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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Validation of Pronghorn for Natural-Circulation Molten Salt Loops

This paper presents the development and validation of a high-fidelity thermal-hydraulic model of a molten salt natural circulation flow loop, designed for integration within a digital twin framework. The study evaluates the performance of Idaho National Laboratory’s Pronghorn against experimental data from Texas A&M University Molten Salt Flow Loop (MSFL) four Hitec-salt test benchmark data. Natural circulation of high-Prandtl-number fluids exhibits complex, counter-intuitive flow patterns that make pointwise thermocouple readings unreliable. Experimental work at TAMU’s MSFL provides benchmark data, including flow visualization at a test-section and centerline steady-state temperature measurements along the loop. Validation includes four single-phase natural circulation test cases with Hitec salt. Key metrics include flow profile agreement and steady-state temperature accuracy. Pronghorn results for two-dimensional single-phase agree qualitatively with the experimental flow profile. This paper illustrates the importance of Computational Fluid Dynamics (CFD) in elucidating the behavior of high-Prandtl-number thermal-hydraulics, along with how misleading centerline temperature measurements can be. Pronghorn reproduces the axial and radial stratification that makes single thermocouple readings unreliable. Future research will focus on reduced-order modeling techniques to enable rapid simulation suitable for real-time digital twin applications. The validated cases provide a basis for developing reduced-order surrogates aimed at real-time digital-twin applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Molecular imaging to guide systemic cancer therapy: Illustrative examples of PET imaging cancer biomarkers

Molecular imaging agents have the ability to non-invasively visualize, characterize, and quantify the molecular biology of disease. Recent advances in nuclear probe development, particularly in PET radiotracers, have generated many new imaging agents with precise molecular targets. With such specificity, PET probes may be utilized as biomarkers to objectively interrogate and evaluate pathology. Whereas the current indications for PET imaging are predominately confined to staging and restaging of malignancy, the utility of PET greatly expands when utilized as a biomarker, the topic of this review. As an imaging biomarker, PET may be used to (1) measure target expression to select subsets of patients who would most benefit from targeted therapy; (2) measure early treatment response to predict therapeutic efficacy; and (3) relate tumor response to survival. This work will discuss the application of radiotracers to targeted cancer therapy. Particular attention is given to new radiotracers evaluated in recently completed clinical trials and those with current or potential clinical utility. The diverse roles of PET in clinical trails for drug development are also examined.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Models, data, and scripts associated with “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning”

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

13C↗
DOE OSTI2019

Diagnostics and Models for Birthing Stress

Understanding the stress development in fluids as they transition to solids is not well-understood. Computational models are needed to represent "birthing stress" for multiphysics applications such as polymer encapsulation around sensitive electronics and additive manufacturing where these stresses can lead to defects such as cracking and voids. The local stress state is also critical to understand and predict the net shape of parts formed in the liquid phase. In this one-year exploratory LDRD, we have worked towards a novel experimental diagnostic to measure the fluid rheology, degree of solidification, and the solid stress development simultaneously. We debugged and made viable a "first-generation" Rheo-Raman system and used it to characterize two types of solidifying systems: paraffin wax, which crystalizes as it solidifies, and thermoset polymers, which form a network of covalent bonds. We used the paraffin wax as a model system to perform flow visualization studies and did some preliminary modeling of the experiment, to demonstrate the inadequacy of the current modeling approaches. This work will inform an advanced fluid constitutive equation that includes a yield stress, temperature dependence, and an evolving viscosity when we pursue the full proposal, which was funded for FY20.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Date Release Report for Large Surface Explosion Coupling Experiment (LSECE) Nevada National Security Site

The DTRA sponsored 2020-2022 Large Surface Explosion Coupling Experiment (LSECE) consists of two large ground surface chemical explosions, data collection, analysis and modeling carried out in 2020-2022. The LSECE chemical explosions were carried out at the site of prior NNSA sponsored buried chemical explosions that were part of the Source Physics Experiment (SPE) Phase II in Dry Alluvium Geology (DAG). This report described the data collected under LSECE that is being made publicly available. The prior buried explosion DAG data are described in a separate report (https://www.osti.gov/biblio/1825534) and are also publicly available. The LSECE explosions, data analysis and numerical modeling work sponsored by DTRA were intended to address three objectives: 1) Generate seismo-acoustic data to test and improve numerical models of explosion energy coupling in dry alluvium geology; 2) To improve seismo-acoustic yield estimation techniques as a function of depth and medium properties; 3) To study and improve acoustic propagation modeling under two different atmospheric conditions. The LSECE data that were collected have expanded the prior DAG buried explosion dataset to include surface chemical explosions recorded on a common set of seismo-acoustic stations. They have allowed a more detailed study of above and below surface seismo-acoustic energy coupling with applications to explosion monitoring and assessment. The two approximately 1-ton TNT equivalent yield LSECE explosions were detonated at different times of the day to explore the effects of the different atmospheric conditions. The first chemical explosion “Artemis” was conducted before dawn when temperature inversions were present. The second chemical explosion “Apollo” was conducted on a sunny afternoon when the temperature gradient was more linear. The LSECE chemical explosion data were also collected across a variety of different sensor types to allow evaluation of their effectiveness in recovering useful information. The LSECE instrumentation included fiber optic or Distributed Acoustic Sensing (DAS), a dense array or Large-N array of seismometers, borehole accelerometers, DAS and a velocity meter, airborne acoustic instruments and a variety of visual and remote sensing data.

