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26 records · Page 2

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DOE OSTI2017

Assessing Air Velocity Distribution in Three Sizes of Commercial Broiler Houses During Tunnel Ventilation

Abstract. Convective cooling is a critical management strategy for maintaining an environment that promotes production efficiency, thermal comfort, and animal well-being in commercial broiler houses. Variations in house size, design, and equipment configuration contribute greatly to the air velocity distribution within the facility. This study assessed total airflow, air velocity distribution, and quantified the floor area in three facilities experiencing insufficient air velocity for maintenance of production efficiency, thermal comfort, and animal well-being. Test facility 1 was an 18.3 × 170.7 m solid side-wall broiler house, test facility 2 was a 15.24 × 144.8 m solid side-wall broiler house, and test facility 3 was a 12.19 × 121.9 m curtain side-wall broiler house. Total airflow of each facility, measured with a Fan Assessment and Numeration System, was 512,730, 389,495, and 329,270 m 3 h -1 for test facilities 1, 2, and 3, respectively. Air velocity distribution patterns were characterized in each house with a Scalable Environment Assessment System (SEAS) and spatial statistics. The air velocity distributions within the test facilities were variable, with notable maxima immediately downstream of the tunnel inlets, which serve as a well-defined vena contracta, and local minima near the leading end of the evaporative pads and the exhaust fans. Equipment within the facilities had an impact on the air velocity distribution by creating reduced cross-sectional areas that resulted in localized increases in air velocity. The percentage of total bird-level floor area in each facility experiencing air velocities below 1.5 m s -1 was 14.3%, 20.7%, and 10.0% for test facilities 1, 2, and 3, respectively. The effective design velocity (V ed ) was calculated from total airflow using the measured building cross-sectional area. The V ed measured 2.97, 2.45, and 2.34 m s -1 for test facilities 1, 2, and 3, respectively. Mean cross-sectional air velocity (V cs ) was calculated from SEAS data and normalized using each facility’s V ed to account for differences in building size for comparison. Test facility 1, the largest of the three houses, generated substantially higher V cs /V ed than test facilities 2 and 3. Test facilities 2 and 3 maintained a larger proportion of V cs above V ed than test facility 1. Test facility 1 showed 26.5% of the total house length below V ed , while test facilities 2 and 3 had only 20.8% and 17.5%, respectively, of the total house length below V ed . The lower-velocity regions were due to the length of the evaporative cooling pad inlet and the use of tunnel doors, and the exhaust fan placement on the side-walls in test facility 1 created an additional pronounced low-velocity area. Placement of tunnel ventilation fans on the end-wall of the facility, rather than the side-wall, eliminated the low-velocity region at the exhaust end of the facility. Modifications to current practices for broiler production facility construction and evaporative cooling pad inlet installation would be required to minimize the low-velocity region at the inlet end of these facilities. Consideration of house width and physical arrangement of the air inlets, tunnel fans, and internal equipment are critical for improving the uniformity of air velocity in commercial broiler houses. Keywords: Air velocity, Anemometer, Broiler house, FANS, Tunnel ventilation.

Agriculture↗

NASA NTRS2003

Predicting and Managing Lighting and Visibility for Human Operations in Space

Lighting is critical to human visual performance. On earth this problem is well understood and solutions are well defined and executed. Because the sun rises and sets on average every 45 minutes during Earth orbit, humans working in space must cope with ~ extremely dynamic lighting conditions varying from very low light conditions to severe glare and contrast conditions. For critical operations, it is essential that lighting conditions be predictable and manageable. Mission planners need to detelmine whether low-light video cameras are required or whether additional luminaires, or lamps, need to be flown . Crew and flight directors need to have up to date daylight orbit time lines showing the best and worst viewing conditions for sunlight and shadowing. Where applicable and possible, lighting conditions need to be part of crew training. In addition, it is desirable to optimize the quantity and quality of light because of the potential impacts on crew safety, delivery costs, electrical power and equipment maintainability for both exterior and interior conditions. Addressing these issues, an illumination modeling system has been developed in the Space Human Factors Laboratory at ASA Johnson Space Center. The system is the integration of a physically based ray-tracing package ("Radiance"), developed at the Lawrence Berkeley Laboratories, a human factors oriented geometric modeling system developed by NASA and an extensive database of humans and their work environments. Measured and published data has been collected for exterior and interior surface reflectivity; luminaire beam spread distribution, color and intensity and video camera light sensitivity and has been associated with their corresponding geometric models. Selecting an eye-point and one or more light sources, including sun and earthshine, a ~ snapshot of the light energy reaching the surfaces or reaching the eye point is computed. This energy map is then used to extract the required information needed for useful predictions. Using a validated, comprehensive illumination model integrated with empirically derived data, predictions of lighting and viewing conditions have been successfully used for Shuttle and Space Station planning and assembly operations. It has successfully balanced the needs for adequate human performance with the utili zation of resources. Keywords: Modeling, ray tracing, luminaires, refl ectivity, luminance, illuminance.

