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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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104 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Interactive automated Bragg peak identification with 3D neutron scattering data

Neutron scattering experiments have undergone significant technological development through large area detectors with concurrent enhancements in neutron transport and electronic functionality. Data collected for neutron events include detector pixel location in 3D, time and associated metadata, such as, sample orientation, neutron wavelength, and environmental conditions. RadiaSoft and Oak Ridge National Laboratory personnel are considering single-crystal diffraction data from the TOPAZ instrument. We are leveraging a new method for rapid, interactive analysis of neutron data using NVIDIA’s IndeX 3D volumetric visualization framework. We have implemented machine learning techniques to automatically identify Bragg peaks and separate them from diffuse backgrounds and analyze the crystalline lattice parameters for further analysis. The implementation of automatic peak identification into IndeX allows scientists to visualize and analyze data in real-time. Our methods include a robust comparison with current analysis techniques which show improvement in a variety of aspects. These improvements will be incorporated into IndeX for visualization to allow scientists an interactive tool for crystal analysis.

Kilpatrick, Matthew↗
DOE OSTI2022

Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations

Abstract Recent progress in the disruption event characterization and forecasting framework has shown that machine learning guided by physics theory can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized random forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict resistive wall mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model’s decisions, a diverse set of counterfactual explanations based on determinantal point processes (DPP) is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical magnetohydrodynamic activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics intuition, it motivates the usage of counterfactuals to simulate real-time control by generating the β N levels that would have kept the RWM stable for a set of unstable discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Concurrent Optimization of Capital Cost and Expected O&M

Concentrating solar power (CSP) technologies can utilize heat from concentrated sunlight from a field of tracking mirrors to generate electricity, reform fuel, provide process heat, or augment fossil plant heat sources. Electricity-generating power tower systems focus light from thousands of independent heliostats onto a thermal receiver, which uses the focused light to warm a heat transfer fluid (HTF), typically, a molten nitrate salt. The HTF is then sent to a power generation cycle or diverted into thermal energy storage (TES) for later use. Thermal storage is – in principle – a straightforward proposition. However, optimal utilization of a TES resource is complex and multi-faceted: thermal energy may be dispatched to produce electricity immediately upon first availability, or thermal energy may be reserved for next-day peak periods at risk of filling storage and dumping energy, or a portion of the thermal energy can be reserved to maintain equipment temperatures, reducing power cycle startup time, etc. Many possible dispatch permutations variously emphasize producing peak power, operating through transients, expediting daily startup, etc. The best operation strategy can change day-to-day throughout the year, depending on the weather and market pricing forecasts. The project we describe in this report develops a software package that allows users to explore design optimization, operations decisions, and performance characterization of concentrating solar power tower plants. Users interface with the tool through a scripting language, and results are reported in time series tables, plots, runtime logs, and design outputs. Users choose from a list of variables such as tower height, solar multiple, design-point irradiance, thermal storage size, etc., and specify information about the system using a list of parameters. The software can then optimize the specified variables to reduce the cost of energy produced by the system while meeting certain production requirements, accounting for uncertain weather and electricity price forecasts, and correcting for equipment failures or repair time. The software we develop is the first comprehensive design tool of its kind to incorporate all of these aspects while being deployed as open source.

14 SOLAR ENERGY↗
DOE OSTI2021

The Application of Differential Scanning Calorimetry to Investigate Precipitation Behavior in Nickel-Base Superalloys Under Continuous Cooling and Heating Conditions

