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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Hourly land-use regression models based on low-cost PM monitor data

Highlights: • A network of 23 low-cost PM monitors was deployed across an urban area. • Land use regression models predict the hourly intra-urban PM variations. • Coefficients of determination for hourly PM predictions ranged 0.66–0.76. • Low-cost PM monitor data need to be carefully evaluated before use. • The combination of low-cost monitors and modeling provides useful PM estimates. Land-use regression (LUR) models provide location and time specific estimates of exposure to air pollution and thereby improve the sensitivity of health effects models. However, they require pollutant concentrations at multiple locations along with land-use variables. Often, monitoring is performed over short durations using mobile monitoring with research-grade instruments. Low-cost PM monitors provide an alternative approach that increases the spatial and temporal resolution of the air quality data. LUR models were developed to predict hourly PM concentrations across a metropolitan area using PM concentrations measured simultaneously at multiple locations with low-cost monitors. Monitors were placed at 23 sites during the 2015/16 heating season. Monitors were externally calibrated using co-located measurements including a reference instrument (GRIMM particle spectrometer). LUR models for each hour of the day and weekdays/weekend days were developed using the deletion/substitution/addition algorithm. Coefficients of determination for hourly PM predictions ranged from 0.66 and 0.76 (average 0.7). The hourly-resolved LUR model results will be used in epidemiological studies to examine if and how quickly, increases in ambient PM concentrations trigger adverse health events by reducing the exposure misclassification that arises from using less time resolved exposure estimates.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

MISSE 5 Thin Films Space Exposure Experiment

The Materials International Space Station Experiment (MISSE) is a set of space exposure experiments using the International Space Station (ISS) as the flight platform. MISSE 5 is a co-operative endeavor by NASA-LaRC, United Stated Naval Academy, Naval Center for Space Technology (NCST), NASA-GRC, NASA-MSFC, Boeing, AZ Technology, MURE, and Team Cooperative. The primary experiment is performance measurement and monitoring of high performance solar cells for U.S. Navy research and development. A secondary experiment is the telemetry of this data to ground stations. A third experiment is the measurement of low-Earth-orbit (LEO) low-Sun-exposure space effects on thin film materials. Thin films can provide extremely efficacious thermal control, designation, and propulsion functions in space to name a few applications. Solar ultraviolet radiation and atomic oxygen are major degradation mechanisms in LEO. This paper is an engineering report of the MISSE 5 thm films 13 months space exposure experiment.

Harvey, Gale A.↗
DOE OSTI2016

National Renewable Energy Laboratory/Habitat for Humanity Zero Energy Home: A Cold Climate Case Study for Affordable Zero Energy Homes

The design of this 1,280-square-foot, three-bedroom Habitat for Humanity of Metro Denver zero energy home carefully combines envelope efficiency, efficient equipment, appliances and lighting, and passive and active solar features to reach the zero energy goal. The home was designed with an early version (July 22, 2004) of the BEOpt building optimization software; DOE2 and TRNSYS were used to perform additional analysis. This engineering approach was tempered by regular discussions with Habitat construction staff and volunteers. These discussions weighed the applicability of the optimized solutions to the special needs and economics of a Habitat house, moving the design toward simple, easily maintained mechanical systems and volunteer-friendly construction techniques. A data acquisition system was installed in the completed home to monitor its performance. The home's energy performance was monitored for 10 years. A comprehensive report on the home was done after the first 2 years of monitoring and a follow-up ACEEE paper on the project including 10 years of performance data was presented in 2016.

Affordable Housing↗
DOE OSTI2023

Systems, devices, and methods for performing augmented reality responsive to monitoring user behavior

Systems, devices, and methods are described for performing augmented reality (AR) to assist user performing a task in an environment. An AR device may be configured to capture real-time data. An AR engine may be configured to monitor user behavior from the real-time data responsive to feature extraction from the real-time data, compare the user behavior to pre-defined work procedures, and generate augmented reality objects to be output by the AR device.