58 GEOSCIENCES↗
DOE OSTI2022

Bioimaging Science Program: 2022 Principal Investigator Meetings Proceedings

The mission of the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program’s Bioimaging Science Program (BSP) is to understand the translation of genomic information into the mechanisms that power living cells, communities of cells, and whole organisms. The goal of BSP is to develop new imaging and measurement technologies to visualize the spatial and temporal relationships of key metabolic processes governing phenotypic expression in plants and microbes. The extended goal of dynamic imaging is to functionally connect cellular components and interdependent organisms. Information about the time and place of chemical reactions in situ can identify causal relationships between biological activators and downstream effectors. BSP held its annual PI meeting virtually February 28–March 1. Contributing investigators are convened to review progress and current state-of-the-art bioimaging research. Holding the 2022 BSP meeting as part of the broader Genomic Science Program (GSP) PI meeting allowed researchers to interact with the extended GSP community. This convergence provided a platform for networking and exchange of ideas with experts in other technologies and in target BSP application areas, helping to forge new multidisciplinary collaborations among investigators from the sister programmatic areas within BER’s Biological Systems Science Division. An important highlight of the BSP meeting was the keynote presentation by Nobel Laureate Dr. Joachim Frank on Time-Resolved Macromolecular Imaging using Cryo-EM. He discussed microfluidic mixing and fast freezing to capture nonequilibrium intermediate states during molecular binding and conformational changes. The action of molecular machines can be captured at nanometer resolution and millisecond discrimination. BSP PIs made presentations describing their research focus and progress in plenary sessions on bioimaging science and on quantum-enabled bioimaging science research projects. BSP research at universities and DOE laboratories is presented in this report. A final discussion of the BSP was organized by meeting plenary session chairs, who prepared the following Executive Summary of current BSP research, research challenges, future opportunities, and potential ideas for expanding the BSP’s impact and interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Heterogeneous 3D Morphological Evolution of Ni Microparticles in Molten Salts: Visualized by Operando Synchrotron X-ray Nano-tomography

Ni-based superalloys are promising materials for high-temperature molten salt (MS) energy generation and storage. Studying morphological and chemical evolution of pure Ni in MS provides fundamental knowledge for MS technologies and corrosion mitigation. Here, in this study, real-time 3D morphological changes of Ni microparticles in molten KCl-MgCl 2 were studied by operando synchrotron X-ray nano-tomography at 700°C. Rapid Ni particle agglomeration occurred, without significant chemical reactions, such as oxide or chloride formation. The morphological growth evolved differently from classical coarsening or sintering behaviors and occurred nonuniformly, with other regions showing slight dissolution of Ni. Ni nanoparticles were found to be dispersed in many areas of the samples, either from microparticle dissolution or other radiation-induced nanoparticle formation mechanisms. This study discusses important factors, i.e., thermal gradient, amounts of salt and metals, and radiation effect, that influence morphological changes of materials in MS, critical for fundamental understanding of material–MS interactions as well as for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Precision Plasmonics with Monomers and Dimers of Spherical Gold Nanoparticles: Nonequilibrium Dynamics at the Time and Space Limits

