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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Optimizing bioenergy biofuel harvest: a comparative analysis of stepwise and integrated methods for economic and environmental sustainability

Switchgrass is a promising bioenergy feedstock due to its high biomass yield potential, adaptability to marginal lands, and low carbon intensity for feedstock production. However, accurate cost estimation and assessment of greenhouse gas (GHG) emissions for the energy-intensive harvesting process are essential for evaluating the sustainability of bioenergy. This study provides a comparative analysis of two harvesting methods: the Stepwise Method, which separates operations into multiple stages, and the Integrated Method, which combines mowing and raking into a single pass. The analysis was conducted under four scenarios based on field sizes and biomass yields. Using three years of field-scale switchgrass harvest data from 125 sites, GHG emissions, energy consumption, and harvesting costs were quantified using the GREET model and techno-economic analysis. Additionally, regression analysis identified key climate and operational factors affecting fuel consumption. The Stepwise method was the most cost-effective for large fields with high biomass yield, achieving the lowest harvesting costs ($37.70 per ton). In contrast, the Integrated Method performed better in small fields and low-yield conditions, reducing GHG emissions by 9 % and energy use by 5 %. Regression analysis confirmed that a larger field size reduced fuel consumption, while higher biomass yield and longer operational time increased fuel use. Maximum temperature also contributed to a slight increase in fuel consumption. Furthermore, these results provide actionable insights for optimizing harvesting strategies based on field-specific conditions and operational goals, contributing to the economic and environmental sustainability of bioenergy production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

High-performance data format for scientific data storage and analysis

Here, in this article, we present the High-Performance Output (HiPO) data format developed at Jefferson Laboratory for storing and analyzing data from Nuclear Physics experiments. The format was designed to efficiently store large amounts of experimental data, utilizing modern fast compression algorithms. The purpose of this development was to provide organized data in the output, facilitating access to relevant information within the large data files. The HiPO data format has features that are suited for storing raw detector data, reconstruction data, and the final physics analysis data efficiently, eliminating the need to do data conversions through the lifecycle of experimental data. The HiPO data format is implemented in C++ and JAVA, and provides bindings to FORTRAN, Python, and Julia, providing users with the choice of data analysis frameworks to use. In this paper, we will present the general design and functionalities of the HiPO library and compare the performance of the library with more established data formats used in data analysis in High Energy and Nuclear Physics (such as ROOT and Parquete). In columnar data analysis, HiPO surpasses established data formats in performance and can be effectively applied to data analysis in other scientific fields.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2024

Physics-based reward driven image analysis in microscopy

The rise of electron microscopy has expanded our ability to acquire nanometer and atomically resolved images of complex materials. The resulting vast datasets are typically analyzed by human operators, an intrinsically challenging process due to the multiple possible analysis steps and the corresponding need to build and optimize complex analysis workflows. We present a methodology based on the concept of a Reward Function coupled with Bayesian Optimization, to optimize image analysis workflows dynamically. The Reward Function is engineered to closely align with the experimental objectives and broader context and is quantifiable upon completion of the analysis. Here, cross-section, high-angle annular dark field (HAADF) images of ion-irradiated (Y, Dy)Ba 2 Cu 3 O 7–δ thin-films were used as a model system. The reward functions were formed based on the expected materials density and atomic spacings and used to drive multi-objective optimization of the classical Laplacian-of-Gaussian (LoG) method. These results can be benchmarked against the DCNN segmentation. This optimized LoG* compares favorably against DCNN in the presence of the additional noise. We further extend the reward function approach towards the identification of partially-disordered regions, creating a physics-driven reward function and action space of high-dimensional clustering. We pose that with correct definition, the reward function approach allows real-time optimization of complex analysis workflows at much higher speeds and lower computational costs than classical DCNN-based inference, ensuring the attainment of results that are both precise and aligned with the human-defined objectives.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Direct and rapid uranium isotopic analysis of environmental sample swipes via microextraction – LS-APGD/Orbitrap mass spectrometry

