Search

According to the International Air Transport Association statistics, the annual fuel cost for the global airline industry is estimated to be about $140 billion in 2017. Therefore, fuel cost is a major cost driver for the airline industry. Advanced future transport aircraft will likely employ adaptive wing technologies that enable the wings of those aircraft to adaptively reconfigure themselves in optimal shapes for improved aerodynamic efficiency throughout the flight envelope. The need for adaptive wing technologies is driven by the cost of fuel consumption in commercial aviation. NASA Ames has developed a novel way to address aerodynamic inefficiencies experienced during aircraft operation. The real-time drag optimization control method uses an on-board, real-time sensor data gathered from the aircraft conditions and performance during flight (such as engine thrust or wing deflection). The sensor data are inputted into an on-board model estimation and drag optimization system which estimates the aerodynamic model and calculates the optimal settings of the flight control surfaces. As the wings deflect during flight, this technology uses an iterative approach whereby the system continuously updates the optimal solution for the flight control surfaces and iteratively optimizes the wing shape to reduce drag continuously during flight. The new control system for the flight control surfaces can be integrated into an existing flight control system. This new technology can be used on passenger aircraft, cargo aircraft, or high performance supersonic jets to optimize drag, improve aerodynamic efficiency, and increase fuel efficiency during flight. In addition, it does not require a specific aircraft math model which means it does not require customization for different aircraft designs. The system promises both economic and environmental benefits to the aviation industry as less fuel is burned.

Gust loads may have detrimental impacts on flight including increased structural and aerodynamic loads, structural deformation, and decreased flight dynamic performance. This technology has been demonstrated to improve current gust load alleviation by use of a trailing-edge, free-floating surface control with a mass balance. Immediately upon impact, the inertial response of the mass balance shifts the center of gravity in front of the hinge line to develop an opposing aerodynamic force alleviating the load felt by the wing. This passive gust alleviation control covering 33% of the span of a cantilever wing was tested in NASA Langleys low speed wind tunnel and found to reduce wing response by 30%. While ongoing experimental work with new laser sensing technologies is predicted to similarly reduce gust load, simplicity of design of the present invention may be advantageous for certification processes. Additionally, this passive technology may provide further gust alleviation upon extending the use of the control to the entire trailing edge of the wing or upon incorporation with current active gust alleviation systems. Importantly, the technology can be easily incorporated into to the build of nearly all fixed wing aircrafts and pilot control can be maintained through a secondary trim tab. Though challenging to retrofit, passive gust alleviation could enable use of thinner, more efficient wings in new plane design.

Composite materials are being used in aerospace design because of their high strength-to-weight ratio. On modern airplanes, composite wings offer a greater degree of aerodynamic efficiency due to weight savings, but at the same time introduce more structural flexibility than their aluminum counterparts. Under off-design flight conditions, changes in the wing shape due to structural flexibility cause the wing aerodynamics to be non-optimal. This effect could offset any weight saving benefits realized by the composite wings. Structural flexibility could also cause adverse interactions with flight control and structural vibration which can compromise aircraft stability, pilot handling qualities, and passenger ride quality. NASA Ames Research Center has developed a novel technology that employs a new multi-objective flight control optimization framework to achieve multiple control objectives simultaneously. This technology leverages the availability of distributed flight control surfaces in modern transports. The multi-objective flight control technology comprises the following objectives all acting in a synergistic manner: 1) traditional stability augmentation and pilot command-following flight control, 2) drag minimization, 3) aeroelastic mode suppression, 4) gust load alleviation, and 5) maneuver load alleviation. Each of these objectives can be a major control system design in its own right. Thus, the multi-objective flight control technology can effectively manage the complex interactions of the individual single-objective flight control system design and take into account multiple competing requirements to achieve optimal flight control solutions that have the best compromise for these requirements. In addition, a real- time drag minimization control strategy is included in the guidance loop. This feature utilizes system identification methods to estimate aerodynamic parameters for the on-line optimization. The aerodynamic parameters are also used in the multi-objective flight control for drag minimization and maneuver/ gust load alleviation control.

