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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

​Understanding a Stratigraphic Hydrothermal Resource – Geophysical Imaging at Steptoe Valley, Nevada

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The stratigraphic reservoir in northern Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, included data which further highlighted potential resource targets in the basin. Geophysical surveys, complemented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a >100MWe power-capable reservoir is likely economically accessible using conventional well placement and stimulation techniques in the Paleozoic carbonates of the deep/central basin of northern Steptoe Valley. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the northern Steptoe resource. The geophysical tools, interpretations, lessons learned, and public data generated by this study establish an exploration methodology to inform decisions for characterization and development of northern Steptoe Valley and other stratigraphic geothermal reservoirs in the western U.S.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Multimodal 3D quantification of particle stimulated nucleation in industrially manufactured aluminium AA5182 sheet

Particle stimulated nucleation is a dominant recrystallisation mechanism observed in many industrially relevant aluminium alloys during thermomechanical processing. Here, in this work, we quantify particle stimulated nucleation in 3D in an aluminium AA5182 alloy sheet cold-rolled to 75% thickness reduction. Second phase particles and nuclei are mapped in the same sample volume by conventional laboratory absorption X-ray tomography and synchrotron X-ray Laue micro-diffraction. The large second phase particles are classified as Fe- and Mg-rich phases. It is found that 84% of the nuclei are particle stimulated and 40% of the particles stimulate nucleation. The critical particle diameter is found to be 4 μm. Deviatoric elastic strains are derived from micro-diffraction data and it is found that elastic strains are present in the recrystallised nuclei. The effects of the different particle types, particle clustering, particle size and aspect ratio as well as strain inheritance are discussed. This work provides a full 3D quantification of particle stimulated nucleation behaviour in AA5182 alloy sheet deformed to high strain.

Deviatoric strain↗
DOE OSTI2025

Extending the operational boundaries of RMP-ELM suppression with optimized 3D field control

The use of 3D magnetic fields is one of the promising approaches to control edge localized modes (ELMs), and ITER has plans to utilize a flexible 3D coil set for ELM suppression using 3D fields. This study focuses on optimizing the 3D field spectrum to expand the operational window for n = 1 resonant magnetic perturbation (RMP) ELM suppression in KSTAR. The optimized n = 1 RMP effectively suppresses ELMs throughout the entire H-mode discharge, including the first ELM crash, while avoiding the onset of disruptive locked modes in low-density L-mode plasmas. The predicted suppression window aligns well with experimental data, highlighting the challenges and solutions of using n = 1 RMP at low densities. Moreover, the optimization successfully achieved n = 1 RMP ELM suppression for the first time in ITER-relevant q 95 and shaping conditions, including cases with q95 as low as 3.6, as well as other q 95 and shape configurations. This highlights the importance and utility of 3D coil optimization while emphasizing the potential of long-wavelength low-n RMP, which will be valuable for ex-vessel coils designed to avoid complications of nuclear degradation.

3D magnetic field control↗
DOE OSTI2020

Three-dimensional seismic characterization and imaging of the Soda Lake geothermal field

Accurate characterization of subsurface geophysical properties and detection of the fault system are essential for geothermal energy exploration and production. The Soda Lake geothermal field is in western Nevada with a complex fault system. Previous seismic characterization only produced a low-resolution, smooth velocity model along with a simple, conceptual fault model. Using optimized correlation-based full-waveform inversion, wavefield-separation-based reverse-time migration, and automatic fault detection techniques, we present 3D seismic characterization for the Soda Lake geothermal field using 3D surface seismic data acquired with Vibroseis sources. Here, we obtain 3D high-resolution velocity, density, and acoustic impedance models, 3D seismic images with different grid spacings, and a high-resolution fault system. Consistency check between the constructed faults and currently active injection and production geothermal wells verifies that our seismic inversion and imaging results and detected faults are reliable. These results can provide valuable information for optimizing well placement and geothermal energy production at the Soda Lake geothermal field.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California

Mesoscale model predictions of wind, turbulence, and wind energy capacity factors are evaluated in the Altamont Pass Wind Resource Area of California (APWRA), where the diurnal regional sea breeze and associated terrain-driven speedup flows drive wind energy production during the summer months. Results from the Weather Research and Forecasting model version 4.4 using a novel three-dimensional planetary boundary layer (3D PBL) scheme, which treats both vertical and horizontal turbulent mixing, are compared to those using a well-established one-dimensional (1D) scheme that treats only vertical turbulent mixing. Each configuration is evaluated over a nearly 3-month-long period during the Hill Flow Study, and due to the recurring nature of the observed speedup flows, diurnal composite averaging is used to capture robust trends in model performance. Both model configurations showed similar overall skill. The general timing and direction of the speedup flows is captured, but their magnitude is overestimated within a typical wind turbine rotor layer. Both also fail to capture a persistent observed near-surface jet-like flow, likely due to the limited grid resolution that is typical of mesoscale models. However, the 3D PBL configuration shows several minor improvements over the 1D PBL configuration, including improved wind speed and turbulence kinetic energy profiles during the accelerating phase of the speedup events, as well as reduced positive wind speed bias at surface stations across the APWRA region. Using a mesoscale wind farm parameterization, modeled capacity factors are also compared to monthly data reported to the US Energy Information Administration (EIA) during the study period. Although the monthly trend in the data is captured, both model configurations overestimate capacity factors by roughly 7 %–11 %. Through model evaluation, this study provides confidence in the 3D PBL scheme for wind energy applications in complex terrain and provides guidance for future testing.