Yoon, SuJong↗
DOE OSTI2017

Evaluation of Optical Fiber Bragg Gratings in a Nuclear Reactor

Optical fiber-based sensors are capable of distributed temperature and/or strain measurements with high accuracy and sub-cm spatial resolution. Optical fibers have a small diameter ({approx}100 {mu}m), a small mass (and therefore fast response time), and are fabricated from high temperature-tolerant, radiation-hard materials. These qualities make optical fiber-based instrumentation a promising candidate for in-pile sensing applications, monitoring fuel performance during irradiation in test reactors, or embedding fibers in other commercial reactor components or structures. The Department of Energy and Idaho National Laboratory are restarting the TREAT reactor (Transient Reactor Test facility) and are interested in updating the reactor's instrumentation regarding the analysis of the test fuel. The ability of optical fibers to make distributed temperature measurements coupled with its small footprint makes optical fiber a viable candidate to monitor fuel performance in the TREAT reactor. The work presented in this paper focuses on the radiation hardness of various types of optical fiber Bragg gratings. Previous experiments have shown that the inscription of Bragg gratings into optical fiber can extend the radiation survivability of distributed sensing in optical fibers, and so the goal is to determine which types of Bragg gratings survive the best in a reactor environment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Production Application Performance Data Streaming for System Monitoring

In this article, we present an approach to streaming collection of application performance data. Practical application performance tuning and troubleshooting in production high-performance computing (HPC) environments requires an understanding of how applications interact with the platform, including (but not limited to) parallel programming libraries such as Message Passing Interface (MPI). Several profiling and tracing tools exist that collect heavy runtime data traces either in memory (released only at application exit) or on a file system (imposing an I/O load that may interfere with the performance being measured). Although these approaches are beneficial in development stages and post-run analysis, a systemwide and low-overhead method is required to monitor deployed applications continuously. This method must be able to collect information at both the application and system levels to yield a complete performance picture. In our approach, an application profiler collects application event counters. A sampler uses an efficient inter-process communication method to periodically extract the application counters and stream them into an infrastructure for performance data collection. We implement a tool-set based on our approach and integrate it with the Lightweight Distributed Metric Service (LDMS) system, a monitoring system used on large-scale computational platforms. LDMS provides the infrastructure to create and gather streams of performance data in a low overhead manner. We demonstrate our approach using applications implemented with MPI, as it is one of the most common standards for the development of large-scale scientific applications. We utilize our tool-set to study the impact of our approach on an open source HPC application, Nalu. Our tool-set enables us to efficiently identify patterns in the behavior of the application without source-level knowledge. We leverage LDMS to collect system-level performance data and explore the correlation between the system and application events. Finally, we demonstrate how our tool-set can help detect anomalies with a low latency. We run tests on two different architectures: a system enabled with Intel Xeon Phi and another system equipped with Intel Xeon processor. Our overhead study shows our method imposes at most 0.5% CPU usage overhead on the application in realistic deployment scenarios.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1982

Improving the computer-human interface: The qualitative monitor display

A technique for displaying the information needed by DSN operators for monitoring station performance is described. Known as a 'qualitative monitor,' it presents continuous variables in quasi-analog form on a digital cathode ray tube (CRT) display. Color changes, field reverses and blinking symbols assist the operator in identifying variables that are within acceptable limits and in performing corrective action when needed.

Baugh, H. W.↗
DOE OSTI2018

ERNIE performance with TSA portals Initial Results

This project extends the “Enhanced Radiological Nuclear Inspection and Evaluation” (ERNIE) system developed with CBP and DNDO to improve performance of PVT-based Radiation Portal Monitors (RPMs). ERNIE was designed to be used with any RPM system. The first implementation was with the SAIC (Leidos) RPM-8 systems. In this project, we are demonstrating how effective the ERNIE approach can be when applied to the VM250 TSA portals used in NSDD programs. Part of the challenge in adapting ERNIE to handle VM250 portals is the lack of gamma spectral information. We report here on the first results showing how the ERNIE analysis can improve analysis of measurements with the VM250 RPMs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

As-Built Characterization and Monitoring System for the RH-LLW Disposal Facility

The performance assessment (PA) for the Idaho National Laboratory (INL) Remote Handled Low Level Waste (RH LLW) disposal facility (U.S. Department of Energy [DOE]-ID 2012) is required to demonstrate that the facility design will meet the performance objectives established for long term protection of the public and the environment following closure of the facility as outlined in DOE Order 435.1, “Radioactive Waste Management.” Protectiveness of the facility in terms of the groundwater pathway is a function of the design features that control the hydrologic and geochemical conditions within and below the vault system. The facility design includes features to promote drainage of infiltrating water to limit accumulation of moisture next to the reinforced concrete disposal vaults and containerized waste. The PA groundwater pathway model accounts for a cement impacted geochemical environment within and below the vault system to inhibit corrosion of stainless steel waste containers (i.e., waste liners), steel reinforcement in the concrete vault, and the effects on release and migration of non anionic radionuclides. Upon closure of the facility, an engineered barrier (i.e., cover) will be placed over the facility to reduce infiltration through the waste zone, retarding the release and migration rate of radionuclides beneath the facility. Crediting the long-term performance of the vault system requires characterization of the hydraulic and concrete performance over time. Verification of the vault performance requires monitoring for radiologic releases from the facility. The purpose of this document is to provide the description of the characterization and monitoring system installed at the facility to accomplish these two objectives.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Comparison of Supervised and Un-Supervised Machine Learning Algorithms for Threat Detection and Scintillator Performance for Radiation Portal Monitoring

