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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.

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).