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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 OSTI2024

Grid-Ready Energy Analytics Training with Data (“GREAT with Data”)

GridEd is a collaborative educational initiative consisting of the Electric Power Research Institute (EPRI), 5 Partner Universities (Stony Brook University, The University of Texas at Austin, University of California – Riverside, Virginia Tech, Washington State University), and participating industry sponsors. This educational initiative focuses on developing and training the next generation of power engineers so they can help shape the electric grid of the future by anticipating and fulfilling the needs of changing electric industry requirements. GridEd is leveraging electric industry research to educate a future electric grid workforce by empowering new and continuing education students, not only to become competent and well-informed engineers, but also to participate and influence major technological, social, and policy decisions that address critical global challenges. GridEd’s activities are centered around four core pillars: Enhancement of university power systems engineering curricula; Professional development and training for a diverse electric industry workforce; Stimulating students to join the movement for the next generation of power engineers, and; Improve workforce development efforts in the electric utility industry. Major accomplishments over the course of the project were: Over 50 unique professional short courses were delivered by more than 30 instructors across the GridEd network; Over 3,600 unique learners, many who took multiple courses, received more than 27,000 professional development hours (PDH) and over 1,000 certificates of completion; Approximately 2,900 unique learners undertook a course that was offered LIVE online or in-person; Approximately 700 unique learners undertook a course that was offered as computer-based training (CBT); Over 35 unique university courses were delivered by more than 30 instructors to 1,500 university students across the GridEd network; One-hundred-and-fifty-seven (157) students were funded to completed 43 student projects in topics of power systems and data science, and; Six (6) Historically Black Colleges and Universities (HBCUs) recruited as Affiliate Universities via Utility Partners. The professional training initiative and workforce development activities launched by this project will be sustained through EPRI’s collaborative business model with industry. Stimulating students to join the power engineering workforce of the future and the enhancement of tertiary training may continue to need government support.

14 SOLAR ENERGY↗
DOE OSTI2025

Navigating Integration: Key Challenges for Data Centers, Nuclear Stakeholders, and Utility Operators

he exponential growth of data centers—driven by artificial intelligence and cloud computing—is reshaping the U.S. energy landscape, presenting urgent challenges and transformative opportunities for data center developers, nuclear energy providers, and utility operators. As data centers are projected to consume up to 12% of U.S. electricity by 2028, stakeholders must address rapid deployment needs, grid congestion, and the demand for reliable, high-quality power. This presentation explores the multifaceted barriers to integrating data centers with nuclear and utility infrastructure, including land use constraints, public perception, regulatory complexity, and workforce alignment. It highlights the distinct priorities and operational cultures of each sector, and the friction that arises from misaligned planning horizons and risk tolerances. We examine collaborative strategies such as co-siting, hybrid power-purchase agreements, unified community engagement, and innovative financing models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗
DOE OSTI2026

Cybersecurity Standards for Distributed Energy Resources: Gaps and Harmonization Strategy

This report examines cybersecurity standards for Distributed Energy Resources (DERs) in light of their rapid growth and increasing integration into energy systems. It identifies critical gaps in existing frameworks, including inadequate coverage of DER-specific challenges, complexities in implementing comprehensive standards, integration issues with legacy systems, adoption hurdles for newer standards, and a lack of harmonization across regulatory landscapes. The analysis highlights vulnerabilities such as data integrity risks, unauthorized device control, and denial-of-service attacks across various DER technologies like solar PV, wind turbines, energy storage systems, and hydrogen fuel cells. The report proposes a harmonization strategy to address these deficiencies by developing unified cybersecurity requirements, certification programs, and training resources while fostering collaboration among stakeholders such as government agencies, industry groups, DER operators, manufacturers, and research institutions. A phased roadmap is outlined to refine and implement these measures through pilot testing and widespread adoption. Ultimately, the report underscores the urgent need for coordinated efforts to enhance DER cybersecurity and ensure the reliable operation of future energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Analytical noise bias correction for precise weak lensing shear inference