In this study, a suite of experimental tools and fast-acting, numerical-simulation techniques was used to quantify the precipitation behavior of three nickel-base superalloys: IN-100, LSHR, and 718. Experimental methods comprised differential scanning calorimetry (DSC) to establish the specific heat as a function of temperature and selected direct-resistance heating trials (using a Gleeble ® machine) to obtain samples for microstructural analysis. For the DSC experiments, each alloy was cooled at a prescribed constant rate (between 5 and 20 K/min) after an initial soak/equilibration in the high-temperature, single-phase (supersolvus) temperature regime. On-heating DSC trials beginning at ambient temperature were also performed on alloy 718 in three different starting conditions: super- δ -solvus solution treated and water quenched (denoted as ST), solution treated and aged (STA), and solution treated and overaged (STOA). DSC results, revealing the thermal signatures associated with the kinetics of precipitation of γ ' (IN-100, LSHR) or γ ' and γ " (718), were interpreted using a previously-developed fast-acting routine that treats concurrent nucleation, growth, coarsening, and dissolution. For these simulations, special attention was paid to various thermo-kinetic input parameters including equilibrium solvus-approach curves, bulk free energies of transformation, matrix-precipitate interface energies, and effective diffusivities. For the γ - γ ' superalloys (IN-100 and LSHR), estimates of precipitate volume fraction as a function of temperature from the specific-heat data revealed semi-quantitative agreement with simulation predictions. For the γ - γ '- γ " superalloy (718), simulation predictions of precipitate volume fractions were converted to specific heat as a function of temperature and showed semi-quantitative agreement with the direct measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Understanding the effect of friction stir processing on electrical steel: Microstructural changes and mechanical transformations

Raising Si beyond 3.5 wt% reduces core loss but embrittles electrical steels, constraining processing and application. Here we assess friction stir processing (FSP) as a solid‑state route to enable Fe–4.0 wt% Si with improved mechanical performance. Temperature‑controlled FSP with a PcBN tool produced a defect‑free processed zone. SEM/EBSD revealed dynamic recrystallization and grain refinement from an average 110 μm in the base material to ∼20 μm in the processed region, with ∼ 80% of grains < 40 μm and a reduction in high‑angle boundary fraction to ∼65% (vs >85% in the base). Comparative mechanical property analysis shows a moderate increase in yield strength in FSP samples relative to the base material, with a significant improvement in ductility (∼35–50%). Post‑fracture EBSD identified 60° twin boundaries in the base while sub-grain formation with elevated KAM in the FSP sample. Fractography revealed a shift from quasi‑cleavage in the Base-faceted planes with river patterns, steps, and occasional secondary cracks—to ductile micro void coalescence after FSP. Following FSP, the material retains soft‑magnetic behavior, with modest shifts in saturation magnetization consistent with grain refinement and stored strain, accompanied by a clear gain in formability. These results indicate that FSP provides a viable route to deploy higher‑Si electrical steels with concurrent gains in strength and ductility.

Electrical steel↗
DOE OSTI2024

Concurrent Inter-Model Spread of Boreal Winter Westerly Jet Meridional Positions Between the Northern and Southern Hemispheres in CMIP6 Models

Here, this study investigates the inter-model spread of climatological extratropical westerly jets in boreal winter, using the historical simulation of 52 Coupled Model Intercomparison Project phase 6 (CMIP6) models from 1851 to 2014. The results show that there is a substantial spread in the latitude of the upper-tropospheric westerly jet across models, characterised by large inter-model standard deviations to both the poleward and equatorward sides of the jet axis, although the multi-model ensemble mean (MME) performs well in simulating meridional position of westerly jets. Furthermore, we detect the consistency of inter-model jet position spread between the Northern and Southern Hemispheres, based on the inter-model empirical orthogonal function (EOF) decomposition and correlation of regional-averaged zonal winds. Specifically, the models that simulate the westerly jets poleward/equatorward relative to the MME position in one hemisphere also tend to simulate the jets poleward/equatorward in the other hemisphere. Accordingly, we define a global jet spread index to depict the concurrence of jet shift in the two hemispheres. The results of inter-model regression analyses based on this index indicate that the models positioning the jets poleward than the MME tend to simulate a wider Hadley Cell, a poleward-shifted Ferrel Cell in the Southern Hemisphere, enhanced precipitation in the subtropics and suppressed precipitation in the tropics, and warmer sea surface temperatures in the subtropics and mid-latitudes. The present results suggest that improving the simulation of jet positions in climate models requires a comprehensive consideration of thermal states in the tropics and subtropics/mid latitudes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment

Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new algorithms, and optimizing resource allocation strategies. However, existing simulators suffer from limited scalability, hardwired algorithms, lack of real-time monitoring, and inability to generate datasets suitable for modern machine learning approaches. We present CGSim, a simulation framework for large-scale distributed computing environments that addresses these limitations. Built upon the validated SimGrid simulation framework, CGSim provides high-level abstractions for modeling heterogeneous grid environments while maintaining accuracy and scalability. Key features include a modular plugin mechanism for testing custom workflow scheduling and data movement policies, interactive real-time visualization dashboards, and automatic generation of event-level datasets suitable for AI-assisted performance modeling. We demonstrate CGSim’s capabilities through a comprehensive evaluation using production ATLAS PanDA workloads, showing significant calibration accuracy improvements across WLCG computing sites. Scalability experiments show near-linear scaling for multi-site simulations, with distributed workloads achieving 6 × better performance compared to single-site execution. The framework enables researchers to simulate WLCG-scale infrastructures with hundreds of sites and thousands of concurrent jobs within practical time budget constraints on commodity hardware.

Vatsavai, Sairam Sri [Brookhaven National Laborato↗
DOE OSTI2024

Integrating in situ environmental covariates in an American lobster catch model to improve impact assessment

The installation and operation of floating offshore wind power is an integral component of societal transition to renewable energy generation where fixed bottom offshore wind is not possible. However, it will cause unique ecosystem changes. To disentangle the effects of offshore wind installations from the concurrent effects of climate change and the fishing practices on commercially significant resources, we must develop detailed characterizations of the resources before development occurs. In the Gulf of Maine, American lobster is the most commercially and culturally important fishery. At the time of writing, this is the largest fishery by value in North America. Our understanding of baseline localized parameters (such as catch per trap at the spatial scale of individual turbines) should be informed by relationships to environmental, biological, and survey-specific functional drivers of catch. A more mechanistic understanding of catch will allow for strategic adjustments to Post- Deployment fishery responses and ultimately, the development of research- and commercial-scale floating offshore wind development. Here, we used survey data from the New England Aqua Ventus Pre-Construction Commercial Trapping Survey to develop Generalized Additive Models describing seasonal catch per trap for legal and sublegal lobsters. We found fall catch to be nearly twice that of spring. Bottom temperature dynamics could be used to predict catch, and the Fall survey was associated with a warmer temperature regime. By using analytical tools that incorporate environmental heterogeneity, we developed monitoring methods from preconstruction baseline data that will be applicable over the post-construction operating period of an offshore wind farm.

BACI↗
DOE OSTI2022

A Common Tracking Software Project

Abstract The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of the detector geometry, the algorithms currently in use by experiments share many common features. At the same time, the intense environment of the High-Luminosity LHC accelerator and other future experiments is expected to put even greater computational stress on track reconstruction software, motivating the development of more performant algorithms. We present here A Common Tracking Software (ACTS) toolkit, which draws on the experience with track reconstruction algorithms in the ATLAS experiment and presents them in an experiment-independent and framework-independent toolkit. It provides a set of high-level track reconstruction tools which are agnostic to the details of the detection technologies and magnetic field configuration and tested for strict thread-safety to support multi-threaded event processing. We discuss the conceptual design and technical implementation of ACTS, selected applications and performance of ACTS, and the lessons learned.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Apatite geochemistry as a tool for understanding the petrogenesis of layered mafic-ultramafic rocks in the Bushveld Complex, South Africa

