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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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96 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Utilities Perspective on Protection Challenges with High IBR Penetration

Utilities have seen rapid increase in Solar, Wind, and Battery Energy Storage Resources that interconnect to their electric system through Inverters. Inverter-Based Resources (IBRs) have fault current characteristics that are unlike the fault current response of traditional rotating-machine-based generators, which is well known and repeatable. IBR’s non-traditional fault current behavior is due to the IBR control scheme, which is configured to provide a clean AC output but also protect the inverter’s sensitive power electronics devices from damage, one source of which is overcurrent. This results in low fault current magnitude, low or no negative sequence current injection, the variability of sequence component currents, the variability of voltage with respect to current angles, and the lack of inertia. The control scheme also results in a fault current response that can vary between manufactures and between models of the same manufacturer. High penetration of IBRs can adversely affect the protection schemes applied in areas with high penetration of IBRs. With the proliferation of IBRs, utilities are finding out that conventional protection schemes are not adequately equipped to protect the electric systems. This is mainly because the existing protection elements and practices have been designed based on the fault current response of conventional rotating machines. In several cases, the available literature does not provide any clear solution for the issues when the protection scheme does not operate properly near IBRs. This presentation identifies various protection challenges due to IBRs that industry is facing, from the utility perspective. Instead of facing on one issue, we are looking broadly on all the challenges that system protection has experienced with high penetration of IBRs. Based on the IBR response from various utilities during real fault events and gathering perspective from different utility SMEs via questionnaire, the presentation summarizes on gathered data and internal experiences.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Assembly and Testing of Scintillator Tile Modules for the CMS High-Granularity Calorimeter Upgrade

The High-Granularity Calorimeter (HGCAL), part of the upgrade to the Compact Muon Solenoid (CMS) experiment, employs silicon and scintillator tile modules in the endcaps to maintain detector performance during the High-Luminosity Large Hadron Collider (HL-LHC) era, scheduled to operate from 2030 to 2040. The upgraded calorimeter will provide highly segmented three-dimensional imaging, energy measurements, and precise timing for particle shower reconstruction. Approximately half of the HGCal plastic scintillator tile modules (1,834 total) will be assembled at Fermilab using an automated pick-and-place (PnP) machine. Following assembly, each tile module undergoes quality control (QC) procedures such as electrical validation, thermal testing, and physics validation using cosmic rays. This poster/talk presents the QC procedures and results from the tile module production in 2026, demonstrating the performance and quality of the assembled tile modules.

Bazimya, Jean Luc [Grambling State U.]↗
DOE OSTI2022

Internet of Things Data Characterization Process: Pattern of Life Behavioral Data Study

The HoneyBee™ TARDIS LDRD team completed a data scoping study that identified the initial processes and procedures to baseline the normal and expected behaviors during operability and interoperability of Internet of Things (IoT) device networks. This research is the initial step in developing a process (or methodology) to inform a much broader information framework incorporating machine learning to determine device pattern-of-life which enables the detection of abnormal IoT behaviors on an individual device, as well as in the context of a larger network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

STANDARD REFERENCE MATERIAL CHARPY TESTING FROM HIGH FLUENCE SURVEILLANCE CAPSULE

The Palisades Nuclear Generating Station included in its original surveillance program a surveillance capsule, designated A-60. The capsule was removed from its surveillance position in early 1995 and has been resident in the spent fuel pool since that time. It was harvested to perform characterization of surveillance specimens in this capsule in 2023. This capsule was irradiated to a fluence of 1.96x1020 n/cm2 (E> 1MeV) that is equivalent for more than 150 effective full power years for the current US reactor pressure vessel (RPV) fleet. This capsule contained several materials, including Charpy specimens of standard reference material (SRM) from Heavy-Section Steel Technology (HSST) A533-B Plate 01. To perform Charpy testing of this highly irradiated material, the new Charpy specimen transfer system was designed and implemented on Charpy impact testing machine in the hot cell to accommodate remote testing of these specimens. This new transfer system includes integrated environmental chamber to cool or heat Charpy specimens to the desired temperature. Testing of this highly irradiated material revealed very large shift of Charpy transition temperature, 188oC.

