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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

The AmeriFlux Data Activity and Data System: An Evolving Collection of Data Management Techniques, Tools, Products and Services

The Carbon Dioxide Information Analysis Center (CDIAC) at Oak Ridge National Laboratory (ORNL), USA has provided scientific data management support for the U.S. Department of Energy and international climate change science since 1982. Over this period, climate change science has expanded from research focusing on basic understanding of geochemical cycles, particularly the carbon cycle, to integrated research addressing climate change impacts, vulnerability, adaptation, and mitigation. Interests in climate change data and information worldwide have grown remarkably and, as a result, so have demands and expectations for CDIAC’s data systems. To meet the growing demands, CDIAC’s strategy has been to design flexible data systems using proven technologies blended with new, evolving technologies and standards. CDIAC development teams are multidisciplinary and include computer science and information technology expertise, but also scientific expertise necessary to address data quality and documentation issues and to identify data products and system capabilities needed by climate change scientists. CDIAC has learned there is rarely a single commercial tool or product readily available to satisfy long-term scientific data system requirements (i.e., one size does not fit all and the breadth and diversity of environmental data are often too complex for easy use with commercial products) and typically deploys a variety of tools and data products in an effort to provide credible data freely to users worldwide. Like many scientific data management applications, CDIAC’s data systems are highly customized to satisfy specific scientific usage requirements (e.g., developing data products specific for model use) but are also designed to be flexible and interoperable to take advantage of new software engineering techniques, standards (e.g., metadata standards) and tools and to support future Earth system data efforts (e.g., ocean acidification). CDIAC has provided data management support for numerous long-term measurement projects crucial to climate change science. One current example is the AmeriFlux measurement network. AmeriFlux provides continuous measurements from forests, grasslands, wetlands, and croplands in North, Central, and South America and offers important insight about carbon cycling in terrestrial ecosystems. We share our approaches in satisfying the challenges of delivering AmeriFlux data worldwide to benefit others with similar challenges handling climate change data, further heighten awareness and use of an outstanding ecological data resource, and highlight expanded software engineering applications being used for climate change measurement data.

Boden, Thomas A.↗
DOE OSTI2018

sympl (v. 0.4.0) and climt (v. 0.15.3) – towards a flexible framework for building model hierarchies in Python

sympl (System for Modelling Planets) andclimt (Climate Modelling and Diagnostics Toolkit) are anattempt to rethink climate modelling frameworks from the ground up. The aimis to use expressive data structures available in the scientific Pythonecosystem along with best practices in software design to allow scientists toeasily and reliably combine model components to represent the climate systemat a desired level of complexity and to enable users to fully understandwhat the model is doing. sympl is a framework which formulates the model in terms of astate that gets evolved forward in time or modified within a specifictime by well-defined components. sympl's design facilitates buildingmodels that are self-documenting, are highly interoperable, and providefine-grained control over model components and behaviour. symplcomponents contain all relevant information about the input they expect andoutput that they provide. Components are designed to be easily interchanged,even when they rely on different units or array configurations.sympl provides basic functions and objects which could be used inany type of Earth system model. climt is an Earth system modelling toolkit that contains scientificcomponents built using sympl base objects. These include both purePython components and wrapped Fortran libraries. climt providesfunctionality requiring model-specific assumptions, such as stateinitialization and grid configuration. climt's programming interfacedesigned to be easy to use and thus appealing to a wide audience. Model building, configuration and execution are performed through a Pythonscript (or Jupyter Notebook), enabling researchers to build an end-to-endPython-based pipeline along with popular Python data analysis andvisualization tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Rapid Evaluation Framework for the CMIP7 Assessment Fast Track

