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At least 1,081 records · Page 60

The applicability of satellite altimetry data to tidal models

The characteristics of satellite altimetry data are reviewed, and techniques for incorporating them into numerical tide models are discussed. The relationship between the geocentric tides observed by satellites and the ocean tides measured by gages or predicted by models is explored, and particular attention is given to the differences between deep-water, shelf, and estuarine tidal regimes. It is concluded that a separate tidal solution should always be performed before assimilating satellite data, including calculation of the tides along a single repeated ground track in the case of shelf and estuarine regions. The potential value of the altimetry to be provided by Topex/Poseidon (scheduled launch in 1991) is indicated.

Parke, Michael E.↗

A feasibility study of using remotely sensed data for water resource models

Remotely sensed data were collected to demonstrate the feasibility of applying the results to water resource problems. Photographs of the Wolf Creek watershed in southwestern Colorado were collected over a one year period. Cloud top temperatures were measured using a radiometer. Thermal imagery of the Wolf Creek Pass area was obtained during one pre-dawn flight. Remote sensing studies of water resource problems for user agencies were also conducted. The results indicated that: (1) remote sensing techniques could be used to assist in the solution of water resource problems; (2) photogrammetric determination of snow depths is feasible; (3) changes in turbidity or suspended material concentration can be observed; and (4) surface turbulence can be related to bed scour; and (5) thermal effluents into rivers can be monitored.

Ruff, J. F.↗

Towards a Methodology and Tooling for Model-Based Probabilistic Risk Assessment (PRA)

A Probabilistic Risk Assessment (PRA) aims to identify and assess potential risks to system technical performance requirements for the purpose of furnishing risk insights into project decisions. PRAs have traditionally been conducted manually using software with an isolated data model. As system complexity rises it becomes difficult to ensure consistency between a PRA, the evolving system design, and other engineering analyses; the techniques for conducting PRAs must evolve to meet this challenge. This work presents progress towards a methodology and tooling for conducting a PRA by leveraging data in the system model, embedded for other purposes and analyses, to conduct a PRA. An approach for identifying the appropriate probabilistic equation for each risk scenario from a standard library is presented, which is a significant step towards the quantification of the likelihood of a risk scenario occurrence. The final calculation of the likelihood of occurrence is left as an item of future work. We also present the development of preliminary tooling to carry out the methodology on a well-formed system model. The information needed to conduct the PRA is embedded in a consistent manner in a system model, so the model-based PRA can be regularly executed as the system model changes. The ability to modify the PRA in concert with lifecycle evolution affords a project the opportunity to track the extent to which system modification impacts compliance with requirements. These aspects of this model-based PRA methodology make it capable of managing risk in increasingly complex technical systems.

Schreiner, Samuel S.↗

Ontologizing health systems data at scale: making translational discovery a reality

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68–99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Modeling and Rapid Prototyping of Integrated Transmission-Distribution OPF Formulations with PowerModelsITD.jl

Conventional electric power systems are composed of different unidirectional power flow stages of generation, transmission, and distribution, managed independently by transmission system and distribution system operators. However, as distribution systems increase in complexity due to the integration of distributed energy resources, coordination between transmission and distribution networks will be imperative for the optimal operation of the power grid. However, coupling models and formulations between transmission and distribution is non-trivial, in particular due to the common practice of modeling transmission systems as single-phase, and distribution systems as multi-conductor phase-unbalanced. To enable the rapid prototyping of power flow formulations, in particular in the modeling of the boundary conditions between these two seemingly incompatible data models, we introduce PowerModelsITD.jl, a free, open-source toolkit written in Julia for integrated transmission-distribution (ITD) optimization that leverages mature optimization libraries from the InfrastructureModels.jl-ecosystem. The primary objective of the proposed framework is to provide baseline implementations of steady-state ITD optimization problems, while providing a common platform for the evaluation of emerging formulations and optimization problems. In this work, we introduce the nonlinear formulations currently supported in PowerModelsITD.jl, which include AC-polar, AC-rectangular, current-voltage, and a linear network transportation model. Results are validated using combinations of IEEE transmission and distribution networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Generating MCNP Input Files for Unstructured Mesh Geometries

