SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
In the context of lab modernization, careful consideration must be given to existing technologies, processes, and knowledge to support the ongoing production, maintenance, and improvement of products for the nuclear enterprise. This consideration often involves analyzing manufacturing processes both to pursue modernization or automation and to ensure the ability to maintain manufacturing capability.
Methods are being developed at Marshall Space Flight Center's Toxicity Lab on a CG/IRD System that will be used to detect ammonia in low part per million (ppm) levels. These methods will allow analysis of gas samples by syringe injections. The GC is equipped with a unique cryogenic-cooled inlet system that will enable our lab to make large injections of a gas sample. Although the initial focus of the work will be analysis of ammonia, this instrument could identify other compounds on a molecular level. If proper methods can be developed, the IRD could work as a powerful addition to our offgassing capabilities.
Detection of life on Mars requires definition of a suitable biomarker and development of sensitive yet compact instrumentation capable of performing in situ analyses. Our studies are focused on amino acid analysis because amino acids are more resistant to decomposition than other biomolecules, and because amino acid chirality is a well-defined biomarker. Amino acid composition and chirality analysis has been previously demonstrated in the lab using microfabricated capillary electrophoresis (CE) chips. To analyze amino acids in the field, we have developed the Mars Organic Analyzer (MOA), a portable analysis system that consists of a compact instrument and a novel multi-layer CE microchip.
Two flash lidars, integrated from a number of cutting-edge components from industry and NASA, are lab characterized and flight tested for determination of maximum operational range under the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project (in its fourth development and field test cycle) which is seeking to develop a guidance, navigation, and control (GN&C) and sensing system based on lidar technology capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The flash lidars incorporate pioneering 3-D imaging cameras based on Indium-Gallium-Arsenide Avalanche Photo Diode (InGaAs APD) and novel micro-electronic technology for a 128 x 128 pixel array operating at 30 Hz, high pulse-energy 1.06 micrometer Nd:YAG lasers, and high performance transmitter and receiver fixed and zoom optics. The two flash lidars are characterized on the NASA-Langley Research Center (LaRC) Sensor Test Range, integrated with other portions of the ALHAT GN&C system from partner organizations into an instrument pod at NASA-JPL, integrated onto an Erickson Aircrane Helicopter at NASA-Dryden, and flight tested at the Edwards AFB Rogers dry lakebed over a field of human-made geometric hazards during the summer of 2010. Results show that the maximum operational range goal of 1 km is met and exceeded up to a value of 1.2 km. In addition, calibrated 3-D images of several hazards are acquired in real-time for later reconstruction into Digital Elevation Maps (DEM's).
The Soldier-Warfighter Operationally Responsive Deployer for Space (SWORDS) rocket is a dedicated small satellite launcher that will minimize danger and complexity in order to allow soldiers in the field to put payloads of up to 25kg into orbit from the field. The SWORDSG2 project is the development of a model, simulation, and ultimately a working application that will control and monitor the cryogenic fluid delivery to the SWORDS rocket for testing purposes. To accomplish this, the project is using the programming language environment Gensym G2. The environment is an all-inclusive application that allows development, testing, modeling, and finally operation of the unique application through graphical and programmatic methods. In addition, observation of the current cryogenic fluid delivery system in the Kennedy Space Center Cry Lab has allowed me to gain valuable experience of fluid systems and propelant delivery that is valuable to our team when developing amd modeling our own system.The ultimate goal of having a test-ready application to show to the heads of the project, and demonstrating G2's capabilities, by late 2014 will require hard work and intense study and understanding of not only the programming aspect but also the physical phenomena we want to model, observe, and control.
The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostlyrely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 100-day and 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-LSTM design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicatethe effectiveness of LSTM and GRUcells for state-of-the-art data processing (interpolation). Additionally, GRUs may be quickly trained with limited data, allowing for exacting improvements with optimizer selection. Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value, requiring support from tailored measurements for Air Traffic Management.
