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At least 559 records · Page 31

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images

The maturation or activation status of dendritic cells (DCs) directly correlates with their behavior and immunofunction. A common means to determine the maturity of dendritic cells is from high-resolution images acquired via scanning electron microscopy (SEM) or atomic force microscopy (AFM). While direct and visual, the determination has been made by directly looking at the images by researchers. Here we report a machine learning approach using pattern recognition in conjunction with cellular biophysical knowledge of dendritic cells to determine the maturation status of dendritic cells automatically. The determination from AFM images reaches 100% accuracy. The results from SEM images reaches 94.9%. The results demonstrate the accuracy of using machine learning for accelerating data analysis, extracting information, and drawing conclusions from high-resolution cellular images, paving the way for future applications requiring high-throughput and automation, such as cellular sorting and selection based on morphology, quantification of cellular structure, and DC-based immunotherapy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations

The dynamics of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution. The latter allows for disentanglement of the particle orientation from other degrees of freedom and compensates for lateral shifts. The disentangled representations in the latent space encode the rich spectrum of local transitions that can now be visualized and explored via continuous variables. The time dependence of ensemble averages allows insight into the time dynamics of the system and, in particular, illustrates the presence of the potential ordering transition. Finally, analysis of the latent variables along the single-particle trajectory allows tracing these parameters on a single-particle level. The proposed approach is expected to be universally applicable for the description of the imaging data in optical, scanning probe, and electron microscopy seeking to understand the dynamics of complex systems where rotations are a significant part of the process.

representation learning↗

DOE OSTI2023

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Turbulent flow characteristics in an 84-pin rod bundle for typical and damaged spacer grids

Hexagonal rod bundles arranged in a tightly packed triangular lattice are extensively used for heat transfer and energy generation applications. Staggered spacer grids are used to maintain the structural integrity of gas-cooled fast reactor (GFR) fuel assemblies, while inducing localized turbulence in flow. Damage to these spacer grids results in a disruption of flow fields within these hexagonal fuel bundles. Experimental flow visualizations are critical to identify the differences in local flow properties that the structural damage may cause. This experimental research investigates the flow-field characteristics at a near-wall and center plane in a prototypical 84-pin GFR fuel assembly. Newly installed typical spacers and spacers subject to naturally occurring damage due to material degradation over prolonged experimentation were investigated. Velocity fields were acquired by utilizing the matched-index-of-refraction method to obtain time-resolved particle image velocimetry measurements for a Reynolds number of 12 000. Reynolds decomposition statistical results divulged differences in the time-averaged velocity, velocity fluctuations, flow anisotropy, and Reynolds stress distributions. Galilean decomposition demarcated the influence of spacer grid damage on the velocity fields. To extract turbulent structures and elucidate mechanisms of flow instabilities, proper orthogonal decomposition analysis was employed. Reduced order flow reconstructions enabled the application of vortex identification algorithms to determine the spatial and statistical characteristics of vortices generated. This research work provides unique experimental data on the spacer grid condition-dependent flow. The results offer a deeper understanding of fluid dynamics behavior to support GFR rod bundle design efforts and computational fluid dynamics model validation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation

Long-term seismic monitoring of carbon capture and storage projects is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. As part of the monitoring program of the Archer Daniels Midland’s large-scale injection of CO 2 in Decatur, Illinois, USA, a continuous seismic monitoring array was installed using a combination of surface orbital vibrator (SOV) sources and fiber-optic cables for distributed acoustic sensing (DAS) acquisition with the objective to build a continuous monitoring array. The aim of the presented project was to build a monitoring array and platform that integrates real-time seismic data with conventional data streams and provides continuous data analysis using dynamic computational models to deliver a comprehensive real-time assessment of subsurface conditions. It is in this context that the Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation project was proposed with the objective to develop an integrated architecture that utilizes a permanent seismic monitoring network, combines the real-time geophysical and process data with reservoir flow and geomechanical models to create a comprehensive monitoring, visualization, and control system that delivers critical information for process surveillance and optimization.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Integrated Monitoring of Macroalgae Farms Using Acoustics and UUV Sensing