The term Advanced Air Mobility (AAM) refers to a new mode of transportation utilizing highly automated airborne vehicles for transporting goods and/or people. The adoption of widespread use of AAM vehicles will necessitate a network of vertiports located throughout a geographical region. A vertiport refers to a physical structure for the departure, arrival, and parking/storage of AAM vehicles. NASA-developed Vertiport Assessment and Mobility Operations System (VAMOS!) enables identifying geographical locations suitable for locating a vertiport or assessing suitability of pre-selected locations. For example, suitability evaluation factors include zoning, land use, transit stations, fire stations, noise, and time-varying factors like congestion and demand. The vertiport assessment system assigns suitability values to these factors based on user-input, and types, including location-based (e.g., proximity to mass transit stations), level-based (e.g., noise levels), characteristic-based (e.g., residential zoning), and time-based (e.g., demand). Based on user input, the system spreads a grid over the geographical area, specifies importance criteria and weights for scaling the impact of the suitability factors, and identifies specific sub-regions as candidate locations. The candidate sub-regions are shown on a user interface map overlay in a color-coded gradient that reflects the suitability strength for a sub-region. Vertiport locations are selected within these sub-regions. These candidate vertiport locations are refined by establishing feasibility of flight between them. VAMOS! includes a modeling component and a simulation component. The modeling component assists a user to identify one or more geographical locations at which a vertiport may be physically built. The simulation component of the technology displays, in real-time, the simulated operational behavior of AAM vehicles and in the context of their projected flight paths combined with data dynamically obtained from live sources. These data sources can be from the Federal Aviation Administration (FAA) or other private or public governing bodies, from one or more AAM vehicles in flight, and from weather sources.

The novel approach for optimizing airspeed for both actual and predicted wind conditions in electric Vertical Takeoff and Landing (eVTOL) aircraft with Distributed Electric Propulsion (DEP) systems includes the process of creating a lookup table for wind‐optimal airspeed as a function of wind magnitude, considering the direction of the wind relative to the cruise segment, considering the cruise altitude for an aircraft type, and incorporating the wind-optimal airspeed lookup table in the performance database for real‐time access by the Flight Management Systems (FMS) to predict wind-optimal airspeed at waypoints of the flight plan. The target wind‐optimal airspeed is updated in real-time throughout the cruise portion of a flight. In a test of the wind-optimal airspeed targeting technique using a multi-rotor aircraft model, results obtained show benefits of flying at the wind‐optimal cruise airspeed compared to the best‐range airspeed. In headwind conditions, energy consumption was reduced by up to 7.5%, and flight duration was reduced by up to 28%. Under uncertain wind magnitudes, flying at wind-optimal airspeed offered lower variability and higher predictability in energy consumption than flying at best‐range airspeed.

The Active Turbulence Suppression (ATS) system for electric Vertical Take-Off and Landing (eVTOL) vehicles employ existing lifting propellers to dampen instabilities during flight, such as Dutch-roll oscillations and other gust-induced oscillations. When a roll angle of an eVTOL aircraft has deviated or is about to deviate from a current stable aircraft state to an undesirable, unstable, and oscillating aircraft state, the ATS system queries a turbulence suppression database that stores a set of propeller speed profiles for mitigation a deviation of a given roll angle for a particular aircraft with specified propellers. Using this data, the eVTOL flight controller adjusts the speed of the propellers for a certain duration of time, according to the propeller speed profiles for mitigating the deviation. In models of aircraft with adjustable propeller angles, the database includes blade angle profiles for mitigating the effects of turbulent conditions. Timing and rate of propeller activation can be pre-computed using higher order computational modeling performed with NASA’s super computing resources. Because the data is pre-computed, the use of the ATS system onboard does not require significant computing resources to implement on eVTOL vehicles. The technology, a mechanism by which existing eVTOL propellers are leveraged to suppress gust-induced oscillations enables a safe and comfortable passenger experience at low-cost and without added hardware.

The sBOOMTraj tool offers an updated approach to accurately predict sonic boom ground signatures for supersonic aircraft. The tool is based on the numeric solution of the augmented Burgers equation where the regular Burgers equation is augmented with absorption, molecular relaxation, atmospheric stratification, and ray tube spreading terms in addition to the non-linear term from the regular equation. The primary idea behind such augmenting is that atmospheric losses are captured, which results in more realistic sonic boom predictions compared to linear theory methods. While previous iterations of the software (sBOOM) were limited to single point analysis (i.e., a point in supersonic climb or cruise), sBOOMTraj extends the prediction of sonic boom to multiple points along the supersonic mission. This includes updated functionality to handle aircraft trajectories and maneuvers as well as inclusion of all relevant noise metrics. The improvements allow efficient computation of sonic boom loudness across the entire supersonic mission of the aircraft. The sBOOMTraj tool can predict ground signatures in the presence of atmospheric wind profiles, and can even handle non-standard atmospheres where users provide temperature, wind, and relative or specific humidity distributions. Furthermore, sBOOMTraj can predict off-track signatures, ground intersection location with respect to the aircraft location, the time taken for the pressure disturbance to reach the ground, lateral cut-off locations, and focus boom locations. The software has the ability to easily interface with other stand-alone tools to predict the magnitude of focus, post-focus, and evanescent booms, and also has the ability to handle different kinds of input waveforms used in design exercises. The sBoomTraj tool could be extremely useful in supersonic aircraft operations as it can predict where sonic booms hit the ground in addition to providing the magnitude of sonic boom loudness levels using physics-based simulations. Using this tool, pilots may be able to steer supersonic aircraft away from populated areas while also allowing real-time adjustments to their flight trajectories, allowing trade-offs associated with sonic boom, performance and acceptability. The predicted sonic boom loudness contours during supersonic flight may also be used in supersonic aircraft design and development, including certification of aircraft under future regulations that may be imposed. sBOOMTraj offers a revolutionary approach to mitigating sonic boom through its unique sonic boom adjoint equations. This potentially has immediate and realizable benefits in supersonic aircraft design when integrated with other disciplines. The NASA technology can potentially aid in supersonic aircraft operations with its integration in a cockpit interactive application that can provide feedback to the pilot on sonic boom impingement areas on the ground with real-time atmospheric and terrain updates. sBOOMTraj has the potential to support both aircraft design and operations, which is extremely rare.

