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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Towards a Global MAP Data Base

Scientific and ecological needs call for the generation of a global data base of measured data from the Earth's atmosphere, similar but complementary to the data of the meteorological service. Right now there is a large gap between these needs and the available financial means. If this prevails, it might be prohibitive for the generation of such a data base. There must be a compromise between the needs and the means. Priorities must be set. Thus, discussions should be started on which data can be or should be stored. Newly generated data for storage in such a data base is recommended to be handled by the World Data Centers (WDCs). This brief report should help to stimulate discussions and ubsequent actions.

Hartmann, G. K.↗
NASA NTRS1978

Comparison of several NASTRAN analytical techniques for large structures

In order to plan for the finite element structural analysis of future aircraft, five static analysis techniques using the MacNeal-Schwendler Corporation version of NASTRAN were evaluated. The structure was analyzed as: (1) a single model with a symmetric loading condition; (2) a single model with symmetric/nonsymmetric loading conditions; (3) three substructures in three phases using tape storage with a symmetric loading condition; (4) three superelements using data base storage with a symmetric loading condition; and (5) three superelements using data base storage with cyclic symmetry for symmetric/nonsymmetric loading conditions. The superelement techniques proved superior to the single model approaches by reducing computer time for redesign work by as much as 70 percent.

Zemer, D. T.↗
DOE OSTI2023

Improving the Quality of Geothermal Data Through Data Standards and Pipelines Within the Geothermal Data Repository: Preprint

For machine learning outputs to be applicable to real world problems, high quality data are needed to ensure high quality results. With the more recent emphasis on machine learning in geothermal, there is an increasing need for greater focus on the quality of the data available for use in these projects. For example, Geothermal Operational Optimization Using Machine Learning (GOOML) utilized large quantities of geothermal power plant operational data to inform power plant operational configurations to maximize power generation. High quality datasets result from dependable sensors or devices collecting data, high frequency of measurements, sufficient data points, adequate metadata, reliable storage of data, and sufficient data curation. Another component that contributes to high quality data is reusability, which can be enhanced through data standardization. Data Standardization creates consistency in formatting and contents of like datasets, lessening preprocessing requirements and ensuring adequate information provided by a given dataset. The Geothermal Data Repository (GDR) aims to help improve data quality through automated data standardization for high-value datasets through the implementation of data pipelines alongside reliable and accessible long-term storage for datasets. As such, the GDR has decided to shift away from recommending the use of Excel-based content models and towards the implementation of automated data pipelines. This takes the burden of data standardization off the user and project team and will increase the availability of standardized geothermal data available through the GDR. A set of recommendations, or a data standard for each data type will exist with each data pipeline in order to advise data collection for maximum usability for future research. This paper serves to describe the GDR's proposed transition towards data standardization through automated data pipelines, to discuss the need for and value of such a shift, and to call for suggestions from the community regarding the most useful data standards and pipelines.

data↗
DOE OSTI2021

Dynamo: Handling Scientific Data Across Sites and Storage Media

Abstract Dynamo is a full-stack software solution for scientific data management. Dynamo’s architecture is modular, extensible, and customizable, making the software suitable for managing data in a wide range of installation scales, from a few terabytes stored at a single location to hundreds of petabytes distributed across a worldwide computing grid. This article documents the core system design of Dynamo and describes the applications that implement various data management tasks. A brief report is also given on the operational experiences of the system at the CMS experiment at the CERN Large Hadron Collider and at a small-scale analysis facility.

Iiyama, Yutaro (ORCID:0000000282975930)↗
NASA NTRS1972

Simulation of mass storage systems operating in a large data processing facility

A mass storage simulation program was written to aid system designers in the design of a data processing facility. It acts as a tool for measuring the overall effect on the facility of on-line mass storage systems, and it provides the means of measuring and comparing the performance of competing mass storage systems. The performance of the simulation program is demonstrated.

Holmes, R.↗
DOE OSTI2024

Underground hydrogen storage leakage detection and characterization based on machine learning of sparse seismic data

Underground hydrogen storage (UHS) is considered as a scalable approach for massive storage and seasonal extraction of hydrogen (H 2 ). Although conventional leakage detection and characterization methods based on time-lapse seismic imaging and inversion generally apply to H 2 leakage detection problem, a high-fidelity yet cost effective geophysics approach is still missing to reliably inform leakage location and properties based on very sparse data. In response, we develop a novel supervised machine learning method to detect and characterize H 2 leakage from UHS. The input to our neural network are sparse time-lapse seismic waveforms, while the output from the neural network includes the spatial location and physical properties of a H 2 leakage. Here, we generate high-quality time-lapse waveforms using the elastic-wave equations to train the neural network. We train and validate our machine learning model and find that it attains high accuracy in using extremely sparse time-lapse seismic data to detect and characterize H 2 leakage. Our investigation is the first systematic study that focuses on applying machine learning to subsurface H 2 leakage detection and characterization and could potentially serve as a cost-effective geophysical tool for underground hydrogen leakage detection and characterization with high fidelity.

