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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A path to intelligent watersheds: coordinating the data to decision pipeline

Operations of multi-reservoir systems are challenged in-part by the interplay of complex physical processes functioning within the watershed. The employment of intelligent systems can be of aid by linking environmental sensing, information technology, data analytics, simulation and decision support to achieve a data-to-decision flow of information. A further challenge is that watershed resources are managed for multiple purposes requiring some level of coordination among numerous resource managers, asset operators and users. System intelligence in this context relies on shared community platforms (data portals, community models), and coordinated communication between decision makers. Opportunities to enrich watershed intelligence has been the subject of a roadmapping exercise for the Department of Energy’s Water Power Technologies Office which has relied on broad stakeholder engagement. Initial phases of engagement involved personal interviews and a series of virtual group meetings, which focused on identifying opportunities to improve the intelligence of the physical infrastructure within our watersheds—examples of feedback include improved sensing of snowpack and runoff, data standards for facilitated data sharing, and better forecasting tools. The latter phase of engagement involved the conduct of a case study in the Upper Colorado River basin where key stakeholders were interviewed to map how their decisions are informed by intelligence from other basin stakeholders. Our presentation will highlight the interdisciplinary flow of information in complex watershed systems and identify physical and institutional opportunities toward the strategic operation of water infrastructure.

Colorado River↗
DOE OSTI2026

Potential of Data Center Controls in Grid Services

The rapid proliferation of large data centers brings both challenges and opportunities for grid reliability. The data center resources and their potential flexibility have the potential to contribute resources to grid operations. Through capabilities like energy shifting and resource coordination, data centers can help reduce their net demand on the transmission network, as well as provide additional grid services to support reliable operation on the grid. While transient and long-term grid planning and operations are the scenarios that draw most attention, the quasi-steady state timeseries (QSTS) operation of data centers and grid bring interesting scenarios that can help evaluate the data center controls to aid grid services. This work is focused on modeling data centers for QSTS applications – incorporating the AI data center load profiles and building on the PNNL digital twin model for the thermal management loads to enable simulation studies to reveal the impact of data center controls on grid performance. This includes the integration of a QSTS battery and natural gas generator model to incorporate local resource impacts to the system. The simulation study is performed with a modified IEEE 24-Bus transmission system. Scenarios are focused on evaluating the data center load impacts on the transmission system and leveraging both data center and local generation controls to mitigate those impacts and provide additional grid services. The data center controls revealed the ability to contribute to two main kinds of grid services: preventing congestion on a weak grid by coordinating the data center resources with the collocated BESS and onsite generation; and the ability to help the grid operations during stressed times of operation like during a contingency. Leveraging these and other capabilities has the potential to help data centers become grid responsive assets, aiding in both their integration into the power system and grid reliability.

power grid simulation↗
DOE OSTI2024

Binding profiles for 961 Drosophila and C. elegans transcription factors reveal tissue-specific regulatory relationships

A catalog of transcription factor (TF) binding sites in the genome is critical for deciphering regulatory relationships. Here, we present the culmination of the efforts of the modENCODE (model organism Encyclopedia of DNA Elements) and modERN (model organism Encyclopedia of Regulatory Networks) consortia to systematically assay TF binding events in vivo in two major model organisms,Drosophila melanogaster(fly) andCaenorhabditis elegans(worm). These data sets comprise 605 TFs identifying 3.6 M sites in the fly and 356 TFs identifying 0.9 M sites in the worm, and represent the majority of the regulatory space in each genome. We demonstrate that TFs associate with chromatin in clusters termed “metapeaks,” that larger metapeaks have characteristics of high-occupancy target (HOT) regions, and that the importance of consensus sequence motifs bound by TFs depends on metapeak size and complexity. Combining ChIP-seq data with single-cell RNA-seq data in a machine-learning model identifies TFs with a prominent role in promoting target gene expression in specific cell types, even differentiating between parent–daughter cells during embryogenesis. These data are a rich resource for the community that should fuel and guide future investigations into TF function. To facilitate data accessibility and utility, all strains expressing green fluorescent protein (GFP)-tagged TFs are available at the stock centers for each organism. The chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center, GEO, and through a direct interface that provides rapid access to processed data sets and summary analyses, as well as widgets to probe the cell-type-specific TF–target relationships.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Analysis of Bis(trifluoromethylsulfonyl)imide Interactions with Metal Cations Through a Chemical Informatics Approach