The traditional scissor spring design for hand controllers has been improved upon with a circumferential spring controller mechanism that facilitates easy customization, enhanced durability, and optimum controller feedback. These advantages are partially facilitated by locating the spring to the outside of the mechanism which allows for easier spring replacement to adjust the deflection force or for maintenance. The new mechanism is comprised of two rounded blades, or cams, that pivot forward and back under operation and meet to form a circle. An expansion spring is looped around the blade perimeter and resides in a channel, providing the restoring force that returns the control stick to a neutral position. Due to the use of a longer circumferential spring, the proportion of spring expansion is smaller for a given distance of deflection, so the forces associated with the deflection remain on a more linear portion of the force deflection curve. The Circumferential Scissor Spring for Controllers is at technology readiness level (TRL) 8 (actual system completed and flight qualified through test and demonstration) and is available for patent licensing. Please note that NASA does not manufacture products itself for commercial sale.

RAM-C interfaces with computational software to provide test logic and manage a unique process that implements three main bodies of theory: (a) aircraft system identification (SID), (b) design of experiment (DOE), and (c) CFD. SID defines any number of alternative estimation methods that can be used effectively under the RAM-C process (e.g., machine learning techniques, regression, neural nets, fuzzy modeling, etc.). DOE provides a statistically rigorous, sequential approach that defines the test points required for a given model complexity. Typical DOE test points are optimized to reduce either estimation error or prediction error. CFD provides a large range of fidelity for estimating aircraft aerodynamic responses. In initial implementations, NASA researchers “wrapped” RAM-C around OVERFLOW, a NASA-developed high-fidelity CFD flow solver. Alternative computational software requiring less time and computational resources could be also utilized. RAM-C generates reduced-order aerodynamic models of aircraft. The software process begins with the user entering a desired level of fidelity and a test configuration defined in terms appropriate for the computational code in use. One can think of the computational code (e.g., high-fidelity CFD flow solver) as the “test facility” with which RAM-C communicates with to guide the modeling process. RAM-C logic determines where data needs to be collected, when the mathematical model structure needs to increase in order, and when the models satisfy the desired level of fidelity. RAM-C is an efficient, statistically rigorous, automated testing process that only collects data required to identify models that achieve user-defined levels of fidelity – streamlining the modeling process and saving computational resources and time. At NASA, the same Rapid Aero Modeling (RAM) concept has also been applied to other “test facilities” (e.g., wind tunnel test facilities in lieu of CFD software).

The goal of this computer vision software is to take the guesswork out of grapple operations aboard the ISS by providing a robotic arm operator with real-time pose estimation of the grapple fixtures relative to the robotic arms end effectors. To solve this Perspective-n-Point challenge, the software uses computer vision algorithms to determine alignment solutions between the position of the camera eyepoint with the position of the end effector as the borescope camera sensors are typically located several centimeters from their respective end effector grasping mechanisms. The software includes a machine learning component that uses a trained regional Convolutional Neural Network (r-CNN) to provide the capability to analyze a live camera feed to determine ISS fixture targets a robotic arm operator can interact with on orbit. This feature is intended to increase the grappling operational range of ISSs main robotic arm from a previous maximum of 0.5 meters for certain target types, to greater than 1.5 meters, while significantly reducing computation times for grasping operations. Industrial automation and robotics applications that rely on computer vision solutions may find value in this softwares capabilities. A wide range of emerging terrestrial robotic applications, outside of controlled environments, may also find value in the dynamic object recognition and state determination capabilities of this technology as successfully demonstrated by NASA on-orbit. This computer vision software is at a technology readiness level (TRL) 6, (system/sub-system model or prototype demonstration in an operational environment.), and the software is now available to license. Please note that NASA does not manufacture products itself for commercial sale.