Maida, James C.↗

DOE OSTI2015

Knowledge management information tool Web analytics - 15182

The Knowledge Management Information Tool (KM-IT) is a web-based [01] system developed to maintain and preserve the Department of Energy's (DOE's) D and D knowledge base. The system was developed by Florida International University's Applied Research Center (FIU-ARC) with the support of the D and D community including the DOE Office of Environmental Management, and with the active collaboration and support of the DOE's Energy Facility Contractors Group (EFCOG). The KM-IT system is a D and D community driven system tailored to serve the technical issues faced by the D and D workforce across the DOE Complex. The D and D KM-IT is an interactive web-based system operational and available for free access at www.dndkm.org. [02] The D and D KM-IT is currently composed of the following tailored D and D modules: Web Crawler, Hotline, Technology, Document Library (including Hanford's and Savannah River Sites' ALARA Center Reports), Specialist Directory, Lessons Learned, Best Practices, Video/Picture Library, Vendors, Collaboration Tools, Training, and Mobile Systems. The Web Crawler dynamically searches through the D and D KM-IT repository as well as the web and displays search results based on the search criteria. The Hotline allows registered users to post questions/problems related to D and D and to receive solutions from a subject-matter specialist. The Technology module serves as repository of D and D technologies and contains technology/ demonstration fact sheets and vendor information. D and D community members can add Lessons Learned, Best Practices, Documents, and Pictures/Videos to the D and D KM-IT repository, after a formal approval process. The Vendor module provides a directory of commercial vendors who provide D and D related technologies, supplies, and services. D and D KM-IT makes excellent use of the knowledge that exists within the D and D community by allowing D and D project managers around the DOE complex to collaborate by sharing innovative ideas, past experiences, and practices and by maintaining a directory of subject matter specialists. The mobile version of the KM-IT can be accessed from m.dndkm.org [03] and currently has six active modules: Vendor, Technology, Picture/Video Library, Hotline, Lessons Learned and Specialist Directory. Web analytics is a key tool being implemented by the D and D KM-IT project team in an effort to enhance the system to better meet users' needs. The D and D KM-IT project team incorporates web analytics as part of its operational elements to analyze information and key data points on how the system is being used. Web analytics is the measurement, collection, analysis and reporting of internet data for the purposes of understanding and optimizing web usage. The use of web analytics retains or attracts D and D community members to access and share knowledge through virtual collaboration on the KM-IT platform. D and D KM-IT web analytics measures details on the overall usage of the system without tracking personal information or specific user behavior: how many users visited the application, how many of those users were unique visitors, how visitors come to the system (i.e., by following a link to the system or by using the web site URL via bookmark or directly typing the address), what keywords were used to perform searches with the search engine, how long the average user stayed on a given screen or on the entire system and what links were clicked on. This information is used to identify which areas of the system are more popular than the others and provides information that can be used to streamline the application for a better user experience. (authors)

