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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Patience is a virtue: A data-driven analysis of rooftop solar PV permitting timelines in the United States

© 2020 The Authors Local permitting can ensure the safe installation and operation of rooftop solar photovoltaic (PV) systems. At the same time, burdensome local permitting processes and local variation in requirements may pose challenges to PV deployment. In this article, we explore new data on the durations between key steps in the PV permitting process in the United States. The data suggest that a typical customer can expect to wait around 25–100 days from permit application until an installed system passes inspection. Permit durations vary significantly across jurisdictions, due in part to differences in local permitting policies. However, permit durations vary as significantly within jurisdictions as across them, in part due to significant variation across installers, suggesting that installer strategies and practices play an important role in permitting timelines. Permit durations have declined over time, reflecting progress from permit streamlining policies and jurisdiction learning-by-doing, though durations have stabilized in recent years. The data suggest that typical PV customers still face long and uncertain permitting timelines in the United States.

O'Shaughnessy, E↗
DOE OSTI2021

Modeling atmospheric data and identifying dynamics Temporal data-driven modeling of air pollutants

Atmospheric modelling has recently experienced a surge with the advent of deep learning. Most of these models, however, predict concentrations of pollutants following a data-driven approach in which the physical laws that govern their behaviors and relationships remain hidden. Furthermore, with the aid of real-world air quality data collected hourly in different stations throughout Madrid, we present a case study using a series of data-driven techniques with the following goals: (1) Find systems of ordinary differential equations that model the concentration of pollutants and their changes over time; (2) assess the performance and limitations of our model using stability analysis; (3) reconstruct the time series of chemical pollutants not measured in certain stations using delay coordinate embedding results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity

Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a Buffer Modification Workflow (BMW), in which the same sample is run by LC-MS in both liquid chromatography solvent with 14 NH 3 -acetate buffer, and in solvent with the buffer modified with 15 NH 3 -formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Altogether, it identified 26% of ~ 27,000 liver LC-MS features as putative metabolites, of which ~ 2600 showed HMDB or KEGG database formula match. This workflow is well-suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Validating Nuclear Data Uncertainties Obtained from a Statistical Analysis of Experimental Data with the “Physical Uncertainty Bounds” Method

Concerns within the nuclear data community led to substantial increases of Neutron Data Standards (NDS) uncertainties from its previous to the current version. For example, those associated with the NDS reference cross section 239 Pu(n,f) increased from 0.6–1.6% to 1.3–1.7% from 0.1–20 MeV. These cross sections, among others, were adopted, e.g., by ENDF/B-VII.1 (previous NDS) and ENDF/B-VIII.0 (current NDS). There has been a strong desire to be able to validate these increases based on objective criteria given their impact on our understanding of various application uncertainties. Here, the “Physical Uncertainty Bounds” method (PUBs) by Vaughan et al. is applied to validate evaluated uncertainties obtained by a statistical analysis of experimental data. We investigate with PUBs whether ENDF/B-VII.1 or ENDF/B-VIII.0 239 Pu(n,f) cross-section uncertainties are more realistic given the information content used for the actual evaluation. It is shown that the associated conservative (1.5–1.8%) and minimal realistic (1.1–1.3%) uncertainty bounds obtained by PUBs enclose ENDF/B-VIII.0 uncertainties and indicate that ENDF/B-VII.1 uncertainties are underestimated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Medium amplitude parallel superposition (MAPS) rheology. Part 2: Experimental protocols and data analysis

An experimental protocol is developed to directly measure the new material functions revealed by medium amplitude parallel superposition (MAPS) rheology. This protocol measures the medium amplitude response of a material to a simple shear deformation composed of three sine waves at different frequencies, revealing a rich dataset consisting of up to 19 measurements of the third-order complex modulus at distinct three-frequency coordinates. We discuss how the choice of input frequencies influences the features of the MAPS domain studied by the experiment. A polynomial interpolation method for reducing the bias of measured values from spectral leakage and reducing variance due to noise is discussed, including a derivation of the optimal range of amplitudes for the input signal. This leads to the conclusion that conducting the experiment in a stress-controlled fashion possesses a distinct advantage to the strain-controlled mode. The experimental protocol is demonstrated through measurements of the MAPS response of a model complex fluid: a surfactant solution of wormlike micelles. The resulting dataset is indeed large and feature-rich, while still acquired in a time comparable to similar medium amplitude oscillatory shear (MAOS) experiments. We demonstrate that the data represent measurements of an intrinsic material function by studying its internal consistency, compatibility with low-frequency predictions for Coleman–Noll simple fluids, and agreement with data obtained via MAOS amplitude sweeps. Finally, the data are compared to predictions from the corotational Maxwell model to demonstrate the power of MAPS rheology in determining whether a constitutive model is consistent with a material’s time-dependent response.

