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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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DOE OSTI2023

Optimization and Comparison of Modern Offshore Wind Turbine Generators Using GeneratorSE 2.0

As the offshore wind industry keeps growing at a rapid pace, developers are bracing themselves for a huge demand in critical rare earth metals which will threaten an already vulnerable supply chain. The wind energy industry is addressing this problem by investing in modern generator technologies that employ magnets with reduced rare earth content and high-field magnets enabled by rare-earth-free superconductors. In this paper we introduce the National Renewable Energy Laboratory's newly advanced GeneratorSE 2.0, which is a design and optimization tool that was developed to investigate the feasibility of such modern generators. Two direct-drive generator topologies with different magnet materials and mounting arrangements are investigated: an outer-rotor, V-shaped interior permanent magnet generator, and an inner-rotor normally conducting armature, paired with a low-temperature superconducting field with race-track coils. These technologies were evaluated for a range of power ratings between 15 and 25 MW, which represent the next generation of offshore wind turbines for both fixed-bottom and floating applications. The analyses indicate a new trend favoring the low-temperature superconducting technology for the direct-drive system.

direct-drive generators↗

DOE OSTI2023

Magnetic imaging of topological phases of matter (Final Report)

This project presents a detailed understanding of the local electronic transport in two different types of conductors. The experimental tools are a combination of local magnetic probes and electronic transport measurements. The first set of samples were superconducting microstructures that exhibited unusual transport behavior. Magnetic imaging revealed the presence of a strain field in the microstructures, which spatially modulated the superconducting properties. This strain field can be controlled through a variety of design parameters opening opportunities for a broad range of quantum materials including topological phases of matter. The second set of samples was fabricated from Cr-doped (Bi,Sb) 2 Te 3 heterostructures that exhibited the quantum anomalous Hall effect (QAHE). Magnetic imaging visualized the current flow in these devices, while simultaneous transport measurements verified that the devices exhibit the QAHE. Surprisingly, the current flowed in the interior of the sample even when exhibiting a high level of quantization.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

First measurement of electron neutrino scattering cross section on argon

We report the first electron neutrino cross section measurements on argon, based on data collected by the ArgoNeuT experiment running in the GeV-scale NuMI beamline at Fermilab. A flux-averaged $\nu_e + \overline{\nu}_e$ total and a lepton angle differential cross section are extracted using 13 $\nu_e$ and $\overline{\nu}_e$ events identified with fully-automated selection and reconstruction. We employ electromagnetic-induced shower characterization and analysis tools developed to identify $\nu_e/\overline{\nu}_e$-like events among complex interaction topologies present in ArgoNeuT data ($\langle E_{\bar{\nu}_e} \rangle = 4.3$ GeV and $\langle E_{\nu_e} \rangle = 10.5$ GeV). The techniques are widely applicable to searches for electron-flavor appearance at short- and long-baseline using liquid argon time projection chamber technology. Notably, the data-driven studies of GeV-scale $\nu_e/\overline{\nu}_e$ interactions presented in this Letter probe an energy regime relevant for future DUNE oscillation physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Calculating topological properties of artificial graphene in B-field

SAND2025-03258O Calculating topological properties of artificial graphene in B-field is a user-friendly tool designed to analyze artificial graphene systems influenced by magnetic fields. It helps researchers understand the unique properties of these materials by calculating the local Chern marker, a key indicator of their behavior. With just one file, this software runs easily on any device with Matlab, making it accessible for scientists and engineers. It provides valuable insights into the electronic characteristics of artificial graphene, which supports advancements in material science and technology, paving the way for innovative applications in electronics and beyond. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Spataru, Dan [Sandia National Lab. (SNL-CA), Liver↗