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2014

THE NEW ONLINE METADATA EDITOR FOR GENERATING STRUCTURED METADATA

Nobody is better suited to “describe” data than the scientist who created it. This “description” about a data is called Metadata. In general terms, Metadata represents the who, what, when, where, why and how of the dataset [1]. eXtensible Markup Language (XML) is the preferred output format for metadata, as it makes it portable and, more importantly, suitable for system discoverability. The newly developed ORNL Metadata Editor (OME) is a Web-based tool that allows users to create and maintain XML files containing key information, or metadata, about the research. Metadata include information about the specific projects, parameters, time periods, and locations associated with the data. Such information helps put the research findings in context. In addition, the metadata produced using OME will allow other researchers to find these data via Metadata clearinghouses like Mercury [2][4]. OME is part of ORNL’s Mercury software fleet [2][3]. It was jointly developed to support projects funded by the United States Geological Survey (USGS), U.S. Department of Energy (DOE), National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA). OME’s architecture provides a customizable interface to support project-specific requirements. Using this new architecture, the ORNL team developed OME instances for USGS’s Core Science Analytics, Synthesis, and Libraries (CSAS&L), DOE’s Next Generation Ecosystem Experiments (NGEE) and Atmospheric Radiation Measurement (ARM) Program, and the international Surface Ocean Carbon Dioxide ATlas (SOCAT).Researchers simply use the ORNL Metadata Editor to enter relevant metadata into a Web-based form. From the information on the form, the Metadata Editor can create an XML file on the server that the editor is installed or to the user’s personal computer. Researchers can also use the ORNL Metadata Editor to modify existing XML metadata files.As an example, an NGEE Arctic scientist use OME to register their datasets to the NGEE data archive and allows the NGEE archive to publish these datasets via a data search portal (http://ngee.ornl.gov/data). These highly descriptive metadata created using OME allows the Archive to enable advanced data search options using keyword, geo-spatial, temporal and ontology filters. Similarly, ARM OME allows scientists or principal investigators (PIs) to submit their data products to the ARM data archive.How would OME help Big Data Centers like the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)?The ORNL DAAC is one of NASA’s Earth Observing System Data and Information System (EOSDIS) data centers managed by the Earth Science Data and Information System (ESDIS) Project. The ORNL DAAC archives data produced by NASA's Terrestrial Ecology Program. The DAAC provides data and information relevant to biogeochemical dynamics, ecological data, and environmental processes, critical for understanding the dynamics relating to the biological, geological, and chemical components of the Earth's environment. Typically data produced, archived and analyzed is at a scale of multiple petabytes, which makes the discoverability of the data very challenging. Without proper metadata associated with the data, it is difficult to find the data you are looking for and equally difficult to use and understand the data.OME will allow data centers like the NGEE and ORNL DAAC to produce meaningful, high quality, standards-based, descriptive information about their data products in-turn helping with the data discoverability and interoperability. Useful Links:USGS OME: http://mercury.ornl.gov/OME/NGEE OME: http://ngee-arctic.ornl.gov/ngeemetadata/ARM OME: http://archive2.ornl.gov/armome/Contact: Ranjeet Devarakonda (devarakondar@ornl.gov)References:[1] Federal Geographic Data Committee. Content standard for digital geospatial metadata. Federal Geographic Data Committee, 1998.[2] Devarakonda, Ranjeet, et al. "Mercury: reusable metadata management, data discovery and access system." Earth Science Informatics 3.1-2 (2010): 87-94.[3] Wilson, B. E., Palanisamy, G., Devarakonda, R., Rhyne, B. T., Lindsley, C., & Green, J. (2010). Mercury Toolset for Spatiotemporal Metadata.[4] Pouchard, L. C., Branstetter, M. L., Cook, R. B., Devarakonda, R., Green, J., Palanisamy, G., ... & Noy, N. F. (2013). A Linked Science investigation: enhancing climate change data discovery with semantic technologies. Earth science informatics, 6(3), 175-185.