The changes in the scope of NASA s mission in the coming decade are profound and demand nimble, yet insightful, responses. On-board clinical and environmental diagnostics must be available for both mid-term lunar and long-term Mars exploration missions in an environment marked by scarce resources. Miniaturization has become an obvious focus. Despite solid achievements in lab-based devices, broad-based, robust tools for application in the field are not yet on the market. The confluence of rapid, wide-ranging technology evolution and internal planning needs are the impetus behind this work. This report presents an analytical tool for the ongoing evaluation of promising technology platforms based on mission- and application-specific attributes. It is not meant to assess specific devices, but rather to provide objective guidelines for a rational down-select of general categories of technology platforms. In this study, we have employed our expertise in the microgravity operation of fluidic devices, laboratory diagnostics for space applications, and terrestrial research in biochip development. A rating of the current state of technology development is presented using the present tool. Two mission scenarios are also investigated: a 30-day lunar mission using proven, tested technology in 5 years; and a 2- to 3-year mission to Mars in 10 to 15 years.
The design and operational requirements which evolved from Sortie Lab (SL) analysis are summarized. A source of requirements for systems is given along with experimental support for the SL, baseline. Basic design data covered include: configuration definition, mission analysis, experimental integration, safety, and logistics. A technical summary outlines characteristics which reflect the influence of the growth in SL capability and the results of the mission and operational analysis. Each of the selected areas is described in terms of objectives, equipment, operational concept, and support requirements.
The complexity of the semiconductor design lifecycle and globalized manufacturing process creates concern over the threat of deliberate malicious alterations, or hardware Trojans, being inserted into microelectronic designs. This has resulted in a significant corpus of hardware Trojan research including Trojan design and benchmarking efforts and development of corresponding metrics and detection and prevention techniques, over the last two decades. In this survey, we first highlight efforts in Trojan design and benchmarking, followed by a cataloging of seminal and recent works in Trojan detection and prevention and their accompanied metrics. Given the volume of literature in this field, this survey considers only pre-silicon techniques. We make this distinction between pre- and post-silicon to properly scope and provide appropriate context into the capabilities of existing hardware Trojan literature. Each major section (design, prevention, and detection) is accompanied by insights, and common pitfalls, which we highlight can be addressed by future research.
We propose a generative quantum learning algorithm using the Adaptive Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss function to be minimized is the maximal quantum Rényi divergence of order two, an unbounded function that mitigates barren plateaus which inhibit training variational circuits. We benchmark this method against other state-of-the-art adaptive algorithms by learning random two-local thermal states. We perform numerical experiments of up to 12 qubits comparing our method learning algorithms that use linear objective functions and show that Rényi-ADAPT is capable of constructing shallow quantum circuits competitive with existing methods, while the gradients remain favorable resulting from the maximal Rényi divergence loss function.
Here, the US High-Luminosity LHC Accelerator Upgrade Project (AUP) is responsible for delivering cryo-assemblies for the Q1/Q3 quadrupole optical components of the High Luminosity LHC upgrade at CERN. Total of 10 cryo-assemblies containing two Nb3Sn quadrupole magnets per cold mass will be delivered within this program. After the successful test of the first pre-series cryo-assembly in 2023, two more cryo-assemblies were tested at Fermilab's horizontal test facility. Production overview and the test results of the LQXFA/B02 and LQXFA/B03 cryo-assemblies are summarized in this paper. After the first test, to increase the capability of the horizontal test facility, various improvements have been made. These improvements are also described in this paper.
The Interplanetary Overlay Network (ION) software at JPL is an implementation of Delay/Disruption Tolerant Networking (DTN) which has been proposed as an interplanetary protocol to support space communication. The JPL Deep Impact Network (DINET) is a technology development experiment intended to increase the technical readiness of the JPL implemented ION suite. The DINET Experiment Operations Center (EOC) developed by JPL's Protocol Technology Lab (PTL) was critical in accomplishing the experiment. EOC, containing all end nodes of simulated spaces and one administrative node, exercised publish and subscribe functions for payload data among all end nodes to verify the effectiveness of data exchange over ION protocol stacks. A Monitor and Control System was created and installed on the administrative node as a multi-tiered internet-based Web application to support the Deep Impact Network Experiment by allowing monitoring and analysis of the data delivery and statistics from ION. This Monitor and Control System includes the capability of receiving protocol status messages, classifying and storing status messages into a database from the ION simulation network, and providing web interfaces for viewing the live results in addition to interactive database queries.