Lennon, Kyle R. (ORCID:0000000212515461)↗
DOE OSTI2020

The ARM Data-oriented Metrics and Diagnostics Package for Climate Models - A New Tool for Evaluating Climate Models with Field Data

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Inter-comparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data-Driven Compositional Optimization in Misspecified Regimes

With a manifold growth in the scale and intricacy of systems, the challenges of parametric misspecification become pronounced. These concerns are further exacerbated in compositional settings, which emerge in problems complicated by modeling risk and robustness. In “Data-Driven Compositional Optimization in Misspecified Regimes,” the authors consider the resolution of compositional stochastic optimization problems, plagued by parametric misspecification. In considering settings where such misspecification may be resolved via a parallel learning process, the authors develop schemes that can contend with diverse forms of risk, dynamics, and nonconvexity. They provide asymptotic and rate guarantees for unaccelerated and accelerated schemes for convex, strongly convex, and nonconvex problems in a two-level regime with extensions to the multilevel setting. Surprisingly, the nonasymptotic rate guarantees show no degradation from the rate statements obtained in a correctly specified regime and the schemes achieve optimal (or near-optimal) sample complexities for general T-level strongly convex and nonconvex compositional problems.

Business & Economics↗
DOE OSTI2020

Data from Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity

Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts, and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a buffer modification workflow (BMW) in which the same sample is run by LC-MS in both liquid chromatography solvent with 14NH3–acetate buffer and in solvent with the buffer modified with 15NH3–formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Overall, it identified 26% of ∼27 000 liver LC-MS features as putative metabolites, of which ∼2600 showed HMDB or KEGG database formula match. This workflow is well suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.

Conversion↗
DOE OSTI2020

ftmsRanalysis: An R package for exploratory data analysis and interactive visualization of FT-MS data

The high-resolution and mass accuracy of Fourier transform mass spectrometry (FT-MS) has made it an increasingly popular technique for discerning the composition of soil, plant and aquatic samples containing complex mixtures of proteins, carbohydrates, lipids, lignins, hydrocarbons, phytochemicals and other compounds. Thus, there is a growing demand for informatics tools to analyze FT-MS data that will aid investigators seeking to understand the availability of carbon compounds to biotic and abiotic oxidation and to compare fundamental chemical properties of complex samples across groups. We present ftmsRanalysis, an R package which provides an extensive collection of data formatting and processing, filtering, visualization, and sample and group comparison functionalities. The package provides a suite of plotting methods and enables expedient, flexible and interactive visualization of complex datasets through functions which link to a powerful and interactive visualization user interface, Trelliscope. Example analysis using FT-MS data from a soil microbiology study demonstrates the core functionality of the package and highlights the capabilities for producing interactive visualizations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Microbial community data from throughfall exclusion experiment: Metadata, SI, community composition, LefSe, and FunGuilR data tables from PARCHED Panama tropical forest soils, 2024-2025