FGDC CSDGM↗

NASA NTRSDate not supplied

A HIRF-Map Certification Approach for UAM, AAM, and UAS Vehicles

Advanced Air Mobility (AAM), Urban Air Mobility (UAM), and Unmanned Aerial Systems (UAS) vehicles will fly in airspace similar to Transport Category Rotorcraft, requiring them to meet stringent requirements for High-Intensity Radiated Fields (HIRF) certification. The environment is notably severe, particularly compared to fixed-wing aircraft, due to operations at lower altitudes. This potentially exposes the vehicles to high-power transmitters on the ground, leading to significant challenges. High-level HIRF exposure can result in avionic system upsets, interference, and undesirable effects. Shielding and circuit protection against HIRF pose significant barriers concerning size, weight, and cost, especially for emerging electric vertical take-off and landing (eVTOL) and electric short take-off and landing (eSTOL) vehicles. This paper proposes a novel HIRF protection approach, aiming to reduce costs by certifying vehicles to a "vehicle tolerance level" lower than that required for Transport Category Rotorcraft. The remaining protection is achieved by maintaining a safe distance from known HIRF sources. This distance is calculated based on the vehicle's tolerance level and transmitter characteristics, such as transmit power, antenna beamwidth, and direction. Tailored flight maps are developed to identify transmitters and avoidance zones within an operating area or along a flight path, enabling restricted vehicle operations. Vehicles with higher tolerance levels could have smaller HIRF avoidance zones, allowing them to operate closer to transmitters. Transmitter data are sourced from government databases, such as those of the Federal Communications Commission (FCC) and the National Oceanic and Atmospheric Administration (NOAA). A map tool is developed using Matlab to calculate and visualize HIRF avoidance zones based on available databases. The tool determines stand-off distances based on input vehicle tolerance levels, displaying results on various base maps depicting HIRF-restricted areas. Illustrations cover various FCC transmitters, including AM/FM/TV transmitters, satellite earth stations, and NOAA weather radars. The HIRF zones of smaller transmitters, such as land-mobile radios, pagers, microwave links, and cellular towers, are also illustrated. An example of flight planning around transmitters is provided. For now, airports and government-owned lands are excluded due to limited access to sensitive transmitter data. Despite this approach, a minimum HIRF tolerance level for vehicles may still be necessary to cover mobile devices, cellular base stations, transmitters on other AAM vehicles, and other small power devices not included in the FCC databases. The paper discusses findings and areas for improving existing databases and suggests an approach for better access to sanitized data in more restricted government databases. This method significantly deviates from the standard approach and introduces slightly higher flight-planning complexity. However, the potential cost savings are considerable. Future AAM/UAM/UAS aeronautical charts could potentially incorporate these new HIRF avoidance zones. Keywords—HIRF; Map; AAM; UAM; UAS; Advanced Air Mobility; Urban Air Mobility; Unmanned Aerial Systems; Certification.

HIRF↗

DOE OSTI2016

SCALE Code System

The SCALE Code System is a widely-used modeling and simulation suite for nuclear safety analysis and design that is developed, maintained, tested, and managed by the Reactor and Nuclear Systems Division (RNSD) of Oak Ridge National Laboratory (ORNL). SCALE provides a comprehensive, verified and validated, user-friendly tool set for criticality safety, reactor and lattice physics, radiation shielding, spent fuel and radioactive source term characterization, and sensitivity and uncertainty analysis. Since 1980, regulators, licensees, and research institutions around the world have used SCALE for safety analysis and design. SCALE provides an integrated framework with dozens of computational modules including three deterministic and three Monte Carlo radiation transport solvers that are selected based on the desired solution strategy. SCALE includes current nuclear data libraries and problem-dependent processing tools for continuous-energy (CE) and multigroup (MG) neutronics and coupled neutron-gamma calculations, as well as activation, depletion, and decay calculations. SCALE includes unique capabilities for automated variance reduction for shielding calculations, as well as sensitivity and uncertainty analysis. SCALE’s graphical user interfaces assist with accurate system modeling, visualization of nuclear data, and convenient access to desired results.SCALE 6.2 provides many new capabilities and significant improvements of existing features.New capabilities include:• ENDF/B-VII.1 nuclear data libraries CE and MG with enhanced group structures,• Neutron covariance data based on ENDF/B-VII.1 and supplemented with ORNL data,• Covariance data for fission product yields and decay constants,• Stochastic uncertainty and correlation quantification for any SCALE sequence with Sampler,• Parallel calculations with KENO,• Problem-dependent temperature corrections for CE calculations,• CE shielding and criticality accident alarm system analysis with MAVRIC,• CE depletion with TRITON (T5-DEPL/T6-DEPL),• CE sensitivity/uncertainty analysis with TSUNAMI-3D,• Simplified and efficient LWR lattice physics with Polaris,• Large scale detailed spent fuel characterization with ORIGAMI and ORIGAMI Automator,• Advanced fission source convergence acceleration capabilities with Sourcerer,• Nuclear data library generation with AMPX, and• Integrated user interface with Fulcrum.Enhanced capabilities include:• Accurate and efficient CE Monte Carlo methods for eigenvalue and fixed source calculations,• Improved MG resonance self-shielding methodologies and data,• Resonance self-shielding with modernized and efficient XSProc integrated into most sequences,• Accelerated calculations with TRITON/NEWT (generally 4x faster than SCALE 6.1),• Spent fuel characterization with 1470 new reactor-specific libraries for ORIGEN,• Modernization of ORIGEN (Chebyshev Rational Approximation Method [CRAM] solver, API for high-performance depletion, new keyword input format)• Extension of the maximum mixture number to values well beyond the previous limit of 2147 to ~2 billion,• Nuclear data formats enabling the use of more than 999 energy groups,• Updated standard composition library to provide more accurate use of natural abundances, andvi• Numerous other enhancements for improved usability and stability.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2023