The sources of the magmas that formed the Rustenburg Layered Suite of the Bushveld Complex in South Africa remain debated, despite decades of research. Vertical and lateral variation in bulk rock and mineral separate Sr-Nd isotopic compositions, which generally indicate enriched sources, demonstrate that the layered sequence was formed by the emplacement of multiple batches of magma, crucially resulting in episodes of PGE-Cr-V mineralisation. The Lu-Hf isotope compositions of zircon are, however, at odds with the bulk rock Sr-Nd isotopic heterogeneity as they show near homogeneous compositions throughout the layered sequence (εHf (2.06 Ga) =−8). This lack of variation in Hf isotope composition has been attributed to deep, continental lithospheric mantle-related and/or crustal contamination of plume-derived Bushveld magmas. In this study, we analysed the major, trace element and Sr-Nd isotope geochemistry of apatite in the Rustenburg Layered Suite. Apatite occurs as an intercumulus mineral in the lowermost regions and a cumulus mineral in the uppermost regions of the layered sequence and can therefore be used to test existing models for the isotopic disequilibrium between bulk rock Sr-Nd and zircon Hf isotopic compositions. Apatite is largely chlorapatite in the lowermost regions and fluorapatite in the uppermost regions of the layered sequence. The Merensky Reef is unusual in that it comprises both chlorapatite and fluorapatite. Apatite throughout the layered sequence is generally unzoned and shows no evidence of late-stage alteration. Trace element data show that apatite is enriched in L/HREE, with common negative Eu-Sr anomalies. These trace element signatures are consistent with a magmatic origin for the apatite grains, with prior, or concurrent, plagioclase crystallization from the same melt. Variability in in situ Sr and Nd isotope compositions of apatite is recorded throughout the layered sequence with εNd (2.06 Ga) compositions varying between −2.5 and − 10.2 and initial 87 Sr/ 86 Sr compositions varying between 0.7079 and 0.7103 (for the Marikana dikes only). The variability in Sr-Nd isotope compositions of apatite is consistent with the bulk rock (and mineral separate) variation in Sr-Nd isotope compositions, suggesting apatite preserves primary magmatic compositions in the Rustenburg Layered Suite.

Apatite↗
DOE OSTI2022

Improved precision in As speciation analysis with HERFD-XANES at the As K -edge: the case of As speciation in mine waste

High-energy-resolution fluorescence-detected (HERFD) X-ray absorption near-edge spectroscopy (XANES) is a spectroscopic method that allows for increased spectral feature resolution, and greater selectivity to decrease complex matrix effects compared with conventional XANES. XANES is an ideal tool for speciation of elements in solid-phase environmental samples. Accurate speciation of As in mine waste materials is important for understanding the mobility and toxicity of As in near-surface environments. In this study, linear combination fitting (LCF) was performed on synthetic spectra generated from mixtures of eight measured reference compounds for both HERFD-XANES and transmission-detected XANES to evaluate the improvement in quantitative speciation with HERFD-XANES spectra. The reference compounds arsenolite (As 2 O 3 ), orpiment (As 2 S 3 ), getchellite (AsSbS 3 ), arsenopyrite (FeAsS), kaňkite (FeAsO 4 ·3.5H 2 O), scorodite (FeAsO 4 ·2H 2 O), sodium arsenate (Na 3 AsO 4 ), and realgar (As 4 S 4 ) were selected for their importance in mine waste systems. Statistical methods of principal component analysis and target transformation were employed to determine whether HERFD improves identification of the components in a dataset of mixtures of reference compounds. LCF was performed on HERFD- and total fluorescence yield (TFY)-XANES spectra collected from mine waste samples. Arsenopyrite, arsenolite, orpiment, and sodium arsenate were more accurately identified in the synthetic HERFD-XANES spectra compared with the transmission-XANES spectra. In mine waste samples containing arsenopyrite and either scorodite or kaňkite, LCF with HERFD-XANES measurements resulted in fits with smaller R -factors than concurrently collected TFY measurements. The improved accuracy of HERFD-XANES analysis may provide enhanced delineation of As phases controlling biogeochemical reactions in mine wastes, contaminated soils, and remediation systems.