17 WIND ENERGY↗
DOE OSTI2022

Qualification of Continuous Fiber Reinforced 3D Printed Material for use in Hot Cell Environments

The ability to employ additively manufactured polymers in highly radioactive environments facilitates rapid prototyping as well as emergency replacement of various equipment components contained in-cell. A technical evaluation (TEV) exists seeking to qualify 3D printed polymers and carbon reinforcements for use in-cell, the data generated in this project seeks to support the TEV. The goal of the study is to determine if the loss in mechanical properties is low enough to justify semi-permanent use in-cell.

3D printing↗
DOE OSTI2024

Novel Full-Ceramic Multi-Tubular Membrane Systems for Pre-Combustion CO2 Capture with Simultaneous H2 Production: Fabrication, Performance Testing, and 3D CFD Modeling

Inorganic membranes show promise for application in pre-combustion CO2 capture with simultaneous H2 production. State-of-the-art systems for use under high temperature and pressure conditions consist of multiple membrane tube bundles prepared in a "candle-filter" configuration, in which the membrane tubes are open at one end and sealed at the other. This configuration is used for practical reasons, specifically the need to minimize potential problems due to thermal expansion mismatch, at high temperatures, between the ceramic tube bundle and the steel housing. The primary technical problem with the candle-filter configuration for use in commercial-scale installations is the inability to purge the permeate side (typically the tube side), a feature that is crucial for high H2 recovery. In this study, we fabricated dual-end open, commercial-size ceramic multiple-tube bundles made of zeolite, palladium (Pd), and carbon molecular sieve (CMS) membranes that enable permeate-side (tube-side) purge for gas separation applications. Experimental gas separation data with these membrane bundles under harsh operating conditions (temperatures up to 350 and pressures up to 800 psig), to be presented at the meeting, manifest excellent performance. Parallel to the membrane bundle construction and testing efforts, we have also developed a detailed 3D CFD modeling package using COMSOL Multiphysics software to gain more insight into the effect on the H2 purity and recovery of the detailed geometry of the multi-tubular membrane system, including the number of tubes used, their dimensions, and placement in the bundle, as well as the number, type, and positioning of internal baffles and other flow-enhancement accessories. The CFD package is validated with experimental data from different systems (1-tube, 3-tube, and 19-tube bundles), and shows high accuracy in predicting the experimental results (<5 % error in all cases). The results of our study show that the detailed internal geometry of these multi-tubular membrane systems has a considerable impact on the performance of the system as the flow maldistribution within the shell-side can substantially decrease (>40%) the H2 recovery.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network

The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. Herein this paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.

42 ENGINEERING↗
DOE OSTI2024

A mesoscale 3D model of irradiated concrete informed via a 2.5 U-Net semantic segmentation

The concrete biological shield in light-water reactors is exposed to neutron and gamma irradiation, which deteriorates the concrete’s mechanical properties in the long term. To assess the irradiation-induced damage, predictive mechanical models are developed and used in parallel with the characterization of irradiated concrete samples. Realistic 3D simulation domains can drastically improve a model’s prediction. In this work, we utilized x-ray computed tomography (XCT) data of a concrete specimen to reconstruct its 3D microstructure. The XCT data shows low contrast between the concrete’s aggregates and cement paste, resulting in poor image segmentation when using traditional unsupervised techniques. To address this issue, we developed and trained a 2.5D U-Net model on only 24 pre-labeled XCT layers to segment 651 layers of the XCT data. The overall F1-score of the model is approximately 96%. Then, we created a 3D finite element (FE) mesh based on the stack of segmented images. The FE model contains radiation-induced expansion, damage, and creep. The constitutive equations are adapted to each phase (aggregates and cement paste). Here, we simulated the effects of neutron irradiation in the concrete specimen as well as the specimen’s mechanical response to uniaxial compression. Finally, model validation was performed using experimental data on similar concrete specimens in the literature.

