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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Carbon Storage Site Mapping Inquiry Tool (MapIT)

To date, 48 projects, consisting of 139 wells, are currently under review with the Environmental Protection Agency’s (EPA) Underground Injection Control (UIC) Program for Class VI – wells used for geologic sequestration of carbon dioxide. The number of applications submitted is expected to increase in coming years with the increase of the 45Q tax credit available to projects that initiate construction prior to 2033. The amount of data collected to submit a Class VI permit is vast, and often disparate, coming from state, federal, and commercial entities, as well as field-specific data collected within an area of interest. When preparing for site selection and permitting, the initial aggregation of relevant public data can be time intensive. The Carbon Storage Site Mapping Inquiry tool (MapIT) was created to support and accelerate the discovery and accessibility of open-source data and information available across the USA. Data was aggregated and organized based on data types described within the EPA UIC Class VI permit documentation. The online tool enables users to explore hundreds of geospatial data layers and connect to additional external resources, leveraging API and REST services where possible to ensure updates to data in real time. MapIT enables users to explore state and federal data related to geologic, geophysical, structural, hydrologic, and contextual information. In addition to displaying spatial data and linking to external resources, MapIT leverages custom widgets to ensure that internal data and external data are discoverable and accessible. The widgets connect users to resources such as the USGS publications and the USGS Earthquake Catalog based on a user-defined location. This talk will describe data aggregation workflows, data types, data preparation, and tool development for MapIT. The Carbon Storage Site Mapping Inquiry Tool and underlying database are valuable, intuitive resources that empower government, academic, commercial and industry stakeholders to explore, analyze, and acquire carbon storage related data.

Morkner, Paige↗
NASA NTRS1992

Latest developments in the display of large-scale ionospheric and thermospheric data sets

Over the past decade, data base sizes have continually increased and will continue to do so in the future. This problem of size is further compounded because the trend in present-day studies is to use data from many different locations and different instruments and then compare it with data from global scale physical models. The latter produce data bases of comparable if not even larger size. Much of the data can be viewed as 'image' time sequences and is most readily viewed on color display terminals. These data sets reside in national or owner-generated data bases linked together by computer networks. As the size increases, just moving this data around, taking 'quick-looks' at the data, or even storing it locally become severe problems compromising the scientific return from the data. Is the present-day technology with these analysis techniques being used in the best way? What are the prospects for reducing the storage and transmission size of the data sets? Examples of such problems and potential solutions are described in this paper.

Sojka, J. J.↗
NASA NTRS2000

Developing a distributed data dictionary service

This paper will explore the use of the Lightweight Directory Access Protocol (LDAP) using the ISO 11179 Data Dictionary Schema as a mechanism for standardizing the structure and communication links between data dictionaries.

datasets data dictionaries Lightweight Directory A↗
NASA NTRS2015

Bundle Data Approach at GES DISC Targeting Natural Hazards

Severe natural phenomena such as hurricane, volcano, blizzard, flood and drought have the potential to cause immeasurable property damages, great socioeconomic impact, and tragic loss of human life. From searching to assessing the Big, i.e., massive and heterogeneous scientific data (particularly, satellite and model products) in order to investigate those natural hazards, it has, however, become a daunting task for Earth scientists and applications researchers, especially during recent decades. The NASA Goddard Earth Sciences Data and Information Service Center (GES DISC) has served Big Earth science data, and the pertinent valuable information and services to the aforementioned users of diverse communities for years. In order to help and guide our users to online readily (i.e., with a minimum effort) acquire their requested data from our enormous resource at GES DISC for studying their targeted hazard event, we have thus initiated a Bundle Data approach in 2014, first targeting the hurricane event topic. We have recently worked on new topics such as volcano and blizzard. The bundle data of a specific hazard event is basically a sophisticated integrated data package consisting of a series of proper datasets containing a group of relevant (knowledge--based) data variables readily accessible to users via a system-prearranged table linking those data variables to the proper datasets (URLs). This online approach has been developed by utilizing a few existing data services such as Mirador as search engine; Giovanni for visualization; and OPeNDAP for data access, etc. The online Data Cookbook site at GES DISC is the current host for the bundle data. We are now also planning on developing an Automated Virtual Collection Framework that shall eventually accommodate the bundle data, as well as further improve our management in Big Data.