The vision of this project was to develop an integrated system for autonomous underwater vehicle (AUV) monitoring of offshore kelp farms using acoustic, environmental, and optical sensors. This project supports the overall MARINER goals of developing an offshore kelp aquaculture industry to produce low-carbon or carbon-neutral biofuels. The project commenced in the spring of 2018 and used laboratory experiments to test the efficacy of acoustic sensors for monitoring kelp farm lines and growing kelp biomass. Sensors were then integrated onto two AUVs as well as an autonomous surface vehicle in order to establish the optimal type, price point, and vehicle to most efficiently monitor kelp farm structures, kelp biomass, and the surrounding environment. Multiple field deployments of these vehicles and sensors in Massachusetts, New Hampshire, and Maine confirmed that farm lines and kelp could be visualized and that monitoring the spatial patterns of environmental variables was possible. The COVID-19 pandemic severely limited fieldwork activities and laboratory testing, with deployments around kelp farms not occurring again until January 2021. Time spent away from the field focused on data visualization and the development of a low-cost, vessel-based sensor system. Unfortunately, the low-cost system experienced a disk failure during its first deployment and due to multiple resignations from the project team further engineering and development was not possible. Additionally, final results from acoustic sensor testing in 2021 were also not able to be completed due to the resignation of the postdoc leading the analysis. Despite these setbacks, multiple avenues for further development of optical imagery processing from the 360-degree Kelpcam camera and testing of the low-cost sensor system may be possible.

09 BIOMASS FUELS↗

DOE OSTI2023

Geospatial Characterization of Low-Temperature Heating and Cooling Demand in the United States: Preprint

Geothermal resources at temperatures below 150 degrees C have great potential as energy sources for various direct-use applications including heating and cooling in residential and commercial buildings. This study geospatially quantifies U.S. heating and cooling demand in residential, commercial, and manufacturing sectors; heating demand in the agricultural sector; and cooling demand in data centers at the county level through end-use energy consumption, expenditure, and commissioned power analyses. Heating and cooling demand in the residential sector was estimated using energy consumption data obtained from the U.S. Energy Information Administration accounting for different U.S. climate zones. For commercial sector analysis, the end-use major fuel energy intensity at the census division level was disaggregated to the county level with respect to principal building activities. Heating and cooling demand analysis for the manufacturing sector was based on end-use energy consumption for direct-use total process categorized by the North America Industry Classification System. Fuel expenditures in the U.S. Department of Agriculture Farm Production Expenditures were examined for heating demand analysis in the agricultural sector, particularly for the greenhouse, nursery, and floriculture production category. Lastly, commissioned power for data centers in the United States were explored for cooling demand analysis. Results indicated a significant fraction of U.S. primary energy consumption is used for low-temperature heating and cooling applications. Heating and cooling demand in residential and commercial sectors is significantly affected by the number of housing units and climate zone designations, while heating and cooling demand in manufacturing and agricultural sectors and data centers are mainly dependent on the number of facilities and their locations. Maps were generated visualizing where heating and cooling demand is high and, overlain with geothermal resource maps, can indicate locations where geothermal energy can supply this heating and cooling demand.

cooling demand↗

DOE OSTI2023

Geospatial Characterization of Low-Temperature Heating and Cooling Demand in the United States

cooling demand↗

DOE OSTI2023

VTAnDeM: A python toolkit for simultaneously visualizing phase stability, defect energetics, and carrier concentrations of materials

Phase stability, defect formation energies, and carrier concentrations are closely interrelated features of semiconductors. Due to their joint dependence on the multidimensional chemical potential space, it is challenging to quantitatively establish patterns between these quantities in a given semiconductor, especially when the semiconductor is comprised of multiple elements. To enable synchronous visualization and analysis of these complementary material properties and their interdependence, we developed the Visualization Toolkit for Analyzing Defects in Materials (VTAnDeM). This python-based toolkit allows users to interactively explore how defect formation energies and carrier concentrations vary across the composition and chemical potential spaces of multicomponent semiconductors. Here, we illustrate the computational workflow that employs VTAnDeM as a post-processing tool for first-principles calculations and describe the data organization and theory underlying the visualization scheme. Furthermore, we believe that this software will serve as a useful tool for simultaneously visualizing the often complex and non-intuitive chemical potential – defect – carrier concentration phase space of semiconductors.

36 MATERIALS SCIENCE↗

DOE OSTI2024

WELLBASE - An Interactive Platform for Wellbore Material Assessment

This project seeks to build an open-source wellbore material data repository with adequate material performance and contextual data to support Geological Carbon Storage (GCS). By appropriately evaluating the data types as mentioned earlier made available by the WELLBASE tool, stakeholders can make more informed decisions regarding well selections, risk assessment, and economic analysis for geologic carbon storage projects. Advanced Natural Language Processing models and other custom python scripts will be deployed in an automated process to extract unstructured data from documents, reports, and web applications and subsequently parse to more usable formats. The processed data will then be integrated into a robust and comprehensive database architecture, optimizing data accessibility, and usability for analytical purposes. The final data products will be accessible through a user-friendly visualization platform that will allow users to query and visualize the data, as well as download data in usable formats.

