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This critical safety tool can be used for a wider variety of aircraft, including general aviation, helicopters, and unmanned aerial vehicles (UAVs) while also improving performance in the fighter aircraft currently using this type of system. <strong>Demonstrations/Testing</strong> This improved approach to ground collision avoidance has been demonstrated on both small UAVs and a Cirrus SR22 while running the technology on a mobile device. These tests were performed to the prove feasibility of the app-based implementation of this technology. The testing also characterized the flight dynamics of the avoidance maneuvers for each platform, evaluated collision avoidance protection, and analyzed nuisance potential (i.e., the tendency to issue false warnings when the pilot does not consider ground impact to be imminent). <strong>Armstrong's Work Toward an Automated Collision Avoidance System</strong> Controlled flight into terrain (CFIT) remains a leading cause of fatalities in aviation, resulting in roughly 100 deaths each year in the United States alone. Although warning systems have virtually eliminated CFIT for large commercial air carriers, the problem still remains for fighter aircraft, helicopters, and GAA. Innovations developed at NASAs Armstrong Flight Research Center are laying the foundation for a collision avoidance system that would automatically take control of an aircraft that is in danger of crashing into the ground and fly it&#8212;and the people inside&#8212;to safety. The technology relies on a navigation system to position the aircraft over a digital terrain elevation data base, algorithms to determine the potential and imminence of a collision, and an autopilot to avoid the potential collision. The system is designed not only to provide nuisance-free warnings to the pilot but also to take over when a pilot is disoriented or unable to control the aircraft. The payoff from implementing the system, designed to operate with minimal modifications on a variety of aircraft, including military jets, UAVs, and GAA, could be billions of dollars and hundreds of lives and aircraft saved. Furthermore, the technology has the potential to be applied beyond aviation and could be adapted for use in any vehicle that has to avoid a collision threat, including aerospace satellites, automobiles, scientific research vehicles, and marine charting systems.

NASA Armstrong collaborated with the U.S. Air Force to develop algorithms that interpret highly encoded large area terrain maps with geographically user-defined error tolerances. A key feature of the software is its ability to locally decode and render DTMs in real time for a high-performance airplane that may need automatic course correction due to unexpected and dynamic events. Armstrong researchers are integrating the algorithms into a Global Elevation Data Adaptive Compression System (GEDACS) software package, which will enable customized maps from a variety of data sources. <strong>How It Works</strong> The DTM software achieves its high performance encoding and decoding processes using a unique combination of regular and semi-regular geometric tiling for optimal rendering of a requested map. This tiling allows the software to retain important slope information and continuously and accurately represent the terrain. Maps and decoding logic are integrated into an aircraft's existing onboard computing environment and can operate on a mobile device, an EFB, or flight control and avionics computer systems. Users can adjust the DTM encoding routines and error tolerances to suit evolving platform and mission requirements. Maps can be tailored to flight profiles of a wide range of aircraft, including fighter jets, UAVs, and general aviation aircraft. The DTM and GEDACS software enable the encoding of global digital terrain data into a file size small enough to fit onto a tablet or other handheld/mobile device for next-generation ground collision avoidance. With improved digital terrain data, aircraft could attain better performance. The system monitors the ground approach and an aircraft's ability to maneuver by predicting several multidirectional escape trajectories, a feature that will be particularly advantageous to general aviation aircraft. <strong>Why It Is Better</strong> Conventional DTM encoding techniques used aboard high-performance aircraft typically achieve relatively low encoding process ratios. Also, the computational complexity of the decoding process can be high, making them unsuitable for the real-time constrained computing environments of high-performance aircraft. Implementation costs are also often prohibitive for general aviation aircraft. This software achieves its high encoding process ratio by intelligently interpreting its maps rather than requiring absolute retention of all data. For example, the DTM software notes the perimeter and depth of a mining pit but ignores contours that are irrelevant based on the climb and turn performance of a particular aircraft and therefore does not waste valuable computational resources. Through this type of intelligent processing, the software eliminates the need to maintain absolute retention of all data and achieves a much higher encoding process ratio than conventional terrain-mapping software. The resulting exceptional encoding process allows users to store a larger library of DTMs in one place, enabling comprehensive map coverage at all times. Additionally, the ability to selectively tailor resolution enables high-fidelity sections of terrain data to be incorporated seamlessly into a map.

The nanoionic-based switches developed by NASA's Glenn Research Center exploit the properties of some amorphous materials that can incorporate relatively large amounts of metal and behave as solid electrolytes. As with liquid electrolytes found in lead-acid batteries, for example, solid electrolytes consist of mobile ions which undergo oxidation/reduction reactions at the anode and cathode of the system. The ionic conductivity of such a material can be of the same order of magnitude as the electronic conductivity of a semiconductor but without the drawbacks of an electromechanical device. In the nanoionic switch, ions are formed at an anode and migrate into the solid electrolyte, while electrons are injected from a cathode, thereby causing the growth of metal nanowires through the electrolyte from the cathode to the corresponding anode when a positive DC bias is applied. Once a nanowire has grown sufficiently to form an electrically conductive path between the electrodes, the switch is closed and no electric power is needed to maintain the connection, unlike in a MEMS or semiconductor-based switch. Moreover, the process of making the connection can easily be reversed by applying a negative bias, causing the wires to ungrow and the switch to open. Thus, NASA's state-of-the-art device is a reversible electrochemical switch that can have geometric features as small as nanometers. The process time for making or breaking the connection is very brief -- about a nanosecond. In addition, this nanoionic material can be deposited in such a way to form multilayer control circuits, which has the potential to minimize circuit footprints, reduce overall circuit losses, and provide unprecedented ease of integration.

