Co-Optimization of Blunt Body Shapes for Moving Vehicles

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.

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).

This technique injects precisely defined stationary transient growth disturbances into the free air slipstream over a wing that develop into streamwise elongated "streaks." These streaks are created with an alternating pattern of low and high streamwise velocity in the boundary layer flow adjacent to the aerodynamic surface of interest. Judicious selection of streak wavelength, amplitude, and profile allows the first-mode instability waves responsible for transition via oblique mode breakdown to be damped while the remaining, uncontrolled waves are kept below an amplification threshold. A similar control concept is also applicable to second mode transition at hypersonic Mach numbers.

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.

According to the International Air Transport Association statistics, the annual fuel cost for the global airline industry is estimated to be about $140 billion in 2017. Therefore, fuel cost is a major cost driver for the airline industry. Advanced future transport aircraft will likely employ adaptive wing technologies that enable the wings of those aircraft to adaptively reconfigure themselves in optimal shapes for improved aerodynamic efficiency throughout the flight envelope. The need for adaptive wing technologies is driven by the cost of fuel consumption in commercial aviation. NASA Ames has developed a novel way to address aerodynamic inefficiencies experienced during aircraft operation. The real-time drag optimization control method uses an on-board, real-time sensor data gathered from the aircraft conditions and performance during flight (such as engine thrust or wing deflection). The sensor data are inputted into an on-board model estimation and drag optimization system which estimates the aerodynamic model and calculates the optimal settings of the flight control surfaces. As the wings deflect during flight, this technology uses an iterative approach whereby the system continuously updates the optimal solution for the flight control surfaces and iteratively optimizes the wing shape to reduce drag continuously during flight. The new control system for the flight control surfaces can be integrated into an existing flight control system. This new technology can be used on passenger aircraft, cargo aircraft, or high performance supersonic jets to optimize drag, improve aerodynamic efficiency, and increase fuel efficiency during flight. In addition, it does not require a specific aircraft math model which means it does not require customization for different aircraft designs. The system promises both economic and environmental benefits to the aviation industry as less fuel is burned.