Monomers and dimers of spherical gold nanoparticles (NPs) exhibit highly uniform plasmonic properties at the single-particle level due to their high structural homogeneity (precision plasmonics). Recent investigations in precision plasmonics have largely focused on static properties using conventional techniques such as transmission electron microscopy and optical dark-field microscopy. Here in this Feature Article, we first highlight the application of femtosecond time-resolved electron diffraction for monitoring the nonequilibrium dynamics of spherical gold NPs after ultrafast optical excitation. The analysis of the transient diffraction patterns allows us to directly obtain quantitative information on the incoherent excitation of the lattice, that is, heating upon electron–lattice equilibration, as well as on the development of strain due to lattice expansion on picosecond time scales. The controlled assembly of two spherical gold NPs into a dimer with a few nanometers gap leads to unique optical properties. Specifically, extremely high electric fields (hot spot) in the gap are generated upon resonant optical excitation. Conventional optical microscopy cannot spatially resolve this unique hot spot due to the optical diffraction limit. We therefore employed nonlinear photoemission electron microscopy to visualize hot spots in single dimers of spherical gold NPs. A quantitative comparison of different single dimers confirms the homogeneity of the hot spots on the single-particle level. Finally, overall, these initial results are highly encouraging because they pave the way to investigate nonequilibrium dynamics in highly uniform plasmonic nanostructures at the time and space limits.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower (DIVERS-H)

U.S. hydropower plants face potential threats from shrinking water supply, rising demands, and warmer stream temperatures from various causes. Power plant owners, operators, and regulators require new tools to take advantage of and interpret the diverse range of scientific data being produced by both observational methods (for example, satellite, radar, stream gauges) and computer modeling methods that evaluate and predict how earth's dynamic systems (atmosphere, oceans, land surface, and sea ice) are changing and interacting. Combining datasets such as these with AI-based analyses introduces a novel decision support system to help users anticipate and address potential impacts on power generation stations. This new technology has been named DIVERS-H for "Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower." In Phase I, technical feasibility was established with the development and demonstration of all the new technologies that are required. Most notably, DIVERS-H will use new artificial intelligence (AI) methods to capture the complex dynamics of water availability, demand, and environmental changes. In addition, new data management software was developed, and a prototype user interface was implemented as the precursor to a full scale decision support system. With technical research complete, the project focus now shifts to development of a commercial software product to provide users with actionable insight into water availability and the risk/resilience of critical systems at their locations of interest. Although DIVER-H was originally conceived as a tool for hydroelectric power applications, the same underlying technology can be readily applied to other water-consuming systems including coal, natural gas, oil, and nuclear power plants.

Chaudhary, Aashish [Kitware, Inc., Clifton Park, N↗
DOE OSTI2016

Quantitative 3D electromagnetic field determination of 1D nanostructures from single projection

One-dimensional (1D) nanostructures have been regarded as the most promising building blocks for nanoelectronics and nanocomposite material systems as well as for alternative energy applications. Although they result in confinement of a material, their properties and interactions with other nanostructures are still very much three-dimensional (3D) in nature. In this work, we present a novel method for quantitative determination of the 3D electromagnetic fields in and around 1D nanostructures using a single electron wave phase image, thereby eliminating the cumbersome acquisition of tomographic data. Using symmetry arguments, we have reconstructed the 3D magnetic field of a nickel nanowire as well as the 3D electric field around a carbon nanotube field emitter, from one single projection. The accuracy of quantitative values determined here is shown to be a better fit to the physics at play than the value obtained by conventional analysis. Moreover the 3D reconstructions can then directly be visualized and used in the design of functional 3D architectures built using 1D nanostructures.

1D nanostructures↗
DOE OSTI2014

Polyclonal and monoclonal antibodies specific for the six-helix bundle of the human respiratory syncytial virus fusion glycoprotein as probes of the protein post-fusion conformation

Human respiratory syncytial virus (hRSV) has two major surface glycoproteins (G and F) anchored in the lipid envelope. Membrane fusion promoted by hRSV{sub F} occurs via refolding from a pre-fusion form to a highly stable post-fusion state involving large conformational changes of the F trimer. One of these changes results in assembly of two heptad repeat sequences (HRA and HRB) into a six-helix bundle (6HB) motif. To assist in distinguishing pre- and post-fusion conformations of hRSV{sub F}, we have prepared polyclonal (α-6HB) and monoclonal (R145) rabbit antibodies specific for the 6HB. Among other applications, these antibodies were used to explore the requirements of 6HB formation by isolated protein segments or peptides and by truncated mutants of the F protein. Site-directed mutagenesis and electron microscopy located the R145 epitope in the post-fusion hRSV{sub F} at a site distantly located from previously mapped epitopes, extending the repertoire of antibodies that can decorate the F molecule. - Highlights: • Antibodies specific for post-fusion respiratory syncytial virus fusion protein are described. • Polyclonal antibodies were obtained in rabbit inoculated with chimeric heptad repeats. • Antibody binding required assembly of a six-helix bundle in the post-fusion protein. • A monoclonal antibody with similar structural requirements is also described. • Binding of this antibody to the post-fusion protein was visualized by electron microscopy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Investigation of grain-boundary geometry and pores morphology in dense and porous cubic zirconia polycrystals

