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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Azure Data Tools (FKA: OEDI (Open Energy Data Initiative) Data Access Tools) [SWR-23-92]

The Open Energy Data Initiative (OEDI) provides a number of tools to enable the use of the open data published through this initiative. The source is largely written in Python, including Jupyter notebooks. The Open Energy Data Initiative (OEDI) is a partnership between the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), Amazon, Microsoft, and Google to provide universal access to big data in the cloud. At the heart of OEDI is a centralized repository of high-value energy research datasets aggregated from DOE Program Offices, National Laboratories and other collaborators.

Jonathan, Weers↗
DOE OSTI2020

Open Energy Data Initiative (OpenEDI) - Open Data Access Tools (OEDI-ODAT) [SWR 20-57]

Open Energy Data Initiative (OpenEDI): Advancing Analytics and Research Innovation through Improved Data Access. The Open Energy Data Initiative (OEDI) aims to improve and automate access of high-value energy data sets across the U.S. Department of Energy’s (DOE’s) programs, offices, and national laboratories. Sponsored by the DOE, this platform is being implemented by the National Renewable Energy Laboratory (NREL) to make data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation. Partners at other national laboratories will help utilize the platform for analysis, can be directly involved in determining requirements, and will be resources for providing new datasets that can be shared with the public to expand innovation.

Rager, David↗
DOE OSTI2021

Source term method for binary neutron stars initial data

The initial condition problem for a binary neutron star system requires a Poisson equation solver for the velocity potential with a Neumann-like boundary condition on the surface of the star. Difficulties that arise in this boundary value problem are: (a) the boundary is not known a priori, but constitutes part of the solution of the problem; (b) various terms become singular at the boundary. In this work, we present a new method to solve the fluid Poisson equation for irrotational/spinning binary neutron stars. The advantage of the new method is that it does not require complex fluid surface fitted coordinates and it can be implemented in a Cartesian grid, which is a standard choice in numerical relativity calculations. This is accomplished by employing the source term method proposed by Towers, where the boundary condition is treated as a jump condition and is incorporated as additional source terms in the Poisson equation, which is then solved iteratively. The issue of singular terms caused by vanishing density on the surface is resolved with an additional separation that shifts the computation boundary to the interior of the star. We present two-dimensional tests to show the convergence of the source term method, and we further apply this solver to a realistic three-dimensional binary neutron star problem. By comparing our solution with the one coming from the initial data solver cocal, we demonstrate agreement to approximately 1%. We report our method can be used in other problems with non-smooth solutions like in magnetized neutron stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Advancing Our Understanding of System Availability through the PV Fleet Performance Data Initiative

The PV Fleet Performance Data Initiative partners with photovoltaic (PV) fleet owners to collect time-series data of PV production data and publishes aggregated anonymized results of system performance metrics. With an extensive dataset drawn from over 2,200 PV systems across the United States, comprising 8.5 GW and 24,000 separate inverter data channels, this initiative aims to ensure that systemic risks in the US PV fleet are detected. The current work explores system availability, revealing a pronounced dependence on time, especially within the initial 6 months of system performance. Following this start-up period, the average system availability stabilizes. Statistical analyses illustrate a median (P5O) monthly availability of 0.991 and a dependence on system size with a negative trend in availability with increasing system size. This finding indicates that larger systems experience lower availability compared to their smaller counterparts.

inverter availability↗
DOE OSTI2022

DOE COVID-19 Data Curation Effort: Overview of Initial Data Collection Coverage (March - June 2020)

During the COVID-19 pandemic of 2020, major case reporting outlets quickly coalesced around two or three primary vendors. Johns Hopkins University and The New York Times were among the more prominent, and all were of great value to the nation, particularly during the uncertain early stages of the pandemic. They primarily focused on three major attributes: number of new cases, deaths, and recovery, but only at the state level. Recognizing that many states were reporting very detailed data sets (e.g., hospital beds) at a count level or finer, the ORNL Pandemic Modeling team embarked on a major data curation effort from March to June 2020 for the purpose of capturing this wealth of detailed data. The challenge of curating this data was daunting. The number of attributes reported by the states grew on almost on a weekly basis. States were routinely shifting their web tool strategies away from easily parsable HTML-based formatting to new Tableau and ArcGIS content. This growth in the sheer number of attributes combined with the unpredictable shifts in data format meant an aggressive and agile combination of automated scripting and manual scraping was required to capture new daily streams. To keep up, the team had to scale up staff and widen its approach for capture and storage. The DOE COVID-19 data collection effort resulted in over 11 million data points being collected, covering over 13,000 unique geographies and over 2,000 unique attributes that spanned predominantly from early March through the end of June 2020.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗
DOE OSTI2025

