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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Wave climate and energy resources in American Samoa from a 42-year high-resolution hindcast

Here, this paper presents an analysis of the wave climate and a characterization of the wave energy resources in American Samoa, a U.S. territory covering seven south central Pacific islands and atolls. A numerical wave model based on WAVEWATCH III® and unstructured SWAN was developed, validated, and executed for 1979–2020 to generate a hindcast dataset suitable for resource characterization. Model-data comparisons were performed with measurements collected in situ and from satellite-based altimeters. The model was found to perform well with a bias in significant wave height of -0.14 m and -0.06 m against buoy and altimeters, respectively. The multimodal sea state of American Samoa is investigated by identifying the sources of energy reaching the islands and partitioning the wave spectrum accordingly. The wave resources characterization follows the International Electrotechnical Commission Technical Specifications to be compatible with studies performed for other U.S. regions. The average omnidirectional wave power at 2 km from shore around Tutuila, the main island of American Samoa, is 14 kW/m. Locations east and west of the islands have a more consistent resource throughout the year because the northwest and southwest swells that are dominant during different seasons complement each other.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Data for Post-Access Report: Tidal Energy Resource Characterization and Model Validation via the Assessment of Cost-effective Spatially Distributed Current Measurements from microFloat Swarms

This project evaluated how high-resolution, spatially distributed field data can be used to refine and validate site-scale hydrodynamic simulations of tidal channels. Use of such spatially-distributed field observations or site-scale hydrodynamic simulations will be needed for producing accurate predictions of tidal energy production over larger arrays of tidal turbines. The data contained in this archive includes field observations of water velocity from a series of short surveys in Agate Pass taken on August 20th, 2020 using station-keeping ADCP (acoustic doppler current profiler) measurements, drifting downward-looking ADCPs, and microFloats, gathered by researchers at APL-UW under a previous project. It also includes high-resolution hydrodynamic model runs for the site during that time generated under TEAMER support. An explanatory file (.pdf format) describes the survey and field data structure. Processed data is stored in .mat format. Survey summary: start time, number of floats, water level. microFloat data: - Cleaned data for each float in survey, - Time series of position (lat., lon.), depth (relative to surface), and horizontal velocity (u, v) Drifting ADCP data: - Cleaned data for each drifting ADCP in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) Stationary ADCP data: - Cleaned data for each station-keeping data set in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) FVCOM (Finite Volume Coastal Ocean Model) model outputs: Water level and velocity, as well as derived variables (e.g., power density) from the final refined model simulations for the Agate Pass subdomain. Contained in NetCDF and ASCII files for the duration of August 18th and 20th 2020 field surveys. Additional scripts for processing the data and generating the figures in the report are included.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

36 MATERIALS SCIENCE↗
DOE OSTI2020

SAS PDF: pair distribution function analysis of nanoparticle assemblies from small-angle scattering data

SASPDF, a method for characterizing the structure of nanoparticle assemblies (NPAs), is presented. The method is an extension of the atomic pair distribution function (PDF) analysis to the small-angle scattering (SAS) regime. The PDFgetS3 software package for computing the PDF from SAS data is also presented. An application of the SASPDF method to characterize structures of representative NPA samples with different levels of structural order is then demonstrated. The SASPDF method quantitatively yields information such as structure, disorder and crystallite sizes of ordered NPA samples. The method was also used to successfully model the data from a disordered NPA sample. The SASPDF method offers the possibility of more quantitative characterizations of NPA structures for a wide class of samples.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Precision Plant Biomass Characterization in Agriculture: Harnessing Machine Learning and Hyperspectral Imaging [Slides]

Efficient Biomass Separation Object detection of anatomical parts (Cob, Stalk, Husk) in IR images enables precise separation, improving preprocessing (e.g., drying, grinding) for biofuel production. Detailed Biomass Characterization with Hyperspectral Data Hyperspectral imaging captures spectral signatures of biomass, allowing for the identification of specific traits like moisture content, lignin levels, and nutrient composition, leading to optimized treatments for each biomass part. Enhanced Feedstock Quality By leveraging hyperspectral data, feedstock can be processed based on its chemical composition, improving conversion efficiency and biofuel yield. Automation for Large-Scale Operations Automated object detection and hyperspectral data analysis reduce manual labor, ensuring accurate sorting and faster processing, making large-scale biofuel production more efficient. Maximized Biomass Utilization Accurate identification of biomass properties minimizes waste and ensures that each part is processed according to its highest biofuel potential.