Highlights: • Cubic zirconia sinters were investigated in three dimensions using dual-beam FEGSEM. • The 3D-EBSD technique was successfully applied to non-conductive ceramics. • New sample preparation approach to automated 3D-EBSD was proposed. • Grain boundary microstructures were reconstructed from inverse pole figure maps. • Pore microstructures were reconstructed from image quality maps. - Abstract: Three-dimensional electron backscatter diffraction technique was used for the visualization of grain boundary geometry and pore morphology in cubic zirconia. A set of four samples sintered under different conditions was investigated. Specimens which were characterized by energy dispersive spectroscopy and X-ray diffraction were entirely composed of cubic phase. Investigations of boundaries and pore structures were carried out in a dual-beam scanning electron microscope. For each sample, a volume of 1000 μm{sup 3} was investigated. The analysis of grain boundary networks reconstructed from inverse pole figure maps revealed a strong dependence between grain boundary density and sample preparation parameters. Sintering also affects the size and distribution of pores. The total number of grains analyzed varied from 17 to 357 and the calculated volume of cavities from 0.01% to 21%. This paper shows the application of three-dimensional crystallographic orientation analysis to characterize the microstructure of yttria stabilized zirconia ceramics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Pulse: An Outlier Sensitive Downsampling Algorithm For Timeseries Data

Pulse is a downsampling algorithm for timeseries data. Frequently datasets become so large that visualization tools and web browsers cannot effectively render graphics due to memory constraints. Downsampling algorithms are commonly applied to minimize the quantity of data required to visualize important features or trends in the data, but some datasets are composed by distinct enough features and trends that most existing downsampling algorithms fail to preserve them. Pule was developed to downsample timeseries data for galvanostatic stack test data at the Idaho National Laboratory. These datasets were composed by approximately 4 million records, most of them being extremely uniform. However, during relatively brief time periods when the stack test changes state, for example when the test article is powered on, or a load is added, the data produce sparse asymptotes. No existing downsampling algorithm was capable of preserving the sparse asymptotes in electrolysis stack test data. Instead, we develop a downsampling algorithm that preserves important outliers in data, and otherwise aggressively downsamples uniform data. The algorithm has applications in other domains like seismology, in the measurement of earthquakes, or astronomy, in the measurement of quasars or transit photometry.

Woodruff, Nathan [Idaho National Laboratory (INL),↗
DOE OSTI2015

Propagation of acoustic shock waves between parallel rigid boundaries and into shadow zones

The study of acoustic shock propagation in complex environments is of great interest for urban acoustics, but also for source localization, an underlying problematic in military applications. To give a better understanding of the phenomenon taking place during the propagation of acoustic shocks, laboratory-scale experiments and numerical simulations were performed to study the propagation of weak shock waves between parallel rigid boundaries, and into shadow zones created by corners. In particular, this work focuses on the study of the local interactions taking place between incident, reflected, and diffracted waves according to the geometry in both regular or irregular – also called Von Neumann – regimes of reflection. In this latter case, an irregular reflection can lead to the formation of a Mach stem that can modify the spatial distribution of the acoustic pressure. Short duration acoustic shock waves were produced by a 20 kilovolts electric spark source and a schlieren optical method was used to visualize the incident shockfront and the reflection/diffraction patterns. Experimental results are compared to numerical simulations based on the high-order finite difference solution of the two dimensional Navier-Stokes equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Electroless Formation of Hybrid Lithium Anodes for Fast Interfacial Ion Transport

Abstract Rechargeable batteries based on metallic anodes are of interest for fundamental and application‐focused studies of chemical and physical kinetics of liquids at solid interfaces. Approaches that allow facile creation of uniform coatings on these metals to prevent physical contact with liquid electrolytes, while enabling fast ion transport, are essential to address chemical instability of the anodes. Here, we report a simple electroless ion‐exchange chemistry for creating coatings of indium on lithium. By means of joint density functional theory and interfacial characterization experiments, we show that In coatings stabilize Li by multiple processes, including exceptionally fast surface diffusion of lithium ions and high chemical resistance to liquid electrolytes. Indium coatings also undergo reversible alloying reactions with lithium ions, facilitating design of high‐capacity hybrid In‐Li anodes that use both alloying and plating approaches for charge storage. By means of direct visualization, we further show that the coatings enable remarkably compact and uniform electrodeposition. The resultant In‐Li anodes are shown to exhibit minimal capacity fade in extended galvanostatic cycling when paired with commercial‐grade cathodes.

Choudhury, Snehashis↗