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Stray-Capacitance As a Simple Tool for Monitoring and Locating Heat Generation Demonstrated in Three Superconducting Magnets
Real-time monitoring of heat loads in cryogenic systems is critical for many applications, particularly high field magnets. In this work, we demonstrated that by monitoring changes in the capacitance of local probes consisting of thin metal strips with a porous glass fiber dielectric, boiling helium from as little as 0.1 J of deposited heat can be located by an analysis of the response speed and amplitude in nearby probes. We further implemented stray-capacitance monitoring of a magnet's metal support structures, a more global probe of the magnet volumes, in three high temperature superconducting Bi-2212 magnet types. Global structural stray-capacitance monitoring was evaluated for a single racetrack coil, a common coil dipole, and a canted cosine theta winding, showing a quench response comparable to voltage signals without inductive effects. The response of the local and global probes was compared in the single racetrack coil with well-known quench properties. Monitoring the cool-down of the common coil dipole demonstrated the added benefit of the global stray-capacitance as a liquid level monitor. This simple method of monitoring the capacitance has proven to be a versatile and robust technique for monitoring and locating heat.
Stellar refraction - A tool to monitor the height of the tropopause from space
Calculations of stellar refraction for a setting or rising star as viewed from a spacecraft show that the tropopause is a discernible feature in a plot of refraction vs time. The height of the tropopause is easily obtained from such a plot. Since the refraction suffered by the starlight appears to be measurable with some precision from orbital altitudes, this technique is suggested as a method for remotely monitoring the height of the tropopause. Although limited to nighttime measurements, the method is independent of supporting data or model fitting and easily lends itself to on-line data reduction.
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An emerging network storage management standard: Media error monitoring and reporting information (MEMRI) - to determine optical tape data integrity
Sophisticated network storage management applications are rapidly evolving to satisfy a market demand for highly reliable data storage systems with large data storage capacities and performance requirements. To preserve a high degree of data integrity, these applications must rely on intelligent data storage devices that can provide reliable indicators of data degradation. Error correction activity generally occurs within storage devices without notification to the host. Early indicators of degradation and media error monitoring 333 and reporting (MEMR) techniques implemented in data storage devices allow network storage management applications to notify system administrators of these events and to take appropriate corrective actions before catastrophic errors occur. Although MEMR techniques have been implemented in data storage devices for many years, until 1996 no MEMR standards existed. In 1996 the American National Standards Institute (ANSI) approved the only known (world-wide) industry standard specifying MEMR techniques to verify stored data on optical disks. This industry standard was developed under the auspices of the Association for Information and Image Management (AIIM). A recently formed AIIM Optical Tape Subcommittee initiated the development of another data integrity standard specifying a set of media error monitoring tools and media error monitoring information (MEMRI) to verify stored data on optical tape media. This paper discusses the need for intelligent storage devices that can provide data integrity metadata, the content of the existing data integrity standard for optical disks, and the content of the MEMRI standard being developed by the AIIM Optical Tape Subcommittee.
SAR Interferometry as a Tool for Monitoring Coastal Changes in the Nile River Delta of Egypt
The Nile River Delta is experiencing rapid rates of coastal change. The rate of both coastal retreat and accretion in the Eastern Nile Delta requires regular, accurate detection and measurement. Current techniques used to monitor coastal changes in the delta are point measurements and, thus, they provide a spatially limited view of the ongoing coastal changes. SAR interferometry can provide measurements of subtle coastal change at a significantly improved spatial resolution and over large areas (100 sq km). Using data provided by the ERS-1&2 satellites, monitoring can be accomplished as frequently as every 35 days when needed. Radar interferometry is employed in this study to detect segments of erosion and accretion during the 1993-2000 period. The average rates of erosion and accretion in the Eastern Nile Delta are measured to be -11.64 m/yr and +5.12 m/yr, respectively. The results of this interferometric study can be used effectively for coastal zone management and integrated sustainable development for the Nile River Delta.
Space Based Measurements for Atmospheric Carbon Dioxide: a New Tool for Monitoring Our Environment
Fossil fuel combustion, deforestation, and other human activities are now adding almost 40 billion tons of carbon dioxide (CO2) to the atmosphere each year. Interestingly, as these emissions have increased over time, natural "sinks" in land biosphere and oceans have absorbed roughly half of this CO2, reducing the rate of atmospheric buildup by a half. Measurements of the increasing acidity (pH) of seawater indicate that the ocean absorbs one quarter of this CO2. Another quarter is apparently being absorbed by the land biosphere, but the identity and location of these natural land CO2 "sinks" are still unknown. The existing ground-based greenhouse gas monitoring network provides an accurate record of the atmospheric buildup, but still does not have the spatial resolution or coverage needed to identify or quantify CO2 sources and sinks.