Following the events of September 11, 2001, international border crossing have been equipped with radiation portal monitors (RPMs) to identify illicit radioactive material. Polyvinyl toluene (PVT) scintillators are commonly used due to their low cost and reasonable maintainability, however they offer low spectral resolution. Despite the fact that over twenty years has transpired since this event, radioisotopes are still typically identified by hand-crafted classification algorithms, e.g., total counts or energy windowing, and exhibit relatively poor performance in detecting threats at the low false alarm rates required to support the stream of commerce. While some improvement to performance has been realized via the use of supervised machine learning, these classification algorithms typically utilize simulations in lieu of real data due to the sparsity of data for one or more classes. Accordingly, the performance of these algorithms is somewhat less than optimal when examining experiments or simulations with model mismatch. Consequently, in this work, we examine the application of a number of unsupervised machine learning, anomaly detection based algorithms, to circumvent the inverse crime when analyzing spectroscopy data for RPMs. We also compare anomaly detection results with those obtained via the use of supervised classification detection ML algorithms when model mismatch is introduced between the simulated threat items utilized for training/testing. Finally, we compared the performance of the PVT scintillators to those obtained with higher resolution detectors using both anomaly detection and supervised classification algorithms.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Excerpt from Oxygen Monitor Clean Testing Report Performed in 2010

The Rosemount X-STREAM Series X2GP is a paramagnetic oxygen analyzer with a 0-25% oxygen measurement range. It claims a drift less than 2% of span (0.10% volume oxygen) per week, and a response time of less than 5 seconds (for the analyzer to report 90% of a step change). Our test equipment was set-up such that the same gas was running through each oxygen analyzer at the same time. This allowed Calibration Error, Calibration Drift, and Loss of Power Testing to be run on all monitors at the same time, eliminating any variability between the analyzers from tolerances in the gas cylinder, ambient temperature, ambient pressure, test duration, etc. An APTech pressure regulator was placed on the gas cylinder manifold and a 23 psia Paroscientific pressure transducer measured the regulator’s outlet pressure. This did not give the inlet pressure on any one analyzer but outlet pressure from the manifold before the stream was split. The APTech pressure regulator was used to keep operating conditions consistent. A DWYER rotameter was installed on each oxygen monitor’s outlet piping. With the exception of Variation of Flow Rate Testing, the rotameter was left fully open for all testing. The flow rate through each oxygen monitor was controlled by a Swagelok needle valve that was installed on each analyzer’s inlet piping. Additionally, a filter was installed in the each monitor’s inlet piping to keep particles from the measurement cells and a Swagelok ball valve was installed on each monitor’s outlet piping to isolate each monitor between tests. No pumps were installed. Each of the cylinders used was at a pressure greater than ambient room barometric pressure. This pressure differential was used to move gas through the testing equipment. The flow rate through each analyzer was controlled by the Swagelok needle valve installed on each analyzer’s inlet piping. Finally, for all tests, a Paroscientific Digiquartz Barometer was used to measure ambient room barometric pressure and a Fluke Thermo-hygrometer was used to measure ambient room temperature.

47 OTHER INSTRUMENTATION↗
NASA NTRS1987

A multiprocessing architecture for real-time monitoring

A multiprocessing architecture for performing real time monitoring and analysis using knowledge-based problem solving techniques is discussed. To handle asynchronous inputs and perform in real time, the system consists of three or more separate processes which run concurrently on one or more processors and communicate via a message passing scheme. The Data Management Process gathers, compresses, scales and sends the incoming telemetry data to other tasks. The Inference Process consists of a proprietary high performance inference engine that runs at 1000 rules per second using telemetry data to perform real time analysis on the state and health of the Space Telescope. The multiprocessing architecture has been interfaced to a simulator and is able to process the incoming telemetry in real time.