Accurate and precise isotopic analysis of actinides collected on environmental sample swipes from within nuclear facilities is an important safeguarding measure for detecting undeclared materials and activities. Traditional isotope ratio (IR) analysis of actinides using a bulk digestion approach can be laborious and time-consuming. Recently, direct analysis of environmental swipe samples using a microextraction approach has been explored as an alternative to conventional bulk digestion methods. The present study further evaluates this approach for accurate and precise isotopic analysis of uranium (U) in cotton swipe samples using the liquid sampling-atmospheric pressure glow discharge (LS-APGD)/Orbitrap-FTMS Booster detection system. The instrumental parameters were optimized, and quantitative capabilities were demonstrated through excellent linearity (R2 = 0.99) across a deposited mass range of 1 to 500 ng, with a limit of detection of 90 pg (in a 2 × 4 mm region) for total U; well below typical U concentrations (ng–mg range) in environmental sample swipes. The 235U/238U ratio showed excellent accuracy (−0.3% relative difference from the certificate value), with ∼20× improvement over previous microextraction-Orbitrap methods. Precision (∼1% relative standard deviation) was also greatly enhanced by ∼5×. This study also demonstrates that figures of merit achieved with neat U solution were not highly degraded when various external interfering elements/matrices were introduced. Finally, the developed method was successfully applied to analyze practical swipe samples representative of laboratory and outdoor environments. The presented figures of merit and applicability to practical samples further validate the capability of the platform for rapid analysis (<7 minutes) of environmental swipe samples.

Shrestha, Suraj [Clemson University, SC]↗
DOE OSTI2026

Validation Studies of RELAP5-3D for High-Temperature Gas-Cooled Reactor Analysis Using a Refined RELAP5-3D Model of the High Temperature Test Facility

RELAP5-3D has been used extensively for high-temperature gas-cooled reactor (HTGR) analysis, but such analysis lies outside the validation basis for the code. Oregon State University (OSU) and the United States Department of Energy’s (DOE) Advanced Reactor Technologies (ART) – Gas-cooled reactor campaign worked together to design and operate the High Temperature Test Facility (HTTF) at OSU to provide validation data for prismatic HTGR thermal hydraulics analysis. Work using a previous RELAP5-3D model of HTTF demonstrated the ability to reproduce trends in HTTF data but an inability to reproduce the values measured in multiple experiments within the uncertainty. It was hypothesized that a new model with a finer radial nodalization would perform better for HTTF analysis. In this paper, we present the development of that more refined model and analyze HTTF experiments PG-27 and PG-29, a pressurized (PCC) and depressurized conduction cooldown (DCC) transient. Comparing PG-27 results to experimental data shows a better prediction of steady state and transient temperatures; however, a deeper dive reveals that better transient temperature predictions are a result of better initial conditions. The transient temperature rise in the core is comparable between the two models, but the legacy model predicts temperatures in the reflectors better. The new model demonstrates the ability to predict unique temperatures in each sector of the core during PG-29, capturing the azimuthal asymmetry; however, the temperature predictions still differ from measured temperatures. One hypothesis is that differences between the assumed and actual power distribution leads to the underprediction of the individual block temperatures. These results suggest that gaps and uncertainties in the measured boundary conditions for the HTTF tests do not allow to fully validate RELAP5-3D for prismatic HTGR analyses. The authors suspect that some of the differences between predicted and measured temperatures may be a result of uncertainties associated with HTTF itself rather than RELAP5-3D. We demonstrated improved validation for steady-state analysis, but significantly improved transient performance has yet to be recognized.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Full-Shape analysis of the power spectrum and bispectrum of DESI DR1 LRG and QSO samples