58 GEOSCIENCES↗
DOE OSTI2024

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Powering the Woods Hole X-Spar Buoy with Ocean Wave Energy—A Control Co-Design Feasibility Study

Despite its success in measuring air–sea exchange, the Woods Hole Oceanographic Institution’s (WHOI) X-Spar Buoy faces operational limitations due to energy constraints, motivating the integration of an energy harvesting apparatus to improve its deployment duration and capabilities. This work explores the feasibility of an augmented, self-powered system in two parts. Part 1 presents the collaborative design between X-Spar developers and wave energy researchers translating user needs into specific functional requirements. Based on requirements like desired power levels, deployability, survivability, and minimal interference with environmental data collection, unsuitable concepts are pre-eliminated from further feasibility study consideration. In part 2, we focus on one of the promising concepts: an internal rigid body wave energy converter. We apply control co-design methods to consider commercial of the shelf hardware components in the dynamic models and investigate the concept’s power conversion capabilities using linear 2-port wave-to-wire models with concurrently optimized control algorithms that are distinct for every considered hardware configuration. During this feasibility study we utilize two different control algorithms, the numerically optimal (but acausal) benchmark and the optimized damping feedback. We assess the sensitivity of average power to variations in drive-train friction, a parameter with high uncertainty, and analyze stroke limitations to ensure operational constraints are met. Our results indicate that a well-designed power take-off (PTO) system could significantly extend the WEC-Spar’s mission by providing additional electrical power without compromising data quality.

autonomous systems↗
DOE OSTI2024

CEBAF Injector for K Long Beam Conditions

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab concurrently operates four experimental Halls with distinct bunch charge specifications and repetition rates. Numerous critical beam parameters within CEBAF are configured in the injector, some remaining unchanged throughout the accelerator. Consequently, the injector plays a crucial role in determining final beam characteristics, including bunch structure, beam sizes, bunch lengths, energy spread, and beam transmission. The Jefferson Lab KL experiment is scheduled to take place at CEBAF in Hall D, featuring a much lower bunch repetition rate of 7.80 MHz or 15.59 MHz, below the nominal values of 249.5 MHz or 499 MHz. Although the proposed average current of 5 ?A or 10 ?A is low compared to the maximum CEBAF cur- rent of approximately 180 ?A, the corresponding bunch charge is unusually high for CEBAF injector operation. This study focuses on the behavior of low-repetition-rate, high-bunch- charge (0.32 to 0.64 pC) beams in the CEBAF injector. We investigated the evolution and transmission of low-charge beams to space-charge dominated high-charge beams in the front end of the CEBAF injector for two configurations: the pre-existing CEBAF Phase 1 injector upgrade, operated at 130 kV, and the existing CEBAF Phase 2 injector upgrade, operated at 140 kV, 180 kV, and to be operated at 200 kV. The electron beam through the CEBAF injector is characterized using beam dynamics simulations and comparisons with the available measurements performed at 130 kV. Multi-objective genetic optimizations of the CEBAF injector were performed to determine the operating magnetic elements and RF settings for the evolution and transmission of low, moderate, and high charge beams in the CEBAF injector at 180 kV and 200 kV DC gun voltages. Subsequently, simulations at the same voltages were conducted to obtain the beam characteristics at the front end of the CEBAF injector. The laser spot size and laser pulse length at the cathode were varied to observe their effects on beam transmission in the injector at different voltages (130 kV, 180 kV, and 200 kV). Experimental studies at 130 kV, 140 kV, and 180 kV validate the simulations. Beam study measurements are carried out using EPICS tools, while optimizations and simulations are facilitated by General Particle Tracer. Based on the findings, optimal parameters for the upcoming Jefferson Lab KL experiment are proposed, utilizing a lower repetition rate and higher bunch charge

Pokharel, Sunil↗