Use of NOAA-N satellites for land/water discrimination and flood monitoring
A tool for monitoring the extent of major floods was developed using data collected by the NOAA-6 advanced very high resolution radiometer (AVHRR). A basic understanding of the spectral returns in AVHRR channels 1 and 2 for water, soil, and vegetation was reached using a large number of NOAA-6 scenes from different seasons and geographic locations. A look-up table classifier was developed based on analysis of the reflective channel relationships for each surface feature. The classifier automatically separated land from water and produced classification maps which were registered for a number of acquisitions, including coverage of a major flood on the Parana River of Argentina.
Integrity Monitoring and Assessment, Prediction, Repair, and Corrosion Control of the Hanford Storage Tanks — 25213
BACKGROUND AND PROJECT TASKS Proposed work addresses focus area 1: Waste Retrieval, Transport and Closure, with particular focus on increasing volume available for tank storage 1) Refurbish/fortify existing double shell storage tanks (Tank Refurbish – Polymer Grout) 2) Robust monitoring tools for internal corrosion (Integrity Monitoring -Reference Electrodes) 3) Mitigate external corrosion (Degradation Prevention - Cathodic Protection) 4) Determine viability of constructing new tanks, and storage room creation by way of evaporation (Cost Benefit Analysis)
Evaluation of Markerless Motion Capture for Monitoring Sensorimotor Performance
BACKGROUND Astronauts returning from long-duration exposure to microgravity frequently exhibit alterations in sensorimotor function leading to postural imbalance, impaired locomotion, and operational challenges to manual control. Mission duration and individual responses often influence both the severity of performance decrements and the variability in adaptation timelines. Postflight disruptions during functional tasks are often detected through body-worn inertial measurement unit (IMU) devices. While IMU sensors are relatively compact, the long-term wear may lead to discomfort, displacement of the sensors on the body, and restrictions in movement or crew behavior. Although IMU data offers valuable insights from a research standpoint, interpreting changes in pre- and post-flight measures can be difficult for crew support personnel beyond the research domain, which can hinder the application for medical assessments and rehabilitation. Finally, the availability of inertial sensors in-flight is limited. There is a need for unobtrusive monitoring tools to improve our ability to monitor adaptation following gravitational transitions in various postflight evaluations and rehabilitation settings. Markerless motion capture (MMC) is an evolving unobtrusive technology that builds upon decades of research with marker-based motion capture systems to provide 3D human pose estimation from multiple synchronized 2D camera views using deep learning algorithms. Markerless technology can revolutionize how data is captured pre- and post-flight and potentially in-flight during intravehicular activity by enabling pose estimation of multiple crew members from onboard camera hardware. METHODS The following presents the initial evaluation of a state-of-the-art commercial-off-the-shelf MMC system, Theia Markerless, compared to IMU devices during various ground-based functional tasks and environmental conditions. The featured functional tasks include assessments from Human Research Program (HRP) funded studies such as Sensorimotor Standard Measures and Sensorimotor Assessments. Synchronous data collected using both motion capture and IMUs are analyzed for six male and female subjects of varying anthropometry. The analysis includes limited assessments of clothing, capture volume configurations, and the tool's sensitivity to detecting performance changes after a spaceflight analog centrifuge exposure. The development of visualization tools to enhance the application of the pose estimation output is also presented. RESULTS Initial results demonstrate comparable root mean square error (RMSE) to existing literature evaluating markerless and marker-based motion capture systems. Considering the relative functional range of motion of the cervical spine, normalized error values for the markerless system’s accuracy of the head was 0.032 in pitch, 0.025 in roll, and 0.018 in yaw plane of motion across a subset of functional tasks. The raw RMSE values were 3.49, 2.25, and 2.81 degrees respectively. For the torso, results suggest normalized errors of 0.469 in pitch, 0.191 in roll, and 0.307 in yaw planes of motion and raw RMSE values of 3.52, 1.53, and 3.07 degrees respectively. The data suggests the functional demands of a particular task influences the estimation accuracy of the MMC system where more dynamic motion and cases where subjects are not upright may introduce diminished tracking accuracy. DISCUSSION The following work lays the foundation for future implementations leveraging markerless motion capture to assess the time course of recovery and provide insight for rehabilitation protocols to enhance crew readiness for the resumption of daily activities. These tools offer effective methods for anonymizing sensitive crew data, facilitating numerous applications across research, medical, and rehabilitation groups. Collaborations with the Anthropometry and Biomechanics Facility will provide further comparisons of the Markerless system to a marker-based system. ACKNOWLEDGEMENT</ This work is supported by NASA’s Exploration Systems Development Mission Directorate Mars Campaign Office Crew Health Countermeasures.
