Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “information processing capacity”

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.

189 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Machine Learning Approach to Improve Air Traffic Management Initiatives

Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.

Farzan Masrour Shalmani↗
NASA NTRS2022

Space Communications Responsive to Events Across Missions (SCREAM): An Investigation of Network Solutions for Transient Science Space Systems

Space Communications Responsive to Events Across Missions (SCREAM): An Investigation of Network Solutions for Transient Science Space Systems The National Academies have prioritized the pursuit of new scientific discoveries using diverse and temporally coordinated measurements from multiple ground and space-based observatories. Networked communications can enable such measurements by connecting individual observatories and allowing them to operate as a cohesive and purposefully designed system. Timely data flows across terrestrial and space communications networks are required to observe transient scientific events and processes. Currently, communications to space-based observatories experience large latencies due to manual service reservation and scheduling procedures, intermittent signal coverage, and network capacity constraints. If space communications network latencies could be reduced, new discoveries about dynamic scientific processes could be realized. However, science mission and network planners lack a systematic framework for defining, quantifying and evaluating timely space data flow implementation options for transient scientific observation scenarios involving multiple ground and space-based observatories. This dissertation presents a model-based systems engineering approach to investigate and develop network solutions to meet the needs of transient science space systems. First, a systematic investigation of the current transient science operations of the National Aeronautics and Space Administration’s (NASA) Tracking and Data Relay Satellite (TDRS) space data network and the Neil Gehrels Swift Observatory resulted in a formal architectural model for transient science space systems. Two methods individual missions may use to achieve timely network services were defined, quantitatively modeled, and experimentally compared. Next, the architectural model was extended to describe two alternative ways to achieve timely and autonomous space data flows to multiple space-based observatories within the context of a purposefully designed transient science observation scenario. A quantitative multipoint space data flow modeling method based in queueing theory was defined. General system suitability metrics for timeliness, throughput, and capacity were specified to support the evaluation of alternative network data flow implementations. A hypothetical design study was performed to demonstrate the multipoint data flow modeling method and to evaluate alternative data flow implementations using TDRS. The merits of a proposed future TDRS broadcast service to implement multipoint data flows were quantified and compared to expected outcomes using the as-built TDRS network. Then, the architectural model was extended to incorporate commercial network service providers. Quantitative models for Globalstar and Iridium short messaging data services were developed based on publicly available sources. Financial cost was added to the set of system suitability metrics. The hypothetical design study was extended to compare the relative suitability of the as-built TDRS network with the commercial Globalstar and Iridium networks. Finally, results from this research are being applied by NASA missions and network planners. In 2020, Swift implemented the first automated command pipeline, increasing its expected gravitational wave follow-up detection rate by greater than 400%. Current NASA technology initiatives informed by this research will enable future space-based observatories to become interoperable sensing devices connected by a diverse ecosystem of network service providers.