DOE OSTI2022

ACES: Infrastructure As Code. Model Optimization and Performance Capability

Infrastructure as Code (IaC) refers to managing infrastructure (networks, physical/virtual machines, storage, and connection topology) in a descriptive model/language, rather than configuring it manually or using interactive configuration tools. Just like source code can be compiled to generate the same binary code, IaC enables generating the same environment every time it is applied. IaC is a key DevOps practice and is generally used in conjunction with continuous integration (CI) and continuous delivery (CD). In CI, all code changes are merged into a mainline branch and validated multiple times a day as developers check in their changes to the source code. In CD on the other hand, code changes are automatically packaged for a new release-to-production on a regular basis. This typically enables teams to deliver software changes much more quickly and often. Developing and managing the ACES platform using (IaC) is vital for the robust deployment and continued sustainment of this foundational computing capability. IaC and DevOps practices will help us solve many of the common challenges often encountered in developing and maintaining compute infrastructure. First, it will make the provisioning, deployment, and maintenance of the compute infrastructure across multiple environments much more efficient. Second, these processes help make the overall system much more stable by continuously testing new changes as they are introduced to the system. Third, it allows us to be much more confident of the security controls in place since they can be tested as part of the CI process and all new changes can be audited and tracked. Finally, IaC enables the ACES Platform to be adaptable to the emerging technologies due to its ability to spin up different test beds to evaluate and incorporate these technologies. This document addresses common infrastructure-management challenges, describes what happens if they are not addressed, and highlights the value of utilizing IaC to tackle them. Finally, we will provide a high-level overview of the IaC and DevOps practices being utilized by the ACES Platform team.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1991

A general multiblock Euler code for propulsion integration. Volume 2: User guide for BCON, pre-processor for grid generation and GMBE

The BCON is a menu-driven graphics interface program whose input consists of strings or arrays of points generated from a computer aided design (CAD) tool or any other surface geometry source. The user needs to design the block topology and prepare the surface geometry definition and surface grids separately. The BCON generates input files that contain the block definitions and the block relationships required for generating a multiblock volume grid with the EAGLE grid generation package. The BCON also generates the block boundary conditions file which is used along with the block relationship file as input for the general multiblock Euler (GMBE) code (GMBE, volumes 1 and 3).

Su, T. Y.↗

NASA NTRS2003

[Development and Use of Hidrosig]

The NASA portion of this joint NSF-NASA grant consists of objective 2 and a part of objective 3. A major effort was made on objective 2, and it consisted of developing a numerical GIs environment called Hidrosig. This major research tool is being developed by the University of Colorado for conducting river-network-based scaling analyses of coupled water-energy-landform-vegetation interactions including water and energy balances, and floods and droughts, at multiple space-time scales.Objective 2: To analyze the relevant remotely sensed products from satellites, radars and ground measurements to compute the transported water mass for each complete Strahler stream using an 'assimilated water balance equation' at daily and other appropriate time scales. This objective requires analysis of concurrent data sets for Precipitation (PPT), Evapotranspiration (ET) and stream flows (Q) on river networks. To solve this major problem, our decision was to develop Hidrosig, a new Open-Source GIs software. A research group in Colombia, South America, developed the first version of Hidrosig, and Ricardo Mantilla was part of this effort as an undergraduate student before joining the graduate program at the University of Colorado in 2001. Hydrosig automatically extracts river networks from large DEMs and creates a "link-based" data structure, which is required to conduct a variety of analyses under objective 2. It is programmed in Java, which is a multi-platform programming language freely distributed by SUN under a GPL license. Some existent commercial tools like Arc-Info, RiverTools and others are not suitable for our purpose for two reasons. First, the source code is not available that is needed to build on the network data structure. Second, these tools use different programming languages that are not most versatile for our purposes. For example, RiverTools uses an IDL platform that is not very efficient for organizing diverse data sets on river networks. Hidrosig establishes a clear data organization framework that allows a simultaneous analysis of spatial fields along river network structures involving Horton- Strahler framework. Software tools for network extraction from DEMs and network-based analysis of geomorphologic and topologic variables were developed during the first year and a part of second year.

Gupta, Vijay K.↗

DOE OSTI2025

Advanced Transmission Technologies –GETs and HPCs Session 3: HPCs and Building Actions Plans to Digital Assurance Risks