Repurposing Drilling Control Diagnostics for Subsurface Edge Detection and Boundary Advisement During Planetary Drilling
Informed decision-making during lunar drilling and sampling missions will require data monitoring tools and specialized ground data systems. Accurate and updated situational awareness, with ongoing data monitoring, is critical for timely responses by to incoming science data. Traverse plans and scheduled activities may need to be flexibly changed in order to react to unexpected data or situations. Unlike (for example) Mars missions, the relative lightspeed closeness of the Moon allows for near-real-time ground processing of incoming mission and instrument data. An Apollo-class lunar regolith drill will in a sense “travel” a meter or two vertically at a given subsurface characterization site. As the drill penetrates into lunar regolith, it is likely to encounter a range of material densities, orientations, fracture toughness, and (perhaps) ice percentages. Lunar drill telemetry can provide science teams with a valuable first look into the subsurface structure, the regolith bulk properties, and constituents at each drilled site. Real-time AI-based recognition and reaction to downhole situations has been developed for automated deeper drilling on Mars and beyond. We can leverage the same knowledge bases and pattern-matching as areal-time interpreter of the subsurface, a situational awareness tool during drilling operations. We recently (Sept. 2019) demonstrated this AI drilling monitoring and analysis capability, in control of in-situ drilling and sampling operations, mounted on a KREX-2 rover in Chile’s Atacama Desert. Terrestrial automated drilling log analyses in oil exploration have used similar machine learning techniques in classifying and identifying features in drilling logs –but these typically are designed assuming a drilling fluid influencing downhole measurements and data (permeability, resistivity). Drilling models and existing AI software designed to detect and respond to drilling faults and hard materials can be repurposed, for near-real-time (ground-based) interpretation of drilling telemetry –a potentially valuable advisory tool for strata boundaries and changes in drilling parameters. On the Moon, this approach could be used to study the structure and to some extent the composition of lunar regolith vs. borehole depth, based on recognizable variations in fracture hardness, drilling energy and penetration rates while actively drilling. Since the early 2000s, a series of increasingly-capable real-time drilling telemetry interpretation and characterization software tools have been developed. These subsurface models and software tools have monitored the real-time drilling data received, and automatically identified changes in drill behavior (e.g., encountering a harder target layer, bit inclusions, drill choking due to infall downhole, and others) correlating these with subsurface structures and features. We discuss the mappings between drill borehole parameters, faults or events detected, and modeled changes in rock layer boundaries, in examples drawn from field testing at analog sites in an Arctic impact crater, Rio Tinto, and Chile’s Atacama Desert. These demonstrate how subsurface structural boundaries led to fault detections and responses by the software.
Salt Sampling FY21 Technical Report
The goal of the salt sampling program at Argonne is to develop and deploy automated molten salt sampling approaches to enable high-precision in-process salt sample analysis to improve the timeliness of sampling-based accountancy measurements. Tools currently under development in support of this goal include (1) a modular vacuum sampler with an accompanying sample handling method for coupling vacuum sampling with high-precision at-line sample analysis, (2) a pneumatic sample generator that enables high-throughput sample analysis to improve the precision of existing analytical techniques, and (3) a windowless flow cell to enable on-line optical analysis of molten salt in a sampling loop. Compared to point sampling approaches (i.e., dip probes), vacuum sampling systems and on-line sampling loops facilitate access to a larger cross-section of a process fluid. This is known to improve the characterization of the process fluid by producing more representative samples and by enabling the analysis of a larger cross section of the fluid. A vacuum sampling approach for molten salts eliminates the risk of dross contamination of samples and avoids the use of moving parts in the salt. In FY21, two methods for integrating a vacuum sampler with a pneumatic sample generator were tested. These included direct fluidic coupling and coupling using a solid salt transfer mechanism. Solid salt transfer was ultimately selected over fluidic coupling, primarily to enable the transport of samples over longer distances to support automated at-line integration with high-precision techniques (such as microcalorimetry) that cannot withstand the extreme conditions near an electrorefining process. To facilitate rapid solid salt coupling, new mechanisms were developed for rapidly charging and discharging salt sample tubes at the vacuum sampler and pneumatic sample generator, respectively. While the charging mechanism will be deployed in FY22, the tube transfer method and discharge mechanism were tested in FY21. These were deployed at one of Argonne’s engineering-scale electrorefiners to implement at-line high-throughput pneumatic micro-sample generation capabilities. The method was used to generate precise uranium- and lanthanide-bearing electrorefiner micro-samples with the specific dimensions requested by researchers at Los Alamos National Laboratory for use in testing their novel microcalorimeter x-ray techniques. The solid salt transfer mechanism proved not only to be an effective means of integrating the precision sample generator with vacuum sampling, but also improved the performance of the sampler generator. Because the modular sampling approach described here eliminates the need for new high-radiation sample handling capabilities, salt-wetted seals, salt-wetted moving parts, and heated transfer lines outside the electrorefiner, it will address most of the remaining technical challenges for the at-line deployment of high-precision analytical techniques. This will enable significant reductions in the time delay for sampling-based accountancy measurements by eliminating the need for manual off-line sample processing and analysis. On-line optical analysis of molten salt in a sampling loop would provide complementary information to at-line and in-situ techniques. In FY21, an open-aperture molten salt gravity flow cell with windowless optical access to flowing salt was successfully demonstrated. Future work should include the refinement and performance testing of the on-line and at-line sampling tools, integration of additional analysis techniques, stakeholder outreach and collaboration, evaluation of the integrated methods, and analyses to determine how the various tools might fit into an integrated safeguards monitoring system of unattended near real time monitoring tools.