Christopher J. Roberts↗
NASA NTRS2019

Predicting Cancer Risk from Ionizing Radiation

The ability to predict cancer risk associated with exposure to low doses of high-LET ionizing radiation (IR) remains a challenge. Epidemiological methods lack the sensitivity and power to provide detailed risk estimates for cancer and ignore individual variance in IR sensitivity. We have hypothesized that DNA repair capacity can be used as a marker to evaluate and differentiate individual radiation sensitivity. More specifically, this work is based on the concept that the combined time-dose dependence of radiation-induced foci (RIF) of p53-binding protein 1 (53BP1) following low-LET exposure contains sufficient information to infer sensitivity to any other LET. Our hypothesis was tested in 15 different mouse strains as well as in primary human immune cells. We first approached individual ionizing radiation sensitivity in a mouse model by culturing primary skin fibroblasts extracted from 76 mice of 15 different genetic backgrounds and exposing them to HZE particles and X-rays. This work is one of the most extensive studies on the kinetics and possible genetic underpinnings of radiation-induced DNA damage and repair. Our results is in agreement with a DNA repair model we previously postulated, where nearby DNA double strand breaks (DSB) in the nucleus are brought together for more efficient repair, leading to RIF clustering. Such mechanism was evidenced by a specific dose and LET dependence of RIF numbers. Briefly, RIF quantification after low-LET X-ray exposure showed an asymptotic saturation for doses between 1 Gy and 4 Gy 4 hours post-irradiation across all 15 strains. The clustering of DSB across all strains also led to more RIF/Gy for lower LET (X-ray and 350 MeV/n Ar) than for higher LET (600 MeV/n Fe) 4 hours post-exposure. Considering the fact that the number of DSB/Gy should be independent of LET, our data suggest there are more DSB in individual RIF as the LET increases. RIF numbers for 24 and 48 hours post-exposure led to the inverse trend, with more remaining RIF/Gy for higher LET (by 600 MeV/n Fe). This result suggests cells have more difficulty resolving RIF from higher LET as they the number DSB/RIF increases. Note that for most conditions, the variance of RIF/Gy was small within individual animals of the same strain and large between strains, suggesting a strong genetics component. Furthermore, we present our preliminary data from an ongoing study on human genetic associations with IR sensitivity. To address the human variability in responses to HZE particle irradiation in a maximally comprehensive manner, we are in the process of collecting and isolating primary blood mononuclear cells from 768 healthy subjects of European descent, 18-75 years of age, 50/50 male/female distribution. We have analyzed 53BP1+ RIF formation as well as oxidative stress and cell death in primary cells from 192 subjects in response to the same HZE particles as used in mice: 600 MeV/n Fe, 350 MeV/n Ar and 350 MeV/n Si, 1.1 and 3 particles/100m2, 4 and 24 hours after irradiation. We will next complete the quantification of HZE particle-induced DNA and cellular damage in the remaining subjects and compare it to their responses to low-LET irradiation. Finally, we will perform GWAS analysis to identify human genomic associations with IR sensitivity and potential targets for biomarker development.

High-LET radiation↗
NASA NTRS2022

National Campaign (NC)-1 Strategic Conflict Management Simulation (X4) Final Report

Urban Air Mobility (UAM) enables highly automated, cooperative, passenger or cargo-carrying air transportation services in and around urban areas. UAM is a subset of the Advanced Air Mobility (AAM) concept under development by the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration (FAA), and industry. The Strategic Conflict Management (SCM) Simulation, dubbed “X4”, was conducted between July 2021 and June 2022 by NASA with the FAA and industry to evolve the Provider of Services for UAM (PSU) that will be needed to ensure initial UAM operations can scale in the National Airspace System (NAS). The FAA UAM Concept of Operations (ConOps) v1 [1] served as an initial guiding document for the X4 airspace management system design to ensure the architecture supports testing of services provided by third-party service providers. To that end, the X4 architecture leveraged concepts and technologies developed for Unmanned Aircraft System (UAS) Traffic Management (UTM) while also developing and testing new capabilities and services needed for UAM. The architecture included an initial prototype of the FAA-Industry Exchange Protocol (FIDXP), third-party services such as the PSUs, and Discovery and Synchronization Service (DSS), and other new services such as Demand-Capacity Balancing (DCB) to facilitate UAM strategic conflict management. During X4, NASA led discussions and collaborated with seven industry airspace partners to develop initial airspace management concepts for UAM that drove what would be tested and evaluated during the simulations. The initial capabilities defined for PSU leveraged UTM UAS Service Supplier (USS) as a starting point and evolved to meet UAM requirements. These discussions were also an opportunity for NASA to collaborate with industry to develop a set of initial Community-Based Rules (CBRs). These CBRs enabled how UAM traffic would be cooperatively managed among UAM operators. The collaborative, iterative process of developing CBRs with industry during X4 provided insight into the challenges involved and identified the need for a suitable and effective forum for future CBR development. In parallel with these discussions, NASA conducted a series of seven software sprints and two collaborative simulations with the airspace partners that built up in complexity. Over the course of the year, all seven partners successfully completed all the sprints by demonstrating the required capabilities. They also participated in the two collaborative simulations to demonstrate how multiple PSUs could work together in a collaborative, more complex environment with higher traffic density. The X4 simulation accomplished NASA's objectives and helped advance the development of the seven participating PSUs. The lessons learned provided insight into key elements of the UAM Notional Architecture from the FAA ConOps v1 [1] and UTM technologies when applied to UAM: - Having a Concept of Use (ConUse) defined prior to the activity would accelerate the time and effort from concept development to testing. - While the UAM Notional Architecture provided a starting point for a federated architecture that support services provided by third-party providers, the USS and PSU differed in their capability definitions for operational intent submission and sharing, conformance monitoring, airspace authorization, strategic conflict management, airspace constraints and dynamic replanning. - While existing DSS developed for UTM provided a way for PSU and other UAM services (such as DCB) to discover relevant operations from each other, additional complexity and challenges were found during X4 testing and illuminated the need for a more suitable solution for UAM. Industry can leverage the results of this demonstration to accelerate UAM requirements, CBRs, and standards development. The FAA and other government municipalities and agencies will be able to leverage results to inform future policies and identify additional gaps that require further analysis, moving toward operationalization of UAM.