GES DISC↗
DOE OSTI2014

Map Matching and Real World Integrated Sensor Data Warehousing (Presentation)

The inclusion of interlinked temporal and spatial elements within integrated sensor data enables a tremendous degree of flexibility when analyzing multi-component datasets. The presentation illustrates how to warehouse, process, and analyze high-resolution integrated sensor datasets to support complex system analysis at the entity and system levels. The example cases presented utilizes in-vehicle sensor system data to assess vehicle performance, while integrating a map matching algorithm to link vehicle data to roads to demonstrate the enhanced analysis possible via interlinking data elements. Furthermore, in addition to the flexibility provided, the examples presented illustrate concepts of maintaining proprietary operational information (Fleet DNA) and privacy of study participants (Transportation Secure Data Center) while producing widely distributed data products. Should real-time operational data be logged at high resolution across multiple infrastructure types, map matched to their associated infrastructure, and distributed employing a similar approach; dependencies between urban environment infrastructures components could be better understood. This understanding is especially crucial for the cities of the future where transportation will rely more on grid infrastructure to support its energy demands.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository

The Department of Energy's (DOE's) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and helping users access data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data. This paper provides an update on recent improvements made to the GDR's data lakes and automated data pipelines, including: (1) streamlining the data lake intake process, (2) better educating users on the process and requirements through a new data lakes page, (3) adding data lake direct access links to GDR data lake submission pages, (4) implementing a DAS data pipeline to convert DAS data uploaded in SEG-Y format to a standardized hierarchical data format v5 (HDF5), (5) extending this pipeline to encompass data in the GDR data lake, (6) adding metadata requirements for geospatial data, (7) making user interface/user experience (UX) enhancements to the data pipelines' documentation pages, and (8) improving the GDR's data standards and pipelines pages to better guide users in ensuring that their data is standardized by the GDR's automated data pipelines. 2024 Geothermal Resources Council. All rights reserved.

accessibility↗
DOE OSTI2017

Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance, porosity and lithofacies

We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and lithofacies within the reservoir conditioned to post-stack seismic and well data. The link between elastic and petrophysical properties is given by a joint prior distribution for the logarithm of impedance and porosity, based on a rock-physics model. The well conditioning is performed through a background model obtained by well log interpolation. Two different approaches are presented: in the first approach, the prior is defined by a single Gaussian distribution, whereas in the second approach it is defined by a Gaussian mixture to represent the well data multimodal distribution and link the Gaussian components to different geological lithofacies. The forward model is based on a linearized convolutional model. For the single Gaussian case, we obtain an analytical expression for the posterior distribution, resulting in a fast algorithm to compute the solution of the inverse problem, i.e. the posterior distribution of acoustic impedance and porosity as well as the facies probability given the observed data. For the Gaussian mixture prior, it is not possible to obtain the distributions analytically, hence we propose a Gibbs algorithm to perform the posterior sampling and obtain several reservoir model realizations, allowing an uncertainty analysis of the estimated properties and lithofacies. Both methodologies are applied to a real seismic dataset with three wells to obtain 3D models of acoustic impedance, porosity and lithofacies. The methodologies are validated through a blind well test and compared to a standard Bayesian inversion approach. Using the probability of the reservoir lithofacies, we also compute a 3D isosurface probability model of the main oil reservoir in the studied field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. Here, the result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this study, a Differential Evolution Adaptive Metropolis (DREAM) algorithm was used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The DREAM is a multi-chain method and uses differential evolution technique for chain movement, allowing it to be efficiently applied to high-dimensional problems, and can reliably estimate heavy-tailed and multimodal distributions that are difficult for single-chain schemes using a Gaussian proposal distribution. The results were evaluated against the popular Adaptive Metropolis (AM) scheme. DREAM indicated that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identified one mode. The calibration of DREAM resulted in a better model fit and predictive performance compared to the AM. DREAM provides means for a good exploration of the posterior distributions of model parameters. Lastly, it reduces the risk of false convergence to a local optimum and potentially improves the predictive performance of the calibrated model.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Spacelab data management subsystem phase B study

The Spacelab data management system is described. The data management subsystem (DMS) integrates the avionics equipment into an operational system by providing the computations, logic, signal flow, and interfaces needed to effectively command, control, monitor, and check out the experiment and subsystem hardware. Also, the DMS collects/retrieves experiment data and other information by recording and by command of the data relay link to ground. The major elements of the DMS are the computer subsystem, data acquisition and distribution subsystem, controls and display subsystem, onboard checkout subsystem, and software. The results of the DMS portion of the Spacelab Phase B Concept Definition Study are analyzed.

Source record↗
NASA NTRS1975

Experiment definition phase shuttle laboratory LDRL-10.6 experiment

The 10.6 microns laser data relay link (LDRL 10.6) program was directed to applying optical communications to NASA's wideband data transmission requirements through the 1980's. The LDRL consists of a transmitter on one or more low earth orbit satellites with an elliptical orbit satellite receivers. Topics discussed include: update of the LDRL design control table to detail the transmitter optical chain losses and to incorporate the change to a reflective beam pre-expander; continued examination of the link establishment sequence, including its dependence upon spacecraft stability; design of the transmitter pointing and tracking control system; and finalization of the transmitter brassboard optical and mechanical design.