Sokolov, Mikhail [ORNL] (ORCID:0000000256373346)↗
DOE OSTI2025

PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals

Abstract The excited-state properties of molecular crystals are important for applications in organic electronic devices. TheGWapproximation and Bethe-Salpeter equation (GW+BSE) is the state-of-the-art method for calculating the excited-state properties of crystalline solids with periodic boundary conditions. We present the PAH101 dataset ofGW+BSE calculations for 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs) with up to ~500 atoms in the unit cell. To the best of our knowledge, this is the firstGW+BSE dataset for molecular crystals. The data records include theGWquasiparticle band structure, the fundamental band gap, the static dielectric constant, the first singlet exciton energy (optical gap), the first triplet exciton energy, the dielectric function, and optical absorption spectra for light polarized along the three lattice vectors. The dataset can be used to (i) discover materials with desired electronic/optical properties, (ii) identify correlations between DFT andGW+BSE quantities, and (iii) train machine learned models to help in materials discovery efforts.

Science & Technology - Other Topics↗
DOE OSTI2024

Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI

Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.

Computer vision↗
DOE OSTI2026

Computing the Critical Temperature of the Affine-Transformed $D=3$ Ising Model Using Masked Autoregressive Flow

The simple Ising model provides a rich environment to build and study lattice field theories. As part of an ongoing project to construct a conformal field theory (CFT) on an arbitrarily curved manifold, in this work we develop methods to measure the critical temperature $β_c$ of the affine-transformed Ising model on the face-centered cubic (FCC) lattice. The main challenge in this endeavor is finding a computationally efficient and accurate method of interpolating and extrapolating Monte Carlo observables with respect to coupling coefficients and temperature. Herein, we compare two such methods. A traditional statistical approach uses the multiple histogram (MH) method, while a newer machine learning approach uses a masked autoregressive flow (MAF) to estimate the underlying probability density function of a set of observables. While the MH method is specifically designed to interpolate and extrapolate Monte Carlo observables, we find that MAF is a viable alternative for measuring $β_c$ with a computational cost that scales more favorably. Furthermore, we comment on additional advantages of MAF relevant to our work, such as extrapolating in system volume.

Svenson, Kai [Texas U.]↗
DOE OSTI2026

Process-level cost analysis of hybrid manufacturing pathways for aerospace structural components

Hybrid manufacturing is a promising route for producing complex aerospace components, yet systematic cost benchmarking across multiple additive-subtractive pathways remains limited. This study presents a comprehensive process-based cost analysis of seven hybrid manufacturing routes, including laser powder bed fusion (L-PBF), powder- and wire-directed energy deposition (DED), wire arc additive manufacturing (WAAM), additive friction stir deposition (AFSD), metal binder jetting (MBJ), and agility forging, followed by scanning and finish machining. Parametric cost models incorporating direct material, labor, and energy costs were developed. L-PBF results are discussed in detail for a pickle fork component and directly compared with commercial pricing. Across all hybrid routes, labor emerged as the dominant cost driver, contributing more than 70% of total manufacturing cost in some cases. AFSD exhibited the lowest cost for aluminum components, with MBJ being its 316 L stainless steel counterpart, after accounting for geometric scaling. Benchmarking against industrial quotes suggests that hybrid manufacturing can achieve cost levels comparable to those of commercial services, although labor-intensive processes exhibit greater deviation. The analysis highlights automation of material handling, setup, and supervision as key opportunities for improving economic competitiveness. Overall, the proposed framework provides a quantitative basis for evaluating and optimizing hybrid manufacturing pathways for aerospace applications.

Baruah, Sweta [ORNL] (ORCID:0009000174256207)↗
DOE OSTI2025

AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks

We propose the artificial intelligence velocimetry-thermometry (AIVT) method to reconstruct a continuous and differentiable representation of the temperature and velocity in turbulent convection from measured three-dimensional (3D) velocity data. AIVT is based on physics-informed Kolmogorov-Arnold networks and trained by optimizing a loss function that minimizes residuals of the velocity data, boundary conditions, and governing equations. We apply AIVT to a set of simultaneously measured 3D temperature and velocity data of Rayleigh-Bénard convection, obtained by combining particle image thermometry and Lagrangian particle tracking. This enables us to directly compare machine learning results to true volumetric, simultaneous temperature and velocity measurements. We demonstrate that AIVT can reconstruct and infer continuous, instantaneous velocity and temperature fields and their gradients from sparse experimental data at a high resolution, providing an additional approach for understanding thermal turbulence.