Noise bias is a significant source of systematic error in weak gravitational lensing measurements that must be corrected to satisfy the stringent standards of modern imaging surveys in the era of precision cosmology. This paper reviews the analytical noise bias correction method and provides analytical derivations demonstrating that we can recover shear to its second order using the ‘renoising’ noise bias correction approach introduced by METACALIBRATION. We implement this analytical noise bias correction within the AnaCal shear estimation framework and propose several enhancements to the noise bias correction algorithm. We evaluate the improved AnaCal using simulations designed to replicate Rubin Legacy Survey of Space and Time (LSST) imaging data. These simulations feature semi-realistic galaxies and stars, complete with representative distributions of magnitudes and Galactic spatial density. We conduct tests under various observational challenges, including cosmic rays, defective CCD columns, bright star saturation, bleed trails, and spatially variable point spread functions. Our results indicate a multiplicative bias in weak lensing shear recovery of less than a few tenths of a per cent, meeting LSST Dark Energy Science Collaboration requirements without requiring calibration from external image simulations. Additionally, our algorithm achieves rapid processing, handling one galaxy in less than a millisecond.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

All Particle In Fission Reactor - Energy Deposition

This product includes software developed by Members of the Geant4 Collaboration (http://cern.ch/geant4). The basic principle of ALFRED consists of a k-eigenvalue module in which all generated particles are tracked and all deposited energy is accounted for. An eigenvalue module updates the neutron source after each run based on the neutrons emitted at each fission in the previous run. As a result, the source distribution converges to the fundamental mode of the steady-state eigenvalue problem of the associated critical reactor. ALFRED leverages the High Precision neutron transport package.

Ferney, PaulA. [Idaho National Laboratory (INL), I↗
DOE OSTI2025

Entanglement as a Probe of Hadronization

Recently, it was discovered that the proton structure at high energies exhibits maximal entanglement. This leads to a simple relation between the proton’s parton distributions and the entropy of hadrons produced in high-energy inelastic interactions, which has been experimentally confirmed. In this Letter, we extend this approach to the production of jets. Here, the maximal entanglement predicts a relation between the jet fragmentation function and the entropy of hadrons produced in jet fragmentation. We test this relation using the ATLAS Collaboration data on jet production at the Large Hadron Collider, and find a good agreement between the prediction based on maximal entanglement within the jet and the data. This study represents the first use of a quantum entanglement framework in an experimental study of the hadronization process, offering a new perspective on the transition from perturbative to nonperturbative QCD. Our results open the door to a more comprehensive understanding of the quantum nature of hadronization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Smart Inverters, Dumb Risk: Taking Control of IBR Security in the Digital Age

This presentation addresses the security challenges posed by Inverter-Based Resources (IBRs) in the modern energy landscape. The presentation highlights the vulnerabilities and risks associated with IBRs, including the potential for cyber-attacks, the impact of insecure defaults, and the systemic risks posed by supply chain dependencies. Key topics covered include: 1) The increasing digital transformation in energy systems and the associated security risks. 2) Specific vulnerabilities in IBRs, including weak passwords, hardcoded credentials, and insecure web application interfaces. 3) The implications of persistent connectivity and the strategic risks posed by foreign-manufactured components. 4) The role of regulatory frameworks, such as NERC CIP, in addressing these challenges and the limitations of current oversight. 5) Practical solutions for mitigating risks, including secure design practices, vendor risk assessments, and the importance of strong passwords and role-based access control. The presentation underscores the necessity of a comprehensive, system-of-systems approach to securing IBRs, emphasizing the need for collaboration across various stakeholders, including operators, developers, and regulators, to ensure the resilience and security of the energy grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

FL‐ADS: Federated learning anomaly detection system for distributed energy resource networks

Abstract With the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)‐based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non‐sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real‐world DER scenarios, showcasing significant improvements in accuracy and F1‐score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.

