequency and order domains, the approach enables detection of harmonics, resonance, and fault signatures tied to rotor dynamics, supporting adaptive control and predictive maintenance. Beyond rotor systems, these techniques are equally effective for monitoring electric motor health, gearbox wear, bearing degradation, and structural coupling effects in composite airframes. They also provide insight into power electronics and thermal management systems by identifying spectral anomalies linked to electrical imbalance or cooling inefficiencies. Aggregated fleet data strengthens prognostic capabilities, enabling early detection of systemic issues and trend analysis. Applications include mitigating ground resonance and modal instabilities, as well as improving reliability of propulsion and structural subsystems. Integration into avionics emphasizes computational efficiency, scalability, and compliance with standards such as DO-160 [1], DO-178 [2], ARP4761 [3] and ARP4764 [4]. Simulation and bench testing confirm feasibility, demonstrating potential to enhance safety, reliability, and lifecycle cost for next-generation urban air mobility platforms.
This paper discusses uncrewed aerial vehicles (UAVs) that can have additional applications beyond their respective civilian, industry, or military applications. The increasing popular electric UAVs in advanced air mobility (AAM) and urban air mobility (UAM) networks can be utilized to increase the efficiency and impact of emergency response in both urban and remote settings. The paper will explore the design considerations and requirements for these dual-use vehicles for specific public good missions, while presenting a survey of additional public good missions that could significantly benefit from additional ready-to-go drones. Additionally, this paper aims to explore the logistics required to implement a system for incorporating civilian, industrial, and military drones into a reserve fleet for emergency and disaster relief efforts.
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.
NASA is conducting investigations in Advanced Air Mobility (AAM) aircraft and operations, including the development of Urban Air Mobility (UAM) aircraft designs that can be used to focus and guide research activities in support of AAM. This report is an investigation of the impact of technology and mission variations on several of the NASA AAM concept aircraft: quadrotor, quiet single main rotor, side-by-side, and tiltrotor configurations, with turboshaft and electric propulsion variants for each. First, the mission and aircraft models of the baseline designs were reassessed and updated, including rotor geometry optimization, update of the rotor performance models, and disk loading optimization. For these eight designs, technology and mission excursions were performed. Relative to the calibration cases that can be considered examples of good design practice, the impact of the weight technology factors is significant. For the electric aircraft, there is a very large impact of battery specific energy (Wh/kg), and correspondingly a very large impact of mission range. The vision of Advanced Air Mobility is driven by missions that will enable new transportation capabilities. Hence it is appropriate to compare Concept Vehicles of different lift and propulsive architectures, all designed to accomplish the same UAM mission. It is also useful however to consider specific missions that can take advantage of the strengths of individual aircraft configurations. So alternate designs were also developed for the concept vehicles: for turboshaft aircraft, longer unrefueled range, including faster cruise speed for the tiltrotor; for electric aircraft, shorter range and more realistic battery weight.
eVTOL Pilot Perspectives, UAM, Challenges of Past V/STOL Aircraft, Modeling and Testing of Complexities Turning Unknowns into Knowns.
Briefing by National AAM Center of Excellence - NAAMCE
EHang's Global Practices and Vision for Advanced Air Mobility Development.
US AAM National Strategy and Comprehensive Plan.
Public Service AAM Applications and Potential Customers/ Operations.
One of the biggest goals for companies in the field of artificial intelligence (AI) is developing “agentic” systems. These metaphorical agents can perform tasks without a guiding human hand. This parallels the goals of the emerging urban air mobility industry, which hopes to bring autonomous flying vehicles to cities around the world. One company wants to do both and got a head start with some help from NASA.
The wing-in-ground effect (WIG) vehicle represents a significant advancement in aerodynamics and vehicle design, leveraging the ground effect phenomenon to enhance lift and reduce drag when flying close to the surface. This unique capability allows WIG vehicles to achieve higher payloads, longer range, and greater fuel efficiency compared to traditional aircraft, making them an attractive option for modern military and global disaster response applications. Wing-in-Ground Effect Vehicles: From Modern Military and Commercial Development to Global Disaster Response discusses future disaster response, logistics, and military applications for WIG vehicles, including the ongoing development of aerospace and transportation technology. Relavant advancements in materials and propulsion systems holds promise for further enhancing WIG performance and operational range. Additionally, cost-effective and powerful flight computers with various types of mission-enabling sensor suites from the advanced air mobility and drone industry further enable the development of modern, safe, and practical WIG platforms. Click here to access the full SAE EDGETM Research Report portfolio.
Advanced Air Mobility Prospectives
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