Soil contains more carbon (C) than terrestrial vegetation and the atmosphere combined, with some of the largest terrestrial C stocks in tropical rainforests. Soil microbes decompose organic matter, playing a vital role in the storage or loss of soil C. With climate change, drought conditions are predicted to increase in many tropical regions, including both chronic drying and extended drought, potentially influencing these processes. This project explored the effects of chronic and seasonal drying on soil microbial communities across four distinct tropical forests in a long-term drying experiment. We investigated the effects of a chronic drying manipulation on soil microbial community abundance and variation across different forests and seasons. We also compared findings with previously published data from these forests after short-term drying. This project used soils from a long-term drying experiment established in 2018 across four seasonal lowland forests in Panama. Soils were collected from 0 – 10 cm depths during three seasonal periods in control and drying plots in 2024 and 2025 from a total of 32 plots (n = 4 per forest per treatment). The forests varied in baseline rainfall and soil fertility. We calculated alpha and beta diversity indices and compared taxonomic community composition. We found significant biogeographic variation in microbial diversity and taxonomy, with significant differences across the forests and significant effects of the drying treatment. Metadata and sample IDs are within Metadata_16S.csv and Metadata_ITS.csv. Relative abundance tables of every sample at every season are shown in the Excel workbooks 16S Relative Abundance.xlsx and ITS Relative Abundance.xlsx. They are then also shown in CSV files by each taxonomic level. Linear discriminant analysis effect size (LefSe) tables are shown for the full 16S and ITS datasets (n = 96), subsets for every site at every season (n = 8), and then for the forests with each plot merged by season (n = 8). FunGuildR data table of ITS data is uploaded.

Bacteria↗
DOE OSTI2021

Deliverable D11 – Data Sharing, Storage, Security Protocols, and a Specification of a Potential Data Sharing Portal

Pacific Northwest National Laboratory (PNNL) and Technical University of Denmark (DTU) completed this deliverable as part of Work Package 2: Data Information Catalog for Distributed Wind Research for the International Energy Agency (IEA) Wind Technology Collaboration Programme Task 41: Enabling Wind to Contribute to a Distributed Energy Future. As part of the work plan, Deliverable D11 requires the development of data sharing, storage, and security protocols for metadata to be stored on the platform, if needed. The specification of a potential data sharing portal that expands on the catalog is also required.

17 WIND ENERGY↗
DOE OSTI2021

Design-Space Data: Informing Common Design Decisions with Pre-Simulated Data

Design Space Exploration (DSE) analysis techniques represent a data-centric approach to integrating performance analysis in early design phases when there is the greatest potential to cheaply improve the energy efficiency of a building. We focus on a novel extension of DSE called Universal Design Space Exploration (UDSE), which leverages massive databases of pre-simulated analysis that represent all possible outcomes of common analysis workflows. These databases, called Design Spaces, become “universal” when a single pre-simulated design space can be re-applied to future unknown projects. Unlike current simulation methods, which require a design to exist before it can be analyzed and often take minutes or hours to simulate, UDSE leverages pre-simulation to deliver rapid and relevant insight as new designs are conceptualized. The data underpinning UDSE enables advanced statistical and Artificial Intelligence methods, allowing UDSE to deliver a greater understanding of the larger problem being explored, rather than simply delivering analysis of several pre-conceived design options. We believe that UDSE can provide instantaneous, relevant analysis for all building design projects at negligible cost. This paper has two main goals, to develop a relevant Universal Design Space that showcases the potential of UDSE and to release this data freely to industry and academia; thereby lowering the barrier to entry to digital literacy in statistics, ML and AI within the architecture, engineering, construction (AEC) industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Unified Software Architecture for Advanced Materials and Manufacturing Technologies Data Management and Processing: FY 2023 Multidimensional Data Correlation Platform

This report details the various digital manufacturing activities ongoing at ORNL as part of the Advanced Materials and Manufacturing Technologies (AMMT) program. The AMMT program is exploring a data-driven approach to demonstrate the use of AM for the fabrication of components for nuclear applications, with the goal of providing a greater understanding of manufacturing quality outcomes that would pave the way toward the development of standards for certification and qualification. The objective of this work package is to establish a digital manufacturing discipline common to all participants of the AMMT program to improve the performance, reliability, and lifetime of nuclear components. As part of this effort, we will develop a unified software architecture for AMMT data management and processing, deploy the digital platform across AMMT participants’ facilities, and generate pedigreed datasets in a common format across multiple labs and facilities. To this end, the MDDC work package has focused on three activities during FY23. First, the MDF Digital Tool was overhauled to better serve the needs of the AMMT program. Next, multiple laser powder bed fusion (L-PBF) systems at the MDF were upgraded to a common sensor package for collecting comparable in situ data across machines. Finally, various improvements relevant to the AMMT program were implemented in the ORNL-developed software tool, Peregrine. This report marks the completion of FY23 milestone M3CR-22OR0403051: Report Describing the Architecture of the Digital Platform to Support AMMT Activities.