Science & Technology - Other Topics↗
DOE OSTI2026

Electrode Erosion and Prefire Studies Towards Fusion Scale Pulsed Power

This study presents a comprehensive investigation of electrode erosion and discharge behavior in spark gap switches over long switching cycle lifetimes. Brass, copper–tungsten (CuW), and stainless steel electrodes are tested under controlled conditions to quantify material degradation, debris accumulation, and changes in breakdown voltage. High-resolution imaging and statistical analysis of spark channel locations and gap breakdown voltages reveal how surface evolution influences long-term performance and reliability. These results provide essential data for lifetime modeling and inform design strategies for pulsed power systems in emerging applications such as private sector fusion energy and large-scale facilities like Sandia’s Z Machine and proposed ZX upgrades, where high repetition reliability and predictable behavior are critical.

electrical breakdown↗
DOE OSTI2025

Utility Experience with Inverter Based Resource Impacts on Transmission Protection

High penetration of inverter-based resources (IBR) can adversely affect the transmission protection schemes in the area. With the proliferation of IBRs, utilities are finding out that conventional protection schemes are not adequately equipped to protect the electric systems because the existing system has been designed based on the fault current response of conventional rotating-machine-based generators. In several cases, the available literature does not provide any clear solution for the issues when the protection scheme does not operate properly near IBRs. This report identifies various protection challenges due to IBRs that industry is facing, from the utility perspective. It includes a broad review of all challenges that system protection has experienced so far with high penetration of IBRs. Key issues have been identified based on a survey of different utility protection engineers. Finally, fault event recordings are shown to demonstrate some actual cases of mis-operations of relays near IBRs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies

The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets.

Computer Science↗
DOE OSTI2026

Predicting Atomistic Transitions with Transformers

Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Polarized target nuclear magnetic resonance measurements with deep neural networks

Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and higher precision in subsequent offline analysis. By reducing analysis-induced uncertainty, the resulting methodology can improve the effective figure of merit for scattering experiments employing dynamically polarized targets and provides a new toolset for NMR-based polarimetry in high-energy and nuclear physics.

Metrology↗
DOE OSTI1998

Fusion Safety Program Peer Review (August 31 and September 1, 1999) [Slides]

The Fusion Safety Program Peer Review conference, held on August 31 and September 1, 1999, focuses on advancing the understanding and management of radioactive and hazardous materials within deuterium-tritium (D-T) fusion machines. The conference aims to investigate the behavior of significant sources of radioactive materials, such as activation products, dust, tritium, and beryllium. Additionally, it seeks to comprehend how various energy sources in fusion facilities—such as magnets, plasma, decay heat, and chemical reactions—can mobilize these materials. A key objective of the conference is to develop integrated, state-of-the-art analytic tools to demonstrate the safety and environmental potential of fusion technology. Furthermore, the conference assesses and evaluates safety and environmental issues associated with emerging fusion concepts, including those in the ARIES, ALPS, APEX, and IFE projects. This comprehensive approach aims to ensure the safe and sustainable advancement of fusion energy.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

A Data-Driven Method for Modeling Creep-Fatigue Stress- Strain Behavior Using Neural ODEs

In this paper, we introduce a data-driven machine learning approach for modeling one-dimensional stress–strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy 617. The study employs uniaxial creep–fatigue test data acquired under various loading histories and compares two distinct neural network-based ODE models. The first model, known as the black-box model, comprehensively describes the strain–stress relationship using a Neural ODE equation. To interpret this black-box model, we apply the Sparse Identification of Nonlinear Dynamical Systems (SINDy) technique, transforming the black-box model into an equation-based model using symbolic regression. The second model, the Neural flow rule model, incorporates Hooke’s Law for the linear elastic component, with the nonlinear part characterized by a Neural ODE. Both models are trained with experimental data to accurately reflect the observed stress–strain behavior. We conduct a detailed comparison with the standard Chaboche model, which includes three back stresses. Our results demonstrate that the neural network-based ODE models precisely capture the experimental creep–fatigue mechanical behavior, exceeding the standard Chaboche model’s accuracy. Furthermore, an interpretable model derived from the black-box neural ODE model through symbolic regression achieves accuracy comparable to the Chaboche model, enhancing its interpretability. The results highlight the potential of neural network-based ODE models to depict complex creep–fatigue behavior, eliminating the necessity for experts to define a specific, material-focused model form.

