Results for “public release” — Page 21

DOE OSTI2020

Multi-Model and Multi-Scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

An environmental model consists of multiple process level sub-models, and each sub-model represents a process that is key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this project develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis to address uncertainty in process models and parameters. The new method yields three process sensitivity indices. The first one is called first-order process sensitivity index, and it is derived as a single summary measure of relative process importance. Evaluating the index is computationally expensive, because it relies in a Monte Carlo scheme that requires thousands and even millions of model executions. To reduce computational cost, this project develops a computationally efficient, quasi Monte Carlo method, and this method is presented in Chapter 2 of this report with and a numerical example for demonstration. The numerical example shows that the results of the quasi Monte Carlo method are substantially close to those of the full Monte Carlo method, but the computational cost of the quasi Monte Carlo method is only 0.7% of that of the full Monte Carlo method. The second index is called total-effect process sensitivity index, and it measures interactions between different processes. Therefore, this sensitivity index includes the first-order process sensitivity index, and can be used to identify influential processes. On the other hand, the total-effect process sensitivity index can also be used to screen non-influential processes. This is demonstrated by two numerical examples using the Sobol-G* functions and groundwater flow models that consider recharge process, geological process, and snowmelt process. The numerical examples shows that the total-effect process sensitivity index is more informative than the first-order process sensitivity. The derivation of the process sensitivity index and the numerical examples are discussed in Chapter 3. Chapter 4 presents two computationally efficient methods for screening non-influential processes to exclude them from further investigation. The two methods are the multi-model difference-based sensitivity (MMDS) analysis method, which can be implemented using the Latin Hypercube Sampling. The second one is the implementation of MMDS method using a binning method. The numerical example for the Sobol-G* function indicates the two methods are capable of identifying non-influential models, and the numerical examples for the groundwater flow and reactive transport show that the two methods are effective for groundwater problems. However, it should be noted that the two methods are numerical approximations, and they can only be used for screening non-influential processes, not for ranking importance of system processes. All the sensitivity analysis methods are implemented by developing python codes, and the codes are in a software called SAMMPY: a python package for process sensitivity analysis under multiple models. The SAMMPY design and structure are discussed in Chapter 5, and the package is released to the public for free download.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

MARSAME Release Report for TA-03 Building 16

Environmental Protection and Compliance, Environmental Stewardship (EPC-ES) has determined that materials associated with technical area 3, Buildings 16 and 18 (collectively known as TA-3-16) (Figure 1) do not meet the criteria for unrestricted release to the public under Department of Energy (DOE) Order 458.1, Radiation Protection for the Public and the Environment (DOE 2020) and are to be treated as Low Level Waste (LLW). These conclusions are based on the known history of the building combined with radiation survey data collected in 2020 and 2021, and findings are consistent with DOE Order 458.1 and Los Alamos National Laboratory (LANL) Functional Series Document EPC-ES-FSD-004, Environmental Radiation Protection (LANL 2020a). Sampling and data analysis, as described in this report, were sufficient to meet measurement quality objectives under the Multi-Agency Radiation Survey and Assessment of Materials and Equipment (MARSAME) manual (MARSAME 2000) and LANL procedures (LANL 2020b). Final approvals for waste disposition will come from LANL’s Waste Management Program.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Formation of Zeolites Responsible for Waste Glass Rate Acceleration: An Experimental and Computational Study for Understanding Thermodynamic and Kinetic Processes

Before nuclear waste glasses can be disposed at geological disposal facilities, a performance assessment (PA) must be conducted to provide confidence to the regulators and public that the release rates of radioisotopes and hazardous materials into the environment are below the regulatory limits. Because the release rate of radionuclides is directly related to the dissolution rate of the radionuclide-containing glass, the corrosion behavior of the glass must be well understood for the disposal site lifetime. For simplicity, glass dissolution in static conditions has been categorized into three different stages, referred to as initial rate (Stage I), residual rate (Stage II) and, depending upon the secondary phases formed, a possible delayed resumption in the alteration rate (Stage III). The last of these stages is the least understood and has the possibility to drive radionuclide release rates above regulatory thresholds.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

The Hot and Cold Wars of the Enola Gay

When the B-29 Superfortress with tail number 44-86292 rolled off the assembly line, no one could have predicted its role in history. Known as the Enola Gay, the bomber carried Little Boy to Hiroshima, helping to end World War II. After the war, largely forgotten and neglected, the Enola Gay once more achieved notoriety, this time at the center of a battle to define its legacy and that of World War II. Personally selected by Colonel Paul Tibbets, the commanding officer of the 509 th Composite Group, 44-86292 was flown to Wendover Airfield in Utah, the training base of Tibbets’ organization. In July 1945, 44-86292 was flown to the Pacific Island of Tinian, where it completed eight training and two combat missions. Tibbets, who seldom flew the bomber, took the controls on July 31 st for a simulated nuclear combat mission. On August 5 th , Tibbetts named 44-86292 the Enola Gay, in honor of his mother, and then piloted the bomber to Hiroshima on August 6 th . When publicity pictures were released, the Enola Gay became famous, ultimately destined for the Smithsonian.