09 BIOMASS FUELS↗
DOE OSTI2022

Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”

The package contains the data layers used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”. Spatial data layers include: topography, wetland vegetation cover, time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. The study aims to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Geospatial Data Workflow Orchestration and Architecture

In an era characterized by explosive growth in geospatial data, the selection of appropriate technologies for data storage, processing, and orchestration is critical for organizations aiming to maintain competitive advantages. This white paper provides a comprehensive analysis of how Oak Ridge National Laboratory (ORNL) has effectively employed various cloud technologies, including containerized applications, container orchestrators, and workflow orchestrators, to develop robust geospatial data processing solutions. We explore the fundamental concepts behind these technologies and compare multiple deployment models tailored to diverse use cases. Our findings conclude that while Kubernetes has emerged as the preferred platform for truly scalable and fault-tolerant production workflows, the choice of workflow orchestration tool requires careful consideration of team needs, pipeline complexity, and deployment environments. This paper aims to serve as a strategic guide for organizations leveraging geospatial data, articulating the balance between technology choices and practical implementation to enhance workflow efficacy and scalability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

bnl/pub-Maffettone_2020_08

The application of the XCA package as first demonstrated in aXiv:2008.00283. ABSTRACT: The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time consuming, error prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications --- rather than absolutes --- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering unprecedented time savings, and is demonstrated on a diverse set of organic and inorganic materials challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

Maffettone, PhillipM [Brookhaven National Lab. (BN↗
DOE OSTI2023

Autonomous Inversion of In Situ Deformation Measurement Data for Injection-Induced Stress Change

Geologic carbon storage (GCS) is likely to play a key part of the global effort to dramatically reduce CO2 emissions and perhaps even reduce atmospheric CO2 concentrations through carbon negative operations. A critical part of effort to commercialize and widely deploy this technology is developing the capability to rapidly assimilate real-time monitoring data into a form that will enable site operators to make decisions to manage the safe and efficient operations. Two of the risks associate with GCS are the risk of inducing fractures in the sealing formations that can create leakage pathways and the risk of inducing earthquakes of sufficient magnitude to cause public concern, property damage, or safety risks. To properly manage these risks the site operator needs to know the initial state of stress, the change in stress induced by injection, and the relationship between operational parameters such as injection rate and pressure and the change in stress. Current methods of estimating the change in stress require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. These characterization methods interrogate the geologic formations using length scales, loading rates or magnitudes that are quite different from those encountered by the actual storage system. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method that uses automatic differentiation and a finite-element based geomechanical model perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. Furthermore, only isotropic linear elasticity is considered in this paper, the approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.

Burghardt, Jeffrey A.↗
DOE OSTI2021

Industry-Average Performance for Components and Initiating Events at U.S. Commercial Nuclear Power Plants: 2020 Update

This report documents the quantitative results of the current industry-average performance for components and initiating events (IEs) at U.S. commercial nuclear power plants (NPPs). It represents the third update of the original analysis in NUREG/CR-6928 with data through 2020. Continuous characterization and updating of current industry-average performance with the latest industry data available are important steps in maintaining up-to-date risk models. Typically, data from 1998–2002 were used in NUREG/CR-6928, data from 1998–2010 in the first update, data from 1998–2015 in the second update, and data from 2006–2020 in this update, although many IEs required longer periods for adequate characterization of frequencies in all these analyses. As with NUREG/CR-6928 and previous updates, four types of events are covered in this report: component unreliability (e.g., a pump that fails to start or fails to run), component or train unavailability resulting from test or maintenance outages, special event probabilities covering operational issues (e.g., pump restarts and injection valve re-openings during unplanned demands), and IE frequencies. Results (in the form of beta distributions for failure probabilities upon demand and gamma distributions for rates) are used as inputs to the U.S. Nuclear Regulatory Commission standardized plant analysis risk models covering U.S. commercial NPPs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Examining the Characteristics of the Cropland Data Layer in the Context of Estimating Land Cover Change