Urban Air Mobility↗
NASA NTRS2012

The Namibia Early Flood Warning System, A CEOS Pilot Project

Over the past year few years, an international collaboration has developed a pilot project under the auspices of Committee on Earth Observation Satellite (CEOS) Disasters team. The overall team consists of civilian satellite agencies. For this pilot effort, the development team consists of NASA, Canadian Space Agency, Univ. of Maryland, Univ. of Colorado, Univ. of Oklahoma, Ukraine Space Research Institute and Joint Research Center(JRC) for European Commission. This development team collaborates with regional , national and international agencies to deliver end-to-end disaster coverage. In particular, the team in collaborating on this effort with the Namibia Department of Hydrology to begin in Namibia . However, the ultimate goal is to expand the functionality to provide early warning over the South Africa region. The initial collaboration was initiated by United Nations Office of Outer Space Affairs and CEOS Working Group for Information Systems and Services (WGISS). The initial driver was to demonstrate international interoperability using various space agency sensors and models along with regional in-situ ground sensors. In 2010, the team created a preliminary semi-manual system to demonstrate moving and combining key data streams and delivering the data to the Namibia Department of Hydrology during their flood season which typically is January through April. In this pilot, a variety of moderate resolution and high resolution satellite flood imagery was rapidly delivered and used in conjunction with flood predictive models in Namibia. This was collected in conjunction with ground measurements and was used to examine how to create a customized flood early warning system. During the first year, the team made use of SensorWeb technology to gather various sensor data which was used to monitor flood waves traveling down basins originating in Angola, but eventually flooding villages in Namibia. The team made use of standardized interfaces such as those articulated under the Open Cloud Consortium (OGC) Sensor Web Enablement (SWE) set of web services was good [1][2]. However, it was discovered that in order to make a system like this functional, there were many performance issues. Data sets were large and located in a variety of location behind firewalls and had to be accessed across open networks, so security was an issue. Furthermore, the network access acted as bottleneck to transfer map products to where they are needed. Finally, during disasters, many users and computer processes act in parallel and thus it was very easy to overload the single string of computers stitched together in a virtual system that was initially developed. To address some of these performance issues, the team partnered with the Open Cloud Consortium (OCC) who supplied a Computation Cloud located at the University of Illinois at Chicago and some manpower to administer this Cloud. The Flood SensorWeb [3] system was interfaced to the Cloud to provide a high performance user interface and product development engine. Figure 1 shows the functional diagram of the Flood SensorWeb. Figure 2 shows some of the functionality of the Computation Cloud that was integrated. A significant portion of the original system was ported to the Cloud and during the past year, technical issues were resolved which included web access to the Cloud, security over the open Internet, beginning experiments on how to handle surge capacity by using the virtual machines in the cloud in parallel, using tiling techniques to render large data sets as layers on map, interfaces to allow user to customize the data processing/product chain and other performance enhancing techniques. The conclusion reached from the effort and this presentation is that defining the interoperability standards in a small fraction of the work. For example, once open web service standards were defined, many users could not make use of the standards due to security restrictions. Furthermore, once an interoperable sysm is functional, then a surge of users can render a system unusable, especially in the disaster domain.