The Los Alamos National Laboratory’s (LANL) Monte Carlo N-Particle (MCNP)1 transport code version 6.3 (also known as MCNP6.3) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into constructive solid geometry (CSG) cells. This feature has been developed for performing calculations of complex geometry models because manually creating CSG models is time-consuming and error-prone as the complexities of geometries increase. A UM geometry model is a collection of finite elements representing a solid geometry. The first step of the MCNP UM calculation is using other software packages to create a finite element mesh representation of a solid 3D geometry because the MCNP code cannot be used to generate a UM model. Computer-aided design (CAD) software is typically used to create a solid geometry model, which is later imported into mesh generation software to create a UM model. Some mesh generation software packages may also be used to create solid geometries and thus CAD files are not needed. The MCNP UM feature was originally designed for models generated by the Abaqus/CAE software suite. The MCNP code version 6.0 and later can process UM models formatted as Abaqus input files. Starting with a 6.3 version, the MCNP code can process HDF5 mesh input files. We only focus on the UM models formatted as Abaqus input files in this report since currently no external software can be used to generate HDF5 mesh input files for MCNP UM calculations. The MCNP code version 6.3 can be used to convert the Abaqus mesh input files into the HDF5 mesh input files, but this option is typically used by the MCNP code development team to test the HDF5 mesh input file feature. Several software packages (such as Abaqus, Attila4MC, or Cubit) can be used to create the Abaqus input files for MCNP UM calculations. An MCNP UM calculation using an Abaqus model requires two input file types: MCNP and Abaqus input files. The Abaqus input files needed for MCNP UM calcu lations must have the correct Abaqus syntax and meet the additional requirements by the MCNP code. The MCNP code can process only Abaqus input files that make use of part and assembly definitions, where elements in each part must be grouped into one or more element sets (i.e., elset) using *Elset keyword lines with specified naming formats. The MCNP and Abaqus input files required for MCNP UM simulations must be related; pseudo-cells in an MCNP input file must be constructed from mesh model data from an Abaqus input file. For large complex UM models, it is tedious to manually create MCNP UM input files. The um pre op (unstructured mesh pre operations) program with the -m option can be used to create a skeleton MCNP input file from an Abaqus input file [6]. Since the um pre op program was written in Fortran and was not written for optimized performance, this program is a deprecated feature in the MCNP code version 6.3 and may be removed in the next release of the code. To improve calculation flow of multiphysics calculations, a Python3 code called write mcnp um input has been developed to generate an MCNP input file instead of using the um_pre_op -m option. This Python code was initially released to the public in 2020. We have updated this Python code for MCNP6.3 and it was used to generate the MCNP input files used to verify the MCNP6.3 code. The write_mcnp_um_input code is included with the MCNP6.3 code package which will be released to the public through the Radiation Safety Information Computational Center (RSICC) at Oak Ridge National Laboratory. This report is a revision of LA-UR-20-27139 report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Developing processing techniques for Skylab data

The author has identified the following significant results. The effects of misregistration and the scan-line-straightening algorithm on multispectral data were found to be: (1) there is greatly increased misregistration in scan-line-straightening data over conic data; (2) scanner caused misregistration between any pairs of channels may not be corrected for in scan-line-straightened data; and (3) this data will have few pure field center pixels than will conic data. A program SIMSIG was developed implementing the signature simulation model. Data processing stages of the experiment were carried out, and an analysis was made of the effects of spatial misregistration on field center classification accuracy. Fifteen signatures originally used for classifying the data were analyzed, showing the following breakdown: corn (4 signatures), trees (2), brush (1), grasses, weeds, etc. (5), bare soil (1), soybeans (1), and alfalfa (1).

Nalepka, R. F.↗

Atmospheric definition for Shuttle aerothermodynamic investigations

A procedure has been developed to estimate the freestream atmospheric properties along the Shuttle trajectory during atmospheric re-entry. The procedure utilizes measurement data obtained by NASA, NOAA, and the Air Force, and model data for regions where measured data are nonexistent. The results obtained are used to determine the best atmosphere-relative trajectory parameters during re-entry and to allow for an evaluation of the aerodynamic and aerothermodynamic characteristics of the Shuttle. A discussion of the method employed by the Langley Atmospheric Information Retrieval System, which is a computer code for the determination of atmospheric parameters, is presented. Results obtained from the first two Shuttle flights, STS-1 and STS-2, are also given with applications of these atmospheric parameters.