2.5D U-Net↗
DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗
DOE OSTI2025

Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography

Obtaining high-quality 3D reconstructions from electron tomography of crystalline particles embedded in lighter support elements is crucial for various material systems such as catalysts for fuel cell applications. However, significant challenges arise due to the limited tilt range, sparse and low signal-to-noise ratio of the measurements. In addition, small metal particles can cause strong streaking and shading artifacts in the 3D reconstructions when using conventional reconstruction algorithms due to the presence of Bragg diffraction and the large scattering cross-section difference between the materials of the particles and the background support regions. These artifacts lead to errors in the downstream characterization affecting extraction of critical features such as the size of the metal particles, their distribution and the volume of the lighter support regions. In this paper, we present a two-stage algorithm based on metal artifact reduction, utilizing model-based iterative reconstruction methods with adaptive adjustment of regularization parameters. Our approach yields high-quality 3D reconstructions compared to traditional algorithms, accurately capturing both the metal particles as well as the background support. We demonstrate the effectiveness of our algorithm through simulated and experimental bright-field electron tomography data, showing significant improvements in reconstruction quality compared to traditional methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Stimulated Raman Scattering Microscopy: Real-Time In-Situ Physical and Chemical Characterization of Reverse Osmosis Desalination Membrane Scaling

We introduce a stimulated Raman scattering (SRS) methodology designed for rapid, real-time, and in situ monitoring of RO membrane scaling adapted for bench-scale desalination flow cells. The methodology can provide new insights into membrane scaling dynamics by offering time-resolved reflection imaging of inorganic crystal growth, coupled with chemical identification from Raman spectral data. These capabilities allow for direct local measurement of the membrane surface area covered by different scalants as well as an approximation of the scalant volume using three-dimensional, integrated Raman intensity. The 2D and 3D SRS results obtained from CaSO 4 scaling experiments are compared to and are in reasonable agreement with those provided by confocal microscopy. The real-time physical and chemical characterization capabilities presented here could be extended to study combinations of inorganic, organic, and biological fouling. Overall, the SRS methodology represents an advancement in real-time sensing of membrane fouling that offers the potential for improved operation, lower cost, and more resilient RO membrane systems for sustainable water management.

42 ENGINEERING↗
DOE OSTI2025

Chimera D-Series Gravitational Wave Emission Sourced from Neutrino Anisotropy

Gravitational wave data sourced from the time-dependent anisotropic neutrino emission, as well as the time-dependent fluid quadrupole motion, in the Chimera D-Series three-dimensional core collapse supernova simulations. Data from three models initiated from three different progenitors are presented: D9.6-3D, D15-3D, and D25-3D. Please see the README for more information about the data structure and progenitors.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2000

Small Scale Variability and the Problem of Data Validation

Numerous measurements taken with a variety of airborne, balloon borne and ground based instruments over the past decade have revealed a complex multiscaled 3D structure in both chemical and dynamical fields in the upper troposphere/lower stratosphere. The variability occurs on scales that are well below the resolution of satellite measurements, leading to problems in measurement validation. We discuss some statistical ideas that can shed some light on the contribution of the natural variability to the inevitable differences in correlative measurements that are not strictly colocated, or that have different spatial resolution.

Sparling, L. C.↗
NASA NTRS2001

Sources of Variability in the Stratospheric Column of Nitrogen Dioxide

Instruments such as the Global Ozone Monitoring Experiment (GOME, on the European Remote Sensing Satellite (ERS-2), launched 1995), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY, on ENVISAT, to be launched July 2001) and the Ozone Monitoring Instrument (OMI on EOS Aura, to be launched 2003) make measurements of the total column of NO2. There is interest in separating the stratospheric and tropospheric contributions to the column, as the tropospheric column provides a measure of pollution. We are using a 3D chemistry and transport model driven by winds from the Goddard Space Flight Center Data Assimilation System to examine variability in the stratospheric NO2 column. Model results for NOx = NO + NO2 + 2N2O5 will be shown to compare well with sunset observations from the Halogen Occultation Experiment on the Upper Atmosphere Research Satellite, and to exhibit similar temporal and spatial dependence. Partitioning between NO, NO2, and N2O5 is also shown to compare well with observations. This good agreement supports the use of simulated fields in the stratosphere to derive the tropospheric column from the total column. Preliminary comparisons of the tropospheric column with model simulations for the troposphere will also be shown.

Douglass, Anne↗
DOE OSTI2024

A method for crystallographic mapping of an alpha-beta titanium alloy with nanometre resolution using scanning precession electron diffraction and open-source software libraries

An approach for the crystallographic mapping of two-phase alloys on the nanoscale using a combination of scanned precession electron diffraction and open-source python libraries is introduced in this paper. This method is demonstrated using the example of a two-phase α/β titanium alloy. The data were recorded using a direct electron detector to collect the patterns, and recently developed algorithms to perform automated indexing and analyse the crystallography from the results. Very high-quality mapping is achieved at a 3 nm step size. The results show the expected Burgers orientation relationships between the α laths and β matrix, as well as the expected misorientations between α laths. A minor issue was found that one area was affected by 180° ambiguities in indexing occur due to this area being aligned too close to a zone axis of the α with twofold projection symmetry (not present in 3D) in the zero-order Laue Zone, and this should be avoided in data acquisition in the future. Nevertheless, this study demonstrates a good workflow for the analysis of nanocrystalline two- or multi-phase materials, which will be of widespread use in analysing two-phase titanium and other systems and how they evolve as a function of thermomechanical treatments.

36 MATERIALS SCIENCE↗