Presently we are positioned to be product focused - Understandably so, since we have been developing a product for the last decade - Focused on developing a single system per mission Going forward we need to look at the system of systems and the coordination among systems that are designed differently, talk differently, and fail differently - Adaptation of the tools we’ve developed along the way - Developing lab to let us, industry, and academia partners to develop and test and evaluate and compare approaches
The improvement of computer hardware and software of the NASA Multipurpose Differential Absorption Lidar (DIAL) system is documented. The NASA DIAL system has undergone development and experimental deployment at NASA/Langley Res. Center for the remote measurement of atmospheric trace gas concentrations from ground and aircraft platforms. A viable DIAL system was developed capable of remotely measuring O3 and H2O concentrations from an aircraft platform. The DIAL Data Acquisition System (DAS) has undergone a number of improvements also. Due to the participation of the DIAL in the Global Tropospheric Experiment, modifications and improvements of the system were tested and used both in the lab and in air. Therefore, this is an operational manual for the DIAL DAS.
The search for existing or past life in the Universe is one of the most important objectives of NASA's mission. In support of this objective, ultrasonic based mechanisms are currently being developed at JPL to allow probing and sampling rocks as well as perform as a sensor platform for in-situ astrobiological analysis. The technology is based on the novel Ultrasonic/Sonic Driller/Corer (USDC), which requires low axial force, thereby overcoming one of the major limitations of planetary sampling in low gravity using conventional drills. The USDC was demonstrated to: 1) drill ice and various rocks including granite, diorite, basalt and limestone, 2) not require bit sharpening, and 3) operate at high and low temperatures. The capabilities that are being investigated including probing the ground to select sampling sites, collecting various forms of samples, and hosting sensors for measuring chemical/physical properties. A series of modifications of the USDC basic configuration were implemented leading an ultrasonic abrasion tool (URAT), Ultrasonic Gopher for deep Drilling, and the lab-on-a-drill.
The Augmented/ Virtual Reality (AVR) Lab has been working to study the applicability of recent virtual and augmented reality hardware and software to KSC operations. This includes the Oculus Rift, HTC Vive, Microsoft HoloLens, and Unity game engine. My project in this lab is to integrate voice recognition and voice commands into an easy to modify system that can be added to an existing portion of a Natural User Interface (NUI). A NUI is an intuitive and simple to use interface incorporating visual, touch, and speech recognition. The inclusion of speech recognition capability will allow users to perform actions or make inquiries using only their voice. The simplicity of needing only to speak to control an on-screen object or enact some digital action means that any user can quickly become accustomed to using this system. Multiple programs were tested for use in a speech command and recognition system. Sphinx4 translates speech to text using a Hidden Markov Model (HMM) based Language Model, an Acoustic Model, and a word Dictionary running on Java. PocketSphinx had similar functionality to Sphinx4 but instead ran on C. However, neither of these programs were ideal as building a Java or C wrapper slowed performance. The most ideal speech recognition system tested was the Unity Engine Grammar Recognizer. A Context Free Grammar (CFG) structure is written in an XML file to specify the structure of phrases and words that will be recognized by Unity Grammar Recognizer. Using Speech Recognition Grammar Specification (SRGS) 1.0 makes modifying the recognized combinations of words and phrases very simple and quick to do. With SRGS 1.0, semantic information can also be added to the XML file, which allows for even more control over how spoken words and phrases are interpreted by Unity. Additionally, using a CFG with SRGS 1.0 produces a Finite State Machine (FSM) functionality limiting the potential for incorrectly heard words or phrases. The purpose of my project was to investigate options for a Speech Recognition System. To that end I attempted to integrate Sphinx4 into a user interface. Sphinx4 had great accuracy and is the only free program able to perform offline speech dictation. However it had a limited dictionary of words that could be recognized, single syllable words were almost impossible for it to hear, and since it ran on Java it could not be integrated into the Unity based NUI. PocketSphinx ran much faster than Sphinx4 which would've made it ideal as a plugin to the Unity NUI, unfortunately creating a C# wrapper for the C code made the program unusable with Unity due to the wrapper slowing code execution and class files becoming unreachable. Unity Grammar Recognizer is the ideal speech recognition interface, it is flexible in recognizing multiple variations of the same command. It is also the most accurate program in recognizing speech due to using an XML grammar to specify speech structure instead of relying solely on a Dictionary and Language model. The Unity Grammar Recognizer will be used with the NUI for these reasons as well as being written in C# which further simplifies the incorporation.