08 HYDROGEN↗
DOE OSTI2019

Grid Data Crossing Platform (GriD-Xing)

This project is to develop a data management and analytics platform called Grid Data Crossing (GriD-Xing) that integrates data processing, storage, analysis, and visualization functionalities for users at industry (e.g., EMS and DMS) and universities. It intends to increase the applicability of large set of multi-source data (e.g., SCADA, PMU, μPMU, AMI, inverter, load/DER forecasting, and cyber data) and eventually improve the resilience, reliability, and security of critical energy infrastructure. GriD-Xing also intends to be a research platform to identify the usefulness of large multi-source data via case studies of interest to industry (e.g., 2011 southwest cold weather and 2016 Blue Cut fire events). In the first year, the team focused on the development and demonstration of large multi-source data (e.g., PMU, μPMU, SCADA, AMI, and cyber data) processing, storage, and analysis. Specifically, the team designed GriD-Xing framework in functional and structural aspects, developed GriD-Xing algorithms for large multi-source grid data processing, storage, and analysis, and finally demonstrated GriD-Xing prototype algorithms performance through a series of tests and case studies using real-world and simulation data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Biomass for Carbon Removal and Storage (BiCRS) Biomass and Bioproduct Data Table

The Biomass for Carbon Removal and Storage (BiCRS) Biomass and Bioproduct Data Table was generated using data from biomass and bioproducts (i.e., biochar, pyrolysis oil) supplied by BiCRS project developers. Data was obtained via analytical characterization methodologies listed in the document at the National Laboratory of the Rockies.

09 BIOMASS FUELS↗
DOE OSTI2018

Mission possible: Unify HPC and Big Data stacks towards application-defined blobs at the storage layer

HPC and Big Data stacks are completely separated today. The storage layer offers opportunities for convergence, as the challenges associated with HPC and Big Data storage are similar: trading versatility for performance. This motivates a global move towards dropping file-based, POSIX-IO compliance systems. However, on HPC platforms this is made difficult by the centralized storage architecture using file-based storage. In this paper we advocate that the growing trend of equipping HPC compute nodes with local storage redistributes the cards by enabling object storage to be deployed alongside the application on the compute nodes. Furthermore, such integration of application and storage not only allows fine-grained configuration of the storage system, but also improves application portability across platforms. In addition, the single-user nature of such application-specific storage obviates the need for resource-consuming storage features like permissions or file hierarchies offered by traditional file systems. In this article we propose and evaluate Blobs (Binary Large Objects) as an alternative to distributed file systems. Finally, we factually demonstrate that it offers drop-in compatibility with a variety of existing applications while improving storage throughput by up to 28%.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

The Challenges Facing Science Data Archiving on Current Mass Storage Systems

This paper discusses the desired characteristics of a tape-based petabyte science data archive and retrieval system required to store and distribute several terabytes (TB) of data per day over an extended period of time, probably more than 115 years, in support of programs such as the Earth Observing System Data and Information System (EOSDIS). These characteristics take into consideration not only cost effective and affordable storage capacity, but also rapid access to selected files, and reading rates that are needed to satisfy thousands of retrieval transactions per day. It seems that where rapid random access to files is not crucial, the tape medium, magnetic or optical, continues to offer cost effective data storage and retrieval solutions, and is likely to do so for many years to come. However, in environments like EOS these tape based archive solutions provide less than full user satisfaction. Therefore, the objective of this paper is to describe the performance and operational enhancements that need to be made to the current tape based archival systems in order to achieve greater acceptance by the EOS and similar user communities.