Nominally weakly coordinating anions are useful for modulating the solubility and chemical properties of metal complexes, but identification and analysis of the systematics of the interactions of anions with cationic metal complexes has not received the attention it deserves. Here, a chemical informatics approach is demonstrated for identifying and quantitatively analyzing the ways that the bis(trifluoromethylsulfonyl)imide anion (TFSI) can interact with metal-containing species. An open access computer program (PyCIFTer) was developed to facilitate large-scale structural analysis of TFSI-containing species by utilization of experimental atomic coordinate data from single-crystal X-ray diffraction (XRD) studies obtained from the Cambridge Structural Database (CSD). PyCIFTer establishes a three-dimensional vector space from the raw atomic coordinates, generating acyclic, undirected graphs that are used to rapidly analyze the structural properties (bond lengths and angles) of TFSI in individual structures in sequential/batch fashion. The structures are sorted by PyCIFTer into groups based on pre-set and chemically sensible criteria, affording a comprehensive and systematic view of TFSI structural chemistry. This approach avoids tedious one-at-a-time interrogation of structures, a prospect unreasonable in this case, and many others of contemporary chemical relevance; there were over 1500 structures in the CSD containing TFSI as of November 2024. The results demonstrate that TFSI only rarely binds to cations in the solid state, favoring the formation of species in which TFSI is found in cations’ outer coordination spheres. The prospect of applying PyCIFTer to other moieties is also discussed. PyCIFTer is also schematically compared to the commercial CSD Python application programming interface (API). Taken together, this work demonstrates the usefulness of modular workflows for sequential/batch analysis of structural data from XRD, an approach that appears poised to accelerate the translation of legacy structural results into new chemical insights and hypotheses.

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

Supporting Data: A Four‐Coordinate Pr 4+ Imidophosphorane Complex

Supporting computational data for the publication titled: A. C. Boggiano, S. Roy Chowdhury, M. D. Roy, M. G. Bernbeck, S. M. Greer, B. Vlaisavljevich,* and H. S. La Pierre* “A Four-Coordinate Pr4+ Imidophosphorane Complex" Angew. Chem. Int. Ed., 2024, e202409789. DOI: 10.1002/anie.202409789

Boggiano, Andrew C.↗
DOE OSTI2024

Utah FORGE: 2024 Discrete Fracture Network Model Data

The Utah FORGE 2024 Discrete Fracture Network (DFN) Model dataset provides a set of files representing discrete fracture network modeling for the FORGE site near Milford, Utah. The dataset includes four distinct DFN model file sets, each corresponding to different time frames and modeling approaches in 2024. These models characterize both natural and induced fractures in the geothermal reservoir, which consists of crystalline granitic and metamorphic rock approximately 8,000 feet below the ground surface. The dataset includes a reference DFN model from February 2024 that incorporates planar fractures and well trajectories, as well as upscaled permeability, porosity, compressibility, and storage values on specified grids. Additionally, there are models based on new microseismic (MEQ) data from May and July 2024, including fracture planes fitted to the latest MEQ catalog datasets, tensile fractures from hydraulic stimulation, and an alternative connected DFN for modeling purposes. Coordinate data is provided in both global and local frames, with detailed instructions on the transformations used to align with principal stress orientations. The dataset also includes notes and calculation files for estimating fracture sizes and differences between various fracture sets. There are subfolders for Global Coordinates and Local Coordinates. To move from the global to the local coordinate frame, fractures and wells were a) rotated 20 degrees counterclockwise looking down about the global point (335376.400482041, 4263189.99998761, 250.093546450195) to better align with the principal stresses; and b) translated by (-335408.68, -4263010.9, 1150). Upscaled permeability values using the _XYZ suffix show directions with respect to the global XYZ coordinate frame, while those using the _IJK suffix are aligned with local coordinate frame.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