Price, J. M.↗

Advanced techniques for the storage and use of very large, heterogeneous spatial databases. The representation of geographic knowledge: Toward a universal framework

A new approach to building geographic data models that is based on the fundamental characteristics of the data is presented. An overall theoretical framework for representing geographic data is proposed. An example of utilizing this framework in a Geographic Information System (GIS) context by combining artificial intelligence techniques with recent developments in spatial data processing techniques is given. Elements of data representation discussed include hierarchical structure, separation of locational and conceptual views, and the ability to store knowledge at variable levels of completeness and precision.

Peuquet, Donna J.↗

NSSDC data listing

In a highly summarized way, data available from the National Space Science Data Center (NSSDC) is identified. Most data are offline data sets (on magnetic tape or as film/print products of various sizes) from individual instruments carried on spacecraft; these compose the Satellite Data Listing. Descriptive names, time spans, data form, and quantity of these data sets are identified in the listing, which is sorted alphabetically-first by spacecraft name and then by the principal investigator's or team leader's last name. Several data sets held at NSSDC, not associated with individual spaceflight instruments, are identified in separate listings following the Satellite Data Listing. These data sets make up the Supplementary Data Listings and include composite spacecraft data sets, ground-based data, models, and computer routines. The identifiers used in the Supplementary Data Listings were created by NSSDC and are explained in the pages preceding the listings. Data set form codes are listed. NSSDC offers primarily archival, retrieval, replication, and dissemination services associated with the data sets discussed in the two major listings identified above. NSSDC also provides documentation which enables the data recipient to use the data received. NSSDC is working toward expanding presently limited capabilities for data subsetting and for promotion of data files to online residence for user downloading. NSSDC data holdings span the range of scientific disciplines in which NASA is involved, and include astrophysics, lunar and planetary science, solar physics, space plasma physics, and Earth science. In addition to the functions mentioned above, NSSDC offers data via special services and systems in a number of areas, including Astronomical Data Center (ADC), Coordinated Data Analysis Workshops (CDAWs), NASA Climate Data System (NCDS), Pilot Land Data System (PLDS), and Crustal Dynamics Data Information System (CDDIS). Furthermore, NSSDC has a no-password account on its SPAN/Telenet-accessible VAX through which the NASA Master Directory and selected online data bases are accessible and through which any data described here may be ordered. Astrophysics data support by NSSDC is not limited to the ADC. Each of these special services/systems is described briefly.

Horowitz, Richard↗

Ground testing for the no-vent fill of cryogenic tanks - Results of tests for a 71 cubic foot tank

NASA Lewis Research has been investigating the no-vent till method, since it is a promising approach to transfer liquid while handling the problems of low-g venting. This paper reports the results of a test series for filling a 71 cu ft tank with liquid hydrogen without venting. Twenty two tests were conducted, ten with a bottom orifice as the inlet and 12 with a spray bar. Parameters investigated included inlet saturation pressures of approximately 5, 15, and 25 psia; transfer pressures of 20, 30, and 45 psia; and various starting wall temperatures. Of the tests, only the one run at the highest wall temperature (238 R) failed to fill the tank. Test results are compared to a thermodynamic equilibrium model. Overall model-data agreement was good except for the tendency of the model to overshoot during the initial wall cool-down of the higher starting wall temperature fills.

Chato, David J.↗

Ground testing for the no-vent fill of cryogenic tanks: Results of tests for a 71 cubic foot tank

NASA Lewis Research has been investigating the no-vent fill method, since it is a promising approach to transfer liquid while handling the problems of low-g venting. This paper reports the results of a test series for filling a 71 cu ft tank with liquid hydrogen without venting. 22 tests were conducted, 10 with a bottom orifice as the inlet and 12 with a spray bar. Parameters investigated included inlet saturation pressures of approximately 5, 15, and 25 psia, transfer pressures of 20, 30, and 45 psia, and various starting wall temperatures. Of the tests, only the one run at the highest wall temperature (238 R) failed to fill the tank. Test results are compared to a thermodynamic equilibrium model. Overall model-data agreement was good except for the tendency of the model to overshoot during the initial wall cool down of the higher starting wall temperature fills.