creep-fatigue↗
DOE OSTI2026

Gear Test Assembly - Experimental Testing and Analysis of Gears and Bearings - FY2026

The Gear Test Assembly (GTA) is an experimental test apparatus built and installed in the Mechanisms and Engineering Test Loop (METL) at Argonne National Laboratory (ANL). While designed to accommodate a variety of components intended for use in advanced compact in-vessel transfer machine, the focus of GTA been the testing of large radial spur gears. The performance of the components used in GTA motivates the choices made in the design, components, and material choices for the forthcoming METL test articles; the Gripper Test Assembly (GrTA), and the Bearing Test Assembly (BTA). To date, GTA has completed nine experimental testing campaigns and achieved over 21 million revolutions under prototypic sodium fast reactor loads and operating conditions. This report provides an overview and results for the two most recent experiments Campaign #8 and Campaign #9. `Campaign #8 was the first campaign to make use of a set of nickel alloy radial spur gears, made from Hastelloy C-22HS, and combined with heat-treated tapered roller bearings. The bearings and torque profile used allowed Campaign #8 to be directly compared with Campaign #1 where Inconel 718 gears were used instead. Campaign #8 attained 1,058,880 shaft revolutions within 23% of the 1,314,855 revolutions achieved in Campaign #1. These results demonstrate that the heat-treated tapered roller bearings show consistent increased lifetimes, approximately three times longer than for non heat-treated taper rollers, and that the particular material of the gear does not play a substantial role in performance. Similar to Campaign #7 where bearing failure resulted in fragmentation and transport of bearing material into the gear teeth led to tooth damage in the Inconel gears, a similar process led to the damage of the Hastelloy gears. Subsequent NDE analysis showed that the damage was only limited to the tooth surface and does not extend into the gear interiors. Based on the NDE reports, the Hastelloy gears can be reconditioned and put back into service for future campaigns as was done for the Inconel gears. Reconditioned Inconel 718 gears damaged in Campaign #7 were returned to service in Campaign #9 and paired with non heat-treated tapered roller bearings. In Campaign #9 a novel set of operating conditions were implemented whereby the gears and bearings were cycled between hot, submerged refueling conditions where fuel handling operations occurred and cold, dry, and inert conditions where the sodium was drained and the vessel was allowed to cool simulating long, ex-vessel storage of the fuel handling machine. A total of eight of these operating cycles were achieved before a bearing failure occurred after the accumulation of 475,692 shaft revolutions were achieved. During Campaign #9, one of the drive motors was damaged and over the course of a 240 day standby period, the entire GTA drive system was upgraded from a 480VAC system to a 240VAC system that provided the opportunity to consolidate wall-space in the METL facility and provide more space for additional test articles being installed in the coming year. The over 475,000 shaft revolutions achieved in Campaign #9 eclipses the previous record for lifetime of non heat-treated tapered roller bearings held by Campaign #3 of 392,000 shaft revolutions. Further analysis and future campaigns hope to shed light on the cause of this approximately 17% increase in lifetime.

42 ENGINEERING↗
DOE OSTI2024

4D-STEM Mapping of Nanocrystal Reaction Dynamics and Heterogeneity in a Graphene Liquid Cell

Chemical reaction kinetics at the nanoscale are intertwined with heterogeneity in structure and composition. However, mapping such heterogeneity in a liquid environment is extremely challenging. Here, in this work, we integrate graphene liquid cell (GLC) transmission electron microscopy and four-dimensional scanning transmission electron microscopy to image the etching dynamics of gold nanorods in the reaction media. Critical to our experiment is the small liquid thickness in a GLC that allows the collection of high-quality electron diffraction patterns at low dose conditions. Machine learning-based data-mining of the diffraction patterns maps the three-dimensional nanocrystal orientation, groups spatial domains of various species in the GLC, and identifies newly generated nanocrystallites during reaction, offering a comprehensive understanding on the reaction mechanism inside a nanoenvironment. This work opens opportunities in probing the interplay of structural properties such as phase and strain with solution-phase reaction dynamics, which is important for applications in catalysis, energy storage, and self-assembly.

four-dimensional scanning transmission electron mi↗