42 ENGINEERING↗

DOE OSTI2023

State Energy Offices’ Engagement in Electric Distribution Planning to Meet State Policy Goals

NASEO and Berkeley Lab released a new publication on State Energy Offices’ Engagement in Electric Distribution Planning to Meet State Policy Goals. State and Territory Energy Offices develop plans, programs, policies, and projects that have a substantial impact on electric distribution systems. They also can participate in distribution system planning (DSP) processes to help ensure that utilities – consumer- and investor-owned – meet the state’s future energy needs. This paper recognizes the wide spectrum of roles that State Energy Offices can play in DSP processes, including planning for distributed energy resources and grid modernization. It highlights various examples of non-regulatory activities by State Energy Offices including planning, conducting studies, convening stakeholder processes, and implementing programs that inform and contribute to distribution system planning. It also provides examples of State Energy Offices’ engagement in proceedings before their respective public utility commissions. As State Energy Offices face myriad challenges associated with meeting state policy goals, preparing for anticipated rates of distributed energy resource deployment, addressing concerns regarding grid reliability and resilience, and recommending and making long-term investment decisions, the examples provided through this guide can serve as a resource in navigating those challenges.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

MARSAME Radiological Release Report for Diamond Drive Widening and Roundabout Construction Projects, Revision 1

Environmental Protection and Compliance, Environmental Stewardship (EPC-ES) has determined that the soil, concrete and asphalt associated with the Diamond Drive Widening/Roundabout Projects (Figure 1) meets the criteria for unrestricted release to the public under Department of Energy (DOE) Order 458.1, Radiation Protection for the Public and the Environment (DOE, 2020). These conclusions are based on the known history of the area combined with soil sample data collected in 2022; the findings are consistent with DOE Order 458.1 and Los Alamos National Laboratory (LANL) Functional Series Document EPC-ES-FSD-004, Environmental Radiation Protection (LANL, 2021). Sampling and data analysis, as described in this report, were sufficient to meet measurement quality objectives (MQOs) under the Multi-Agency Radiation Survey and Assessment of Materials and Equipment (MARSAME) manual (NUREG, 2009) and LANL procedures (LANL, 2020). Final approvals for waste disposition will come from LANL’s Waste Management Program.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

U.S. Electronic Neutron Generator Use in Research and Academia – A Survey [Slides]

This is a slide deck intended for use at the upcoming IAEA workshop "EVT2002488 Consultancy Meeting on Industrial Applications of Nucleonic Control Systems based on Neutrons from sealed sources and generators". This slide deck provides a survey of U.S. commercial neutron generator technology, including manufacturers, and a few examples of use in academia, and some prior research at INL. All data and figures in this presentation are either publicly available (note citations) or are INL materials that have been previously release through STISM for public presentations or papers.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2021

SPRUCE Diurnal and Seasonal Patterns of Water Potential in S1 Bog and SPRUCE Experimental Plot Vegetation beginning in 2010

This data set reports on water potential from excised terminal shoots or leaves (Mainthemum only) of vegetation at the SPRUCE site located in the S1 bog. Pretreatment measurements were collected from outside plots within the S1 bog. Post treatments were collected from inside SPRUCE plots. Water potential was measured on the overstory canopy species, consisting of the evergreen Picea mariana (Mill.) B.S.P. (black spruce) and deciduous Larix laricina (Du Roi) K. Koch (tamarack) trees, and ericaceous shrubs < 1 m tall, consisting of the evergreen Rhododendron groenlandicum (Oeder) Kron & Judd (Labrador tea) and Chamaedaphne calyculata (L.) Moench. (leatherleaf). Some measurement campaigns also included the evergreen Kalmia polifolia, Wangenh. (bog laurel), the deciduous Vaccinium angustifolium (Aiton) (blueberry), and the herbaceous Maianthemum trifolium (L.) Sloboda (three-leaf false Solomon’s seal). This dataset contains one data file in comma-separated (*.csv) format. This is the second release of water potential data from the site with additional data from 2018 and 2019. These data cover 2010-06-22 to 2019-07-16. Further results will be added to this data set and released to the public periodically as quality assurance and publication of results are accomplished. There are 10 experimental plots in SPRUCE: five temperature treatments (+0, +2.25, +4.5, +6.75, +9°C) at ambient CO2, and the same five temperature treatments at elevated CO2 (+500 ppm). These data span the pre- and post-treatment periods when enclosed plots were exposed to warming and elevated carbon dioxide (CO2) within the SPRUCE experiment. The pretreatment samples were collected from south end of the S1 bog prior to SPRUCE boardwalk construction, then throughout the bog once the boardwalks were completed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Acoustic Rocket Signatures Collected by Smartphones