NCAR/EOL ISFS Data for LASSO-CACTI Overview Paper

5 minute averages of surface meteorology and flux data collected by the NCAR/EOL Integrated Surface Flux System (ISFS) at 15 sites during the RELAMPAGO field campaign. These data have been quality-controlled and are available in NetCDF format. Winds reported by the sonic anemometers have been tilt corrected and rotated into geographic coordinates. Data providence, citation, and acknowledgement This ARM data set is a copy of v2.0 of the NCAR data set obtained on 6-Jun-2024 from https://doi.org/10.26023/ZPHJ-JW9W-2B0Y. The citation for the original data source is: NCAR/EOL In-situ Sensing Facility, Oncley, S. 2021. NCAR/EOL ISFS Surface Meteorology and Flux Products, 5-minute. Version 2.0. UCAR/NCAR - Earth Observing Laboratory. https://doi.org/10.26023/ZPHJ-JW9W-2B0Y Accessed 06 Jun 2024. In addition to the citation reference and any other acknowledgements, please acknowledge NCAR/EOL in your publications with text such as: "Data provided by NCAR/EOL under the sponsorship of the National Science Foundation. https://data.eol.ucar.edu/"

atmosphere: surface↗
DOE OSTI2026

An Autonomous MCP Bridge to Rucio: Enhancing Data Management Accessibility for High Energy Physics

The Rucio Data Management System [1] is an important tool used by High Energy Physics experiments, including those at Fermi National Accelerator Laboratory, to store and manage exabyte-scale scientific datasets. Despite its central role in coordinating data across globally distributed storage sites, Rucio's command line interface (CLI) presents a steep learning curve, and makes it difficult for scientists to navigate through. To solve this issue, a containerized Model Context Protocol (MCP) [2] server was built that connects Large Language Models directly to Rucio, allowing AI agents to handle data tasks by using simple, natural language rather than memorized terminal commands. The core engineering focus of this project was moving the server away from slow terminal commands that require text parsing and replacing them with a native Python Client API toolset and a planned REST API framework. Moving to the Python API handles data operations directly in memory, which helps clear up formatting errors, provides the AI with clean, structured JSON data and speeds up tool execution. To prove that the system actually works, a benchmarking pipeline was also built with various questions to test the AI across four different model configurations. The questions included finding data scopes, tracking down specific datasets, and checking replication rules. Through benchmarking, early runs showed that with raw terminal text, the model would get confused and stuck, whereas switching to the Python API to feed the AI clean, structured data yielded massive improvement. By creating an intelligent and autonomous bridge to a storage network, this project shows how AI can be implemented in scientific data management, which ultimately helps scientists at Fermilab spend less time sorting through data and more time focusing on their experiments and analysis.

Akella, Kashyap [William Rainey Harper Coll.]↗
DOE OSTI2026

Location Identifiers, Metadata, and Map for Field Measurements at the East-Taylor Watershed Community Observatory, Colorado, USA (Version 3.3)

