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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.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Monitoring Human Performance During Suited Operations: A Technology Feasibility Study Using EMU Gloves

Mobility tracking of human subjects while conducting suited operations still remains focused on the external movement of the suit and little is known about the human movement within it. For this study, accelerometers and bend sensitive resistors were integrated into a custom carrier glove to quantify range of motion and dexterity from within the pressurized glove environment as a first stage feasibility study of sensor hardware, integration, and reporting capabilities. Sensors were also placed on the exterior of the pressurized glove to determine if it was possible to compare a glove joint angle to the anatomical joint angle of the subject during tasks. Quantifying human movement within the suit was feasible, with accelerometers clearly detecting movements in the wrist and reporting expected joint angles at maximum flexion or extension postures with repeatability of plus or minus 5 degrees between trials. Bend sensors placed on the proximal interphalangeal and distal interphalangeal joints performed less well. It was not possible to accurately determine the actual joint angle using these bend sensors, but these sensors could be used to determine when the joint was flexed to its maximum and provide a general range of mobility needed to complete a task. Further work includes additional testing with accelerometers and the possible inclusion of hardware such as magnetometers or gyroscopes to more precisely locate the joint in 3D space. We hope to eventually expand beyond the hand and glove and develop a more comprehensive suit sensor suite to characterize motion across more joints (knee, elbow, shoulder, etc.) and fully monitor the human body operating within the suit environment.

Bekdash, Omar↗
DOE OSTI2021

High-Resolution 3D Acoustic Borehole Integrity Monitoring (Research Performance Progress Report Q2 2021)

Real-time, in-situ, high spatial resolution (sub-cm) imaging of the near-borehole environment would revolutionize wellbore diagnostics and integrity assessment by direct observation of defects. It is becoming increasingly apparent, that better understanding of the near-wellbore environment is required to meet the safety and operational needs in challenging environments such as those present in subsurface energy extraction (geothermal) and storage (CO2 sequestration) applications. Therefore, it is important to have a more robust ability to image the near-borehole and reliably detect defects. It was proposed to further develop and improve our advanced 3D imaging system to evaluate casing defects (e.g. corrosion) and cement quality in either open- or cased-borehole with the ultimate goal to develop a commercially deployable technology. The system consists of a unique acoustic source (LANL) and advanced inversion techniques for image processing (LANL, ORNL). This system will provide comprehensive borehole integrity monitoring with improved resolution over existing techniques. As an application of this imaging system, we will characterize the effectiveness of next-generation wellbore completion technology (NETL, SNL), and will demonstrate that, unlike current technology, the proposed approach can successfully characterize foamed cements.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Groundwater Monitoring Report, U.S. Department of Energy Y-12 National Security Complex, Oak Ridge, Tennessee

This report contains the groundwater and surface water monitoring data obtained during calendar year (CY) 2019 at the U.S. Department of Energy (DOE) Y-12 National Security Complex (Y-12) on the DOE Oak Ridge Reservation (ORR) in Oak Ridge, Tennessee. The monitoring data were obtained from wells, springs, and surface water sampling locations in three hydrogeologic regimes at Y-12. The Bear Creek Hydrogeologic Regime (Bear Creek Regime) encompasses a section of Bear Creek Valley (BCV) between the west end of Y-12 and the west end of the Bear Creek Watershed (directions are in reference to the Y-12 grid system, shown as Plant North. The Upper East Fork Poplar Creek Hydrogeologic Regime (East Fork Regime) encompasses the Y-12 industrial facilities and support structures in BCV. The Chestnut Ridge Hydrogeologic Regime (Chestnut Ridge Regime) encompasses a section of Chestnut Ridge directly south of Y-12. Background information in Section 2 of this report outlines the hydrogeologic framework for groundwater and surface water quality monitoring at Y-12 and includes an overview of the groundwater contamination in each hydrogeologic regime. Section 3 provides details regarding the groundwater and surface water sampling and analysis activities implemented under the Y-12 GWPP, including sampling locations and frequency, sample collection and handling, field measurements and laboratory analytes, quality assurance (QA)/quality control (QC) sampling, data management, and data quality assessment (DQA). However, the equivalent QA/QC or DQA information for the groundwater and surface water data associated with the monitoring programs implemented by UCOR are not included in this report and instead are deferred to referenced programmatic plans and reports issued by OREM and UCOR. Section 4 of this report presents a summary evaluation of the CY 2019 monitoring data with regard to the respective objectives of surveillance monitoring and exit pathway/perimeter monitoring. The evaluation is based primarily on the analytical results for the following principal groundwater contaminants at Y-12: nitrate, uranium, gross alpha activity, gross beta activity, and volatile organic compounds (VOCs). Section 5 summarizes the most significant findings with respect to the principal contaminants along with recommendations for any proposed changes to the ongoing groundwater and surface water quality monitoring performed under the Y-12 GWPP. Technical reports and plans cited in the narrative sections of the report are listed in Section 6. Narrative sections of this report reference several appendices. Figures (maps and diagrams) and data tables (excluding data summary tables incorporated in the narrative sections) are in Appendix A and Appendix B, respectively. Appendix C contains construction details for each well sampled during CY 2019 by either the Y-12 GWPP or UCOR, along with schematic diagrams for wells equipped with Westbay™ multiport sampling equipment or Barcad® pump systems. Appendix D supports the background summary discussion in Section 2 and provides more detailed information about the hydrogeologic framework for groundwater and surface water monitoring at Y-12, including the primary sources of groundwater contamination in each hydrogeologic regime. Results for all field measurements and laboratory analyses obtained by the Y-12 GWPP and UCOR are presented in Appendix E, which also includes the sample numbers for the QA/QC samples associated with groundwater and surface water monitoring performed by the Y-12 GWPP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗
DOE OSTI2023

