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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Accelerating Resilience of the Community through Holistic Engagement and use of Renewables (ARCHER) Planning Framework

The primary objective of the Accelerating Resilience of the Community through Holistic Engagement and Use of Renewables (ARCHER) initiative was to identify and incorporate the unique variations in energy burden, social vulnerability, living conditions, and access to essential services that differ across communities. By accounting for these localized factors—down to the neighborhood level—the project supports more targeted and effective investments in community resilience. The framework seeks to establish practical planning guidance, methods, and performance measures for community energy resilience, integrate community-level and electric utility system resilience planning, and assess its effectiveness through comparison with conventional and operational planning approaches. A key component of the project was its data exchange platform, which is used to evaluate and demonstrate the tools, methodologies, and planning approaches developed through ARCHER. This open-source platform enables developers and vendors of distribution and outage management systems to build upon the research by incorporating its concepts into their own tools and workflows. This capability is enabled by the transparent availability of data, functional requirements, and the underlying information model. The project yielded several important insights. First, meaningful engagement with communities is essential to achieving comprehensive resilience outcomes. Second, resilience planning is most effective when electric grid considerations and broader community needs are addressed in a coordinated manner. Third, the use of platforms that allow for real-time input from communities can enhance utility responsiveness during restoration activities. Fourth, a structured and systematic planning approach can successfully translate ARCHER concepts into practice. Fifth, the development of an integrated metric that reflects both grid performance and community impacts provides a more holistic basis for evaluating resilience. Finally, incorporating community engagement and equity considerations into grid operations is critical, particularly during severe weather events that result in extended outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Regulatory Implications of Embedded Grid Energy Storage

Electricity is unique among commodities in that its supply chain was developed without a storage component. Every other resource commodity has the ability to store excess quantities built into its supply chain – in the form of granaries, warehouses, reservoirs, etc. This embedded storage creates a buffer for mismatches between supply and demand, stabilizing prices and protecting customers. Recent technological advances in cost-competitive energy storage technologies that are scalable and flexible have made the concept of embedded electrical storage feasible, but several regulatory questions remain. This paper summarizes energy regulatory structures in the U.S. and the implications that they would have for embedded storage on the electric grid. Regulatory challenges for embedded storage include the lack of underlying standards, inclusion in planning processes, ownership models, compensation, and metrics. Two possible pathways forward are explored: an incremental one including regulatory guidance regarding the evaluation of embedded storage in existing planning processes, and a complex one involving the revision of existing reliability standards or the development of a new standard specific to embedded storage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Integrated Hourly Meteorological Database of 20 Meteorological Stations (1981-2022) for Watershed Function SFA Hydrological Modeling

This dataset contains (a) a script “R_met_integrated_for_modeling.R”, and (b) associated input CSV files: 3 CSV files per location to create a 5-variable integrated meteorological dataset file (air temperature, precipitation, wind speed, relative humidity, and solar radiation) for 19 meteorological stations and 1 location within Trail Creek from the modeling team within the East River Community Observatory as part of the Watershed Function Scientific Focus Area (SFA). As meteorological forcings varied across the watershed, a high-frequency database is needed to ensure consistency in the data analysis and modeling. We evaluated several data sources, including gridded meteorological products and field data from meteorological stations. We determined that our modeling efforts required multiple data sources to meet all their needs. As output, this dataset contains (c) a single CSV data file (*_1981-2022.csv) for each location (20 CSV output files total) containing hourly time series data for 1981 to 2022 and (d) five PNG files of time series and density plots for each variable per location (100 PNG files). Detailed location metadata is contained within the Integrated_Met_Database_Locations.csv file for each point location included within this dataset, obtained from Varadharajan et al., 2023 doi:10.15485/1660962. This dataset also includes (e) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and (f) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Review the (g) ReadMe_Integrated_Met_Database.pdf file for additional details on the script, methods, and structure of the dataset.The script integrates Northwest Alliance for Computational Science and Engineering’s PRISM gridded data product, National Oceanic and Atmospheric Administration’s NCEP-NCAR Reanalysis 1 gridded data product (through the `RCNEP` R package, Kemp et al., doi:10.32614/CRAN.package.RNCEP), and analytical-based calculations. Further, this script downscales the input data into hourly frequency, which is necessary for the modeling efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Modulating Solvation Structure in Concentrated Aqueous Organic Redox Flow Battery Electrolyte for Solubility and Transport Enhancement via Polycomplex Ion