Rockets generate complex acoustic signatures that can be detected over a thousand kilometers from their source. While many far-field acoustic rocket signatures have been collected and released to the public, very few signatures collected at distances less than 100 km are available. This work presents a curated and annotated dataset of acoustic signatures of 243 rocket launches collected by a network of smartphones stationed at distances between 10 and 70 km from the launch sites, resulting in 1089 individual recordings. Due to the frequency dependence of atmospheric attenuation and the relatively short propagation distances, higher-frequency features not preserved in most publicly available data are observed. The signals are time-aligned to allow for different segments of the signal (ignition, launch, trajectory, chronology) to be more easily examined and compared. Initial analysis of the features of these rocket launch stages is performed, observed features are compared to those found in the existing literature, and comparisons between signals from launches of different rocket types are made. The dataset is annotated and made available to the public to aid future analysis of the characteristics and source mechanisms of rocket acoustics as well as applications such as rocket detection and classification models.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

IceCube Search for Neutrinos Coincident with Gravitational Wave Events from LIGO/Virgo Run O3

Using data from the IceCube Neutrino Observatory, we searched for high-energy neutrino emission from the gravitational-wave events detected by the advanced LIGO and Virgo detectors during their third observing run. We did a low-latency follow-up on the public candidate events released during the detectors' third observing run and an archival search on the 80 confident events reported in the GWTC-2.1 and GWTC-3 catalogs. An extended search was also conducted for neutrino emission on longer timescales from neutron star containing mergers. Follow-up searches on the candidate optical counterpart of GW190521 were also conducted. We used two methods; an unbinned maximum likelihood analysis and a Bayesian analysis using astrophysical priors, both of which were previously used to search for high-energy neutrino emission from gravitational-wave events. No significant neutrino emission was observed by any analysis, and upper limits were placed on the time-integrated neutrino flux as well as the total isotropic equivalent energy emitted in high-energy neutrinos.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a collection of objects stored within an RDate file, stateWaterRightsHarmonized.RData. Each object either describes how one should interact with the other objects, or contains the harmonized water rights data. Here is each object described in detail: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. The column headers for each data frame are: waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. basinNum - The alpha-numeric identifier of the WMA the record belongs to. priorityDate - The priority date associated with the right. allocatedFlow - The maximum flow of the allocation in cubic feet per second (ft3s-1). source - Whether the right is for surface water or groundwater. origWaterUse - The original stated water use from the state. waterUse - The water use category under the unified use categories established here. rightsByState_surface: A list of the cleaned surface water rights collected from each state. See rightsByState_ground for more details on how the data is formatted. fullRightsRecs: A list of the combined groundwater and surface water records for each state. Essentially, these lists are the merging of rightsByState_ground and rightsByState_surface by state. See rightsByState_ground for more details on how the data is formatted. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page.