We present the first joint analysis of the power spectrum and bispectrum using the Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI), focusing on Luminous Red Galaxies (LRGs) and quasars (QSOs) across a redshift range of 0.4 ≤ z ≤ 2.1. By combining the two- and three-point statistics, we are able to partially break the degeneracy between the logarithmic growth rate, f(z), and the amplitude of dark matter fluctuations, σ s8 (z), which cannot be measured separately in analyses that only involve the power spectrum. In comparison with the (fiducial) Planck ΛCDM cosmology we obtain f/f fid = {0.888 -0.089 +0.186 ,0.977 -0.220 +0.182 ,1.030 -0.085 +0.368 }, σ s8 /σ fid s8 = {1.224 -0.133 +0.091 ,1.071 -0.163 +0.278 ,1.00 0 -0.223 +0.088 } respectively for the three LRG redshift bins, corresponding to a cumulative 10.1% constraint on f, and of 8.4% on σ s8 , including the systematic error budget. Additionally, we obtain constraints for the ShapeFit compressed parameters describing the isotropic scaling parameter, α iso (z), the Alcock-Paczyński parameter, α AP (z), the combined growth of structure parameter fσ s8 (z), and the combined shape parameter m(z)+n(z). Their cumulative constraints from our joint power spectrum-bispectrum analysis are respectively σ αiso = 0.9% (9% improvement with respect to our power spectrum-only analysis); σ α AP = 2.3% (no improvement with respect to power spectrum-only analysis, which is expected given that the bispectrum monopole has no significant anisotropic signal); σ fσs8 = 5.1% (9% improvement); σ m+n = 2.3% (11% improvement). These results are fully consistent with the main DESI power spectrum analysis, demonstrating the robustness of the DESI cosmological constraints, and compatible with Planck ΛCDM cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Application of Weak-Beam Dark-Field STEM for Dislocation Loop Analysis

Nanoscale dislocation loops formed by irradiation can significantly contribute to both irradiation hardening and embrittlement of materials when subjected to extreme nuclear reactor environments. Here, this study explores the application of weak-beam dark-field (WBDF) scanning transmission electron microscopy (STEM) methods for quantitative irradiation-induced defect analysis in crystalline materials, with a specific focus on dislocation loop imaging and analysis. A high-purity Fe-5 wt% Cr model alloy was irradiated with 8 MeV Fe 2+ ions at 450°C to a fluence of 8.8 × 10 19 m -2 , inducing dislocation loops for analysis. While transmission electron microscopy (TEM) has traditionally been the primary tool for dislocation imaging, recent advancements in STEM technology have reignited interest in using STEM for defect imaging. This study introduces and compares three WBDF STEM methods, demonstrating their effectiveness in suppressing background contrasts, isolating defect information for dislocation loop type classification, providing finer dislocation line images for small loop analysis, and presenting inside–outside contrast for identifying loop nature. Experimental findings indicate that WBDF STEM methods surpass traditional TEM approaches, yielding clearer and more detailed images of dislocation loops. The study concludes by discussing the potential applications of WBDF STEM techniques in defect analysis, emphasizing their adaptability across various material systems beyond nuclear materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Analysis of a Disturbance Event with Inverter-Based Resources Using EMT Simulations

Increasing penetration of inverter-based resources (IBRs) necessitates newer methods of planning and analysis of disturbances. The existing phasor-domain transient stability (TS) analysis may not capture the dynamics of IBRs during fault events. Here, in this paper, electromagnetic transient (EMT) simulations using high-fidelity detailed model of power grid and one of the affected photovoltaic (PV) plants during the Angeles Forest disturbance in 2018 are performed. In these simulations, the processes to develop EMT models of power grid from traditional phasor-domain TS data and PV plant from collected data are described. Thereafter, using these simulations, the response of the PV plant during the fault event in 2018 is replicated and a sensitivity analysis is performed. The sensitivity analysis consists of making changes to the components within the PV plant and in the power grid to evaluate the impact they have on the response observed by the PV plant during the fault event. This analysis provides an understanding of the components that impact the operation of a PV plant during fault events and provide guidance to system planners on the studies that need to be performed to maintain a reliable power grid as new IBR plants are integrated.

42 ENGINEERING↗
DOE OSTI2025

Machine-learning-enabled on-the-fly analysis of RHEED patterns during thin film deposition by molecular beam epitaxy

Thin film deposition is a fundamental technology for the discovery, optimization, and manufacturing of functional materials. Deposition by molecular beam epitaxy (MBE) typically employs reflection high-energy electron diffraction (RHEED) as a real-time in situ probe of the growing film. However, the state-of-the-art for RHEED analysis during deposition requires human observation. Here, we present an approach using machine learning (ML) methods to monitor, analyze, and interpret RHEED images on-the-fly during thin film deposition. In the analysis workflow, RHEED pattern images are collected at one frame per second and featurized using a pretrained deep convolutional neural network. The feature vectors are then statistically analyzed to identify changepoints; these changepoints can be related to changes in the deposition mode from initial film nucleation to a transition regime, smooth film deposition, and in some cases, an additional transition to a rough, islanded deposition regime. The feature vectors are additionally analyzed via graph analysis and community classification. The graph is quantified as a stabilization plot, and we show that inflection points in the stabilization plot correspond to changes in the growth regime. The full RHEED analysis workflow is termed RHAAPsody and includes data transfer and output to a visual dashboard. We demonstrate the functionality of RHAAPsody by analyzing the precaptured RHEED images from epitaxial depositions of anatase TiO2 on SrTiO3(001) and show that the analysis workflow can be executed in less than 1 s. Our approach shows promise as one component of ML-enabled real-time feedback control of the MBE deposition process.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nuclear Integrated Hydrogen Production Analysis Tool

