Aerodynamic Framework for Parachute Deployment from Aerial Vehicle

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).

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.

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.

Vehicles designed for purposes of exploration of the planets and other atmospheric bodies in the Solar System favor the use of mid-Lift/Drag blunt body geometries. Such shapes can be designed so as to yield favorable hypersonic aerothermodynamic properties for low heating and hypersonic aerodynamic properties for maneuverability and stability. The entry trajectory selected influences entry peak heating and integrated heating loads which in turn influences the design of the thermal protection system. A nominal is used to compare each shape considered. The vehicle will be subject to both launch and entry loading along with structural integrity constraints that may further influence shape design. Further, such vehicles must be sized so as to fit on existing or realizable launch vehicles, often within existing launch payload shroud constraints.

The parametric modeling system allows for the integrated design and optimization of aerospace vehicles by unifying physical and control subsystems within a single computational model. The system includes representations of the vehicle’s geometry, structural load, propulsion, energy storage, and GNC systems. The system performs sensitivity analysis on key performance metrics (e.g., fuel consumption, heat load, and mechanical forces) to determine how changes in design parameters affect overall performance. By incorporating real-world conditions, such as wind variations and sensor noise, the system allows for the use of real-time feedback to refine vehicle designs. The optimization process uses a gradient-based algorithm to iteratively adjust parameters so that constraints such as structural integrity, thermal protection, and fuel capacity are met. The system generates a Pareto front representing trade-offs between performance metrics that allow engineers to visualize optimal designs for different mission profiles, which enhances design accuracy while reducing the need for expensive physical testing.