PACE

Performance Analytics for Computational Experiments (PACE) is an end-to-end performance monitoring infrastructure to summarize performance data collected from Department of Energy’s (DOE) climate model experiments to derive insights and present them through a web portal. PACE automatically captures provenance/timing data from simulations run on DOE supercomputers.

Sreepathi, Sarat [Oak Ridge National Lab. (ORNL), ↗
DOE OSTI2026

Filtration Performance of Simulated 200 West Area Waste Feeds

This report describes the scaled experimental system and approach used to examine dead-end filtration performance of representative 200W waste feeds. The scaled system, which was originally designed and assembled to test Tank Side Cesium Removal (TSCR) system performance with higher-than-expected solid loadings in 2021 (Schonewill et al. 2021), was repurposed to conduct the current experiments at ~1/145 of full scale (based on throughput). Six experimental runs were conducted with five different 200W waste feed simulants: three using a DEF module scaled for TSCR and three using a DEF module scaled for the 200W process modules (based on the current design for the Advanced Modular Pretreatment System). Each experiment was run continuously for multiple days with an operating approach prototypic of the full-scale system. Staff performing the experimental runs monitored performance, obtained data from calibrated process instruments, and collected samples for observation and analysis. The measured data are presented with a focus on assessing DEF performance – specifically, the filters’ differential pressure response to the five waste simulants, frequency and efficacy of backwashing, and baseline recovery between experimental runs; data related to ion exchange column performance are also discussed in cases where the opportunity arose. The experimental campaign demonstrated that the DEFs satisfied their primary function of protecting the ion exchange column from solid intrusion for all the representative simulants used. The filters readily handled solids loadings of =500 ppm (and even greater), especially the modules scaled to the 200W process modules. Adjustments to the processing flow rate and reductions in feed temperature were observed to affect the rate of differential pressure increase on the filters, but neither adversely affected the ability of the DEFs to perform their primary function. Backflushing reliably recovered filter performance in all runs, although it did not prevent irreversible fouling for one simulant. The run that exhibited irreversible fouling established that both the quantity and the nature of the solids being filtered need to be considered when projecting filter performance.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1986

Characterization of photovoltaic array performance - An overview

Techniques are described for standardized performance monitoring of photovoltaic arrays. Performance is measured in terms of the array electrical output for a given set of environmental or standard reporting conditions or the total output per given time interval. The environmental conditions can be a standard 25 C or the normal operating temperature in the presence of 80 mW/sq cm irradiance, 20 C air temperature and 1 m/sec wind velocity. Methods are presented for converting the measurements over time into I-V characteristics representations for entire arrays. The techniques are accurate to within 5 percent, provided in situ data are collected and the distribution of the measured parameters is known.

Ross, Ronald G., Jr.↗
NASA NTRS2004

Monitoring the Performance of a Neuro-Adaptive Controller

Traditional control has proven to be ineffective to deal with catastrophic changes or slow degradation of complex, highly nonlinear systems like aircraft or spacecraft, robotics, or flexible manufacturing systems. Control systems which can adapt toward changes in the plant have been proposed as they offer many advantages (e.g., better performance, controllability of aircraft despite of a damaged wing). In the last few years, use of neural networks in adaptive controllers (neuro-adaptive control) has been studied actively. Neural networks of various architectures have been used successfully for online learning adaptive controllers. In such a typical control architecture, the neural network receives as an input the current deviation between desired and actual plant behavior and, by on-line training, tries to minimize this discrepancy (e.g.; by producing a control augmentation signal). Even though neuro-adaptive controllers offer many advantages, they have not been used in mission- or safety-critical applications, because performance and safety guarantees cannot b e provided at development time-a major prerequisite for safety certification (e.g., by the FAA or NASA). Verification and Validation (V&V) of an adaptive controller requires the development of new analysis techniques which can demonstrate that the control system behaves safely under all operating conditions. Because of the requirement to adapt toward unforeseen changes during operation, i.e., in real time, design-time V&V is not sufficient.