Tetteh, Daniel A.↗

DOE OSTI2025

The Aerosol Model Benchmarking Repository: A toolkit for model intercomparison

The Aerosol Model Benchmarking Repository and Standards (AMBRS) project was initiated to provide tools and to establish community standards for benchmarking aerosol models. This report describes a set of open-source tools for building, running, and analyzing aerosol box model simulations in a standardized framework. The framework consists of three core components: AMBuilder, a CMake-based build system that compiles supported models consistently; AMBRS, a Python module that defines unified numerical experiments and executes them with aligned inputs; and PyParticle, an aerosol analysis package that standardizes output, computes diagnostics, and visualizes simulation results. Together, these tools enable reproducible intercomparison of aerosol schemes and support process-level evaluation of how model simplifications affect predictions of size distributions, cloud condensation nuclei activity, and other relevant properties relevant for the Earth-Energy system. Beyond its role in benchmarking, AMBRS provides a platform for studying aerosol processes across scales and can be used to generate training data for AI/ML applications in support of a broader hierarchical aerosol modeling strategy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Understanding the interplay between pilot fuel mixing and auto-ignition chemistry in hydrogen-enriched environment

The diesel-piloted dual-fuel compression ignition combustion strategy is well-suited to accelerate the decarbonization of transportation by adopting hydrogen as a renewable energy carrier into the existing internal combustion engine with minimal engine modifications. Despite the simplicity of engine modification, many questions remain unanswered regarding the optimal pilot injection strategy for reliable ignition with minimum pilot fuel consumption. The present study uses a single-cylinder heavy-duty optical engine to explore the phenomenology and underlying mechanisms governing the pilot fuel ignition and the subsequent combustion of a premixed hydrogen-air charge. The engine is operated in a dual-fuel mode with hydrogen premixed into the engine intake charge with a direct pilot injection of n-heptane as a diesel pilot fuel surrogate. Optical diagnostics used to visualize in-cylinder combustion phenomena include high-speed IR imaging of the pilot fuel spray evolution as well as high-speed HCHO* and OH* chemiluminescence as indicators of low-temperature and high-temperature heat release, respectively. Three pilot injection strategies are compared to explore the effects of pilot fuel mass, injection pressure, and injection duration on the probability and repeatability of successful ignition. The thermodynamic and imaging data analysis supported by zero-dimensional chemical kinetics simulations revealed a complex interplay between the physical and chemical processes governing the pilot fuel ignition process in a hydrogen containing charge. Hydrogen strongly inhibits the ignition of pilot fuel mixtures and therefore requires longer injection duration to create zones with sufficiently high pilot fuel concentration for successful ignition. Results show that ignition typically tends to rely on stochastic pockets with high pilot fuel concentration, which results in poor repeatability of combustion and frequent misfiring. In conclusion, this work has improved the understanding on how the unique chemical properties of hydrogen pose a challenge for maximization of hydrogen’s energy share in hydrogen dual-fuel engines and highlights a potential mitigation pathway.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Updimensioning strategy derived from synthetic equiaxed grain structures for approximating 3D grain size distributions from 2D visualizations with 1D parameters

We generated synthetic equiaxed grain structures using computer graphics software to explore the relationship between various grain size determination methods and true three-dimensional (3D) grain diameters. Mirroring grain measurement techniques, the synthetic 3D grain structures are imaged as 2D micrographs which are measured to yield 1D grain size parameters. Synthetic grain structures provide data at a mass scale and permit exploration of both polished and fractured surface micrographs, revealing one-to-one correspondence between exposed 2D grain cross-sections and individual 3D grains. Analysis of this correspondence yielded a procedure to approximate 3D equiaxed grain size and volume distributions based on the mode of the 2D fractograph grain size distribution. The 3D approximation procedure is shown to be less susceptible to different imaging conditions that affect small, undiscernible grains compared to the standard planimetric and linear intercept methods, which by design also tend to underestimate the 3D grain diameter. The procedure requires larger sample sizes to lower variance and a deeper analysis which could become more practical with machine learning (ML) models for grain boundary segmentation, which synthetic grain structures can help train. This work lays the foundation for analyzing other grain distributions such as columnar and composite grains in similar depth.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

