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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

NGEE Arctic Meteorological Data from Instrumented PCDC Stations, Council Site, Seward Peninsula, Alaska, 2018 - 2023

This dataset provides meteorological data collected at two instrumented Power, Communication, and Data Collection (PCDC) Stations, located at Council Road Mile Marker 71 (CN_MM71) and Council Road Mile Marker 68 (Rocky Top). Measurements began in June of 2018 at CN_MM71 and in September 2018 at Rocky Top and ended in 2023. Raw data included. Meteorological data include air temperature, relative humidity, wind speed and direction, and snow depth. Measurements have been aggregated to both 10-minute and 30-minute reporting intervals provided in four comma-separated (*.csv) data files.• The 30-minute data files include only the primary meteorological variables, leaf wetness, and snow depth.. The 30-minute reporting interval matches other coincident site data collections.• The 10-minute files provide the primary meteorological variables and numerous system operational parameters such as battery charge and solar and wind turbine status. This 10-minute data product is valuable for analyzing solar and wind charging system performance in arctic landscapes.Daily photographic images (09:00, 12:00, and 15:00 AKST/AKDT) of the PCDC Stations and the surrounding landscape that are captured by an adjacent web camera, are also provided.The daily 12:00 images from CN_MM71 camera have been compiled into time-lapse movies:• October 2018 – June 12, 2019 = Council_winter_2018_2019.mov• January 2019 – December 2019 = Council_2019.mp4The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

NGEE Arctic Tram: Overview of Tram-mounted Instruments, Co-located Data, Measurement Position, Elevation and Polygon Characterization, Utqiagvik (Barrow), Alaska, 2014-2018

The NGEE-Arctic Tram operated from 2014 - 2018. It was installed on the Barrow Environmental Observatory in Barrow, Alaska in late May of 2014 and operated through mid-November 2014. The subsequent 2015 through 2018 years followed a similar schedule, but with different deployment dates and data gaps, with the goal always to capture the surface dynamics of the snow melt, growing, and freeze-up seasons. This Tram overview dataset has three objectives: (1) provide new data users with a brief description of the Tram system and its research purposes; (2) provide generally useful Tram site characterization details, including measurement position ground surface elevation, characteristics of the Arctic polygonal tundra under the Tram, vegetation community composition (future), and derived image products; and (3) reference all of the data products and archived datasets that were collected with the Tram-mounted sensors and co-located instruments. This dataset contains two csv files, two zipped files, one tif file, and one pdf file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Nuclear Instrumentation and Control Cyber Testbed Considerations – Lessons Learned

Abstract: Identifying and understanding digital instrumentation and control (I&C) cyber vulnerabilities within nuclear power plants and other nuclear facilities, is critical if nation states desire to operate nuclear facilities safely, reliably, and securely. In order to demonstrate objective evidence that cyber vulnerabilities have been adequately identified and mitigated, a testbed representing a facility’s critical nuclear equipment must be replicated. Idaho National Laboratory (INL) has built and operated similar testbeds for common critical infrastructure I&C for over ten years. This experience developing, operating, and maintaining an I&C testbed in support of research identifying cyber vulnerabilities has led the Korean Atomic Energy Research Institute of the Republic of Korea to solicit the experiences of INL to help mitigate problems early in the design, development, operation, and maintenance of a similar testbed. The following information will discuss I&C testbed lessons learned and the impact of these experiences to KAERI.

control systems↗
DOE OSTI2015

PROGRESS IN DESIGN OF THE INSTRUMENTATION AND CONTROL OF THE TOKAMAK COOLING WATER SYSTEM

This paper discusses progress in the design of the control, interlock and safety systems of the Tokamak Cooling Water System (TCWS) for the ITER fusion reactor. The TCWS instrumentation and control (I&C) is one of approximately 200 separate plant I&C systems (e.g., vacuum system I&C, magnets system I&C) that interface to a common central I&C system through standardized networks. Several aspects of the I&C are similar to the I&C of fission-based power plants. However, some of the unique features of the ITER fusion reactor and the TCWS (e.g., high quasi-static magnetic field, need for baking and drying as well as cooling operations), also demand some unique safety and qualification considerations. The paper compares the design strategy/guidelines of the TCWS I&C and the I&C of conventional nuclear power plants. Issues such as safety classifications, independence between control and safety systems, sensor sharing, redundancy, voting schemes, and qualification methodologies are discussed. It is concluded that independence and separation requirements are similar in both designs. However, the voting schemes for safety systems in nuclear power plants typically use 2oo4 (i.e., 4 divisions of safety I&C, any 2 of which is sufficient to trigger a safety action), while 2oo3 voting logic - within each of 2 independent trains - is used in the TCWS I&C. It is also noted that 2oo3 voting is also acceptable in nuclear power plants if adequate risk assessment and reliability is demonstrated. Finally, while qualification requirements provide similar guidance [e.g., both IEC 60780 (invoked in ITER-space), and IEEE 323 (invoked in fission power plant space) provide similar guidance], an important qualification consideration is the susceptibility of I&C to the magnetic fields of ITER. Also, the radiation environments are different. In the case of magnetic fields the paper discusses some options that are being considered.