Open Energy Data Initiative (OEDI) FY22-24 (Final Technical Report)

Final technical report for the Open Energy Data Initiative (OEDI) project covering fiscal years FY22 through FY24. The DOE Open Energy Data Initiative (OEDI) is a partnership between the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and major cloud providers including Amazon, Microsoft, and Google to provide universal access to big data in the cloud. At the heart of OEDI is a centralized repository of high-value energy research datasets aggregated from the U.S. Department of Energy's Program Offices, National Laboratories and other collaborators. It aggregates smaller, domain-specific repositories, allows direct data submissions, and includes support for big data through its energy data lakes. OEDI's data lakes make high-value data universally accessible and help researchers, collaborators and the general public overcome many of the obstacles to accessing and using big data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Data-Centric AI and the Open Energy Data Initiative (OEDI)

This presentation emphasizes the critical importance of data-centric AI. The limitations of model-centric AI when dealing with poor or insufficient data are highlighted, and it is illustrated how training models on inaccurate or noisy data leads to suboptimal results. This talk advocates for a hybrid approach that combines a focus on data quality and model parameters to achieve optimal results. The Open Energy Data Initiative (OEDI) is introduced as a valuable resource for obtaining high-quality energy-related datasets, hosting nearly 2,000 publicly accessible datasets, including 99 solar-related datasets, totaling over 2.7 petabytes of data. OEDI's data lakes enable users to query and work with data without extensive transfers. In conclusion, the significance of data-centric AI and adherence to data curation best practices is emphasized, positioning OEDI as a prime source of high-quality data for AI and machine learning in the renewable energy sector.

AI↗
DOE OSTI2024

Survey of Use Cases and Scenarios on the Open Energy Data Initiative Solar Systems Integration (OEDI SI) Platform

The Open Energy Data Initiative Solar Systems Integration (OEDI SI) Data and Modeling Platform offers a comprehensive set of use cases tailored for power systems analysis. Each use case is centered around a specific power system analysis problem, supported by composite input data and reference algorithms. These composite input datasets are meticulously assembled using OEDI SI's data preprocessing tools, which integrate raw data from various sources. The primary objectives of the OEDI SI Platform include facilitating access to composite input data through widely accepted input/output formats and verified results. This accessibility enables power system network researchers and developers to validate their algorithms and showcase their applications' capabilities to the broader community. Moreover, the platform strives to promote reproducible, robust, replicable, and generalizable solar systems integration research.

14 SOLAR ENERGY↗
DOE OSTI2020

PV Fleet Performance Data Initiative (March 2020 Methodology Report)

Data for thousands of systems by multiple sources were transmitted to NREL for evaluation. Initial automated data quality assurance (QA) checks identified hundreds of systems (QA Tier 1) with high-quality meteorological and AC production data and hundreds of QA Tier 2 systems with adequate data quality. A standard RdTools analysis was conducted for these systems, evaluating performance loss on an annualized basis. To date with the systems evaluated so far, we find a median performance loss rate (aka Rd or degradation rates) for the entire fleet of -0.72%/year based on individual inverter data, which is in line with historical degradation rates previously published for modules and systems (-0.5% to -0.9% / yr, Jordan et al. 2016). A fleet-scale distribution based on revenue-grade meter data rather than inverter AC (alternating current) data yields a slightly different result. We are working to understand the difference between this result and the inverter-based result of -0.72%/year.