Purohit, Shaurya [Iowa State University Ames Iowa ↗
DOE OSTI2024

Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. Here, we develop particle-flow neural-assisted simulations (parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the Compact Muon Solenoid (CMS) experiment. We show that parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Measurement of inclusive jet cross section and substructure in 𝑝 + 𝑝 collisions at $\sqrt{s}$ = 200 GeV

The jet cross section and jet-substructure observables in 𝑝 + 𝑝 collisions at $\sqrt{s}$ =200 GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-𝑘 𝑡 algorithm with a jet radius of 𝑅 = 0.3 for jets with transverse momentum within 8.0 < 𝑝 𝑇 < 40.0 GeV/𝑐 and pseudorapidity |𝜂| < 0.15. Measurements include the jet cross section, as well as distributions of SoftDrop-groomed momentum fraction (𝑧 𝑔 ), charged-particle transverse momentum with respect to jet axis (𝑗 𝑇 ), and radial distributions of charged particles within jets (𝑟). Also measured was the distribution of 𝜉 = −ln⁡(𝑧), where 𝑧 is the fraction of the jet momentum carried by the charged particle. The measurements are compared to theoretical next-to and next-to-next-to-leading-order calculations, the PYTHIA and H erwig event generators, and to other existing experimental results. Indicated from these measurements is a lower particle multiplicity in jets at RHIC energies when compared to models. Also noted are implications for future jet measurements with sPHENIX at RHIC as well as at the future Electron-Ion Collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Erratum to: Measurements of higher-order cumulants of multiplicity and net-electric charge distributions in inelastic proton-proton interactions by NA61/SHINE

This Erratum replaces, due to a discovery of coding mistakes, the following quantities: κ 3 /κ 1 of the h + − h − distribution presented in Fig. 6 and Table 6, κ 4 listed in Table 4, and $\hat{C}$ 4 presented in Fig. 7 and Table 5. All mentioned figures and tables were updated.

High-Energy Particle Collision Data Analysis↗
DOE OSTI2026

ESnet Watershed Field Science Data Backhaul Campaign Report

ESnet’s Watershed Field Science Data Backhaul Campaign is intended to improve the ability to gather data from sensors deployed across the East River and Trail Creek watersheds, with a special focus on the Snodgrass distributed sensor area, the Pumphouse instruments with co-located U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Surface Atmosphere Integrated Field Laboratory (SAIL) tethered balloon setup, and the former ARM SAIL campaign’s second ARM Mobile Facility (AMF2) site by the Watershed Function Science Focus Area (WFSFA) program. This program is not funded by ARM, but collaborates with ARM as part of the SAIL campaign, collocated with the WFSFA field sites in Colorado’s East River Watershed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Standardized nomenclature for photovoltaic connectors

Photovoltaic (PV) systems rely on discrete connectors for the efficient and safe flow of power from module to module and from strings to combiner boxes and inverters. Despite their functional importance, no common nomenclature for PV connectors currently exists, resulting in confusion and miscommunication. Misunderstood terms like "MC4 compatible", "cross-mating", "intermating", "female", and "male" can lead to installation and maintenance errors and compromise system reliability. We believe a standardized terminology will reduce confusion, help support installation best practices, aid in maintenance and repair, inform next-generation designs, and provide a technical basis for improved codes and standards. To that end, we are proposing a standardized glossary for 4 mm PV connectors (the most common type of connector used in PV applications) based on, and validated by, a Sandia National Laboratories' investigation that included the following sources: 1) a comprehensive review of official documents from 20 connector manufacturers, including schematics, datasheets, installation manuals, and catalogs, as well as relevant patents; 2) two rounds of surveys distributed to stakeholders, including connector manufacturers, engineers, asset owners, test labs, and researchers; and 3) visual examination of 25 different models of 4 mm single-pole DC PV connectors to document variations in design and functionality. This work provides a foundation for establishing a clear and consistent terminology for PV connectors that will in turn enable progress toward greater reliability and collaboration across the industry.

14 SOLAR ENERGY↗
DOE OSTI2025

Queued Up: 2025 Edition – Characteristics of Power Plants Seeking Transmission Interconnection As of the End of 2024 [Slides]