This dataset contains identifiers, metadata, and a map of the locations where field measurements have been conducted at the East-Taylor Watershed Community Observatory located in the Upper Colorado River Basin, United States. This is version 3.3 of the dataset and replaces the prior version 3.2 (see below for details on changes between the versions). Dataset description: The East River-Taylor Watershed is the primary field site of the Watershed Function Scientific Focus Area (WFSFA) and the Rocky Mountain Biological Laboratory. Researchers from several institutions generate highly diverse hydrological, biogeochemical, climate, vegetation, geological, remote sensing, and model data at the East-Taylor Watershed in collaboration with the WFSFA. Thus, the purpose of this dataset is to maintain an inventory of the field locations and instrumentation to provide information on the field activities in the East-Taylor Watershed and coordinate data collected across different locations, researchers, and institutions. The dataset contains (1) a README file with information on the various files, (2) three csv files describing the metadata collected for each surface point location, plot and region registered with the WFSFA, (3) csv files with metadata and contact information for each surface point location registered with the WFSFA, (4) a csv file with with metadata and contact information for plots, (5) a csv file with metadata for geographic regions and sub-regions within the watershed, (6) a compiled xlsx file with all the data and metadata which can be opened in Microsoft Excel, (7) a kml map of the locations plotted in the watershed which can be opened in Google Earth, (8) a jpg image of the kml map which can be viewed in any photo viewer, and (9) a zipped file with the registration templates used by the SFA team to collect location metadata. The zipped template file contains two csv files with the blank templates (point and plot), two csv files with instructions for filling out the location templates, and one compiled xlsx file with the instructions and blank templates together. Additionally, the templates in the xlsx include drop down validation for any controlled metadata fields. Persistent location identifiers (Location_ID) are determined by the WFSFA data management team and are used to track data and samples across locations. Dataset uses: This location metadata is used to update the Watershed SFA’s publicly accessible Field Information Portal (an interactive field sampling metadata exploration tool; https://wfsfa-data.lbl.gov/watershed/), the kml map file included in this dataset, and other data management tools internal to the Watershed SFA team. Version Information: The latest version of this dataset publication is version 3.3. This version contains 167 new point locations, 1 new plot, and 2 new geographic regions. Overall, there are a total of 1439 point locations, 75 plots, and 54 geographic regions. Additionally, the kml map of locations and image now includes two boundaries (Upper Ohio Creek (UO) and Carbon Creek (CA)) outside of the East River watershed (USGS HUC-10) and accompanying stream network that represents areas of focus. Refer to methods for further details on the version history. This dataset will be updated on a periodic basis with new measurement location information. Researchers interested in having their East-Taylor Watershed measurement locations added to this list should reach out to the WFSFA data management team at wfsfa-data@googlegroups.com. Acknowledgments: Please cite this dataset if using any of the location metadata in other publications or derived products. If using the location metadata for the 2018 NEON hyperspectral campaign, additionally cite Chadwick et al. (2020). doi:10.15485/1618130. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2026

REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization

Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model’s prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.

Saklani, Shanu [Indian Institute of Technology, Ka↗
DOE OSTI2025

Reference Correlations for the Density and Viscosity of Molten Alkali and Alkaline Earth Fluoride Salts

While there is a significant body of literature pertaining to thermophysical property measurements of molten salts, there is often a wide degree of variability among independent measurements of the same compounds. As such, the scientific community benefits greatly from an unbiased, independent assessment of duplicate datasets, so that reference correlations which describe these thermophysical properties as functions of temperature can be determined and then commonly used by researchers, scientists, and engineers. With regard to molten fluoride compounds, a significant time has elapsed since density and viscosity reference correlations have been determined; Janz conducted the most recent effort, in 1988, to provide reference correlations for the densities and viscosities of molten fluoride compounds via the National Standard Reference Data System coordinated by the National Bureau of Standards. Since then, new data have been published for molten fluoride compounds, and a new precedent has surfaced for putting forth reference correlations that involve fitting to multiple primary datasets. In this work, reference correlations are put forth for molten alkali and alkaline earth fluoride compounds in an effort to provide updated, improved correlations for general use. For molten alkali fluoride densities, estimated uncertainties with a 95% confidence interval are summarized as follows: LiF (0.63%), NaF (0.48%), KF (0.76%), RbF (0.93%), and CsF (0.75%). For molten alkaline earth fluoride densities, an estimated uncertainty was not able to be quantified for BeF 2 because of limited data; however, estimated uncertainties with a 95% confidence interval are summarized as follows for the remaining alkaline earth fluorides: MgF 2 (1.5%), CaF 2 (0.92%), SrF 2 (1.6%), and BaF 2 (0.23%). For molten alkali fluoride viscosities, uncertainty was not able to be quantified for RbF and CsF because of limited data; however, estimated uncertainties with a 95% confidence interval are summarized as follows for the remaining alkali fluorides: LiF (4.4%), NaF (3.0%), and KF (4.0%). For molten alkaline earth fluoride viscosities, limited consistent data resulted in the recommendation of single datasets (from literature) that are deemed to be the most trustworthy based on the quality of the underlying experimental studies.

