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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Three-Dimensional Heat Flux and Thermal Analysis of Angled Tungsten Samples on DIII-D

ITER-grade tungsten and dispersoid-strengthened tungsten samples with the top surface angled at ~15° towards the incident plasma flux were exposed to 9 H-mode discharges with edge-localized modes (ELMs) in the lower divertor of DIII-D tokamak using the Divertor Material Evaluation System (DiMES). Surface damage included cracking and flaking of material on the two samples farthest away from the plasma strike point, and significant melting of the two samples closest to the strike point. Heat flux and thermal analysis tools new to DIII-D have been applied to better understand this material response and to help optimize the exposure conditions for future experiments. SMITER field-line tracing simulations based on IRTV data and EFIT equilibria estimate an average inter-ELM perpendicular heat flux, 𝑞⊥,𝑖nter−𝐸LM , on the angled surfaces of 10.1 – 19.6 MW/m² for a majority of the 9 discharges, increasing to 15.6 – 24.5 MW/m² for the single, higher-power shot where samples melted. Fast camera data showed shallow intra-ELM melting and re-solidification, which transitioned to bulk inter-ELM melting with melt motion in the 𝐽⃗ 𝑥 𝐵⃗ direction. About 50% of the protruding volume of the most affected sample was displaced via melt-motion. SIERRA thermal modeling software was able to reproduce an onset time of melting consistent with fast camera data and final sample conditions, within < 200 ms. Maximum surface temperatures of 3122 K and 2787 K are estimated for the samples farthest away from the strike point, while the closest samples achieve melting at 4067 ms and 4750 ms into the ~5000 ms plasma exposure. A +10% increase in both the SMITER 𝑞⊥,𝑖nter−𝐸LM calculations and the estimated ELM heat loads 𝑞⊥, 𝐸LM was required to achieve this result, which is within the uncertainty of the diagnostic data but likely accounts for non-ideal geometry effects plus other physics uncertainties not included in this first iteration of modeling. This work provided valuable estimates of the 3D temperature evolution to help better understand the observed surface morphology and internal recrystallization of samples, which are discussed in detail in a complementary manuscript [1]. Benchmarking efforts with more diagnosed DIII-D experiments are underway to further refine the SMITER and SIERRA models for DiMES. Future use of these tools will enable researchers to precisely target heat flux exposure conditions in DIII-D to test, but not exceed, the thermomechanical limitations of novel plasma-facing materials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model

The National Solar Radiation Database (NSRDB) is a widely used resource providing satellite-derived solar data across the United States and globally. While the NSRDB employs a physical model for computing global horizontal irradiance (GHI), its current method for estimating cloudy-sky direct normal irradiance (DNI) relies on surface observations and empirical models. Recently, a novel physics-based approach, the Fast All-Sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to enhance the DNI forecasting. FARMS-DNI incorporates both direct and scattered solar radiation within the circumsolar region, resulting in improved day-ahead DNI predictions when integrated into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study integrates FARMS-DNI into the NSRDB algorithm to generate high-resolution DNI data from satellite resources. Our findings reveal that FARMS-DNI effectively mitigates the substantial DNI overestimation present in the conventional NSRDB across surface sites, particularly in conditions categorized as cloudy overcast. Consequently, this innovative model substantially enhances the overall accuracy of the NSRDB.

Xie, Yu↗
DOE OSTI2024

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model: Preprint

The National Solar Radiation Database (NSRDB) is a widely used resource providing satellite-derived solar data across the United States and globally. While the NSRDB employs a physical model for computing global horizontal irradiance (GHI), its current method for estimating cloudy-sky direct normal irradiance (DNI) relies on surface observations and empirical models. Recently, a novel physics-based approach, the Fast All-Sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to enhance the DNI forecasting. FARMS-DNI incorporates both direct and scattered solar radiation within the circumsolar region, resulting in improved day-ahead DNI predictions when integrated into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study integrates FARMS-DNI into the NSRDB algorithm to generate high-resolution DNI data from satellite resources. Our findings reveal that FARMS-DNI effectively mitigates the substantial DNI overestimation present in the conventional NSRDB across surface sites, particularly in conditions categorized as cloudy overcast. Consequently, this innovative model substantially enhances the overall accuracy of the NSRDB.