The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) provides spatially explicit information about crop production area and has served as a prevalent data source for characterizing cropland change in the U.S. in the last decade. Understanding the accuracy of the CDL is paramount because of the reliance on it for management and policy making. This study examined the characteristics of the CDL from 2007 to 2017 using comparisons to other USDA datasets. The results showed when examining the cropland area for the same year, the CDL produced comparable trends with other datasets (R 2 > 0.95), but absolute area differed. The estimated area of cropland changes from 2007 to 2012, 2008 to 2012 and 2012 to 2017 varied from weak to moderate correlation between the CDL and the tabular data (R 2 = 0.005~0.63). Differences in area of cropland change varied widely between data sources with the CDL estimating much larger change area. A series of image processing techniques designed to improve the confidence in cropland change estimated using the CDL reduced the area of estimated cropland change. The techniques also, unexpectedly, lowered the correlation in change estimated between the CDL and the tabular datasets. Estimated land cover change area varied widely based on analyses applied and could reverse from increasing to declining area in cropland. Further analyses showed unlikely change scenarios when comparing different year combinations. The authors recommend the CDL only be used for land cover change analysis if the error can be estimated and is within change estimates.

58 GEOSCIENCES↗
DOE OSTI2020

Hydrodynamic and Radiographic Toolbox (HART)

With a multi-lab and university team, we propose to develop new methods for a Hydrodynamic and Radiographic Toolbox (HART) that will enable a fuller and more extensive use of experimental radiographic data towards better characterizing and reducing uncertainties in predictive modeling of weapons performance. We will achieve this by leveraging recent developments in areas of computational imaging, statistical and machine learning, and reduced order modeling of hydrodynamics. In terms of software practices, by partnering with XCP (RISTRA Project) we will conform to recent XCP standards that are inline with modern software practices and standards and ensure compatibility and ease of inter-operability with existing codes. Three new activities under this proposal include (a) the development and use of deep learning-based surrogates to accelerate reconstruction and variational inference of density fields from radiographs of hydrotests, (b) a model-data fusion strategy that couples deep learning-based density reconstructions with fast hydrodynamics simulators to better constrain the reconstruction, and (c) a method for treating asymmetries using techniques adopted from limited view tomography. All three new activities will be based on improved treatment of scatter, noise, beam spot movement, detector blur, and flat fielding in the forward model, and a use of sophisticated priors to aid in the re construction. The improvements to the forward model and improved algorithmic design of the reconstruction when complete will be contained in the iterative reconstruction code SHIVA—a code project that we have recently initiated. The many ways in which machine learning can be used in the reconstruction work will be contained in a code HERMES that has been initiated with DTRA support. For example, the significant levels of acceleration that will likely be achieved by the use of machine learning techniques will permit us (and are required) to quantify uncertainties in density retrievals. Next, the two-way coupling between density reconstruction and model-based simulation of the hydrodynamics will be contained in code EREBUS, and will permit a fuller realization of the potential of the data to constrain the hydrodynamic model and better address issues related to asymmetries in the problem. Finally, we anticipate that the better consistency with physics achieved in our reconstructions will allow them to be used by X-Division more so than today.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams

Abstract We present Lightcurve Anomaly Identification and Similarity Search ( LAISS ), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes ( spectroscopic anomalies), of uncommon host galaxy environments ( contextual anomalies), and of peculiar or interaction-powered phenomena ( behavioral anomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host galaxy environments. We use analogs for data-driven discovery, characterization, (re)classification, and imputation in retrospective and real-time searches. To date, we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to superluminous supernovae (SLSNe), tidal disruption events, SNe IIn, SNe IIb, SNe I-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018 and 2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the “needle in the haystack” in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Characterization of Gramicidin A in Triblock and Diblock Polymersomes and Hybrid Vesicles via Continuous Wave Electron Paramagnetic Resonance Spectroscopy