Birri, A. [Oak Ridge National Laboratory (ORNL), O↗
DOE OSTI2026

Mitigating Data Center Impact on Grid Stability: A Coordinated Control Strategy Using Verrus StabiliGrid Architecture

Large data centers, which now represent a significant and growing share of the total U.S. grid load, can inadvertently destabilize the electrical grid when they disconnect simultaneously during brief voltage disturbances. The July 10, 2024, Eastern Interconnection incident, in which a sub-100-millisecond transmission fault triggered the cascading loss of approximately 1,500 MW of data center load, illustrates this vulnerability. While commercial battery energy storage systems (BESS) deployed in data centers provide device-level fault ride-through per IEEE 1547, they lack coordination with facility protection logic and uninterruptible power supplies (UPS), limiting their effectiveness as grid-stabilizing assets. This report presents the Verrus StabiliGrid architecture, a coordinated control framework that integrates BESS, UPS, and point-of-interconnection (POI) protection settings to enable data centers to ride through both undervoltage and overvoltage grid contingencies without disconnecting. The four-step strategy encompasses: (1) high-resolution power quality monitoring to detect the grid state during events such as undervoltage, overvoltage, underfrequency, and overfrequency; (2) POI protection settings that allow for extended ride-through and grid-connected operation during grid contingencies; (3) grid state-driven autonomous dispatch of assets to improve grid resilience by reducing power draw during undervoltage or absorbing more power during overvoltage events; and (4) coordinated post-recovery dispatch of data center assets to restore firm load to pre-contingency levels. Validated through controller-hardware-in-the-loop (C-HIL) simulations at the National Laboratory of the Rockies, results show grid import restoration to pre-fault levels within 100 milliseconds of voltage recovery. This work advances the ability of data centers to transition from passive, disturbance-sensitive loads to active participants in grid stability, a capability increasingly required by emerging NERC and ERCOT regulatory frameworks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Data for Physiological Controls on Carbon Fluxes and Biomass Production in Miscanthus: Insights From a Process- Based Agroecosystem Model

Biomass crops serve as essential feedstocks for renewable energy and bioproducts and play a critical role in achieving lower emissions in the transportation sector. However, dedicated perennial biomass crops such as Miscanthus × giganteus (Miscanthus) remain underrepresented in process- based agroecosystem models, limiting robust evaluation of their economic and environmental performance. In this study, we developed a data- constrained representation of the sterile triploid Miscanthus (IL clone) within the process- based model ecosys, integrating global sensitivity analysis, ensemble simulation, and parameter calibration. Planting, harvesting, and fertilization practices consistent with field management were incorporated, and phenology was constrained using PhenoCam- derived Green Chromatic Coordinate (GCC) data. Using the Morris global sensitivity analysis method, we identified 11 key physiological parameters governing plant carbon, water, and nutrient relations, particularly processes associated with CO2 assimilation. We then conducted ensemble simulations by perturbing these parameters and calibrated the model against eddy covariance fluxes and field- measured biomass. Building on the calibrated operating state, parameter- response analyses show that different photosynthetic processes influence productivity in different ways. Protein allocation determines whether productivity increases toward a higher level, whereas electron transport capacity controls additional gains once protein allocation approaches saturation. These findings demonstrate that parameter importance depends on physiological context and on which photosynthetic processes remain limiting. Calibration and validation against observations show that ecosys can reliably reproduce carbon and water fluxes, as well as both above- and belowground biomass, with post- calibration GPP R2 improving from 0.67 to 0.95 during the calibration period and remaining high during validation (R2 = 0.95). These results provide a mechanistic foundation for regional simulations and sustainable bioenergy assessments.

Carbon↗
DOE OSTI2025

Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights

GeoGov Summit Final Presentation entitled "Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights". Maintaining the integrity of energy infrastructure plays a critical role in ensuring energy security. Robust foundational AI models using data from federal, state, industry, and other sources can help address integrity risk management & mitigation issues as well as evaluate extended use strategies. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. Coordination, collaboration & data sharing to develop robust models to aid in: Optimizing operations; Minimizing costs; Ensuring energy security.

