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Rudders have long served as the primary flight control surface as is pertains to aircraft yaw. Breaking this mold, NASA's SAW technology is a game-changing development in aircraft wing engineering that reduces rudder motion required to control aircraft. The benefits of reduced rudder dependency led NASA to develop the outer aileron yaw damper to further decrease or eliminate rudder dependency for aircraft using SAWs. As mentioned, SAWs use shape memory alloy actuators to articulate the outer portion of the wing, effectively creating a movable wingtip. NASA's invention uses an outer aileron located on the wingtips, which is driven (along with the inner ailerons) by a novel control algorithm. The control algorithm, taking into account the wingtip positions, manipulates the outer ailerons to achieve the desired yaw rate. At the same time, it positions the inner ailerons to counter roll rate resulting from the outer aileron. In other words, the control algorithm calculates a control surface ratio (i.e., position of inboard aileron and outboard aileron) that produces desired yaw and roll accelerations. The system can also be used to offset the existing rudder in current or future aircraft designs. A second part of NASAs novel outer aileron control algorithm modifies the aircrafts rudder loop gain in proportion to outer aileron usage. This allows the outer ailerons and rudder to work in tandem, while at the same time reducing rudder usage. As a result of this NASA invention, required rudder usage can be reduced or eliminated for aircraft with SAWs. Consequently, the size of rudders and vertical tail structures can be reduced, which in turn reduces weight and parasitic drag. The result is an aircraft with increased performance and fuel efficiency.

Derived from a design approach for a new wing known as PRANDTL-D, this technology achieves similar improvements for propellers and other rotating machinery. <b><em>How It Works</b></em> To achieve the innovation's alternate spanload, Armstrong designers applied a non-linear twist to the propeller blade. The twist moves the load inward and dissipates the tip vortex over a wider area, minimizing its effect on drag. It also results in a decrease in load at the tip and reduced torque at the tip. These changes combine to achieve a dramatic reduction in power consumption without compromising the blade's other parameters. Specifically, the blade's diameter and rpm remain unchanged. <b><em>What Makes It Better</b></em> Unlike the conventional minimum induced loss (elliptical) spanload, which consumes large amounts of power at the tip of the blade, the new design unloads the tip and reduces torque, achieving significant improvements in efficiency. First-order analysis shows a more than 15 percent improvement in power consumption while producing the same thrust. The design also produces significantly less noise than conventional blade designs.

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.

Transonic flutter is a pacing item in transport aircraft design in that it is crucial to characterize this phenomenon for each aircraft to prevent catastrophic failure. Aerodynamic study of flows around airfoils is a canonical problem that entails both experimental and computational approaches. While the transonic flutter prediction can be more accurate with high-fidelity Computational Fluid Dynamics (CFD) methods than with unsteady potential flow methods, the computational cost is high. Therefore, computationally efficient methods for transonic flutter prediction continue to be of high interest to the aircraft design community. NASA Ames has developed a novel method that eliminates the need for expensive calculations of aerodynamics of wing flutter, which typically takes tens of hours on a supercomputer. Such calculations are now replaced by machine-learning-based closed form solutions that provide the solution almost instantaneously. The technology presents a new approach to predict the flow around pitching NACA00 series airfoils. NACA airfoils are generally symmetric, and thus they do not possess camber. However, the invention can readily extend to wings with camber. This novel data modeling approach is orders of magnitude faster than the traditional CFD approach of predicting aerodynamic effects of transonic pitching airfoils. The data model is based on a subset of unsteady CFD simulations that train the model. The trained model then resolves the pitching airfoil in time for any other set on the order of a second, as compared with a complete CFD simulation that typically takes 30 hours on a supercomputer. The data model is demonstrated in this invention for transonic flow corresponding to Mach number of 0.755 over pitching NACA00 series airfoils for a reduced frequency range typical of flutter, i.e., k lies in the range 0.02 - 0.25.

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.

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 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.

Commercial soft magnetic cores used in power electronics are limited by core loss and decreased ferromagnetism at high temperatures. Extending functional performance to high temperatures allows for increased power density in electric systems with fixed power output and elevated operating temperature. The innovators at Glenn developed a unique composition and process to improve the temperature capability of the material. Nanocomposite soft magnetic materials are typically comprised of a combination of raw materials including iron, silicon, niobium, boron, and copper. Instead of niobium, NASA&#39;s material utilizes small cobalt and tantalum additions. The raw materials are combined to form an amorphous precursor through melt spinning. NASA&#39s innovation with the fabrication lies in the thermal annealing step, which nucleates and crystallizes the precursor to form the composite structure of the material. By adjusting the temperature and magnetic field of the thermal annealing step, Glenn&#39;s process results in good coupling between the crystalline and amorphous matrix phases. Innovators at Glenn demonstrated the temperature robustness using small test cores of their material and are investigating additional quality attributes compared to other well-known soft magnetic materials (see two Figures below).

This materials system is comprised of an Al metal matrix with high-performance SMA reinforcements. The combination of the unique matrix composition and SMA elements allow for this material system to self-repair via a two-step crack repair method. When a crack is present in the matrix material, the MMC is heated above the SMA&#39;s austenite start (As) temperature. This initiates shape recovery of the SMA, pulling the crack together as the SMA reinforcements return to their initial length. Concurrently, the increased temperature causes softening and liquefaction of the eutectic micro-constituent in the matrix, which enables the recovery of plastic strain in the matrix as well as crack filling. Combined with the crack closure force provided by the SMA reinforcements completely reverting to their original length, the MMC welds itself together and, upon cooling, results in a solidified composite able to realize its pre-cracked, original strength. The research team has demonstrated and tested the new materials. The team induced cracks in prototype materials based on Al-Si matrix with SMA (NiTi) reinforcements and demonstrated the recovery of tensile strength after healing. Data from tensile and fatigue tests of the samples before and after the fatigue crack healing shows a 91.6% healing efficiency on average under tensile conditions.

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.