As Earth system models (ESMs) grow in complexity and in volume of output data, there is an increasing need for rapid, comprehensive evaluation of their scientific performance. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require expeditious response for model analyses designed to inform and drive integrated Earth system assessments. To meet this challenge, the Rapid Evaluation Framework (REF), a community-driven platform for benchmarking and performance assessment of ESMs, was designed and developed. The initial implementation of the REF, constructed to meet the near-term needs of the CMIP7 Assessment Fast Track, builds upon four disparate community evaluation and benchmarking tools that are coupled together using the Coordinated Model Evaluation Capabilities (CMEC) framework. The REF runs within a containerized workflow for portability and reproducibility and is aimed at generating and organizing diagnostics covering a variety of model variables. The REF leverages well documented observational datasets to provide assessments of model fidelity across a collection of diagnostics. All diagnostics were identified and selected with community involvement and consultation. Operational integration with the Earth System Grid Federation (ESGF) will permit automated execution of the REF for selected diagnostics as soon as model output data are published on ESGF by the originating modeling centers. The REF is designed to be portable across a range of current computational platforms to facilitate use by modeling centers for assessing the evolution of model versions or gauging the relative performance of CMIP simulations before being published on ESGF. When integrated into production simulation workflows, results from the REF provide immediate quantitative feedback that allows model developers and scientists to quickly identify model biases and performance issues. After the REF is released to the community, its subsequent development and support will be prioritized by an international consortium of scientists and engineers, enabling a broader impact across Earth science disciplines. For instance, the REF will facilitate improvements to models and will enhance confidence in model projections through process-based selection of models based on their performance with respect to observations. Production of reproducible diagnostics and community-based assessments are key features of the REF. Furthermore, providing interoperability with existing evaluation packages assures that contributions from previous community efforts will be available for use in future model intercomparison projects.

Hoffman, Forrest [ORNL] (ORCID:0000000158024134)↗
DOE OSTI2022

Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis

Scientific communities are increasingly publishing data to evaluate, accredit, and build on published research. However, guidelines for curating data for publication are sparse for model-related research, limiting the usability of archived simulation data. In particular, there are no established guidelines for archiving data related to terrestrial models that simulate land processes and their coupled interactions with climate. Terrestrial modelers have a unique set of challenges when publishing data due to the diversity of scientific domains, research questions, and the types and scales of simulations. Researchers in the U.S. Department of Energy’s (DOE) projects use a variety of multiscale models to advance robust predictions of terrestrial and subsurface ecosystem processes. Here, we synthesize archiving needs for data associated with different DOE models, and provide guidelines for publishing terrestrial model data components following FAIR (Findable, Accessible, Interoperable, Reusable) principles. The guidelines recommend archiving model inputs and testing data used in final simulation runs along with associated codes, workflow scripts, and metadata in public repositories. Researchers should consider archiving model outputs if they are within the storage limits of the repository. We also provide considerations for how to bundle files into different data publications with citable digital object identifiers. Finally, we identify repository features and tools that would enable storage and reuse of model data. Given the diversity of DOE terrestrial models, these guidelines are transferable to other model types and will enable efficient reuse of simulation data for purposes such as model intercomparisons, initialization, benchmarking, synthesis, and comparisons with field observations.

58 GEOSCIENCES↗
DOE OSTI2013

Retracted: Eddy-Covariance and Auxiliary Measurements, NGEE-Barrow, 2012-2013

Redirect - This dataset has been superseded and is no longer available to download but is available by contacting the archive. Users are now directed to "Flux Tower Eddy Covariance and Meteorological Measurements for Barrow, Alaska: 2012-2016" https://doi.org/10.5440/1362279. This record does include the three original files, two .xlsx files and one .docx, as well as three versions created for interoperability in csv and pdf. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Calibrated Hydrothermal Parameters, Barrow, Alaska, 2013

A model-observation-experiment process (ModEx) is used to generate three 1D models of characteristic micro-topographical land-formations, which are capable of simulating present active layer thickness (ALT) from current climate conditions. Each column was used in a coupled calibration to identify moss, peat and mineral soil hydrothermal properties to be used in up-scaled simulations. Observational soil temperature data from a tundra site located near Barrow, AK (Area C) is used to calibrate thermal properties of moss, peat, and sandy loam soil to be used in the multiphysics Advanced Terrestrial Simulator (ATS) models. Simulation results are a list of calibrated hydrothermal parameters for moss, peat, and mineral soil hydrothermal parameters drawn from literature and field observations at the NGEE-Arctic site. Find more details in the associated publication by Atchley, et.al, 2015.There is one data file as both a .xlsx and .csv. The datafile was transformed into a csv for interoperability, however some information might have been lost in the transformation.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Soil Organic Horizon Exometabolomics Extracts and Soil Characteristics with Depth, Utqiagvik (Barrow), Alaska, 2014