The tool developed through this work merges information from the electric propulsion system design phase with diagnostic tools. Information from the failure mode and effect analysis (FMEA) from the system design phase is embedded within a Bayesian network (BN). Each node in the network can represent either a fault, failure mode, root cause or effect, and the causal relationships between different elements are described through the connecting edges. This novel approach can help Fault Detection and Isolation (FDI), producing a framework capable of isolating the cause of sub-system level fault and degradation. This system: Identifies and quantifies the effects of the identified hazards, the severity and probability of their effects, their root cause, and the likelihood of each cause; Uses a Bayesian framework for fault detection and isolation (FDI); Based on the FDI output, estimates health of the faulty component and predicts the remaining useful life (RUL) by also performing uncertainty quantification (UQ); Identifies potential electric powertrain hazards and performs a functional hazard analysis (FHA) for unmanned aerial vehicles (UAVs)/Urban Air Mobility (UAM) vehicles. Despite being developed for and demonstrated with an application to an electric UAV, the methodology is generalized and can be implemented in other domains, ranging from manufacturing facilities to various autonomous vehicles.

The technology is an all-printed Physically Unclonable Function (PUF) electronic device based on a nanomaterial (such as single-walled carbon nanotube) network. The network may be a mixture of semiconducting and metallic nanotubes randomly tangled with each other through the printing process. The all-printed PUF electronic device comprises a nanomaterial ink that is inkjet deposited, dried, and randomly tangled on a substrate, creating a network. A plurality of electrode pairs is attached to the substrate around the substrate perimeter. Each nanotube in the network can be a conduction path between electrode pairs, with the resistance values varying among individual pairs and between networks due to inherent inter-device and intra-device variability. The unique resistance distribution pattern for each network may be visualized using a contour map based on the electrode information, providing a PUF key that is a 2D pattern of analog values. The PUF security keys remain stable and maintain robustness against security attacks. Although local resistance change may occur inside the network (e.g., due to environmental impact), such change has little effect on the overall pattern. In addition, when a network-wide resistance change occurs, all resistances are affected together, so that the unique pattern is maintained.

Advances in additive manufacturing have enabled development of printable electronic sensor elements that can be deposited onto flexible substrates. To benchmark performance of printed sensors against the state of the art, NASA developed a low power flexible sensor platform. The platform integrates the following key components and features: -Flexible substrate: DuPont Kapton allows bending around cylindrical surfaces as small as in diameter. -Embedded microcontroller: Cypress CY8C4248 LQI-BL583 Arm Cortex M0 processor with BLE wireless controller, max frequency 48 MHz. Supports low power modes of operation, capacitive sensing support, and a single-channel 12-bit AD converter. -Commercial sensor suite: Bosch BNO080 inertial sensor; Bosch BME280 humidity, pressure, and temperature sensor; AMS CCS811 air quality sensor (VOCs and CO2). -Prototyping area for custom-printed sensors: 1) thermistor, uses carbon-based PTC resistor paste DuPont2792; 2) capacitive humidity sensor using a NASA-developed dielectric ink. NASA researchers have used the platform to study performance of the printed capacitive humidity sensor. The 2x4 mm co-doped barium titanate sensing element is highly sensitive to water vapor and performs as an unobtrusive breathing monitor, sensitive to breath at distances of up to 20 cm. Average change of sensor capacitance at a distance of 7.5 cm was observed to be 6.23.5 pF.

NASA's Extensible Traffic Management (xTM) system allows for distributed management of the airspace where disparate entities collaborate to maintain a safe and accessible environment. This digital ecosystem relies on a common data generation and transfer framework enabled by well-defined data collection requirements, algorithms, protocols, and Application Programming Interfaces (APIs). The key components in this new paradigm are: Data Standardization: Defines the list of data attributes/variables that are required to inform and safely perform the intended missions and operations. Automated Real Time And/or Post-Flight Data Verification Process: Verifies system criteria, specifications, and data quality requirements using predefined, rule-based, or human-in-the-loop verification. Autonomous Evolving Real Time And/or Post-Flight Data Validation Process: Validates data integrity, quantity, and quality for audit, oversight, and optimization. The verification and validation process determines whether an operation’s performance, conformance, and compliance are within known variation. The technology can verify thousands of flight operations in near-real time or post flight in the span of a few minutes, depending on networking and computing capacity. In contrast, manual processing would have required hours, if not days, for a team of 2-3 experts to review an individual flight.