Electric transmission system operators (ISOs, RTOs, or utilities) require proposed power plants seeking to connect to the transmission grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. The lists of projects in this process are known as “interconnection queues”. In collaboration with interconnection.fyi, Berkeley Lab compiled, aggregated, and cleaned interconnection queue data from >50 transmission grid operators (7 ISO/RTOs and 49 non-ISO balancing areas), which collectively represent ~97% of currently installed U.S. electric generating capacity. The dataset includes requests submitted to queues through the end of 2024, and only includes requests seeking to connect to the transmission grid (not distribution-connected or behind-the-meter projects). The files below include both a PDF report and an Excel data file. The PDF report analyzes interconnection data and metrics through the end of 2024. The Excel data file includes (a) the full project-level interconnection queue dataset through 2024, (b) a codebook (data dictionary) describing each data field, and (c) 35 additional tabs featuring tables summarizing a range of interconnection metrics. Key highlights from the Queued Up: 2025 Edition (featuring data through 2024) include: • As of the end of 2024, there were ~10,300 projects actively seeking grid interconnection in the U.S., representing 1,400 GW of generation and approximately 890 GW of storage. • Historic withdrawal rates alongside relatively fewer new requests resulted in a 12% decrease in total active queue volume compared to the prior year. • Active natural gas capacity (136 GW, +72% year-over-year) increased in 2024, while solar (956 GW, -12%), storage (890 GW, -13%), and wind (271 GW, -26%) capacity decreased. • 408 GW of capacity already has a draft or executed interconnection agreement (IA) but has not yet reached commercial operations. • The time projects spend in queues before reaching COD is increasing. For the regions with available data, the median duration from IR to COD has doubled from <2 years for projects built in 2000-2007 to over 4 years for those built in 2018-2024. • Ultimately, most of this proposed capacity will not be built. Only 13% of capacity that submitted interconnection requests from 2000-2019 had reached commercial operations by the end of 2024; 77% of that capacity had been withdrawn and 10% was still active. • FERC Order 2023 and various other reforms are being implemented. These are important measures to reduce interconnection bottlenecks and enhance grid system reliability, but it is too early to measure and assess their full impact. • New additions for the 2025 edition include: (a) additional detail on data processing and gaps; (b) updates on interconnection reforms; (c) new analysis on interconnection agreements, and more.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Natural Hazard Forecast Alert Grid Risk System

Weather events cause most power outages. Often, we even get notifications on our phones to take cover or be prepared for an imminent event. If electric grid utilities had a similar warning that also included probable scenarios and the equipment involved, they could prepare and minimize the effects. Idaho National Laboratory had a project with the U.S. Department of Energy’s Cybersecurity, Energy Security, and Emergency Response program to develop a grid alert application that receives messages from the existing emergency alert system, filters and determines components possibly affected by the emergency event, calculates probable scenarios using MASTERRI (Modeling And Simulation for Targeted Reliability and Resilience Improvement). For high-risk events, the application can then send alert links to subscribed electric distribution utility operations staff to allow them to see and evaluate the scenarios and the impact in a web based interactive map tool. This proof of concept application used data from utilities and organizations, such as the international regulatory body North American Electric Reliability Corporation, which have complied historical failure data of elements that comprise the U.S. electric grid. Nominal failure rates are obtained from this data. To make this tool possible, estimated failure rates were calculated for different component types given the alert type, severity, and location. Historic weather-related grid element failures were correlated with historic weather events from the Integrated Public Alert & Warning System. These correlated events and failures are used along with Bayesian updates from the historical norms to provide a modified failure rate for grid elements in the alert areas and calculate probable scenarios. Working with an industry collaborator, actual grid models and data were used for demonstration cases. This report outlines the work performed for this project.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Measurement of Energy Correlators inside Jets and Determination of the Strong Coupling α S ( m Z )

Energy correlators that describe energy-weighted distances between two or three particles in a hadronic jet are measured using an event sample of s = 13 TeV proton-proton collisions collected by the CMS experiment and corresponding to an integrated luminosity of 36.3 fb − 1 . The measured distributions are consistent with the trends in the simulation that reveal two key features of the strong interaction: confinement and asymptotic freedom. By comparing the ratio of the measured three- and two-particle energy correlator distributions with theoretical calculations that resum collinear emissions at approximate next-to-next-to-leading-logarithmic accuracy matched to a next-to-leading-order calculation, the strong coupling is determined at the Z boson mass: α S ( m Z ) = 0.122 9 − 0.0050 + 0.0040 , the most precise α S ( m Z ) value obtained using jet substructure observables. © 2024 CERN, for the CMS Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