This dataset includes chemical characterization data for four soil cores (organic horizon) collected in August 2014 from beneath two vegetation types (Eriophorum angustifolium and Carex aquatilis) from the centers of a low-centered and high-centered polygon at NGEE Areas A and B, respectively, on the Barrow Environmental Observatory near Barrow, Alaska. Measurements include gravimetric soil water content, total carbon and nitrogen, total organic carbon, and metabolomics data (low molecular weight organics). Data from the file Processed_LMW_DOM_Results_20190422.xlsx was separated out into csv files for interoperability. This dataset contains one xlsx file, twelve csv files, two pdf files, one txt file, and four zipped files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Common practices for quantifying methane emissions from plumes detected by remote sensing

This document provides a set of community-accepted practices for quantifying methane emissions based on plumes detected via spectroscopic remote sensing. Its primary goal is to promote consistency in the generation, validation, reporting, and quality assessment of methane emission estimates derived from remote sensing radiances. Developed by subject matter experts with deep experience across all stages of the measurement process, this guidance reflects a critical evaluation of current methodologies and highlights key practices needed to produce reliable, interoperable, and traceable products. The focus is specifically on methane emissions quantified from distinct plumes originating from localized sources, rather than diffuse emissions spread over large regions, which are beyond the scope of this work. This document is intended to serve both data producers and users. For producers, it offers a framework for aligning with field-recognized standards to ensure their outputs meet rigorous quality and transparency criteria. For users, it provides a reference to assess dataset fitness-for-purpose by highlighting essential metadata, assumptions, and methodological choices that underpin emission estimates. By fostering a shared understanding of best practices, this work aims to enhance comparability, confidence, and utility of remotely sensed methane emission products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗
DOE OSTI2026

Building a Trusted Roaming Hub [Slides]

The Trusted Roaming Hub is a U.S. Department of Energy-backed initiative led by the National Laboratory of the Rockies (NLR) to address one of the most persistent challenges in electric vehicle (EV) charging: fragmented roaming, inconsistent interoperability, and insufficient digital trust across charging networks. As EV adoption accelerates and charging infrastructure scales nationwide, today's many-to-many integration model between eMobility Service Providers (eMSPs) and Charge Point Operators (CPOs) has become increasingly brittle, costly, and difficult to secure. The Trusted Roaming Hub introduces a neutral, cybersecurity-forward "switchboard" architecture that enables standardized, secure, and scalable roaming interactions across the EV charging ecosystem. Rather than replacing existing networks or commercial relationships, the hub acts as a trusted intermediary that enforces consistent identity, authentication, authorization, and routing across participants improving reliability for drivers, lowering integration burden for industry, and creating a foundation for future grid-interactive charging services. This read-ahead provides an overview of the problem the hub is designed to solve, the core functional and security concepts behind the architecture, the value proposition to key stakeholders, and the near-term trajectory of the work.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Science Uses Deployment Operations-Advanced Wireless: Exploring Open Radio Access Network Technologies for Energy Science

Open Radio Access Network is emerging as a solution to the increasing demand for more flexible, cost-effective, and advanced mobile network infrastructures. This evolution is driven by advancements in wireless technologies and the growing complexity of deploying and managing these networks. O-RAN represents a significant shift in wireless technology, building upon the 3rd Generation Partnership Project framework to foster openness, flexibility, and interoperability. By decoupling hardware and software components, Open Radio Access Network enables a multi-vendor ecosystem that encourages innovation and diverse solutions. Open Radio Access Network's potential extends beyond traditional wireless applications, with growing interest in its role in advancing energy systems, particularly in the context of smart grids, microgrids, and the integration of renewable energy sources. While the role of open-wireless technologies in driving energy transformation is increasingly recognized, further exploration is needed. Vendors and utilities are investigating how Open Radio Access Network technologies can optimize energy use cases and improve the performance of 5G and beyond applications. This report outlines efforts under the Science Uses Deployment Operations Advance Wireless project, a collaboration between the National Laboratory of the Rockies' Cybersecurity Research Center, Argonne National Laboratory, Lawrence Berkeley National Laboratory, and the Department of Energy's Energy Science Network research and operations staff. The focus of this project is on due diligence, through testing and evaluation, preparing for the deployment of advanced wireless infrastructure for scientific use cases, with an emphasis on Open Radio Access Network technology, its components, integrations, and its ability to support vertical stack application across the energy sector. Additionally, the report highlights the value cases for utilities, underscoring how adopting open wireless standards can accelerate the evolution of energy systems, foster innovation, and improve the integration of critical energy technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