This is an Excel-based time-independent discount cash flow calculator for LWR-HTSE systems. The tool incorporates (1) discounted cash flow and levelized cost of hydrogen (LCOH) analysis, (2) sensitivity analysis with respect to select financial performance metrics with output ‘tornado’ charts, (3) profitability analysis represented by heat maps using the two most sensitive parameters, (4) electricity versus hydrogen production preference analysis by comparing change in net present value (?NPV) between NPP-HTSE and business-as-usual electricity production for the grid, and (5) competitiveness analysis by comparing the calculated LCOH for NPP-HTSE with that of steam methane reforming, which is the conventional process to produce hydrogen.

Cheng, WenChi [Idaho National Laboratory (INL), Id↗
DOE OSTI2024

Refining HPCToolkit for application performance analysis at exascale

As part of the US Department of Energy’s Exascale Computing Project (ECP), Rice University has been refining its HPCToolkit performance tools to better support measurement and analysis of applications executing on exascale supercomputers. To efficiently collect performance measurements of GPU-accelerated applications, HPCToolkit employs novel non-blocking data structures to communicate performance measurements between tool threads and application threads. To attribute performance information in detail to source lines, loop nests, and inlined call chains, HPCToolkit performs parallel analysis of large CPU and GPU binaries involved in the execution of an exascale application to rapidly recover mappings between machine instructions and source code. To analyze terabytes of performance measurements gathered during executions at exascale, HPCToolkit employs distributed-memory parallelism, multithreading, sparse data structures, and out-of-core streaming analysis algorithms. To support interactive exploration of profiles up to terabytes in size, HPCToolkit’s hpcviewer graphical user interface uses out-of-core methods to visualize performance data. The result of these efforts is that HPCToolkit now supports collection, analysis, and presentation of profiles and traces of GPU-accelerated applications at exascale. These improvements have enabled HPCToolkit to efficiently measure, analyze and explore terabytes of performance data for executions using as many as 64K MPI ranks and 64K GPU tiles on ORNL’s Frontier supercomputer. HPCToolkit’s support for measurement and analysis of GPU-accelerated applications has been employed to study a collection of open-science applications developed as part of ECP. This paper reports on these experiences, which provided insight into opportunities for tuning applications, strengths and weaknesses of HPCToolkit itself, as well as unexpected behaviors in executions at exascale.

Adhianto, Laksono↗
DOE OSTI2024

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray (Hoyer & Hamman, 2017) for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT’s key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools (CDAT) library (Dean N. Williams et al., 2009) (D. N. Williams, 2014) (Doutriaux et al., 2019) and leverage powerful packages in the Xarray ecosystem including xESMF (Zhuang et al., 2023), xgcm (Abernathey et al., 2022), and CF xarray (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al., 2017).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Screening analysis of enhanced weathering of igneous rocks and industrial waste materials

Enhanced weathering (EW) is a promising emerging carbon dioxide removal approach that involves harnessing and accelerating the natural weathering process by which atmospheric CO2 passively reacts with exposed alkaline minerals and is removed from the atmosphere. This manuscript reports on a screening level techno-economic analysis of EW. Two primary cases utilizing different sources of alkaline material are considered: (1) utilizing naturally occurring mined igneous rocks, and (2) utilizing industrial waste materials. The modeled EW process encompasses material purchase, comminution, transport, distribution of material on farmland, and measurement, reporting and verification of CO2 removal. Detailed sensitivities are performed to highlight promising scenarios for application. The analysis highlights that utilizing materials with high weathering potential in suitable locations may result in relatively low levelized cost of captured (less than $100 per total tonnes of CO2 captured from the atmosphere). NETL is publishing a detailed and transparent report titled “Enhanced Weathering: Techno-Economic and Life Cycle Screening Analysis” that includes more detail on the screening level techno-economic analysis and includes a life cycle analysis.

