Method And System for Enhancing Vehicle Performance and Design Using Parametric Modeling and Gradient-Based Control Integration

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.

Composite materials are being used in aerospace design because of their high strength-to-weight ratio. On modern airplanes, composite wings offer a greater degree of aerodynamic efficiency due to weight savings, but at the same time introduce more structural flexibility than their aluminum counterparts. Under off-design flight conditions, changes in the wing shape due to structural flexibility cause the wing aerodynamics to be non-optimal. This effect could offset any weight saving benefits realized by the composite wings. Structural flexibility could also cause adverse interactions with flight control and structural vibration which can compromise aircraft stability, pilot handling qualities, and passenger ride quality. NASA Ames Research Center has developed a novel technology that employs a new multi-objective flight control optimization framework to achieve multiple control objectives simultaneously. This technology leverages the availability of distributed flight control surfaces in modern transports. The multi-objective flight control technology comprises the following objectives all acting in a synergistic manner: 1) traditional stability augmentation and pilot command-following flight control, 2) drag minimization, 3) aeroelastic mode suppression, 4) gust load alleviation, and 5) maneuver load alleviation. Each of these objectives can be a major control system design in its own right. Thus, the multi-objective flight control technology can effectively manage the complex interactions of the individual single-objective flight control system design and take into account multiple competing requirements to achieve optimal flight control solutions that have the best compromise for these requirements. In addition, a real- time drag minimization control strategy is included in the guidance loop. This feature utilizes system identification methods to estimate aerodynamic parameters for the on-line optimization. The aerodynamic parameters are also used in the multi-objective flight control for drag minimization and maneuver/ gust load alleviation control.

For rapid parachute deployment simulation, the framework and methodology provided by the simulation database uses parametrized aerodynamic data for a variety of environmental conditions, air taxi design parameters, and landing system designs. The database also includes a compilation of drag coefficients, thrust and lift forces, and further relevant aerodynamic parameters utilized in the simulated flight of a proposed air taxi. The database and framework can be constructed using simulated data that accounts for oscillatory breathing of parachutes. The methodology can further employ an overset grid of body-fitted meshes to accurately capture deployment of an internally-stored parachute, as well as descent of the air taxi and deployed parachute. The systems and methods of the disclosed technology can be utilized with existing CFD solvers in a plug-and-play manner, such that the framework can be integrated to directly improve the performance of these solvers and the machines on which they are installed. The framework itself can employ parallelization to enable distributed solution of intensive CFD simulations to build a robust database of simulated data. Further, as up to 90% of computational time is spent in the calculation of aerodynamic parameters for use in coupled trajectory equations, the framework can significantly reduce the computational costs and design time for safe landing systems for air taxis. These reductions can lead to lower costs for design processes, while enabling rapid design and testing prior to physical prototyping.

Many control problems can benefit from the adaptive control algorithm described. This algorithm is well-suited to nonlinear applications that have unknown modeling parameters and poorly known operating conditions. Disturbance accommodation is a critical component of many systems. By using feedback control with disturbance accommodation, system performance and reliability can be increased considerably. Often the form of the disturbance is known, but the amplitude is unknown. For instance, a motor operating on a structure used for accurate pointing would cause a sinusoidal disturbance of a known frequency content. The algorithm described is able to accurately cancel these disturbances, without needing knowledge of their amplitude. In markets needing controllers, the efficiency, uptime, and lifespan of equipment can be dramatically increased due to the robustness of this technologys 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).