Mandl, Daniel↗
NASA NTRS2014

Forecasting Sensorimotor Adaptability from Baseline Inter-Trial Correlations

One of the greatest challenges surrounding adaptation to the spaceflight environment is the large variability in symptoms, and corresponding functional impairments, from one crewmember to the next. This renders preflight training and countermeasure development difficult, as a "one-size-fits-all" approach is inappropriate. Therefore, it would be highly advantageous to know ahead of time which crewmembers might have more difficulty adjusting to the novel g-levels inherent to spaceflight. Such knowledge could guide individually customized countermeasures, which would enable more efficient use of crew time, both preflight and inflight, and provide better outcomes. The primary goal of this project is to look for a baseline performance metric that can forecast sensorimotor adaptability without exposure to an adaptive stimulus. We propose a novel hypothesis that considers baseline inter-trial correlations, the trial-to-trial fluctuations in motor performance, as a predictor of individual sensorimotor adaptive capabilities. To-date, a strong relationship has been found between baseline inter-trial correlations and adaptability in two oculomotor systems. For this project, we will explore an analogous predictive mechanism in the locomotion system. METHODS: Baseline Inter-trial Correlations: Inter-trial correlations specify the relationships among repeated trials of a given task that transpire as a consequence of correcting for previous performance errors over multiple timescales. We can quantify the strength of inter-trial correlations by measuring the decay of the autocorrelation function (ACF), which describes how rapidly information from past trials is "forgotten." Processes whose ACFs decay more slowly exhibit longer-term inter-trial correlations (longer memory processes), while processes whose ACFs decay more rapidly exhibit shorterterm inter-trial correlations (shorter memory processes). Longer-term correlations reflect low-frequency activity, which is more easily measured in the frequency domain. Therefore, we use the power spectrum (PS), which is the Fourier transform of the ACF, to describe our inter-trial correlations. The decay of the PS yields a straight line on a log-log frequency plot, which we quantify by Beta = - (slope of PS on log-log axes). Hence, Beta is a measure of the strength of inter- trial correlations in the baseline data. Larger Beta values are indicative of longer inter-trial correlations. Experimental Approach: We will begin by performing a retrospective analysis of treadmill-gait adaptation data previously collected by Dr. Bloomberg and colleagues. Specifically, we will quantify the strength of inter-trial correlations in the baseline step cadence and heart rate data and compare it to the locomotor adaptability performance results already described by these investigators. Incorporating these datasets will also allow us to explore the applicability of (and potential limitations surrounding) the use of Beta in forecasting physiological performance. We will also perform a new experiment, in which Beta will be derived from baseline data collected during over-ground (non-treadmill) walking, which will enable us to consider locomotor performance, through the parameter Beta, under the most functionallyrelevant, natural gait condition. This experiment will incorporate two baseline and five post-training over-ground locomotion tests to explore the consistency and potential adaptability of the Beta values themselves. HYPOTHESES: We hypothesize that the strength of baseline inter-trial correlations of step cadence and heart rate will relate to locomotor adaptability. Specifically, we anticipate that individuals who show weaker longer-term inter-trial correlations in baseline step cadence data will be the better adaptors, as step cadence can be modified in real-time (i.e., online corrections are an inherent property of the locomotor system; analogous to results observed in the VOR). Conversely, because heart rate is not altered mid-beat, we expect that individuals who demonstrate stronger longer-term correlations in heart rate will be the better adaptors (analogous to results observed in the saccadic system). CONCLUSIONS: At the conclusion of this project we hope to uncover a baseline predictor of locomotor adaptability. If our hypotheses hold true, our results will demonstrate that the temporal structure of baseline behavioral data contains important information that may aid in forecasting adaptive capacities. The ability to predict such adaptability in the sensorimotor system has significant implications for spaceflight, where astronauts must adjust their motor programs following a change in g-level to retain movement accuracy.

Beaton, K. H.↗
NASA NTRSDate not supplied

Scalar Atomic Defect-Based Solid-State Self-calibrating Magnetometer (3SM) for Space Plasma Analysis