14 SOLAR ENERGY↗
DOE OSTI2026

PV Fleet Performance Data Initiative 2026 Update

We provide an update on the PV Fleet Performance Data Initiative at the 2026 PV Reliability Workshop. Our latest runs incorporate additional data sources and an integrated analysis pipeline run on our Kestrel HPC cluster. Initial degradation findings suggest that single-axis tracked PV systems exhibit higher performance loss rates than fixed-tilt systems, an increase of 0.5 %/yr, almost double. We discuss multiple methods for identifying stuck tracker rows, which are suspected to be a contributor to the enhanced degradation. Through satellite image detection and data-driven approaches we address the topic of identifying when stuck trackers are occuring and to what extent the problem exists. Preliminary results suggest that the increased performance loss detected for the tracked systems would be consistent with stuck tracker rows affecting on the order of 5% - 10% of the system.

14 SOLAR ENERGY↗
DOE OSTI2021

PV Fleet Performance Data Initiative Program and Methodology

The US Department of Energy’s PV Fleet Performance Data Initiative has been launched in order to collect and evaluate production data across multiple PV fleet partners. Performance statistics are anonymized, aggregated and shared to represent a snapshot of the US commercial and utility-scale fleet. Production data have been collected from over 1500 systems representing more than 1.3 GWdc capacity. Preliminary analysis indicates median performance loss rates are in line with previous publications of system degradation, on the order of –0.6%/yr to –0.9%/yr (preliminary numbers subject to change). These values are higher than module-only degradation rates which are often used in pro-forma estimates of project performance and economics, potentially exposing owner/operators to increased risk if systems under-perform over time.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

Loss Factor Assessment in the 8GW PV Fleet Performance Data Initiative

This presentation is divided into the following sections: (1) photovoltaics (PV) current and future deployment; (2) the PV Fleet Performance Data Initiative; (3) fleet degradation trends; (4) high-efficiency module performance; (5) other system loss factors; and (6) conclusions.

deployment↗
DOE OSTI2024

Initial data for first-order causal viscous conformal fluids in general relativity

We solve the Einstein constraint equations for a first-order causal viscous relativistic hydrodynamic theory in the case of a conformal fluid. For such a theory, a direct application of the conformal method does not lead to a decoupling of the equations, even for constant-mean curvature initial data. We combine the conformal method applied to a background perfect fluid theory with a perturbative argument in order to obtain the result.

Disconzi, Marcelo (ORCID:0000000234497778)↗
DOE OSTI2024

Fast permeability measurement for tight reservoir cores using only initial data of the one chamber pressure pulse decay test

Here, in this study, a mathematical model for fast determination of the permeabilities of tight rocks using measurements taken from the initial period of the One Chamber Pressure Pulse Decay (OC-PPD) test is presented. The model applies to measurements taken both before and after the pressure pulse front has reached the downstream end of the specimen. The analytical solutions for the pressure decay in the upstream chamber are derived based on a parabolic arc approximation of pore pressure distribution along the test specimen. This approximation allows converting the initial–boundary value problem of fluid diffusion in the specimen, governed by partial differential equations, to a system of ordinary differential equations that can be easily solved by explicit formulae. Thus, an explicit formula for the pressure decay rate is obtained, which enables inverse analysis of the initial experimental data to estimate the rock permeability. The proposed method expedites the pulse decay test as it does not require the system to reach equilibrium. The method is validated with three sets of experimental data of the OC-PPD test using helium as the diffusing fluid, for which the relative error of the permeability is found to be less than 6%. This method is particularly useful if the equilibrium time of the pulse decay test for rock specimens with permeabilities in the range of nano-Darcy takes hours or days.

early-time solution↗
DOE OSTI2026

Data Cards for Standardized Metadata Across DOE-Aligned Data Initiatives: Toward Transparent, Interoperable, and Governed Dataset Documentation

As data-intensive research, advanced computing, and artificial intelligence become increasingly central to scientific and operational workflows, the need for consistent, transparent, and machine-actionable documentation has grown correspondingly. Multiple DOE-aligned communities—including Office of Science, Genesis Mission, American Science Cloud (AmSC), National Nuclear Security Administration (NNSA) stewardship and governance, and related cross-laboratory collaborations—have independently developed metadata practices to support discovery, access, reuse, repository deposit, and compliance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