Source record↗
NASA NTRS2012

Development of the Optical Communications Telescope Laboratory: A Laser Communications Relay Demonstration Ground Station

The Laser Communications Relay Demonstration (LCRD) project will demonstrate high bandwidth space to ground bi-directional optical communications links between a geosynchronous satellite and two LCRD optical ground stations located in the southwestern United States. The project plans to operate for two years with a possible extension to five. Objectives of the demonstration include the development of operational strategies to prototype optical link and relay services for the next generation tracking and data relay satellites. Key technologies to be demonstrated include adaptive optics to correct for clear air turbulence-induced wave front aberrations on the downlink, and advanced networking concepts for assured and automated data delivery. Expanded link availability will be demonstrated by supporting operations at small sun-Earth-probe angles. Planned optical modulation formats support future concepts of near-Earth satellite user services to a maximum of 1.244 Gb/s differential phase shift keying modulation and pulse position modulations formats for deep space links at data rates up to 311 Mb/s. Atmospheric monitoring instruments that will characterize the optical channel during the link include a sun photometer to measure atmospheric transmittance, a solar scintillometer, and a cloud camera to measure the line of sight cloud cover. This paper describes the planned development of the JPL optical ground station.

optical commuication↗
DOE OSTI2011

Designing the Cloud-based DOE Systems Biology Knowledgebase

Systems Biology research, even more than many other scientific domains, is becoming increasingly data-intensive. Not only have advances in experimental and computational technologies lead to an exponential increase in scientific data volumes and their complexity, but increasingly such databases themselves are providing the basis for new scientific discoveries. To engage effectively with these community resources, integrated analyses, synthesis and simulation software is needed, regularly supported by scientific workflows. In order to provide a more collaborative, community driven research environment for this heterogeneous setting, the Department of Energy (DOE) has decided to develop a federated, cloud based cyber infrastructure - the Systems Biology Knowledgebase (Kbase). Pacific Northwest National Laboratory (PNNL) with its long tradition in data intensive science lead two of the five initial pilot projects, these two focusing on defining and testing the basic federated cloud-based system architecture and develop a prototype implementation. Hereby the community wide accessibility of biological data and the capability to integrate and analyze this data within its changing research context were seen as key technical functionalities the Kbase needed to enable. In this paper we describe the results of our investigations into the design of a cloud based federated infrastructure for: (1) Semantics driven data discovery, access and integration; (2) Data annotation, publication and sharing; (3) Workflow enabled data analysis; and (4) Project based collaborative working. We describe our approach, exemplary use cases and our prototype implementation that demonstrates the feasibility of this approach.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

PNNL-ANL Hydrometeorological Super Ensemble

The current dataset contains data upload links to the following **hydrologic (water balance), river routing (water management), and hydropower simulation** data over CONUS: * Climate Forcing: **Livneh** (https://www.nature.com/articles/sdata201542) * Simulation Scenario: **Historical** * Simulation Period: **1971-2013 (1972-2013 for Hydropower)** * Simulation Models: **VIC (Variable Infiltration Capacity)**, **mosartwmpy (Model for Scale Adaptive River Transport-Water Management in Python)**, and **PNNL B1Hydro** * Output Format: **NetCDF** and **CSV**

Tidwell, Vincent C [Pacific Northwest National Lab↗
DOE OSTI2025

PNNL-ANL Hydrometeorological Super Ensemble

The current dataset contains data upload links to the following hydrologic (water balance), river routing (water management), and hydropower simulation data over CONUS: Climate Forcing: ClimRR (https://climrr.anl.gov/climrrdata) Simulation Scenario: Historical, Mid-Century, End-Century Simulation Period: 1995-2004, 2045-2054, 2085-2094 Simulation Models: VIC (Variable Infiltration Capacity), mosartwmpy (Model for Scale Adaptive River Transport-Water Management in Python), and PNNL B1Hydro Output Format: NetCDF and CSV

Tidwell, Vincent C [Pacific Northwest National Lab↗
DOE OSTI2025

TGW Hydrology, River Routing, and Hydropower Simulation Datasets

he current dataset contains data upload links to the following hydrologic (water balance), river routing (water management), and hydropower simulation data over CONUS: Climate Forcing: TGW (https://tgw-data.msdlive.org/) Simulation Scenario: Historical, Mid-Century, End-Century Simulation Period: 1980-2024, 2020-2059, 2060-2099 Simulation Models: VIC (Variable Infiltration Capacity), mosartwmpy (Model for Scale Adaptive River Transport-Water Management in Python), and PNNL B1Hydro Output Format: NetCDF and CSV

Tidwell, Vincent C [Pacific Northwest National Lab↗
NASA NTRS1988

Optical links in the angle-data assembly of the 70-meter antennas

In the precision-pointing mode the 70 meter antennas utilize an optical link provided by an autocollimator. In an effort to improve reliability and performance, commercial instruments were evaluated as replacement candidates, and upgraded versions of the existing instruments were designed and tested. The latter were selected for the Neptune encounter, but commercial instruments with digital output show promise of significant performance improvement for the post-encounter period.

Nelson, M. D.↗