Chato, David J.↗

Near-simultaneous ROSAT and Ginga observations of the 1991 X-ray transient in Musca

The quasi simultaneous Rosat/Ginga observations of the Musca X-ray transient are reported. During its all sky survey, Rosat observed the Musca 1991 X-ray transient on 24-25 Jan. 1991, two weeks after outbursts, for about 170 seconds. The intensity was found to be nearly 6 Crab in the Rosat band. A combined fit of Rosat and Ginga data from 25 Jan. 1991 with a multitemperature disk blackbody model data plus a power law component results in a maximal temperature of the disk of about kT = 0.96 keV at an absorbing column of N(sub H) = 2.2 x 10(exp 21)/sq cm. A minimum distance to the black hole binary of at least 4 to 5 kpc was derived. Including the accretion disk inclination angle of i = 26 +/- 25 degrees determined from the shape of the positron annihilation line, and the quiescent optical brightness together with the most probable spectral type of the companion, a black hole mass of M = (6 +/- 1.5) of the solar mass and a distance of about (11 +/- 3) kpc, were derived. Additional Rosat observations in Aug. 1991 and Mar. 1992 suggest that the exponential intensity decay until 240 days after the outburst is followed by a steeper decline between 240 and 410.

Greiner, J.↗

Time Manager Software for a Flight Processor

Data analysis is a process of inspecting, cleaning, transforming, and modeling data to highlight useful information and suggest conclusions. Accurate timestamps and a timeline of vehicle events are needed to analyze flight data. By moving the timekeeping to the flight processor, there is no longer a need for a redundant time source. If each flight processor is initially synchronized to GPS, they can freewheel and maintain a fairly accurate time throughout the flight with no additional GPS time messages received. How ever, additional GPS time messages will ensure an even greater accuracy. When a timestamp is required, a gettime function is called that immediately reads the time-base register.

Zoerne, Roger↗

NASA's ATM Ontology: Semantic Integration and Querying Across NAS Data Sources

NASA is developing an Air Traffic Management (ATM) Ontology as part of an advanced prototyping activity that demonstrates the utility of semantic technologies for integrating, querying, and searching over various sources of heterogeneous ATM data. The ontology encodes an overarching data model that functions as the backbone upon which to overlay data from multiple sources published by FAA, NOAA, NASA, and others. The integrated data can be queried to produce results not achievable using any single source alone. The ontology incorporates flight data, weather data, traffic management advisory data, airport delay data, and national airspace infrastructure data for a very limited spatial and temporal slice of airspace operations (one day of operations at a major airport).

air traffic management↗

Post-flight Analysis of Atmospheric Properties from Mars 2020 Entry, Descent, and Landing

The Mars 2020 spacecraft landed the Perseverance rover and Ingenuity helicopter successfully in Jezero crater on Feb. 18, 2021. The entry, descent, and landing (EDL) sequence of the spacecraft largely leveraged the previous 2012 Mars Science Laboratory (MSL) mission. The atmospheric modeling approach for Mars 2020 was also borrowed from MSL. It consisted of two mesoscale atmospheric models of the target site during the Martian season of landing, and a statistical model of the pressure, density, temperature, and winds based on the mesoscale model data. This paper briefly describes the pre-flight atmospheric models used for Mars 2020, but focuses on the post-flight assessment of these models and comparison to near-landing day orbiter sounder and other onboard atmospheric measurements. Observations from post-flight analysis showed that density was under-predicted in the upper atmosphere, but within the altitudes covered by the mesoscale models, the pre-flight modeling matched post-flight results, including for quantities like wind velocities. Potential improvements to address the upper atmosphere and other deficiencies of the Mars 2020 pre-flight model are also discussed.

Villar, Gregorio↗

Data Mining for Science of the Sun-Earth Connection as a Single System

Establishing the Sun-Earth connection requires overcoming the challenges of exploring the data from past and current missions and leveraging tools and models (data mining) to create an efficient system treatment of the Sun and heliosphere. However, solar and heliospheric environment data constitute a vast source of information whose potential is far from being optimally exploited. In the next decade, the solar and heliospheric community will have to manage the increasing amount of information coming from new missions, improve reanalysis of data from past and current missions, and create new data products from the application of new methodologies. This complex task is further complicated by practical challenges such as different datasets and catalogs in different formats that may require different pre-processing and analysis tools, and the need for numerous analysis approaches that are not all fully optimized for large volumes of data. While several ongoing efforts aim at addressing these problems, the available datasets and tools are not always used to their full potential often due to lack of awareness of available resources. In this paper, we summarize the issues raised and goals discussed by members of the community during recent conference sessions focused on data mining for science.

Sun-Earth connection↗