The Earth’s magnetosphere is a system of multiple, co-located particle populations interacting via plasma waves. Things to understand are driving processes, radiation belt and ring current issues, auroral physics, internal plasma processes, and magnetosphere-ionosphere mapping issues. These plasma-physics processes enable the Earth’s magnetosphere to evolve in response to temporal changes in the solar wind and they underlie the phenomena of space weather, which impacts spacecraft systems, astronauts, radio communications, and ground based electric-power grids. The objective of 3SM is to measure magnetic field strength and to calibrate the Vector Magnetometer (VM) device to maintain absolute accuracy during a mission. The 3SM features make the instrument preferably suited not only for the traditional role of scalar magnetometers as absolute references for the calibration of the on-board vector instruments, but also for extended operational capacities, such as higher frequency scalar measurements (of potential interest for magnetosphere studies for the low frequency part of the spectrum) or autonomous scalar / vector operations. Diamond has been the solid-state platform of choice for quantum device technologies for some time, however, it suffers from difficulties such as scalability, integration, and cost. While the diamond platform is very useful for quantum technologies, further development is needed to make it viable. The material platform of choice for NASA Glenn’s Quantum Sensing And Spin Physics (Q-SASP) is silicon carbide (SiC). This is due to the much higher industry development of the SiC material platform for high-power and high-temperature electronics. It leverages both the decades-long SiC development expertise and infrastructure at NASA Glenn and its growing capabilities in quantum metrology. To make SiC devices usable for quantum technologies such as quantum sources, a much deeper understanding of defects is needed. Q-SASP is developing quantum metrology capabilities to evaluate the energy structure, defect formation energy, band structure augmentation, generation/recombination rates, and limits of dipole-dipole coupling in non-metal implanted SiC devices. This can be achieved by analysis of zero-field splitting, low-field resonance, and singlet-triplet mixing through various forms of Electrically Detectable Magnetic Resonance (EDMR) and Near-Zero Field Magnetic Resonance (NZFMR) spectroscopy. This work will discuss recent system developments, device developments, computational modeling, and spectroscopy results and analysis of defects created by non-metal implantations in SiC devices. The defect formation energies of V Si ,V C , V C V Si , N C V Si , N Si , N C in 4H-SiC are previously reported values in other research [2]-[3]. The defect formation energies of PSi and PC were calculated in GPAW [fig 1A]. The basic underlying mechanism of the zero-field phenomenon is the mixing of singlet and triplet states [4]-[5]. In most spin-dependent transport, two electron spins are involved, and thus one must consider each of their interactions with the field. We investigated the electronic and magnetic properties of 4H-SiC and 6H-SiC. The defect formation energy helps us determine what types of defects we are observing in the SiC EDMR experiment. They have very low formation energy (it is negative). The phosphorus substitution in 4H-SiC is a very stable defect. The band diagrams provide us with vital information about how the electronic properties of SiC (such as band gap) change as we add non-metal defects. The zero-field splitting parameters allow us to study the inflection point in the NZFMR [fig 1B]. We clearly observed zero-field splitting. We also noted that the zero-field splitting remained constant with changing bias. We aspect it zero-field splitting to remain constant while the hyperfine and exchange interaction perturbations shift under the influence of an external magnetic field. This is the essence of quantum magnetometry and self-calibration.

space plasma↗
NASA NTRS2004

Resolving the Issues with Flywheel Position Sensors

For the past few years, the Advanced Electrical Systems Branch here at NASA Glenn has been pursuing research in the area of flywheels. The purpose of these pursuits has been t o explore the potential for flywheels to replace current battery-powered systems in space. So far it has been learned that flywheels offer large momentum storage capacity, comparatively small volume, high durability, and near- complete discharge capabilities, all of which are advancements over the existing nickel hydrogen and nickel cadmium batteries. Another significant advantage of flywheels is the potential they offer for combining the function of attitude control with energy storage. During the summer of 2004, I worked with Dr. Barbara Kenny in the Advanced Electrical Systems Branch, supporting the work she is doing by analyzing and testing some new components for the new Generation-2 flywheel. To monitor the speed and angular position of the flywheel rotor, a once-around (OAR) signal along with a sensorless algorithm is used. The OAR signal is used for the magnetic bearings that keep the flywheel suspended for frictionless operation. The sensorless algorithm is used for the flywheel motor/generator control. The OAR is generated from position sensors that monitor a circular plate. The plate has a cut down the middle such that one half of the circle is on a slightly lower level than the other. Every half-turn, or 180, the sensors detect the "cut" on the plate, and trigger the OAR, telling the computer that the rotor has made half a revolution. This, however, doesn't provide needed detailed information about the angular position of the rotor, since it only provides a signal alert every half- revolution. This is enough information for the magnetic bearing control but is insufficient for the motor/generator control. A new resolver was designed such that it would give continuous angle information rather than the 180 degree information of the OAR. The new resolver has two separate observable pieces: a flat middle section to monitor vertical motion, and an angled section around the circumference, which, when observed from above, produces a sine-wave displacement through the entire 360" revolution. My first job when I arrived this summer was to calibrate the sensors that would be mounted on the inside of the flywheel casing to monitor the position (angular and vertical) of the shaft. After calibration, I used the sensors to evaluate voltage outputs created by position differences between two pairs of sensors on the angled portion of the resolver for eight different angular positions, moving the resolver vertically and laterally through its entire potential range of motion. The results of these tests will be used to determine the rotor angular (and axial) position from the sensor readings once the new flywheel unit is assembled. The sensorless algorighm mentioned above consists of two operations: the signal injection method and the back electro-motive force (EMF). The signal injection is meant to work at low speeds, while the back EMF algorithm is meant to work at higher speeds. Both work together to determine the correct estimate of rotor position and speed based on the measured motor/generator current. It was determined that we wanted to know exactly how accurate our estimation methods were, and so a resolver (a commercially available mechanical sensor mounted to the motor/generator shaft to measure rotor position and speed) and a "Resolver to Digital" (R2D) circuit board was purchased to make the comparison to the existing estimation. My work related to the R2D board has included the following: creating two connector cables (one to power the circuit and one to get readable output off the board), writing Simulink code to process the board's output, and building a dSpace panel to control and monitor the circuit. The next step in the process will be to perform tests to compare the estimated rotor position and speed from the sensorless algorithm to the actual rotor and speed from the resolver signal.