Effectiveness of Residue and Tillage Management on Runoff Pollutant Reduction from Agricultural Areas

Highlights No-till and no-till residue systems were effective in reducing runoff particulate and total nutrients but increased dissolved nutrients. Maintaining >30% residue cover reduced most runoff constituents, irrespective of no-till or tillage. No-till-residue prevented runoff nutrient losses and benefitted farm revenue by avoiding tillage. Abstract. Reduced tillage management conservation practices (No-till and Reduced-till) are widely adopted in agriculture; however, understanding their overall effectiveness for water quality protection is challenging. A meta-analysis was conducted to understand and quantify the effectiveness of residue and tillage management on runoff, sediment, and nutrient losses from agricultural fields. Annual runoff and the associated sediment, and nutrient (nitrogen and phosphorus) loads were compiled from 60 peer reviewed research articles published across the United States and Canada. A total of 1575 site-years of data were categorized into tillage (<30% surface cover), no-tillage (<30% surface cover), tillage with residue (>30% surface cover), no-tillage with residue (>30% surface cover), and pasture management. No-tillage, no-tillage-residue, and tillage-residue managements were evaluated for their effectiveness in reducing runoff, nutrients, and sediment loads compared to tillage. Synthesized and surveyed corn yield data were used to evaluate the economic cost effectiveness of no-tillage-residue management with respect to tillage. Across the site years (1968-2019) studied, median runoff depth for no-tillage and no-tillage-residue were 84% and 70% greater than tillage and tillage-residue management, respectively. No-tillage-residue management had up to 86% less sediment losses than tillage systems, on average, for both >30% and <30% surface cover. No-tillage-residue management was most effective, with a positive performance effectiveness of 65% to 90% in controlling sediments, particulate, and total nutrient losses in runoff compared to tillage. Cost effectiveness analysis revealed the benefits of no-tillage-residue management in reducing nutrient loads and increasing net-farm revenue by avoiding tillage operational costs. Except for dissolved phosphorus, no-tillage-residue management cost effectiveness for sediments and nutrient loads ranged from negative $6 to negative $102 per every Mg or kg of load reduction, indicating it had both economic and environmental benefits compared to tillage management. Overall, these results indicate that over the long-term, no-tillage and tillage, combined with greater than 30% residue cover, can effectively reduce sediment and nutrient losses. This work highlights the importance of crop residues on the soil surface to reduce runoff losses, even in no-tillage systems. Keywords: Conservation tillage, No-tillage, Residue cover, Tillage, Water quality.

Agriculture↗

DOE OSTI2021

Analysis of Loss-of-Offsite-Power Events: Update

Loss-of-offsite power (LOOP) can have a negative impact on a nuclear power plant’s ability to achieve and maintain safe shutdown conditions. LOOP event frequencies and times required for subsequent restoration of offsite power are important inputs to plant probabilistic risk assessments. This report presents a statistical and engineering analysis of LOOP frequencies and durations at U.S. commercial nuclear power plants. The data used in this study were based on the operating experience during calendar years 1987–2020, while the most recent 15-year data (i.e., from 2006–2020) were used for most analyses in this report. LOOP events during critical operation that did not result in a reactor trip are not included. Frequencies and durations were determined for four LOOP event categories: plant-centered, switchyard-centered, grid-related, and weather related. Highly significant decreasing trends in the LOOP occurrence rates were identified for All-LOOPs during critical operation (p-value = 0.001) and switchyard-centered LOOPs during critical operation (p-value = 0.002) for the most recent 10-year period (2011–2020). Adverse trends in LOOP durations continue for switchyard-centered LOOPs (p-value = 0.005), All-LOOPs (p-value = 0.019), as well as All-LOOPs during shutdown operation (p-value = 0.003). Statistical tests show the LOOP counts are not uniformly distributed across the 12 months, and variation among the months exists for plant-centered LOOPs (p-value = 0.028), grid-related LOOPs (p-value = 0.021), All-LOOPs (p-value = 0.003), and All-LOOPs during critical operation (p-value = 0.008). The engineering analysis of LOOP data showed for the period of 2006–2020, the equipment failure events were dominated by failures of circuits and relay; human errors have been less frequent and occurred primarily in maintenance; and weather events were dominated by tornadoes and lightning.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