Studying membrane proteins in a native environment is crucial to understanding their structural and/or functional studies. Often, widely accepted mimetic systems have limitations that prevent the study of some membrane proteins. Micelles, bicelles, and liposomes are common biomimetic systems but have problems with membrane compatibility, limited lipid composition, and heterogeneity. To overcome these limitations, polymersomes and hybrid vesicles have become popular alternatives. Polymersomes form from amphiphilic triblock or diblock copolymers and are considered more robust than liposomes. Hybrid vesicles are a combination of lipids and block copolymers that form vesicles composed of a mixture of the two. These hybrid vesicles are appealing because they have the native lipid environment of bilayers but also the stability and customizability of polymersomes. Gramicidin A was incorporated into these polymersomes and characterized using continuous wave electron paramagnetic resonance (CW-EPR) and transmission electron microscopy (TEM). EPR spectroscopy is a powerful biophysical technique used to study the structure and dynamic properties of membrane proteins in their native environment. Spectroscopic studies of gramicidin A have been limited to liposomes; in this study, the membrane peptide is studied in both polymersomes and hybrid vesicles using CW-EPR spectroscopy. Lineshape analysis of spin-labeled gramicidin A revealed linewidth broadening, suggesting that the thicker polymersome membranes restrict the motion of the spin label more when compared to liposome membranes. Statement of Significance: Understanding membrane proteins’ structures and functions is critical in the study of many diseases. In order to study them in a native environment, membrane mimetics must be developed that can be suitable for obtaining superior biophysical data quality to characterize structural dynamics while maintaining their native functions and structures. Many currently widely accepted methods have limitations, such as a loss of native structure and function, heterogeneous vesicle formation, restricted lipid types for the vesicle formation for many proteins, and experimental artifacts, which leaves rooms for the development of new biomembrane mimetics. The triblock and diblock polymersomes and hybrid versicles utilized in this study may overcome these limitations and provide the stability and customizability of polymersomes, keeping the biocompatibility and functionality of liposomes for EPR studies of membrane proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Rapid retrieval of first-order spatiotemporal distortions for ultrashort laser pulses

High-intensity, ultrashort laser sources are a foundational pillar for High-Energy-Density (HED) physics, and as the repetition-rates of these systems increase, diagnostics must be developed to match their data collection speed. Characterization of any spatiotemporal distortions in the laser pulse is necessary in order to standardize results across facilities. Here, we have previously developed a laser diagnostic called Spatially and Temporally Resolved Intensity and Phase Evaluation Device: Full Information from a Single Hologram, or STRIPED FISH, to measure the full spatiotemporal laser electric field on a single shot. In order to provide rapid feedback, we here adapt the STRIPED FISH retrieval algorithm, which typically has computation times of up to 30 minutes due to the high spatial resolution of the device and its retrieval of the complete four-dimensional pulse field, to distill the key electric field quantities into a handful of scalars for rapid assessment of the pulse's first order distortions. Here, our new, rapid non-iterative retrieval algorithm is validated with simulated and experimental data, and it provides, within seconds, the same first-order information as given by the full analysis. This algorithm is suitable for on-shot assessment of spatiotemporal distortions and can be adapted to assess high repetition-rate laser quality in HED experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Analysis of Previously Classified White Dwarf–Main-sequence Binaries Using Data from the APOGEE Survey

We present analyses of near-infrared spectroscopic data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey for 45 previously confirmed or candidate white dwarf–main-sequence (WDMS) binaries identified by the optical Sloan Digital Sky Survey (SDSS) and LAMOST surveys. Among these 45 systems, we classify three as having red giant primaries in the LAMOST sample and 14 as young stellar object contaminants in the photometrically identified SDSS sample. From among the subsample of 28 systems that we confirm to have MS primaries, we derive and place limits on orbital periods and velocity amplitudes for 14. Seven systems have significant velocity variations that warrant a post-common-envelope (PCE) binary classification, four of which are newly classified, three of which are newly confirmed, and five for which we can derive full orbital parameters. If confirmed, one of these newly discovered systems (2M14544500+4626456) will have the second-longest orbital period reported for a typical compact PCE WDMS binary (P = 15.1 days). In addition to the seven above, we also recover and characterize with APOGEE data the well-known PCE WDMS systems EG UMa and HZ 9. We also investigate the overall metallicity distribution of the WDMS sample, which is a parameter space not often explored for these systems. Of note, we find one system (2M14244053+4929580) to be extremely metal-poor ([Fe/H] = − 1.42) relative to the rest of the near-solar sample. Additionally, the PCE systems in our sample are found to be, on average, higher in metallicity than their wide-binary counterparts, though we caution that with this small number of systems, the sample may not be representative of the overall distribution of WDMS systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Application of an Embedded Fracture and Borehole Modeling Approach to the Understanding of EGS Collab Experiment 1