Fehrmann, Elizabeth A.↗
NASA NTRSDate not supplied

Localization of Ad-Hoc Lunar Constellations in Communication Failure Modes for Distributed Spacecraft Autonomy

As lunar missions increase in complexity inspired by NASA’s Artemis Program, they will require reliable and sufficient capability of the Position, Navigation, and Timing (PNT) system to support their scientific objectives. In addition, NASA's Commercial Lunar Payload Services (CLPS) program initiates the proliferation of public and private exploration partnerships using small satellites from commercial and private organizations, expanding traditionally confined low Earth orbit to be used for missions beyond geosynchronous orbit (Zucherman et al., 2022). Therefore, the Lunar PNT system is also required to provide navigation services compatible with the smaller platforms being sent by the public and private sectors, like CubeSats. However, traditional approaches to deep space missions’ navigation based on ground radio facilities have difficulties in providing sufficient support for the increasing number of users and communication at a distance from the Earth (Kaplev et al., 2022). In particular, the existing Lunar navigation technologies such as weak signal global positioning system (GPS) and deep space network (DSN) are not able to ensure operations of the upcoming small-scale Lunar missions due to their limitations in localization performance as well as capacity aspects. Another way to provide Lunar PNT service is to create a dedicated Lunar global navigation satellite system (GNSS) constellation, like GNSS systems on Earth. Space agencies like NASA, ESA, and JAXA are now developing the lunar communications relay and navigation systems (LCRNS) and Lunar navigation satellite systems (LNSS). In their systems, satellites will be deployed in moon orbits to provide the communication, positioning, navigation, and timing (CPNT) service at the lunar south pole region where the Artemis base camp will be expected (Murata et al., 2022). Meanwhile, common challenges considered in lunar PNT research arise from poor geometry of the terrestrial GNSS satellites when seen from the lunar user, highly perturbed lunar orbits, and limitations in power, size, and cost of the equipment on lunar satellites (Iiyama et al., 2023). It is also not clear if there will be enough Lunar users to support the cost and resources this would require as the Low-cost surface missions may not be able to support the large power, mass, and weight requirements that these navigation solutions entail (Niemoeller et al., 2022). As an alternative, existing Lunar science and exploration assets could be used to create a low-cost, autonomous, ad-hoc, and on-demand mission-centric Lunar PNT swarm capable of providing PNT services to these low-cost lunar missions (Hagenau et al., 2021). Introducing the non-dedicated and ad-hoc Lunar navigation constellation gives a way to provide PNT services on-demand. The non-dedicated swarm assets of Lunar constellations are designed to localize themselves with minimal interaction with Earth by adding cooperative autonomous localization to lunar missions, freeing up valuable bandwidth and ground segment resources. An autonomous localization of Lunar constellations is based on the concept of the decentralized PNT system with a distributed extended Kalman filter (DEKF) approach to state estimation for minimal onboard operating costs. In the distributed data processing algorithm, computation is broken down and assigned to each satellite, resulting in a considerably decreased computational amount while maintaining the accuracy of the orbit ephemeris and clock offsets as the result of centralized data processing (Wen et al., 2019). The DEKF requires spacecraft to perform two-way ranging operations with each other to communicate simultaneously, leveraging neighbor two-way intersatellite link (ISL) measurements such as pseudoranges to, and relative velocities between, visible satellites as sensor values (Frank et al., 2021). The Lunar autonomous PNT simulation (LAPS) demonstrated the feasibility of orbital asset localization among ad-hoc Lunar small-sat constellations based on the DEKF in Hagenau et al. (2021) and evaluated the matching algorithm proposed by Frank et al. (2021) in scheduling position estimation updates. In previous papers, all assets and measurements are assumed to be always available without consideration of the impact of intermittent and permanent communication failure. This study presents localization performance with increasing levels of network degradation for