The Neurodata Without Borders ecosystem for neurophysiological data science

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Emerging semantics to link phenotype and environment

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. Lastly, in this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Software Development for a CompactRIO-Based Wire Scanner Control and Data Acquisition SYstem

The Beam Diagnostics and Instrumentation Team at the Los Alamos Neutron Science Center is developing a wire scanner data acquisition and control system with a National Instrument's compactRIO at its core. For this application, the compactRIO controller not only requires programming the FPGA and RT computer internal to the compactRIO, but also requires programming a client computer and a touch panel display. This article will summarize the hardware interfaces and describe the software design approach utilized for programming and interfacing the four systems together in order to fulfill the design requirements and promote reliable interoperability.

42 ENGINEERING↗
DOE OSTI2012

Rapid Application Development with OpenStudio: Preprint

This paper presents several case studies of rapidly implemented, audience-specific applications for whole building energy modeling and standards analysis. By tailoring each application to the audience and the task at hand, the required learning curve for new users was greatly reduced. Each case study used OpenStudio, the U.S. Department of Energy's middleware software development kit (SDK).OpenStudio provides an easy interface to the EnergyPlus whole building simulation engine, while extending its capability and providing higher-level functionality such as software interoperability, standards, analysis, and optimization. Each case study is unique in the technology employed to interface with OpenStudio as well as the methods used for user interaction and data presentation. Fourcase studies are presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

The Earth System Grid Federation: An Open Infrastructure for Access to Distributed Geospatial Data

The Earth System Grid Federation (ESGF) is a multi-agency, international collaboration that aims at developing the software infrastructure needed to facilitate and empower the study of climate change on a global scale. The ESGF s architecture employs a system of geographically distributed peer nodes, which are independently administered yet united by the adoption of common federation protocols and application programming interfaces (APIs). The cornerstones of its interoperability are the peer-to-peer messaging that is continuously exchanged among all nodes in the federation; a shared architecture and API for search and discovery; and a security infrastructure based on industry standards (OpenID, SSL, GSI and SAML). The ESGF software is developed collaboratively across institutional boundaries and made available to the community as open source. It has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the entire model output used for the next international assessment report on climate change (IPCC-AR5) and a suite of satellite observations (obs4MIPs) and reanalysis data sets (ANA4MIPs).

Climate Modelling↗
DOE OSTI2013

NATIONAL GEOTHERMAL DATA SYSTEM: AN EXEMPLAR OF OPEN ACCESS TO DATA

The formal launch of National Geothermal Data System (NGDS – www.geothermaldata.org) in 2014 will provide open access to technical geothermal-relevant data from all of the Department of Energy- sponsored geothermal development and research projects and geologic data from all 50 states. By making data easily discoverable and accessible this system will open new exploration opportunities and shorten project development. The prototype data system currently includes multiple data nodes, and nationwide data online and available to the public, indexed through a single catalog under construction at http://search.geothermaldata.org. Data from state geological surveys and partners includes more than 5 million records online, including 1.48 million well headers (oil and gas, water, geothermal), 732,000 well logs, and 314,000 borehole temperatures and is growing rapidly. There are over 250 Web services and another 138 WMS (Web Map Services) registered in the system as of August, 2013. Additional data record is being added by companion projects run by Boise State University, Southern Methodist University, and the USGS. The National Renewable Energy Laboratory is managing the Geothermal Data Repository, an NGDS node that will be a clearinghouse for data from hundreds of DOE-funded geothermal projects. NGDS is built on the US Geoscience Information Network (USGIN) data integration framework, which is a joint undertaking of the USGS and the Association of American State Geologists (AASG). NGDS is fully compliant with the White House Executive Order of May 2013, requiring all federal agencies to make their data holdings publicly accessible online in open source, interoperable formats with common core and extensible metadata. The National Geothermal Data System is being designed, built, deployed, and populated primarily with grants from the US Department of Energy, Geothermal Technologies Office. To keep this operational system sustainable after the original implementation will require four core elements: continued serving of data and applications by providers; maintenance of system operations; a governance structure; and an effective business model. Each of these presents a number of challenges currently under consideration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

Argonne's SpEC Module

Jason Harper, an electrical engineer in Argonne National Laboratory's EV-Smart Grid Interoperability Center, discusses his SpEC Module invention that will enable fast charging of electric vehicles in under 15 minutes. The module has been licensed to BTCPower.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