36 MATERIALS SCIENCE↗
DOE OSTI2026

MINERvA s Open Data Product: A First for Neutrino Data Preservation

Access to information on neutrino nucleus interactions is critical to the success of all neutrino oscillation experiments. MINERvA's rich dataset covers a range of energies and nuclei unique amongst experiments, and as such is critical to the community in building the important shared knowledge needed to unravel the mysteries of the neutrino. In particular, its dataset provides the greatest statistical coverage in in the range of neutrino energies pertinent for DUNE until DUNE's near detector begins operation. Historically, such significant datasets in neutrino physics have been preserved primarily through their published results. While meaningful and useful, this limits the ability to explore the data to its fullest extent as new perspectives continue to form. MINERvA has undertaken a major effort to break this trend and preserve its data in a format to be as analyzable as possible from outside the collaboration. This has culminated in the officially-released MINERvA Open Data Product for the community to take advantage of and utilize. Maintaining direct access to the dataset in an analyzable form will allow new insights to continue to be extracted indefinitely. This talk will cover the contents of this product, the information included (and excluded), the tools provided to utilize the product effectively, the support MINERvA intends to provide in its use, and some lessons learned through the process.

Last, David [Rochester U.] (ORCID:0000000245147183↗
DOE OSTI2023

The Wind Data Hub, also known as The Data Archive and Portal.

The Wind Data Hub, or WDH (formerly known as the Data Archive and Portal), is established by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy’s Wind Energy Technologies Office (WETO). The WDH provides secure, timely, easy, and open access to all laboratory, field, and benchmark model data produced by land-based and offshore projects funded by WETO.

17 WIND ENERGY↗
DOE OSTI2026

Multi-omics data resource: Data package 22 (Pck022)

In type 1 diabetes (T1D), autoimmune response and inflammation cause the death of pancreatic ß cells, leading to the body’s inability to produce insulin and maintain glucose homeostasis. This process is at least in part mediated by pro-inflammatory cytokines, such as interferon (IFN)a, IFN?, interleukin (IL)-1ß, and tumor necrosis factor (TNF)a, which induce ß-cell dysfunction and apoptosis. A deep understanding of the ß-cell signaling and regulatory networks induced by these cytokines could lead to the identification of therapeutic targets to prevent T1D development. To study cytokine-mediated islets/ß-cell signaling and regulatory networks, a variety of omics experiments have been conducted, including transcriptomics, epigenomics (DNA methylation, UMI-4C, ATAC-seq & ChIP-seq), proteomics (bottom-up, top-down, post-translational modification analysis), lipidomics, and metabolomics. The combination of these datasets can be instrumental in identifying signaling components and regulatory factors involved in ß-cell stress/death. Here, we aggregated these multiple omics datasets into a centralized location, providing a quality-controlled and statistically rigorous resource for investigators seeking to holistically study ß-cell regulation by pro-inflammatory cytokines. The data package consists of isolated pancreatic islets from adult male C57BL6/J mice treated with IL-1β, IFNγ or IL-1β + IFNγ for 6 h and submitted for scRNA-seq. This study focused on understanding the heterogeneity of the cytokine-mediated response. Data contributors: Jennifer S Stancill & John A Corbett: Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA Data repository: GSE156175 Publication: 10.26508/lsa.202000949

Sarkar, Soumyadeep [Pacific Northwest National Lab↗
DOE OSTI2026

Multi-omics data resource: Data package 23 (Pck023)

The data package consists of isolated pancreatic islets from adult male C57BL6/J mice treated with IL-1β + IFNγ, IL-1β + IFNγ + NMMA, or NMMA alone for 18 h and submitted for scRNA-seq. This study focused on the cell-type-specific effects of nitric oxide signaling in islets and characterized the heterogeneity of responses. Data contributors: Jennifer S Stancill & John A Corbett: Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA Data repository: GSE183010 Publication: 10.1093/function/zqab063

Sarkar, Soumyadeep [Pacific Northwest National Lab↗