The NASA Orbital Debris Program Office has a long-standing optical program begun over three and a half decades ago in 1984, designed to observe the Earth-orbiting environment with optical telescopes. Photometrically calibrated optical data provides a statistical sample for input to NASA models of the debris population for understanding the current and future debris environment around the Earth. Tracked objects and orbits allow for analysis of break-up events. Both known (correlated target in the SSN catalogue, or CT) and unknown (uncorrelated target, or UCT) objects are of interest to better understand how to protect current spacecraft and design more robust future operational satellites, and advise on how policies and practices can lead to protecting the environment itself for future generations. In 2015, a joint NASA JSC – Air Force Research Labs (AFRL) project culminated in the installation of the 1.3-meter Eugene Stansbery Meter Class Autonomous Telescope, ES-MCAT (a.k.a. MCAT) on Ascension Island. This DFM Engineering designed telescope provides nearly five-times greater light-collecting power than its predecessor, the 0.6-m MODEST telescope, and faster tracking capabilities by both the telescope and the 7-m ObservaDome. This allows for all orbital regimes to be easily within reach, ranging from low Earth to geosynchronous orbits. Extensive testing and commissioning activities of this custom system led to successfully reaching Initial Operational Capability in 2018, and the facility is currently on track to reach Full Operational Capability. The John Africano Observatory (JAO) comprises the primary 1.3-m ES-MCAT facility, the adjacent tower platform with a 0.4-m telescope, a sophisticated suite of weather instruments, and custom software by Euclid Research for autonomously running the entire system, including monitoring the weather and hardware, tasking all components, and collecting, processing, and analyzing the data. The mission of JAO and MCAT will be discussed, including survey and tracking tasking, a full discussion of data calibration, and both optics and weather-dependent performance.
Over time, advances in unmanned aircraft systems (UAS) have enabled a shift in the operational paradigm from one operator managing one aircraft to that of multiple operators working together to manage multiple aircraft. This shift has highlighted the need for effective human-autonomy teaming methods to maintain manageable workload levels for operators as well as high standards of system performance and safety. This paper presents a study aimed at evaluating whether automation can help operators manage workload during small UAS (sUAS) package delivery scenarios featuring contingency situations. These contingency situations, resulting from unplanned UAS Volume Reservations (UVRs), required flight path reroutes for multiple aircraft simultaneously. The study manipulated the number of aircraft affected by the UVRs and the level of automation support. The presence of terrain conflicts was also controlled within each scenario. Due to the COVID-19 pandemic, subjects were not able to gain direct access to the Ground Control System (GCS). Therefore, the study was conducted using a subject-surrogate paradigm that required subjects to relay commands through a verbal protocol from remote locations outside of the lab to a researcher surrogate who had direct control of the GCS interfaces at the lab location. Results show that the automated support condition was associated with faster reroute response times, more efficient reroute maneuvers, and significantly lower levels of perceived workload than the manual reroute condition. However, the automation support level did not significantly impact pilots’ ability to avoid the UVR successfully; pilots were overwhelmingly capable of avoiding the UVR in all conditions. The presence of terrain conflicts primarily impacted pilot performance by leading to multiple uploads per vehicle, which was not typically required when pilots only needed to maneuver laterally. Although subjects did not have direct control over the GCS, subjective ratings indicate that the displays under test provided them with sufficient information to manage their aircraft and promptly respond to the unplanned UVRs. Overall, the objective and subjective data strongly suggest that the verbal protocol and subject-surrogate paradigm were effective methods for collecting data remotely amid the COVID-19 pandemic.