Laffey, Thomas J.↗
NASA NTRS2017

In-Flight Performance of the Ozone Monitoring Instrument

The Dutch-Finnish Ozone Monitoring Instrument (OMI) is an imaging spectrograph flying on NASA's EOS Aura satellite since 15 July 2004. OMI is primarily used to map trace-gas concentrations in the Earth's atmosphere, obtaining mid-resolution (0.4-0.6 nm) ultraviolet-visible (UV- VIS; 264-504 nm) spectra at multiple (30-60) simultaneous fields of view. Assessed via various approaches that include monitoring of radiances from selected ocean, land ice and cloud areas, as well as measurements of line profiles in the solar spectra, the instrument shows low optical degradation and high wavelength stability over the mission lifetime. In the regions relatively free from the slowly unraveling "row anomaly" (RA) the OMI irradiances have degraded by 3- 8 %, while radiances have changed by 1-2 %. The long-term wavelength calibration of the instrument remains stable to 0.005-0.020 nm.

ultraviolet↗
DOE OSTI2018

Development of a Kalman filter estimator for simulation and control of particulate matter distribution of a diesel catalyzed particulate filter

The knowledge of the temperature and particulate matter mass distribution is essential for monitoring the performance and durability of a catalyzed particulate filter. A catalyzed particulate filter model was developed, and it showed capability to accurately predict temperature and particulate matter mass distribution and pressure drop across the catalyzed particulate filter. However, the high-fidelity model is computationally demanding. Therefore, a reduced order multi-zone particulate filter model was developed to reduce computational complexity with an acceptable level of accuracy. In order to develop a reduced order model, a parametric study was carried out to determine the number of zones necessary for aftertreatment control applications. The catalyzed particulate filter model was further reduced by carrying out a sensitivity study of the selected model assumptions. The reduced order multi-zone particulate filter model with 5 × 5 zones was selected to develop a catalyzed particulate filter state estimator considering its computational time and accuracy. Next, a Kalman filter–based catalyzed particulate filter estimator was developed to estimate unknown states of the catalyzed particulate filter such as temperature and particulate matter mass distribution and pressure drop (Δ P) using the sensor inputs to the engine electronic control unit and the reduced order multi-zone particulate filter model. A diesel oxidation catalyst estimator was also integrated with the catalyzed particulate filter estimator in order to provide estimates of diesel oxidation catalyst outlet concentrations of NO 2 and hydrocarbons and inlet temperature for the catalyzed particulate filter estimator. The combined diesel oxidation catalyst–catalyzed particulate filter estimator was validated for an active regeneration experiment. The validation results for catalyzed particulate filter temperature distribution showed that the root mean square temperature error by using the diesel oxidation catalyst–catalyzed particulate filter estimator is within 3.2 °C compared to the experimental data. Similarly, the Δ P estimator closely simulated the measured total Δ P and the estimated cake pressure drop error is within 0.2 kPa compared to the high-fidelity catalyzed particulate filter model.

Engineering↗
DOE OSTI2017

Pinellas County, Florida Site Environmental Restoration Project Semiannual Progress Report for the 4.5 Acre Site June through November 2016 January 2017

This Pinellas County, Florida, Site Environmental Restoration Project Semiannual Progress Report for the 4.5 Acre Site describes environmental restoration activities for the 4.5 Acre Site located in Pinellas County, Largo, Florida (Figure 1). The former U.S. Department of Energy (DOE) Pinellas Plant facility consisted of the 4.5 Acre Site and what is now the STAR Center (Young - Rainey Science, Technology, and Research Center). Both the 4.5 Acre Site and the STAR Center are part of the overall Pinellas County, Florida, Site (Figure 2). The 4.5 Acre Site is located immediately northwest of the STAR Center, in the northeast quarter of Section 13, Township 30 South, Range 15 East. DOE owned this parcel from 1957 to 1972, at which time it was sold to a private landowner. During the period of DOE ownership, the property was used for the disposal of drums of waste resins and solvents. As a result of this practice, the surficial aquifer was impacted by volatile organic compounds (VOCs)—trichloroethene (TCE), cis-1,2-dichloroethene (cDCE), trans-1,2-dichloroethene (tDCE), vinyl chloride (VC), and benzene. Detailed background information for the site is contained in the Long-Term Surveillance and Maintenance Plan for the Pinellas Site (DOE 2016). That document and other site-related documents can be accessed at this website: http://www.lm.doe.gov/Pinellas/Sites.aspx. Recent remediation activities consist of the injection of emulsified soybean oil and the microorganism Dehalococcoides mccartyi (formerly known as Dehalococcoides ethenogenes) into the subsurface in February 2010 and again in July 2013 to enhance contaminant biodegradation (hereafter described as bioinjection). Monitoring the performance of these actions, in the form of monitoring well sampling, is ongoing.

2016 Pinellas County↗