Wind-Optimal Cruise Airspeed Mode for Flight Management Systems (FMS)

While previous eVTOLs often require a near 90° wing tilt to position propellers in an optimal location to generate vertical force for takeoff, NASA has taken a very different approach. NASA's design instead uses a slight wing angle and large flaps designed to deflect slipstream generated by the propellers to create a net positive force in the vertical direction, all while preventing forward movement. This unique configuration allows for takeoff and landing operations without the need for near 90° wing tilt angles. After takeoff, the transition to forward flight only requires a slight change in attitude of the vehicle and retraction of the flaps. Similar solutions require large changes in attitude to accomplish this transition which is often undesirable, especially for air taxi operations that involve passengers. Given the effectiveness of this configuration for generating upward force, the requirement for wing angle tilt has been reduced from near 90° to approximately 15° during takeoff. Further iterations may reduce this requirement even further to 0°. By eliminating the need for near 90° wing tilt, NASA's eVTOL design removes the need for mechanisms to perform active tilting of the wings or rotors, reducing system mass and thereby improving performance. Flaps represent the only components that require actuation for takeoff and landing operations. Innovators at NASA leveraged the Langley Aerodrome 8 (LA-8), a modular testbed vehicle that allows for rapid prototyping and testing of eVTOLs with various configurations, to design and test this novel concept.

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 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.

Distributed propulsion and lightweight flexible structures on air vehicles pose a significant opportunity to improve mission performance while meeting next generation requirements including reduced fuel burn, lower emissions, and enhanced takeoff and landing performance. Flexible wing-shaping aircraft using distributed propulsion enable the ability to achieve improved aerodynamic efficiency while maintaining aeroelastic stability. Wing shaping concepts using distributed propulsion leverage the ability to introduce forces/ moments into the wing structure to affect the wing aerodynamics. This can be performed throughout the flight envelope to alter wing twist, hence local angle of attack, as the wing loading changes with air vehicle weight during cruise. Thrust-induced lift can be achieved by distributed propulsion for enhanced lift during take-off and landing. For a highly flexible wing structure, this concept could achieve a 4% improvement in lift-to-drag ratio, hence reduced fuel burn, as compared to a conventional stiff wing. This benefit is attributed to a reduction in lift-induced drag throughout the flight envelope by actively shaping the spanwise lift distribution using distributed propulsion. Vertical tail size could be reduced by utilizing differential thrust flight-propulsion control. This will result in weight reduction to achieve further fuel savings. Aeroelastic stability is addressed in the design process to meet flutter clearance requirements by proper placement of the propulsion units. This technology enables synergistic interactions between lightweight materials, propulsion, flight control, and active aeroelastic wing shaping control for reducing the environmental impact of future air vehicles.

The SABERS innovators developed novel lithium-sulfur designs, including sulfur-selenium on graphene cathodes, and lightweight bipolar plate stacking and packaging designs. SABERS is unique in several aspects: it deploys graphene-based manufacturing processes for the cathode and bipolar plates, and it uses a solid-state electrolyte in place of the liquid electrolyte found in other lithium-sulfur battery designs. The team has achieved energy densities over 500 W-hr/kg, and further improvements are expected. SABERS can meet the high-power requirements needed for aircraft take-off. SABERS is lightweight, safe, robust, and reliable. Furthermore, its manufacturing processes are scalable and environmentally friendly. Coin cell and pouch prototypes have been demonstrated to date. Development efforts continue and new portfolio innovations are expected. Major component technologies in SABERS include the following (as listed here and shown in the figure below). S/Se Cathode – Sulfur/Selenium on graphene scaffold (LAR-19556-1, LEW-20228-1) Solid Electrolyte – Solid-state electrolyte composites (LEW-20445-1) Bipolar Stack – Graphene plates (LAR-20257-1) Li-Metal Anode (Proprietary, under development) Packaging (Proprietary, under development) Robust computational models have been developed to support the battery materials design and are available to licensees to evaluate and optimize different materials combinations and performance targets.