Korsah, Kofi↗
DOE OSTI2015

Neutron xyz - polarization analysis at a time-of-flight instrument

When implementing a dedicated polarization analysis setup at a neutron time-of-flight instrument with a large area detector, one faces enormous challenges. Nevertheless, significant progress has been made towards this goal over the last few years. This paper addresses systematic limitations of the traditional method that is used to make these measurements, and a possible strategy to overcome these limitations. This will be important, for diffraction as well as inelastic experiments, where the scattering occurs mostly out-of-plane.

Ehlers, Georg↗
DOE OSTI2017

Robust Online Monitoring for Calibration Assessment of Transmitters and Instrumentation

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this article, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation • Virtual sensing • Sensor response-time assessment These algorithms incorporate, at their base, a Gaussian Process-based uncertainty quantification (UQ) method. Various plant models (using kernel regression, GP, or hierarchical models) may be used to predict sensor responses under various plant conditions. These predicted responses can then be applied in fault detection (sensor output and response time) and in computing the correct value (virtual sensing) of a failing physical sensor. The methods being evaluated in this work can compute confidence levels along with the predicted sensor responses, and as a result, may have the potential for compensating for sensor drift in real-time (online recalibration). Evaluation was conducted using data from multiple sources (laboratory flow loops and plant data). Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.

fault detection↗
DOE OSTI2018

Comparison and Analysis of Instruments Measuring Plane-of-Array Irradiance for One-Axis Tracking Systems: Preprint

A variety of sensors are studied on a one-axis tracking surface and a horizontal surface in Golden, Colorado, and Eugene, Oregon. This is the first year of a long-term study that will look at not only a comparison between the instruments but also the longer-term degradation in calibration and/or performance. Initially, results from each location will be analyzed, and then results will be compared and contrasted between the two locations. A quick comparison at Eugene indicates that reference solar cells seem to compare better against a secondary standard pyranometer on a one-axis tracker than photodiode-based pyranometers. More study is needed to characterize and confirm this finding.

14 SOLAR ENERGY↗
DOE OSTI2017

ROBUST ONLINE MONITORING FOR CALIBRATION ASSESSMENT OF TRANSMITTERS AND INSTRUMENTATION

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. Specifically, the next generation of OLM technology is expected to include newly developed advanced algorithms that improve monitoring of sensor/system performance and enable the use of plant data to derive information that currently cannot be measured. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this paper, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation – fault detection and selection of acceptance criteria • Virtual sensing – signal value prediction and acceptance criteria • Response-time assessment – fault detection and acceptance criteria selection A GP-based uncertainty quantification (UQ) method previously developed for UQ in OLM, was adapted for use in sensor-fault detection and virtual sensing. For signal validation, the various components to the OLM residual (which is computed using an AAKR model) were explicitly defined and modeled using a GP. Evaluation was conducted using flow loop data from multiple sources. Results using experimental data from laboratory-scale flow loops indicate that the approach, while capable of detecting sensor drift, may be incapable of discriminating between sensor drift and model inadequacy. This may be due to a simplification applied in the initial modeling, where the sensor degradation is assumed to be stationary. In the case of virtual sensors, the GP model was used in a predictive mode to estimate the correct sensor reading for sensors that may have failed. Results have indicated the viability of using this approach for virtual sensing. However, the GP model has proven to be computationally expensive, and so alternative algorithms for virtual sensing are being evaluated. Finally, automated approaches to performing noise analysis for extracting sensor response time were developed. Evaluation of this technique using laboratory-scale data indicates that it compares well with manual techniques previously used for noise analysis. Moreover, the automated and manual approaches for noise analysis also compare well with the current “gold standard”, hydraulic ramp testing, for response time monitoring. Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.

calibration interval extension↗