The third session of the Idaho National Laboratory’s (INL) Technical Assistance for Digital Assurance (TADA) program, held on November 11, 2025, centered on High Performance Conductors (HPCs) and the formulation of action plans to address digital assurance risks associated with Grid-Enhancing Technologies (GETs). This session convened experts from utilities, vendors, and government agencies to examine the technical, operational, and cybersecurity aspects of HPC deployment. Discussions highlighted the benefits of HPCs, such as their ability to rapidly increase transmission capacity using existing corridors, improve grid resilience, reduce system losses, and align with FERC Orders 2023 and 1920. Participants evaluated supply chain and digital assurance risks, including reliance on imported materials, limited domestic manufacturing capacity, workforce shortages, and traceability issues. The session also emphasized the importance of digital trust, integration-layer cybersecurity, and unified risk frameworks, introducing tools like intrusion detection systems, encryption, zero trust networking, and firmware integrity. Recaps of earlier workshops on Dynamic Line Ratings (DLRs), Advanced Power Flow Control (APFC), and Transmission Topology Optimization (TTO) underscored institutional barriers and integration challenges. Action plans were proposed to mitigate issues such as inconsistent cybersecurity practices, SBOM usage, supply chain visibility, operator trust, and misaligned incentives. Additionally, INL presented its supply chain risk management tools and Cyber-Informed Engineering (CIE) principles to support secure procurement and system design. The session concluded with a commitment to share key takeaways, incorporate cohort feedback into future policy development, and continue collaborative engagement through upcoming pilot activities. Session 3 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Imaging mesoscopic antiferromagnetic spin textures in the dilute limit from single-geometry resonant coherent x-ray diffraction

The detection and manipulation of antiferromagnetic domains and topological antiferromagnetic textures are of central interest to solid-state physics. A fundamental step is identifying tools to probe the mesoscopic texture of an antiferromagnetic order parameter. In this work, we demonstrate that Bragg coherent diffractive imaging can be extended to study the mesoscopic texture of an antiferromagnetic order parameter using resonant magnetic x-ray scattering. We study the onset of the antiferromagnet transition in PrNiO 3 , focusing on a temperature regime in which the antiferromagnetic domains are dilute in the beam spot and the coherent diffraction pattern modulating the antiferromagnetic peak is greatly simplified. We demonstrate that it is possible to extract the arrangements and sizes of these domains from single diffraction patterns and show that the approach could be extended to a time-structured light source to study the motion of dilute domains or the motion of topological defects in an antiferromagnetic spin texture.

36 MATERIALS SCIENCE↗

DOE OSTI2020

The concept of spin ice graphs and a field theory for their charges

Originally detected in rare earth pyrochlores, spin ice physics is now being artificially extended to a variety of geometries that control collective behavior and exotic properties, making graph theory their proper framework. We relate spin ice notions, such as ice rule, ice manifold, Coulomb phases, charges, and monopoles, to graph-theoretical notions, such as balance, in/out-degrees, and Euler paths. We then propose a field-theoretical treatment in which topological charges and monopoles are the degrees of freedom, while the binary spins are subsumed in an entropic interaction among charges. We show that for a spin ice on a graph in a Gaussian approximation, the kernel of the entropic interaction is the inverse of the graph Laplacian, and we compute screening functions from the graph spectra as Green operators for the screened Poisson problem on a graph. We then apply the treatment to star graphs, tournaments, cycles, and regular spin ice in different dimensions. Our aim is twofold: to set spin ice physics in a proper graph setting, where only topological rather than geometrical notions hold, and to invite graph theorists to contribute their powerful tools to the field of spin ice.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Visual Analytics of Crosstalk in Quantum Hardware

Crosstalk remains a major obstacle to building scalable and fault-tolerant quantum computers. Conventional diagnostic techniques-often based on numerical simulation or statistical modeling-struggle to scale with hardware complexity and offer limited interpretability. In this work, we present a visual analytics framework for diagnosing qubit crosstalk using lightweight, circuit-based models integrated with an interactive user interface. Our approach quantifies correlations between active and idle qubits under parameterized single- and twoqubit operations, enabling detection of both spatial and gateinduced crosstalk. The system incorporates qubit topology and gate performance data to support sector-based exploration and correlation mapping. This tool assists users in identifying correlated error sources, informing qubit placement strategies, and guiding noise-aware circuit design.

Chae, Junghoon [ORNL] (ORCID:0000000206016746)↗

DOE OSTI2022

Crosslink V.0.11.x User Manual

CrossLink is a novel two-dimensional and three-dimensional geometry and mesh generation software package developed by the Simulation Tools team at Los Alamos National Laboratory. This software represents the third generation of topology-based mesh generation technology developed by the Department of Defense and the Department of Energy with a special focus on complex multi-material hydrodynamic applications, mesh scalability, and high-order element mesh generation. The topology-based meshing approach offered by CrossLink enables users to quickly and easily mesh complex geometries in a repeatable and robust manner. CrossLink’s topology-based meshing approach is well-suited for parametric design studies, parametric design optimization, damage scenario assessment, and iterative design modification (i.e. feature addition and/or removal). CrossLink’s python API allows workflow scripting of the geometry creation and mesh generation process for traceability, repeatability, data provenance, and version control. CrossLink consists of three main components: a graphical user interface (GUI), a geometry creation and mesh generation engine, and a python API that provides a workflow scripting interface to the geometry and meshing functions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1995