Advanced Infrastructure Integrity Model (AIIM)↗
DOE OSTI2026

Physiological Controls on Carbon Fluxes and Biomass Production in Miscanthus: Insights From a Process‐Based Agroecosystem Model

Biomass crops serve as essential feedstocks for renewable energy and bioproducts and play a critical role in achieving lower emissions in the transportation sector. However, dedicated perennial biomass crops such as Miscanthus × giganteus (Miscanthus) remain underrepresented in process-based agroecosystem models, limiting robust evaluation of their economic and environmental performance. In this study, we developed a data-constrained representation of the sterile triploid Miscanthus (IL clone) within the process-based model ecosys, integrating global sensitivity analysis, ensemble simulation, and parameter calibration. Planting, harvesting, and fertilization practices consistent with field management were incorporated, and phenology was constrained using PhenoCam-derived Green Chromatic Coordinate (GCC) data. Using the Morris global sensitivity analysis method, we identified 11 key physiological parameters governing plant carbon, water, and nutrient relations, particularly processes associated with CO 2 assimilation. We then conducted ensemble simulations by perturbing these parameters and calibrated the model against eddy covariance fluxes and field-measured biomass. Building on the calibrated operating state, parameter-response analyses show that different photosynthetic processes influence productivity in different ways. Protein allocation determines whether productivity increases toward a higher level, whereas electron transport capacity controls additional gains once protein allocation approaches saturation. These findings demonstrate that parameter importance depends on physiological context and on which photosynthetic processes remain limiting. Calibration and validation against observations show that ecosys can reliably reproduce carbon and water fluxes, as well as both above- and belowground biomass, with post-calibration GPP R 2 improving from 0.67 to 0.95 during the calibration period and remaining high during validation (R 2 = 0.95). These results provide a mechanistic foundation for regional simulations and sustainable bioenergy assessments.

ecosys↗
DOE OSTI2024

Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example

Deriving closed-form analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM—the Desai-Zwanzig model—in its mean-field limit, using a smaller number of variables than traditional closed-form models. To this end, we use the manifold learning algorithm Diffusion Maps to identify a parsimonious set of data-driven latent variables, and we show that they are in one-to-one correspondence with the expected theoretical order parameter of the ABM. We then utilize a deep learning framework to obtain a conformal reparametrization of the data-driven coordinates that facilitates, in our example, the identification of a single parameter-dependent ordinary differential equation (ODE) in these coordinates. Additionally, we identify this ODE through a residual neural network inspired by a numerical integration scheme (forward Euler). We then use the identified ODE—enabled through an odd symmetry transformation—to construct the bifurcation diagram exhibiting the phase transition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Utility Managed Distributed Energy Resources Intelligent Community - UDERMS iCommunity (Final Technical Report)

The Utility Managed Distributed Energy Resources Intelligent Community (UDERMS iCommunity) project, led by PacifiCorp and supported by the U.S. Department of Energy’s Building Technologies Office (Award No. DEEE0009782), aimed to demonstrate an integrated, utility-managed “behind-the-meter” distributed energy resource (DER) program connecting multifamily, commercial, institutional, and industrial buildings to deliver grid services and improve energy efficiency.

14 SOLAR ENERGY↗
DOE OSTI2025

Site A5 - NREL Scanning Lidar (Halo Allsky SN235) / Reviewed Data

This dataset contains lidar data that have been standardized and quality-controlled through NREL/FIEXTA/LiDARGO (https://github.com/NREL/FIEXTA/tree/main/lidargo). Standardization rearranges the lidar data into convenient range vs beamID vs scanID coordinates that facilitates data analysis. The scan geometry (i.e., azimuth, elevation) is shifted on a regular grid based on the most likely angles within the scan file. Quality control of radial wind speed is performed through a generalized version of the dynamic lidar filter (Beck and Kuhn, 2017).

17 WIND ENERGY↗