Windowless Optical Cell Flow System
One of the goals for advanced integrated nuclear safeguards is on-line optical analysis of molten salt in molten salt reactors and electrochemical fuel reprocessing facilities. While progress has been made in the development of optical techniques that may be suitable for on-line analysis of molten salts, significant technology gaps exist with regard to integrating these analytical techniques into industrial-scale processes. One key technological hurdle is the short service life of optical window materials, which are subject to clouding and scaling during extended exposure to molten salts. To address this issue, Argonne is developing a gravity flow cell with open-apertures to support on-line optical analysis without the need for salt-wetted windows. The flow cell uses gravity flow and fluid dynamics principles to enable molten salt flow through a pipe that has holes in its wall through which salt does not flow. In FY21, an open-aperture gravity flow cell with windowless optical access to flowing salt was successfully demonstrated. Future work should include the integration of the flow cell into a sampling loop, stakeholder outreach and collaboration, integration of multiple optical analysis techniques, evaluation of the integrated methods, and analyses to determine how the various tools might fit into an integrated safeguards monitoring system of unattended near real time monitoring tools.
Strain - The Heart of the Matter
Objective: Identify how much exercise is needed to maintain pre-bedrest / pre-spaceflight strength, minimize any flight complications and reduce time required to reacclimate to Earth's gravity. Subjects volunteered to participate in a 70 day 6deg head down tilt (HDT) study at UTMB hospital in Galveston. HDT are used to study microgravity for several reasons: 1. Allows subjects to experience atrophy of lower extremities from disuse. 2. Allows scientist to study the fluid shifts and the subject's cardiovascular and physiological effects. 3. Allows several studies to be measured and conducted simultaneously in a safe and monitored environment. Throughout those 10 weeks, subjects were either in an exercise or control group. Never before has cardiac strain been monitored at set time points throughout spaceflight or bed rest study. Once we clearly understand this technique, protocols can be formulated for astronauts to use on the ISS as both a diagnostic and monitoring tool. Echoes could have the possibility to predict & monitor heart health, cardiac strength, endurance, and overall rate of muscle degradation. Also, echoes can act as an additional factor in determining exercise prescriptions and effectiveness.
DER Translate
SAND2024-13723O This software translates and maps any .xlsx-based distributed energy resource (DER) device from the SunSpec certification registry (https://sunspec.org/certified-registry/) to a JSON-based DER models map. It is then read by DER monitoring tools for applications such as network intrusion detection and system health monitoring. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Let’s speak FRETish
FRET (https://github.com/NASA-SW-VnV/fret [github.com]) is a framework for the elicitation, formalization and analysis of requirements. FRET allows its user to enter requirements in a structured natural language called FRETish. Requirements written in FRETish are assigned unambiguous semantics. FRET supports its users in understanding this semantics and repairing requirements if applicable, by utilizing a variety of forms for each requirement: natural language description, formal mathematical logics, diagrams, and interactive simulation. FRET exports requirements into forms that can be used by a variety of analysis tools, including state-of-the-art model checkers and runtime monitoring tools. The talk will cover some of the theory behind the framework, present case studies from the aerospace and robotics domains, as well as current work on extending FRET for specifying requirements for software that learns.
Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data
Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine
Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)
Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.