EGS Collab is a series of meso-scale experiments and associated numerical simulation activities being funded by the United States Department of Energy, Geothermal Technologies Office (GTO) to investigate enhanced geothermal system processes under in-situ stress and slightly elevated temperature conditions. This project is designed to provide scientists and engineers with immediate access to impermeable rock at scales larger than possible in the laboratory, but generally smaller than those for commercial production. Immediate access to rock is provided via the existing drifts of the former Homestake Gold Mine, now operated as the Sanford Underground Research Facility in Lead, South Dakota. The objectives of the EGS Collab project are to develop well controlled fracture networks between injector and producer boreholes using normal and shear stimulation for permeability enhancement. The first experimental site was located off the West Access drift on the 4850 Level (4850 feet below ground surface) in phyllite of the Precambrian Poorman formation, and involved the creation of a fracture network comprising a combination of hydraulic and natural fractures. A second experimental site is now being considered near the battery alcove on the 4100 Level in amphibolite and rhyolite of the Yates Unit. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO’s flagship EGS research effort. Experiment 1 has comprised a series of successful tests, including a long-term chilled-water circulation test, but the testbed has two atypical EGS elements. Active ventilation in the adjacent drift over a 50-year period cooled the testbed rock mass and hydraulic fracturing intersected monitoring boreholes making them conduits for fluid flow. Numerical simulations executed in support of the design of the EGS Collab Experiment 1, computed a radial temperature distribution and stress gradient orthogonal to the drift axis, resulting in the forecast of an oblong hydraulic fracture geometry, extended in the direction of the drift from the stimulation borehole. This paper describes the numerical simulation of long-term chilled-water circulation test with an embedded fracture and borehole modeling approach. The principal objective of the simulation work is show agreement between experimental observations in terms of production fluid temperatures, monitoring borehole temperatures, injection and production pressures, and tracer recoveries using a single conceptual model for the fracture network, using characterization and monitoring data generally available for EGS. A secondary objective is to improve the numerical simulation result comparisons with additional data available from the broader monitoring equipment within the testbed versus deeper, hotter, and more remote EGS. One complicating factor for comparisons between the numerical simulation results and experimental observations, is the Joule-Thomson heating associated with large pressure drops across the fracture network.

numerical simulation, embedded fractures and boreh↗
DOE OSTI2024

The POINTER Imaging baseline cohort: Associations between multimodal neuroimaging biomarkers, cardiovascular health, and cognition

Abstract INTRODUCTION The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is evaluating lifestyle interventions in older adults at risk for cognitive decline and dementia. Here we characterize the baseline data set of the POINTER Imaging ancillary study. METHODS Participants underwent health and cognitive assessments and neuroimaging with multimodal positron emission tomography (PET) (beta‐amyloid [Aβ] and tau) and magnetic resonance imaging (MRI). Framingham risk score (FRS) was used to quantify cardiovascular disease (CVD) risk. RESULTS A total of 1052 participants (31% from underrepresented ethnoracial groups) were enrolled. Compared to Aβ−, Aβ+ (29%) participants were older, had higher apolipoprotein E (APOE) ε4 carriage rate and white matter hyperintensity volume, and greater temporal tau. FRS was related to MRI measures, but not AD biomarkers. FRS and tau had independent effects on cognition. DISCUSSION In this heterogenous, at‐risk cohort, CVD risk was related to more abnormal brain structure and poorer cognition, representing a putative non‐AD (Alzheimer's disease) pathway to brain injury and cognitive decline. Highlights The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) cohort is enriched for cardiovascular disease (CVD) and poor lifestyle POINTER Imaging collected multimodal neuroimaging data in this unique, at‐risk cohort Amyloid burden was related to age, apolipoprotein E (APOE) ε4 carriage, and measures of disease progression Associations between amyloid and tau, and tau and cognition, were relatively weak CVD risk and tau pathology were independently related to memory

Neurosciences & Neurology↗