High latitude ion composition

A data base comprised of all available ionosphere satellite ion composition measurements - the Goddard Comprehensive Ionosphere Data Base (GCID) - has been set upon optical disks for convenient merging and accessing of data from different satellites. This data has recentely been expanded to include all accessible satellite electron density and plasma temperature measurements. This paper demonstrates with a couple examples, the potential of GCID as an alternative to empirical models for undertanding ionsophere physics and chemistry. Through binning techniques GCID provdes a tool that complements and improves on the International Reference Ionosphere model (IRI) in delineating the topology of high latitude ion composition. GCID provides at a glance a measure of the local statistical variabilty of the ion compostions that is not in empirical models such as IRI. Furthermusing the data to determine the spatial and geophysical parameter range over which the minor ion species are approximately in chemical equilibrium, the number of data points available for empirical ion composition models can be increased by using the statiscally more reliable neutral and electron empirical models to derive ions composition. Currently available empirical models are not capable of adequately defining the comples high latitude distribution - a measure of the variablity is needed.

Grebowsky, J. M.↗

NASA NTRSDate not supplied

Model-Based Systems Engineering, Real-Time Operations, and Autonomy

Model-Based Systems Engineering has been enabled by the development of the SysML language and software tools to create systems models. Systems models described in SysML incorporate frames (Diagrams) that represent behaviors (activities, sequences, state machines, use cases), requirements, and structure (definitions, internal structure, parametric formulation, and packaging). The SysML models are, in turn, used by applications to do analysis and studies of the designs and operational capabilities. These uses of the model are based on simulations, and do not include hardware. This paper presents a software environment and processes that enables more comprehensive systems models for MBSE, and use of these rich models for real-time operations. The paper describes a software platform that enables creation of comprehensive models, beyond what is now possible with SysML and related software tools, called the NASA Platform for Autonomous Systems (NPAS). The platform encapsulates a paradigm and infrastructure for creating systems models with complexity levels comparable to the ones handled by SysML software tools, but with additional fidelity that includes detailed design diagrams encompassing sensors, components, and design topologies. Furthermore, NPAS enables incorporation of data, information, and knowledge (DIaK) to implement autonomy and Integrated System Health Management (ISHM) and the inherent integration of content encompassing SysML structure and behavior diagrams throughout the NPAS modelAnd lastly, the NPAS models are used in real-time operations, taking advantage of the fidelity and complexity encompassed in the models in order to implement “thinking” ISHM and/or autonomous operations. . Incorporation of SysML model content into an NPAS model is briefly discussed.

MBSE↗

DOE OSTI2022

Gate-defined wires in twisted bilayer graphene: From electrical detection of intervalley coherence to internally engineered Majorana modes

Twisted bilayer graphene (TBG) realizes a highly tunable, strongly interacting system featuring superconductivity and various correlated insulating states. We establish gate-defined wires in TBG with proximity-induced spin-orbit coupling as (i) a tool for revealing the nature of correlated insulators and (ii) a platform for Majorana-based topological qubits. We show that the band structure of a gate-defined wire immersed in an intervalley coherent correlated insulator inherits electrically detectable fingerprints of symmetry breaking native to the latter. Surrounding the wire by a superconducting TBG region on one side and an intervalley coherent correlated insulator on the other further enables the formation of Majorana zero modes—possibly even at zero magnetic field depending on the precise symmetry-breaking order present. Our proposal not only introduces a highly gate-tunable topological qubit medium relying on internally generated proximity effects but can also shed light on the Cooper-pairing mechanism in TBG.

36 MATERIALS SCIENCE↗

DOE OSTI2020

GridSTAGE (aka PowerDrone)

Simulation tool for electrical grid security. It is possible to select from pre-loaded power system topologies, enable different grid controls (like AGC, power system stabilizer), add faults, tweak different parameters, add pre-modeled attack scenarios, and run batch simulations to generate data needed for research.

Nandanoori, Sai Pushpak↗