Schumann, Johann↗
NASA NTRS2022

FY22 X-Hab: Rochester Institute of Technology: Gamification and Performance-Based Monitoring of Sensorimotor Training Activities

Proprioception is the body’s ability to sense movement, action, and location. Proprioceptive training is crucial for astronauts because humans balance system, cognitive functions, and muscle tone heavily rely on Earth’s gravity. The goal of this project is to create a training system that strengthens those characteristics to ensure astronauts not only maintain their coordination and cognitive skills, but improve upon them as well. Furthermore, the system’s live biofeedback provides astronauts with sensorimotor feedback to correct any balance dysfunction. The completed system prototype and data collected from it will support NASA’s knowledge base about the use of gamification for sensorimotor training activities.

Griffin Brown↗
DOE OSTI2023

Statistical Approaches for Pebble Bed Reactor Operations and Safeguards

The design of pebble bed reactors (PBRs) and their method of operation align more closely with statistical approaches used in manufacturing and process control than traditional safeguards statistical approaches. The reason is PBRs will employ a nondestructive assay (NDA) measurement (burnup measurement system [BUMS]) that is part of the fuel handling system supporting discharge decisions in addition to the reactor code which monitor performance. The integration of these two approaches provides the opportunity to monitor reactor performance statistically for both operations and safeguards in ways not achievable using other reactor designs. For light water reactors (LWRs), knowledge about reactor code performance in predicting irradiated special nuclear material (SNM) content historically was only achieved from special measurement campaigns or from fuel reprocessing. Conversely, through statistical comparison of the BUMS with the reactor code-predicted values, PBRs can achieve this in real time. The resulting SNM distribution is an indicator of reactor performance because factors such as transit time and path of the pebble fuel through the reactor determine the plutonium production and uranium depletion. By analyzing the predicted and measured values, opportunities exist to adjust operating parameters, fuel design, and other characteristics to optimize performance and fuel utilization. From a safeguards perspective, this approach also provides the information necessary to validate declared values and evaluate whether the reactor is being operated as expected. This paper outlines statistical approaches for PBRs that can be used to support both operations and safeguards.

Ball, Cory↗
NASA NTRS2008

Inductive System Monitors Tasks

The Inductive Monitoring System (IMS) software developed at Ames Research Center uses artificial intelligence and data mining techniques to build system-monitoring knowledge bases from archived or simulated sensor data. This information is then used to detect unusual or anomalous behavior that may indicate an impending system failure. Currently helping analyze data from systems that help fly and maintain the space shuttle and the International Space Station (ISS), the IMS has also been employed by data classes are then used to build a monitoring knowledge base. In real time, IMS performs monitoring functions: determining and displaying the degree of deviation from nominal performance. IMS trend analyses can detect conditions that may indicate a failure or required system maintenance. The development of IMS was motivated by the difficulty of producing detailed diagnostic models of some system components due to complexity or unavailability of design information. Successful applications have ranged from real-time monitoring of aircraft engine and control systems to anomaly detection in space shuttle and ISS data. IMS was used on shuttle missions STS-121, STS-115, and STS-116 to search the Wing Leading Edge Impact Detection System (WLEIDS) data for signs of possible damaging impacts during launch. It independently verified findings of the WLEIDS Mission Evaluation Room (MER) analysts and indicated additional points of interest that were subsequently investigated by the MER team. In support of the Exploration Systems Mission Directorate, IMS is being deployed as an anomaly detection tool on ISS mission control consoles in the Johnson Space Center Mission Operations Directorate. IMS has been trained to detect faults in the ISS Control Moment Gyroscope (CMG) systems. In laboratory tests, it has already detected several minor anomalies in real-time CMG data. When tested on archived data, IMS was able to detect precursors of the CMG1 failure nearly 15 hours in advance of the actual failure event. In the Aeronautics Research Mission Directorate, IMS successfully performed real-time engine health analysis. IMS was able to detect simulated failures and actual engine anomalies in an F/A-18 aircraft during the course of 25 test flights. IMS is also being used in colla

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