Peavey, Bernard↗
DOE OSTI2021

Programming Abstractions for Managing Workflows on Tiered Storage Systems

Scientific workflows in High Performance Computing (HPC) environments are processing large amounts of data. The storage hierarchy on HPC systems is getting deeper, driven by new technologies (NVRAMs, SSDs, etc.) There is a need for new programming abstractions that allow users to seamlessly manage data at the workflow level on multi-tiered storage systems, and provide optimal workflow performance and use of storage resources. In previous work, we introduced a software architecture Managing Data on Tiered Storage for Scientific Workflows (MaDaTS) that used a Virtual Data Space (VDS) abstraction to hide the complexities of the underlying storage system while allowing users to control data management strategies. In this article, we detail the data-centric programming abstractions that allow users to manage a workflow around its data on the storage layer. The programming abstractions simplify data management for scientific workflows on multi-tiered storage systems, without affecting workflow performance or storage capacity. We measure the overheads and effectiveness introduced by the programming abstractions of MaDaTS. Our results show that these abstractions can optimally use the storage capacity in lesser capacity storage tiers, and simplify data management without adding any performance overheads.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Multi-system analysis of offshore geologic carbon storage: a review of open-source data science solutions

Geologic carbon storage projects are maturing worldwide and the footprint of deployment in the offshore is expanding. At present, there are ten projects in operation or that have been completed, more than 50 in construction and development, and dozens of characterization studies completed or underway. Offshore geologic carbon storage offers potential benefits over onshore geologic carbon storage. These offshore projects are generally remote in location, distant from population centers, and avoid complicated pore space rights while having abundant prospective storage potential. Some offshore fields targeted for carbon storage have comparatively fewer prior borehole penetrations except for areas that have been explored for petroleum production, minimizing potential issues such as pressure interference and infrastructure impacts. Yet offshore geologic carbon storage projects face distinctive technical and economic challenges, such as seafloor geohazards (e.g., seabed instability), expensive maritime transport, and meteorological-oceanographic conditions that can damage infrastructure and impact operations. Analytical capabilities and improved computational speeds have advanced engineering, earth and energy sciences in the wake of the arrival of modern data science over the last decade. These advancements have created an opportunity for integrated, multi-systems modeling approaches utilizing artificial intelligence and machine learning that are no longer limited by computational issues. Analytical tools developed alongside this advancement in data science can be leveraged to calibrate the potential advantages and challenges of carbon storage operations in the offshore. New methods and approaches that incorporate data science to analyze multiple aspects of engineered and natural systems can provide insights that complement the characterization and onsite engineering that traditional commercial and operational software addresses. These new methods and approaches can potentially improve the outcome of energy operations and carbon storage. Providing multi-system, science-driven data analytics enhances the knowledge base that offshore developers, operators, and regulatory bodies may draw from to improve offshore site selection and operational efficiency. Here, we provide a brief synopsis of geologic carbon storage efforts to date, an overview of the engineered and natural systems involved in offshore geologic carbon storage, and a review of publicly available, open-source, offshore and/or carbon storage related data- and science-driven tools developed by 2010 or later that are suitable for screening and assessing regions for offshore geologic carbon storage.

artificial intelligence↗
DOE OSTI2025

Carbon Storage Technical Viability Approach (CS TVA) Data Availability Results Database

The Carbon Storage Technical Viability Approach (CS TVA) Data Availability Results Database v1.0 is the output of a data availability assessment workflow that was developed by NETL to create a tool for spatial data availability assessment. This results database is the output of the first version of the workflow performed on the data with the CS TVA Database v2.0.

carbon storage↗
DOE OSTI2022

Community Resilience through Low-Temperature Geothermal Reservoir Thermal Energy Storage

Submitted data include simulations related to underground thermal battery (UTB) simulations described in Modeling and efficiency study of large scale underground thermal battery deployment, presented at GRC, October 2021. The UTB is comprised of a tank of water, a helical heat exchanger in the center of tank and connected to a water source heat pump, and a phase change material (PCM). Compared to a conventional VBGHE, the UTB is designed to be installed at a much shallower depth, therefore, with a cheaper cost. In addition, the GSHP efficiency is improved due to natural convection of water and additional load capacity provided by PCM. The goal of this study is to explore factors that may affect the efficiency of large-scale UTB deployment. The simulations found in this submission relate to the report on UTB deployment.

15 GEOTHERMAL ENERGY↗
NASA NTRS1975

Use of information-retrieval languages in automated retrieval of experimental data from long-term storage

Problems and methods are discussed of automating information retrieval operations in a data bank used for long term storage and retrieval of data from scientific experiments. Existing information retrieval languages are analyzed along with those being developed. The results of studies discussing the application of the descriptive 'Kristall' language used in the 'ASIOR' automated information retrieval system are presented. The development and use of a specialized language of the classification-descriptive type, using universal decimal classification indices as the main descriptors, is described.

Khovanskiy, Y. D.↗