Economics↗

DOE OSTI2024

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a series of various files organized by state sub-directories. The first two characters of each file name is the abbreviation for the state the in which the file contains data for. After the abbreviation is the text which describes the contents of the file. Here is each file type described in detail: XXFullHarmonizedRights.csv: A file of the combined groundwater and surface water records for each state. Essentially, this file is the merging of XXGroundwaterHarmonizedRights.csv and XXSurfaceWaterHarmonizedRights.csv by state. The column headers for each of this type of file are: state - The name of the state the data comes from. FIPS - The two digit numeric state ID code. waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. priorityDate - The priority date associated with the right. origWaterUse - The original stated water use from the state. waterUse - The water use category under the unified use categories established here. source - Whether the right is for surface water or groundwater. basinNum - The alpha-numeric identifier of the WMA the record belongs to. CFS - The maximum flow of the allocation in cubic feet per second (ft3s-1). Arizona is unique among the states, as its surface and groundwater resources are managed with two different sets of boundaries. So, for Arizona, the basinNum column is missing and instead there are two columns: surBasinNum - The alpha-numeric identifier of the surface water WMA the record belongs to. grdBasinNum - The alpha-numeric identifier of the groundwater WMA the record belongs to. XXStatePOD.shp: A shapefile which identifies the location of the Points of Diversion for the state's water rights. It should be noted that not all water right records in XXFullHarmonizedRights.csv have coordinates, and therefore may be missing from this file. XXStatePOU.shp: A shapefile which contains the area(s) in which each water right is claimed to be used. Currently, only Idaho and Washington provided valid data to included within this file. XXGroundwaterHarmonizedRights.csv: A file which contains only harmonized groundwater rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. XXSurfaceWaterHarmonizedRights.csv: A file which contains only harmonized surface water rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. Additionally, one file, stateWMALabels.csv, is not stored within a sub-directory. While we have referred to the spatial boundaries that each state uses to manage its water resources as WMAs, this term is not shared across all states. This file lists the proper name for each boundary set, by state.

Economics↗

DOE OSTI2024

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a series of various files organized by state sub-directories. The first two characters of each file name is the abbreviation for the state the in which the file contains data for. After the abbreviation is the text which describes the contents of the file. Here is each file type described in detail: XXFullHarmonizedRights.csv: A file of the combined groundwater and surface water records for each state. Essentially, this file is the merging of XXGroundwaterHarmonizedRights.csv and XXSurfaceWaterHarmonizedRights.csv by state. The column headers for each of this type of file are: state - The name of the state the data comes from. FIPS - The two-digit numeric state ID code. waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. priorityDate - The priority date associated with the right. origWaterUse - The original stated water use(s) from the state. waterUse - The water use category under the unified use categories established here. source - Whether the right is for surface water or groundwater. basinNum - The alpha-numeric identifier of the WMA the record belongs to. CFS - The maximum flow of the allocation in cubic feet per second (ft3s-1). Arizona is unique among the states, as its surface and groundwater resources are managed with two different sets of boundaries. So, for Arizona, the basinNum column is missing and instead there are two columns: surBasinNum - The alpha-numeric identifier of the surface water WMA the record belongs to. grdBasinNum - The alpha-numeric identifier of the groundwater WMA the record belongs to. XXStatePOD.shp: A shapefile which identifies the location of the Points of Diversion for the state's water rights. It should be noted that not all water right records in XXFullHarmonizedRights.csv have coordinates, and therefore may be missing from this file. XXStatePOU.shp: A shapefile which contains the area(s) in which each water right is claimed to be used. Currently, only Idaho and Washington provided valid data to include within this file. XXGroundwaterHarmonizedRights.csv: A file which contains only harmonized groundwater rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. XXSurfaceWaterHarmonizedRights.csv: A file which contains only harmonized surface water rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. Additionally, one file, stateWMALabels.csv, is not stored within a sub-directory. While we have referred to the spatial boundaries that each state uses to manage its water resources as WMAs, this term is not shared across all states. This file lists the proper name for each boundary set, by state. For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. This object holds the the data that is exported to created the xxGroundwaterHarmonizedRights.csv files. rightsByState_surface: A list of data frames with the cleaned surface water rights collected from each state. This object holds the the data that is exported to created the xxSurfaceWaterHarmonizedRights.csv files. fullRightsRecs: A list of the combined groundwater and surface water records for each state. This object holds the the data that is exported to created the xxFullHarmonizedRights.csv files. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page. The code related to the creation of this dataset can be viewed within HarDWR GitHub Repository/dataHarmonization.

Economics↗

DOE OSTI2026

RTN-117: Image Calibration and Instrument Signal Removal for the First Year of the LSST

The NSF-DOE Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) requires calibration products that provide uniform, stable, and accurate photometric and astrometric performance across the 3.2 gigapixel focal plane and throughout the 10-year survey. This paper details the algorithms and workflows used to produce instrument calibrations and remove instrumental artifacts for Data Preview 2 (DP2)---the first end-to-end processing demonstration using on-sky data with the LSST Camera (LSSTCam). We describe the verification, acceptance, and certification framework used to assess calibration quality and quantify residual systematics. We show baseline metrics on calibrated science images to evaluate the robustness of the calibration and instrument signature removal (ISR) data processing pipelines for DP2. Finally, we summarize the known limitations observed in DP2 production and outline expected algorithmic improvements for the first public LSST data release (Data Release~1, DR1).

