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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).

Modeling additive manufacturing processes can be difficult due to the scale difference between the active processing point (e.g., a sub-millimeter melt pool) and the part itself. Typically, the tools used to model these processes are either too computationally intensive (due to high physical fidelity or inefficient computations) or are focused solely on either the microscale (e.g., microstructure) or macroscale (e.g., cracks). These pitfalls make the tools unsuitable for fast and efficient evaluations of additive manufacturing build files and parts. Failures in parts made by laser powder bed fusion (L-PBF) often come when there is a lack of fusion or overheating of the metal powder that causes areas of high porosity. AM-PM uses a point field-based method to model L-PBF process conditions from either the build instructions (pre-build) or in situ measurements (during the build). The AM-PM modeling technique has been tested in several builds including a Ti-6Al-4V test article that was divided into 16 parts, each with different build conditions. With AM-PM, calculations are performed faster than similar methods and the technique can be generalized to other additive manufacturing processes. The AM-PM method is at technology readiness level (TRL) 6 (system/subsystem model or prototype demonstration in a relevant environment) and is available for patent licensing.

The difference between Layered Energy Depletion Radiation Shielding (LEDRS) and Stacked Energy Depletion Radiation Shielding (SEDRS) is how the piece of matter, or shield, is analyzed as radiation passes through the matter. SEDRS involves using a defined and ordered stack of layers of shielding with different material properties such that the thickness and chemical properties of each material maximizes the absorption of energy from the radiation particles that are most damaging to the target. The SEDRS shielding method aims to provide the maximum level of energy absorption while still keeping shielding mass and volume low. The process of LEDRS involves using layers of shielding material such that the thickness of each material is designed to absorb the maximum amount of energy from the radiation particles that are most damaging to the target after subsequent layers of shielding. The more energy is absorbed by the shielding material, the less energy will be deposited in the target minimizing the required mass to achieve a resulting lower dose for a given geometrical feature. The LEDRS shielding method aims to provide the maximum level of energy absorption. The process for designing LEDRS views potential radiation shields as a cascade of effects from each shielding layer to the next and is helpful for investigating the particular effects of each layer. SEDRS and LEDRS can improve any technology that relies on the controlled manipulation of a radiation field by interaction with a material element.

In order to maintain the low cost, simplicity, and quick turnaround of cold-flow testing while improving accuracy, NASA evaluated unconventional gases for use as simulants. During such evaluations, NASA discovered that by adjusting stagnation temperature, the isentropic exponent of ethane can be tuned to approximate those of common rocket propellants (e.g., hydrogen, hypergols, alcohols, and hydrocarbons). Furthermore, due to ethanes high auto-ignition temperature and resistance to condensation, tuned ethane enables testing of expansion ratios much larger than conventional inert-gas testing. To leverage this discovery, NASA developed a hardware-based system to treat ethane and obtain nozzle chamber conditions that match the appropriate aerodynamics for a specific test. The system, named HYPERFIRE, works in the following manner. Liquid ethane is transferred to a piston-style run tank, where it is pressurized. Then, the ethane is run through two insulated pebble beds where it is heated, vaporized, and stabilized. Finally, the treated ethane is transferred from the second pebble bed to a small thrust takeout structure, and through the test article. Control of valves and regulators is managed by an onboard computer, accessed via a LabVIEW™ interface. The system is mounted on a hurricane-resistant steel frame to enable transportation via forklift. Heated ethane reproduces the aerodynamics of combustion products at low temperatures relative to alternative testing methods. Thus, test articles can be manufactured using low-cost, low temperature rated, transparent materials (e.g., acrylic). In addition to reducing testing cost, this grants optical access to internal flowfields, enabling advanced diagnostic techniques (e.g., Schlieren imaging, particle image velocimetry) not possible with hot-fire testing and less meaningful with conventional cold-flow testing.

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.

Proprotors on tiltrotor aircraft have complex aeroelastic properties, experiencing torsion, bending, and chord movement vibrational modes, in addition to whirl flutter dynamic instabilities. These dynamics can be stabilized by high-frequency swashplate adjustments to alter the incidence angle between the swashplate and the rotor shaft (cyclic control) and blade pitch (collective control). To make these high-speed adjustments while minimizing control inputs, generalized predictive control (GPC) algorithms predict future outputs based on previous system behavior. However, these algorithms are limited by the fact that tiltrotor systems can substantially change in orientation and airspeed during a normal flight regime, breaking system continuity for predictive modeling. NASA’s Advanced GPC (AGPC) is a self-adaptive algorithm that overcomes these limitations by identifying system changes and adapting its predictive behavior as flight conditions change. If system vibration conditions deteriorate below a set threshold for a set time interval, the AGPC will incrementally update its model parameters to improve damping response. AGPC has shown significant performance enhancements over conventional GPC algorithms in comparative simulations based on an analytical model of NASA’s TiltRotor Aeroelastic Stability Testbed (TRAST). Research for Hardware-In-the-Loop testing and flight vehicle deployment is ongoing, and hover data show improved vibration reduction and stability performance using AGPC over other methods. The example presented here is an application to tiltrotor aircraft for envelope expansion and vibration reduction. However, AGPC can be employed on many dynamic systems.