Aqueous organic redox flow batteries hold great promise as a technology for creating economical grid energy storage using sustainable materials. Nonetheless, the solubility limit presents a universal barrier for all redox-active organic molecules. In this paper, a new approach is proposed to surpass the solubility limit by manipulating the solvation structure with polycomplex ion additives (PIA). Using poly(3,4-ethylenedioxythiophene) polystyrenesulfonate colloids as one example, its role in dismantling the rigid supramolecular clusters within the highly concentrated 7,8-dihydroxyphenazine-2-sulfonic acid electrolyte is investigated. 1 H and 23 Na NMR spectra and molecular dynamics simulation studies demonstrate that the bipolar structure of the PIA effectively disrupts the aggregations of DHPS and Na + ion in the highly concentrated anolyte, thus rendering a more flexible solvation structure and less restrictive ion transport, leading to substantially improved battery performance of an AORFB cell. The anolyte with PIA achieved 1.6 M and 74.3 Ah L –1 anolyte energy capacity.

25 ENERGY STORAGE↗
DOE OSTI2022

Shifts in hydropower operation to balance wind and solar will modify effects on aquatic biota

To avoid negative consequences to freshwater biota from climate change, society must complete the transition from fossil to renewable electricity sources. However, temporal patterns in hydropower generation (and flow releases that affect aquatic biota) may change with increased wind and solar penetration. We used power cost modeling to characterize current and future within-day and seasonal patterns in hydropower generation across the Eastern Interconnection in a wet and a dry year. Compared to the baseline, future hydropower generation across the grid decreased during the day and increased before dawn and after dusk. At a project level, such a pattern would suggest 'double peaking' operation (up- and down-ramping before dawn and after dusk, with lower releases midday). Variation in generation was higher in wet years than dry years, foreshadowing possible flow constraints on hydropower flexibility. At the grid scale, projected ramping rates were higher in all seasons. A review of the ecological literature suggests that these changes would shift the timing of invertebrate drift and elevate the risk of nest scouring during up-ramping and the risk of stranding or dewatering during down ramping. Thermal conditions may be moderated by increased ramping. Strategies for adapting to future shifts in the renewable portfolio range from re-regulation in reservoir cascades to providing flow refuge (structures and vegetation) below individual projects. Coordinated basin-scale operation can distribute peaking operation to maintain grid support while restricting local ramping at critical ecological times. In addition, research to design hybrid renewable systems that add battery storage is needed to understand how we can mitigate future risks to aquatic communities while promoting the use of renewable energy. This study, which is among the first to examine ecological side-effects of the shift to renewable energy in freshwater ecosystems, lays out a path toward understanding and navigating changes to flow regimes under the energy transition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗
DOE OSTI2020

Additive Manufacturing of Controlled Porous Elastomeric Nanocomposites for Enhanced Sensing Function

Sensors manufactured using piezoresistive elastomeric nanocomposites have a wide range of applications in many fields such as structural health monitoring, robotics, and biomedical industries. Additive manufacturing of these sensors, achieved using Direct Ink Writing (DIW), has provided numerous advantages including increased sensitivity and the fabrication of complex geometry. However, solid sensors manufactured either additively or using conventional methods, display a greater level of hysteresis, especially at strains higher than 10%, which limits their sensitivity. The solid sensors also display a greater level of piezoresistive relaxation where the resistance change with time either in a static or dynamic loading environment. Porosity can be introduced in the form of varying infill patterns and densities to reduce bulk material effects. In this paper, a lattice structure with four infill densities are investigated, and the samples are printed using a grid infill pattern. The fabricated samples are characterized using a scanning electron microscope (SEM) to validate the microstructural features and layer bonding. The pressure-sensing capability of each sensor is investigated using cyclic compression loading at various strain levels. Sensing results show that an increase in sensitivity, as well as a decrease in hysteresis with an increase in porosity, compared to solid samples (100% in-fill). The highest porosity was measured to be 62.3% with a corresponding 112% increase in the gauge factor compared to the solid bulk. These results indicate that introducing porosity using DIW is a sensible strategy to improve the piezoresistive performance of nanocomposites and to allow for the tunability of sensing capacity in pressure sensors