swarm assets and users to demonstrate the robustness of the decentralized Lunar PNT service in more realistic scenarios. Main issues arising from communication failure include spacecraft permanent or transient loss, antenna failures, message delays, etc. We tested four possible reasons for network degradation for 7 days in 21 satellites frozen with an altitude of 5500 km, evenly spaced around 3 circular, 40 inclination orbital planes where each spacecraft has two directional antennas. As anchor nodes with an independent estimate of their position are required in the DEKF approach, two ground nodes in each pole and one node in the gateway were implemented in the simulation. First, the most probable failure scenario involves the loss of a single spacecraft due to solar interference and technical malfunctions of the assets. Losing the availability of a single spacecraft means losing the two-way ISL measurement of the asset in the DEKF update. In order to provide the best possible quality of PNT service with limited time and resources, the distributed Lunar constellations must schedule the communication activities. The scheduler leverages mixed-integer linear programming (MILP) for the coordination and scheduling of the desired “as-needed” localization service (Niemoeller et al., 2022). We assume the scheduler has completely excluded the spacecraft information before the DEKF update in the failure scenario. When a random spacecraft has been turned off at a specific time, the robustness of the autonomous Lunar PNT system is evaluated. The simulation results give an 11.5% degradation in median position accuracy compared to the idealized performance excluding the asset loss. Second, a large number of assets may vanish due to major hardware problems or meteor strikes around the moon. A multiple spacecraft loss can degrade the localization performance very fast by losing the communication ability to do cross-plane measurements and in-plane measurements in a 3-plane constellation. When the matching-based scheduler is aware of ISL availability, we investigate a large number of in-plane and cross-plane asset vanishments both in close proximity and equally spaced throughout the orbital plane. According to the simulations, the loss of in-plane measurements gives 40.2% degradation while cross-plane measurements degrade 50.5% of asset localization performance among available assets. Therefore, it is concluded that cross-plane measurements are more important in improving the position estimation accuracy. Third, spacecraft failure information can be lost due to the internal message delay, resulting in the DEKF update scheduler to solve the matching problem with unavailable assets. The DEKF update cycle is comprised of network setup, communication, and computations where a global broadcast network and a 2-way ISL network setup take 6 minutes in total (Frank et al., 2021). Once the broadcast network successfully transmits and receives information, a random spacecraft may lose its availability right before solving the matching problem. This means the matching solution is no longer optimal, resulting in degradation in the localization performance. A numerical assessment shows the matching-based scheduler with knowing failure holds 11.5% of position accuracy degradation, whereas the scheduler without knowing failure gives 34% degraded localization performance without asset loss. Fourth, a transient loss of a single or multiple spacecraft may occur due to their antenna outages. After losing the two-way ISL availability for a few DEKF update cycles, the availability of spacecraft can easily be recovered as their states have been independently updated using measurements from anchor nodes. It is likely that the longer failure will result in worse localization performance. We have tested the transient failure of a random single asset for 30 min in the simulation, which is losing 3 update cycles in the DEKF system. From the simulation results, the position accuracy has been degraded to 4.84% which is better than the degraded localization performance of 11.5% from the permanent loss scenario among available assets. In conclusion, the autonomous Lunar PNT system based on the DEKF approach shows the ability to maintain resilience and robustness in the possible communication failure scenarios, ensuring that localization accuracy is preserved across various network degradation and outages. Future studies on investigating user localization performance near the South Pole and the broadcast network system will be continued in the following months.

Yeji Kim↗