Leptinsky, Sarah [NETL Site Support Contractor, Na↗
DOE OSTI2024

Method Development and Implementation for Characterizing Uncertainties in Tank Leak Volumes for Composite Analysis.

The purpose of this environmental calculation file (ECF) is to provide a summary of methodology, inputs, assumptions, and results of a Special Analysis (SA) titled “Method Development and Implementation for Characterizing Uncertainties in Tank Leak Volumes for the Updated Composite Analysis.” This SA is one of the three SAs conducted in Fiscal Year (FY) 2024 for the Composite Analysis (CA) for the Hanford Site (DOE/RL-2019-52, Composite Analysis for Low-Level Waste Disposal in the Hanford Site Central Plateau (FY 2020)), and it is performed following the Unreviewed Composite Analysis Question Evaluation procedure, UCAQ-24-01-E (UCAQ-24-01-E, Rev. 0).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2025)

The Spent Fuel and Waste Science and Technology Campaign (SFWST) of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE) is conducting research and development on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two priorities for SFWST are design concept development and disposal system modeling. These priorities are directly addressed in the Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. This report describes specific activities in the Fiscal Year (FY) 2025 associated with the GDSA Uncertainty and Sensitivity Analysis Methods work package. This report fulfills the GDSA Uncertainty and Sensitivity Analysis Methods work package (SF-25SN01030407) level 3 milestone, Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework (FY2025) (M3SF-25SN010304072). This work was closely coordinated with the other Sandia National Laboratory GDSA work packages: the GDSA Framework Development work package (SF-25SN01030408), the GDSA Repository Systems Analysis work package (SF-25SN01030409), and the GDSA PFLOTRAN Development work package (SF-25SN01030410). This report builds on developments reported in previous GDSA Framework milestones, particularly M3SF-24SN010304072.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Modal Analysis and Testing of a Cantilever Beam: Results and Guide

This report discusses modal analysis and testing of a cantilever beam including an introduction to modal testing, simulation, analysis, and a comparison of results. An accompanying presentation can be found from the LLNL CASIS website. Modal testing and analysis are essential for characterizing the response of a structure during vibration environments. Determining natural frequencies and mode shapes helps determine whether resonances will be reached during operations and if so, how the structure will respond and how the response can be tuned. The goal of this work is to convey the basics of modal testing and analysis through a simple project. This can be applied later to more complex, applicable structures. This includes a variety of work including data collection, finite element analysis and data processing. For this project a cantilever beam was chosen because it is a simple, well-characterized structure.

42 ENGINEERING↗
DOE OSTI2024

Imaging and Analysis of Insoluble Electrorefiner Material

Throughout pyroprocessing efforts at the Hot Fuel Examination Facility (HFEF), insoluble material has accumulated in the Electrorefiner (ER) vessel. The objective of this effort was to analyze the accumulated material to determine its origin. Material was removed from the ER salt bath and distilled to remove excess salt prior to performing analysis. Three samples were sent for chemical and isotopic analysis and underwent an ethyl acetate-bromine dissolution to segregate the oxide fraction from the metal fraction. Actinide concentrations were determined using a quadrupole–inductively coupled plasma–mass spectrometer (Q-ICP-MS). Three additional samples were sent for morphologic and elemental analysis by scanning electron microscopy (SEM) with Energy Dispersive X-ray (EDX) analysis at the Irradiated Materials Characterization Laboratory (IMCL). Analyses suggest that the material is primarily composed of UO2. with a small fraction of metal. This study draws no single conclusion as to the origin of the insoluble material in the ER. The particle sizes and morphologies observed in SEM micrographs indicate that the larger particles observed may be a result of introducing material to the ER that has not been completely reduced in Oxide Reduction (OR) operations, which precede electrorefining. Smaller particles may be due to reactions with oxygen and moisture present in HFEF. This research suggests that microscopic analysis of fuel particles before and after OR operations (including after distillation) as well as the uranium product collected on the cathode in the ER would increase understanding about particle morphology within the pyrochemical process.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