Sukati, Musa↗
DOE OSTI2020

"Russians in the Grid" Enrichment Process

Threat intelligence sources are numerous, and the quality of their intelligence is equally variable. The need for quality, actionable, and shareable intelligence is becoming increasingly important as cyber-threats continue to increase. In this paper, we describe the process we used to enrich U.S. CERT’s Technical Alert entitled “Russian Government Cyber-Activity Targeting Energy and Other Critical Infrastructure Sectors,” affectionately referred to as “Russians In the Grid,” from a series of separate but related reports to a final, actionable, unified Structured Threat Information Expression (STIX) v2 representation of the report. In the process, we detail the tools and procedures we used, some of which are automated and others highly manual. We identify some of the limitations of current STIX enrichment tools and techniques, and propose enhancements to allow for more effective enrichment and better overall threat intelligence to be used for more effective threat data-sharing, as well as machine learning-based predictive threat work being conducted at Idaho National Laboratory.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Benchmarking the PCMCI Causal Discovery Algorithm for Spatiotemporal Systems

Causal discovery algorithms construct hypothesized causal graphs that depict causal dependencies among variables in observational data. While powerful, the accuracy of these algorithms is highly sensitive to the underlying dynamics of the system in ways that have not been fully characterized in the literature. In this report, we benchmark the PCMCI causal discovery algorithm in its application to gridded spatiotemporal systems. Effectively computing grid-level causal graphs on large grids will enable analysis of the causal impacts of transient and mobile spatial phenomena in large systems, such as the Earth’s climate. We evaluate the performance of PCMCI with a set of structural causal models, using simulated spatial vector autoregressive processes in one- and two-dimensions. We develop computational and analytical tools for characterizing these processes and their associated causal graphs. Our findings suggest that direct application of PCMCI is not suitable for the analysis of dynamical spatiotemporal gridded systems, such as climatological data, without significant preprocessing and downscaling of the data. PCMCI requires unrealistic sample sizes to achieve acceptable performance on even modestly sized problems and suffers from a notable curse of dimensionality. This work suggests that, even under generous structural assumptions, significant additional algorithmic improvements are needed before causal discovery algorithms can be reliably applied to grid-level outputs of earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Areal interpolation of population projections consistent with different SSPs from 1-km resolution to block level based on USA Structures dataset

Population data are normally collected at various census administrative levels, and areal interpolation of population is often required to transform population to the desired spatial resolution. Building footprint datasets, such as Microsoft building footprints, have proven to be useful in estimating population distribution and can therefore be used for areal interpolation of population. In addition to Microsoft building footprints, the recently released USA Structures dataset provides additional information such as building type and building height for some regions, which may provide valuable information for a better depiction of population distribution and improved population areal interpolation accuracy. In this study, we have conducted areal interpolation of population projections consistent with three different Shared Socioeconomic Pathways (SSP2, SSP3, and SSP5) from 1-km grid cells to block level in Washington state for every ten years from 2020 to 2040 based on USA Structures. We assessed USA Structures-based population downscaling accuracy using U.S. decennial survey data in 2020 under three different downscaling schemes, including population downscaling from census tracts to block groups, from census tracts to blocks, and from block groups to blocks. The resulting accuracies were compared with those based on Microsoft building footprints. The comparison showed that USA Structures achieved higher accuracies across different population density regions and areas with different urbanization extent within our study area.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements is the foundation for analyzing system vulnerability to natural hazards and connecting loads with end users and their communities. However, power system models and geospatial data for power grid assets may have been developed asynchronously without close coordination. Creating a direct mapping between the two may be a challenging task, considering heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect power system model elements and geospatial data for transmission network, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.

