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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.

The virtual air data sensor leverages smartphone-grade inertial and GPS sensors with advanced computational methods to generate accurate air flow data in real time. The innovation uses inexpensive sensors typically present on smartphones, along with real-time modeling, filtering, and data reconstruction using kinematic equations. Operating within the aircraft fuselage, the algorithm avoids environmental exposure and flow field complications affecting traditional external sensors. The algorithm employs a dual-methodology approach for real-time air flow estimation. It calibrates an aerodynamic model during calm air conditions, using aircraft response characteristics to compute air flow angles from vertical and lateral acceleration data through frequency-domain modeling. Simultaneously, kinematic relationships with GPS-corrected sensor bias estimation reconstruct independent air flow data at lower update rates. Advanced complementary filtering blends these streams to generate continuous airspeed, angle of attack, and sideslip angle measurements. The algorithm incorporates automated calibration, vertical acceleration-based alpha estimation, and GPS-based low-frequency angle reconstruction using kinematic expressions. The innovative algorithm is self-calibrating and provides independent, reliable, and accurate virtual sensing that can be implemented with readily-available hardware. The technology is currently TRL 5 (component validated in relevant environment) and available for licensing.

NASA’s digital projection focusing Schlieren system is attached to a commercial-off-the-shelf camera. For focusing Schlieren measurements, it directs light from the light source through a condenser lens and linear polarizer towards a beam-splitter where linear, vertically-polarized component of light is reflected onto the optical axis of the instrument. The light passes through the patterned LCD element, a polarizing prism, and a quarter-wave plate prior to projection from the assembly as left- or right-circularly polarized light. The grid-patterned light (having passed through the LCD element) is directed past the density object onto a retroreflective background (RBG) that serves as the source grid. Upon reflection off the RBG, the polarization state of light is mirrored. It passes the density object a second time and is then reimaged by the system. Upon encountering the polarizing prism the second time, the light is slightly offset. This refracted light passes through the LCD element, now serving as the cutoff grid, for a second time before being imaged by the camera. The LCD element can be programmed to display a variety of grid patterns to enable sensitivity to different density gradients. The color properties of the LCD can be leveraged in combination with multiple colored light sources to enable simultaneous multi-color, multi-technique data collection. This system is ready for integration into commercial flow visualization and diagnostic equipment, offering manufacturers and research facilities an efficient, cost-effective solution for multi-technique imaging. The Schlieren system is currently available for patent licensing.

The goal of this computer vision software is to take the guesswork out of grapple operations aboard the ISS by providing a robotic arm operator with real-time pose estimation of the grapple fixtures relative to the robotic arm’s end effectors. To solve this Perspective-n-Point challenge, the software uses computer vision algorithms to determine alignment solutions between the position of the camera eyepoint with the position of the end effector – as the borescope camera sensors are typically located several centimeters from their respective end effector grasping mechanisms. The software includes a machine learning component that uses a trained Region-based Convolutional Neural Network (R-CNN) to provide the capability to analyze a live camera feed to determine ISS fixture targets a robotic arm operator can interact with on orbit. This feature is intended to increase the grappling operational range of ISS’s main robotic arm from a previous maximum of 0.5 meters for certain target types, to greater than 1.5 meters, while significantly reducing computation times for grasping operations. Industrial automation and robotics applications that rely on computer vision solutions may find value in this software’s capabilities. A wide range of emerging terrestrial robotic applications, outside of controlled environments, may also find value in the dynamic object recognition and state determination capabilities of this technology as successfully demonstrated by NASA on-orbit. This computer vision software is at a technology readiness level (TRL) 6, (system/sub-system model or prototype demonstration in an operational environment.), and the software is now available to license. Please note that NASA does not manufacture products itself for commercial sale.

Environmental Control and Life Support Systems (ECLSS) used for extended space missions must recover and process wastewater to provide potable water for crew consumption and oxygen generation. Exploration mission spacecraft will have a smaller crew than the ISS, meaning demands would typically be less than what full-featured commercial TOC analyzers are designed to provide. Current analyzer technology also has limitations and uncertainties for spaceflight integration, such as part traceability, reliability, material properties for flammability or off-gassing, software and interface that are inconsis-tent with spaceflight needs, human factors, and structural reliability. The MiniTOCA provides a compact solution to the performance demands of onboard water quality analysis for crewed exploration missions through a unique core technology process that facilitates the detection of trace organic compounds in a water sample. It utilizes an ultra-violet oxidation method to activate the dissolved oxygen in the water which results in oxidation of the organic chemicals into carbon dioxide. The carbon dioxide is then measured by a Miniature Tunable Laser Spectrometer (MTLS) by sweeping the carbon dioxide out of the water in a gas / liquid separator using nitrogen gas. This novel process allows for small system sample volumes, small overall size/mass, zero consumables, low average power con-sumption (less than 60W), projected long-life (~10 years), and reliable analytical performance – all addressing critical performance gaps within the current TOC analyzer industry. Lab and environmental testing demonstrated that the MiniTOCA’s architecture is both feasible and is excellent in performance. Potential commercial applications for the MiniTOCA include, but are not limited to, ultra-pure water (UPW) systems; remote, mobile, and distributed environmental water quality monitoring; and specialized industrial process control. Technologies comprising the device lend themselves to miniaturization and are forward leaning in exploration applications. The MiniTOCA is scheduled to be flown and imple-mented aboard the ISS in late 2025.