HIResist: a database of HIV-1 resistance to broadly neutralizing antibodies

Changing the course of the human immunodeficiency virus type I (HIV-1) pandemic is a high public health priority with approximately 39 million people currently living with HIV-1 (PLWH) and about 1.5 million new infections annually worldwide. Broadly neutralizing antibodies (bnAbs) typically target highly conserved sites on the HIV-1 envelope glycoproteins (Envs), which mediate viral entry, and block the infection of diverse HIV-1 strains. But different mechanisms of HIV-1 resistance to bnAbs prevent robust application of bnAbs for therapeutic and preventive interventions. Here we report the development of a new database that provides data and computational tools to aid the discovery of resistant features and may assist in analysis of HIV-1 resistance to bnAbs. Bioinformatic tools allow identification of specific patterns in Env sequences of resistant strains and development of strategies to elucidate the mechanisms of HIV-1 escape; comparison of resistant and sensitive HIV-1 strains for each bnAb; identification of resistance and sensitivity signatures associated with specific bnAbs or groups of bnAbs; and visualization of antibody pairs on cross-sensitivity plots. The database has been designed with a particular focus on user-friendly and interactive interface. Our database is a valuable resource for the scientific community and provides opportunities to investigate patterns of HIV-1 resistance and to develop new approaches aimed to overcome HIV-1 resistance to bnAbs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Waveform Simulation Framework: User Manual with Tutorials

This manuscript describes the Waveform Simulation Framework (WSF), a Python-based framework that provides a unified, programmable interface for generating synthetic seismograms for applications such as seismic array design, method development, and special event analysis. WSF standardizes how users define sources, receivers, and velocity models while abstracting simulator-specific configuration details, enabling workflows that are largely independent of the underlying numerical engine. The document provides installation guidance and tutorial-driven examples for three WSF simulator wrappers—WSF PyFK, WSF SW4, and WSF SPECFEM2D—illustrating end-to-end workflows from forward waveform simulation to common post-processing tasks (e.g., visualization and backprojection) using consistent data products (e.g., ObsPy Stream objects and SAC files).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Materials Fingerprinting Classification

Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such as atom probe tomography (APT), which routinely generate datasets comprised of millions of atoms, are an important step in realizing this goal. However, state-of-the-art APT instruments generate noisy and sparse datasets that provide information about elemental type, but obscure atomic structures, thus limiting their subsequent value for materials discovery. The application of a materials fingerprinting process, a machine learning algorithm coupled with topological data analysis, provides an avenue by which here-to-fore unprecedented structural information can be extracted from an APT dataset. As a proof of concept, the material fingerprint is applied to high-entropy alloy APT datasets containing body-centered cubic (BCC) and face-centered cubic (FCC) crystal structures. A local atomic configuration centered on an arbitrary atom is assigned a topological descriptor, with which it can be characterized as a BCC or FCC lattice with near perfect accuracy, despite the inherent noise in the dataset. Finally, this successful identification of a fingerprint is a crucial first step in the development of algorithms which can extract more nuanced information, such as chemical ordering, from existing datasets of complex materials.

36 MATERIALS SCIENCE↗

DOE OSTI2021

LL18-Signal Analysis-NDD2Ad (Final Report)

Under favorable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially-distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection, and scaling with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning, and possible application to studies of repeating events in induced, volcanic, and glacial seismicity. Our results place limits on the widely-held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.

58 GEOSCIENCES↗

DOE OSTI2022

Computed Tomography Scanning and Geophysical Measurements of the Enhanced Geothermal Systems (EGS) Collab SURF Core

The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia were used to characterize core from 8 wells from the Enhanced Geothermal Systems (EGS) Collab project located in the Sanford Underground Research Facility’s (SURF) 4850 level. The work in this report falls in Experiment 1 in a three-phase experiment, with the primary purpose to increase understanding of hydraulic fracturing. The primary impetus of this work is a collaboration between U.S. Department of Energy (DOE) and the EGS Collab. The resultant datasets are presented in this report and can be accessed from NETL's Energy Data eXchange (EDX) online system using the following link: https://edx.netl.doe.gov/dataset/surf-collab-core. All equipment and techniques used were non-destructive, enabling future examinations and analyses to be performed on these cores. None of the equipment used was suitable for direct visualization of the pore space in the core, although fractures and discontinuities were detectable with the methods tested. Low resolution CT imagery with the NETL medical CT scanner was performed on the entire core. Qualitative analysis of the medical CT images, coupled with X-ray fluorescence (XRF), P-wave, and magnetic susceptibility measurements from the MSCL were useful in identifying zones of interest for more detailed analysis as well as fractured zones. The ability to quickly identify key areas for more detailed study with higher resolution will save time and resources in future studies. The combination of methods used provides a multi-scale analysis of the core; the resulting macro and micro descriptions are relevant to many subsurface energy related examinations traditionally performed at NETL.

47 OTHER INSTRUMENTATION↗