Resilience, geospatial data, grid transmission net↗
DOE OSTI2021

Subaru Hyper Suprime-Cam excavates colossal over- and underdense structures over 360 deg 2 out to z = 1

Subaru Strategic Program with the Hyper-Suprime Cam (HSC-SSP) has proven to be successful with its extremely wide area coverage in past years. Taking advantages of this feature, we report initial results from exploration and research of expansive over- and underdense structures at z = 0.3–1 based on the second Public Data Release where optical 5-band photometric data for ~ eight million sources with i < 23 mag are available over ~360 deg 2 . We not only confirm known superclusters but also find candidates of titanic over- and underdense regions out to z = 1. The mock data analysis suggests that the density peaks would involve one or more massive dark matter haloes (>1014 M ⊙ ) of the redshift, and the density troughs tend to be empty of massive haloes over >10 comoving Mpc. Besides, the density peaks and troughs at z ≲ 0.6 are in part identified as positive and negative weak lensing signals respectively, in mean tangential shear profiles, showing a good agreement with those inferred from the full-sky weak lensing simulation. The coming extensive spectroscopic surveys will be able to resolve these colossal structures in 3D space. Finally, the number density information over the entire survey field is available as grid-point data on the website of the HSC-SSP data release (https://hsc.mtk.nao.ac.jp/ssp/data-release/).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications

A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.

42 ENGINEERING↗
DOE OSTI2023

Electron-phonon coupling from GW perturbation theory: Practical workflow combining BerkeleyGW, ABINIT, and EPW

Here we present a workflow of practical calculations of electron-phonon (e-ph) coupling with many-electron correlation effects included using the GW perturbation theory (GWPT). This workflow combines BerkeleyGW, ABINIT, and EPW software packages to enable accurate e-ph calculations at the GW self-energy level, going beyond standard calculations based on density functional theory (DFT) and density-functional perturbation theory (DFPT). This workflow begins with DFT and DFPT calculations (ABINIT) as starting point, followed by GW and GWPT calculations (BerkeleyGW) for the quasiparticle band structures and e-ph matrix elements on coarse electron k- and phonon q-grids, which are then interpolated to finer grids through Wannier interpolation (EPW) for computations of various e-ph coupling determined physical quantities such as the electron self-energies or solutions of anisotropic Eliashberg equations, among others. A gauge-recovering symmetry unfolding technique is developed to reduce the computational cost of GWPT (as well as DFPT) while fulfilling the gauge consistency requirement for Wannier interpolation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Hierarchical analysis of US electric vehicle subsidies for carbon emission mitigation

Electric vehicle (EV) adoptions are promoted with subsidies to reduce greenhouse gas emissions from ground transportation. In this paper, a hierarchical analysis is presented on the potential of greenhouse gas emission mitigation via the electric vehicle subsidy policy at state level in the US, through research of environmental and economic fundamentals of electric vehicle operations, energy consumptions, battery degradation and service life. It has been found that restructuring the federal subsidies to promote EV adoption can significantly reduce greenhouse gas emissions across the US. The reduction costs of greenhouse gas emissions vary between $\$1167.44$/ton in Vermont to $\$6880.13$/ton in Wyoming. A case study reveals that 15.24 % more greenhouse gas emissions can be reduced with a tiered federal subsidy structure. The restructuring of subsidies will also encourage the adoption of clean energies in the grid fuel mix and drive technological advancements to extend the battery lifetime in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ~ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). Here, we consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Cobalt-free composite-structured cathodes with lithium-stoichiometry control for sustainable lithium-ion batteries

Abstract Lithium-ion batteries play a crucial role in decarbonizing transportation and power grids, but their reliance on high-cost, earth-scarce cobalt in the commonly employed high-energy layered Li(NiMnCo)O 2 cathodes raises supply-chain and sustainability concerns. Despite numerous attempts to address this challenge, eliminating Co from Li(NiMnCo)O 2 remains elusive, as doing so detrimentally affects its layering and cycling stability. Here, we report on the rational stoichiometry control in synthesizing Li-deficient composite-structured LiNi 0.95 Mn 0.05 O 2 , comprising intergrown layered and rocksalt phases, which outperforms traditional layered counterparts. Through multiscale-correlated experimental characterization and computational modeling on the calcination process, we unveil the role of Li-deficiency in suppressing the rocksalt-to-layered phase transformation and crystal growth, leading to small-sized composites with the desired low anisotropic lattice expansion/contraction during charging and discharging. As a consequence, Li-deficient LiNi 0.95 Mn 0.05 O 2 delivers 90% first-cycle Coulombic efficiency, 90% capacity retention, and close-to-zero voltage fade for 100 deep cycles, showing its potential as a Co-free cathode for sustainable Li-ion batteries.

36 MATERIALS SCIENCE↗