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1983

A new method for generating and maintaining rigid formats in NASTRAN

A new method for generating and updating Rigid Formats in NASTRAN is discussed. The heart of this method is a Rigid Format data base that is in card-image format and that can therefore be easily maintained by the use of standard text editors. Each Rigid Format entry in this data base will contain the Direct Matrix Abstraction Program (DMAP) for that Rigid Format along with the related restart, subset and substructure control tables. NASTRAN will read this data base directly in every NASTRAN run and perform the necessary transformations to allow the DMAP to be processed and compiled by the NASTRAN executive. This approach will permit Rigid Formats to be changed without unnecessary compilations and relinking of NASTRAN. Furthermore, this approach will also make it very easy for users to make permanent changes to existing Rigid Formats as well as to generate their own Rigid Formats. This new method will be incorporated in a future release of the public version of NASTRAN.

Pamidi, P. R.↗

NASA NTRS1992

UIT support observations archive

We are in the process of archiving the approximately 1.2Tbytes of imaging data acquired in support of UIT observations. The UIT is one of three telescopes comprising the ASTRO spacecraft and is a 38-cm f/9 Ritchey-Chretien telescope with wavelength coverage 1200 A to 3000 A and a 40 arcminute diameter field-of-view at 4 min FWHM resolution. During the ASTRO shuttle mission, there were 65 different pointings (some with multiple targets) of the UIT and hence 65 fields. Our support data of these fields were obtained with the KPNO and CTIO 0.9m telescopes and several of the 2048x2048 CCD's. Our images are typically about 20 acrminutes on a side and contain the entire UV target except for nearby galaxies, for which mosaiced images were created. All good quality images in the broadband and narrow band filters for every target are being archived. Though all of the UIT targets were well studied astronomical objects, these frames are many of the first large field format images of them and, when combined with the soon to be released UV frames, provide a unique dataset. These data were already used to address a wide variety of astronomical questions. Vacuum ultraviolet (VUV) observations were used to study star formation in a sample of nearby galaxies, since integrated VUV - optical colors provide the most sensitive available measure of the formation rate of massive stars. The blue stages later in stellar evolution are also being studied. These data allow more accurate determination of the helium and metallicities of hothorzontal branch stars. These data are being prepared to release to the public in CD-ROM format through the National Space Science Data Center.

Smith, Eric P.↗

NASA NTRS1994

Technology transfer initiatives

This report summarizes the University of Alabama in Huntsville (UAH) technology transfer activities with the Marshall Space Flight Center (MSFC) for the period of April 1993 through December 1993. Early in 1993, the MSFC/TUO and UAH conceived of the concept of developing stand-alone, integrated data packages on MSFC technology that would serve industrial needs previously determined to be critical. Furthermore, after reviewing over 500 problem statements received by MSFC, it became obvious that many of these requests could be satisfied by a standard type of response. As a result, UAH has developed two critical area response (CAR) packages: CFC (chlorofluorocarbon) replacements and modular manufacturing and simulation. Publicity included news releases, seminars, articles and conference papers. The Huntsville Chamber of Commerce established the Technology Transfer Subcommittee with the charge to identify approaches for the Chamber to assist its members, as well as non-members, access to the technologies at the federal laboratories in North Alabama. The Birmingham Chamber of Commerce has expressed interest in establishing a similar technology transfer program. This report concludes with a section containing a tabulation of the problem statements, including CAR packages, submitted to MSFC from January 1992 through December 1993.

Mccain, Wayne↗

NASA NTRS1994

An overview of the EOSDIS V0 information management system: Lessons learned from the implementation of a distributed data system

The EOSDIS Version 0 system, released in July, 1994, is a working prototype of a distributed data system. One of the purposes of the V0 project is to take several existing data systems and coordinate them into one system while maintaining the independent nature of the original systems. The project is a learning experience and the lessons are being passed on to the architects of the system which will distribute the data received from the planned EOS satellites. In the V0 system, the data resides on heterogeneous systems across the globe but users are presented with a single, integrated interface. This interface allows users to query the participating data centers based on a wide set of criteria. Because this system is a prototype, we used many novel approaches in trying to connect a diverse group of users with the huge amount of available data. Some of these methods worked and others did not. Now that V0 has been released to the public, we can look back at the design and implementation of the system and also consider some possible future directions for the next generation of EOSDIS.

Ryan, Patrick M.↗