DNI↗
DOE OSTI2023

Arctic tropospheric ozone: assessment of current knowledge and model performance

As the third most important greenhouse gas (GHG) after carbon dioxide (CO 2 ) and methane (CH 4 ), tropospheric ozone (O 3 ) is also an air pollutant causing damage to human health and ecosystems. This study brings together recent research on observations and modeling of tropospheric O 3 in the Arctic, a rapidly warming and sensitive environment. At different locations in the Arctic, the observed surface O 3 seasonal cycles are quite different. Coastal Arctic locations, for example, have a minimum in the springtime due to O 3 depletion events resulting from surface bromine chemistry. In contrast, other Arctic locations have a maximum in the spring. The 12 state-of-the-art models used in this study lack the surface halogen chemistry needed to simulate coastal Arctic surface O 3 depletion in the springtime; however, the multi-model median (MMM) has accurate seasonal cycles at non-coastal Arctic locations. There is a large amount of variability among models, which has been previously reported, and we show that there continues to be no convergence among models or improved accuracy in simulating tropospheric O 3 and its precursor species. The MMM underestimates Arctic surface O 3 by 5% to 15% depending on the location. The vertical distribution of tropospheric O 3 is studied from recent ozonesonde measurements and the models. The models are highly variable, simulating free-tropospheric O 3 within a range of ±50% depending on the model and the altitude. The MMM performs best, within ±8% for most locations and seasons. However, nearly all models overestimate O 3 near the tropopause (~300 hPa or ~8 km), likely due to ongoing issues with underestimating the altitude of the tropopause and excessive downward transport of stratospheric O 3 at high latitudes. For example, the MMM is biased high by about 20% at Eureka. Observed and simulated O 3 precursors (CO, NO x , and reservoir PAN) are evaluated throughout the troposphere. Models underestimate wintertime CO everywhere, likely due to a combination of underestimating CO emissions and possibly overestimating OH. Throughout the vertical profile (compared to aircraft measurements), the MMM underestimates both CO and NO x but overestimates PAN. Perhaps as a result of competing deficiencies, the MMM O 3 matches the observed O 3 reasonably well. Our findings suggest that despite model updates over the last decade, model results are as highly variable as ever and have not increased in accuracy for representing Arctic tropospheric O 3 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
DOE OSTI2020

Why Boron Nitride is such a Selective Catalyst for the Oxidative Dehydrogenation of Propane

Abstract Boron‐containing materials, and in particular boron nitride, have recently been identified as highly selective catalysts for the oxidative dehydrogenation of alkanes such as propane. To date, no mechanism exists that can explain both the unprecedented selectivity, the observed surface oxyfunctionalization, and the peculiar kinetic features of this reaction. We combine catalytic activity measurements with quantum chemical calculations to put forward a bold new hypothesis. We argue that the remarkable product distribution can be rationalized by a combination of surface‐mediated formation of radicals over metastable sites, and their sequential propagation in the gas phase. Based on known radical propagation steps, we quantitatively describe the oxygen pressure‐dependent relative formation of the main product propylene and by‐product ethylene. Free radical intermediates most likely differentiate this catalytic system from less selective vanadium‐based catalysts.

Venegas, Juan M.↗
DOE OSTI2020

Why Boron Nitride is such a Selective Catalyst for the Oxidative Dehydrogenation of Propane

Boron–containing materials, and in particular boron nitride, have recently been identified as highly selective catalysts for the oxidative dehydrogenation of alkanes such as propane. To date, no mechanism exists that can explain both the unprecedented selectivity, the observed surface oxyfunctionalization, and the peculiar kinetic features of this reaction. Here, we combine catalytic activity measurements with quantum chemical calculations to put forward a bold new hypothesis. We argue that the remarkable product distribution can be rationalized by a combination of surface–mediated formation of radicals over metastable sites, and their sequential propagation in the gas phase. Based on known radical propagation steps, we quantitatively describe the oxygen pressure–dependent relative formation of the main product propylene and by–product ethylene. Free radical intermediates most likely differentiate this catalytic system from less selective vanadium–based catalysts.

03 NATURAL GAS↗
DOE OSTI2025

A 30-yr high-resolution weather research and forecasting model downscaling data over California and Nevada

This dataset presents a 30-year high resolution meteorological dataset obtained using the WRF model (Advanced version Research WRF version 4.4). We used WRF and European Centre for Medium-Range Weather Forecasts Reanalysis v5 as initial and boundary conditions to generate gridded meteorological variables. A large number of surface weather stations was used for model validation. A multi-physics analysis was first developed to identify a good physics suite extended from 6 November 00 UTC to 10 November 23 UTC, 2018, which included the Camp Fire in northern California. Based on the best physics suite, the downscaling dataset extends from 1 December to 28 February, 1990–2021 and the horizontal domain has 1.5 km grid spacing covering the entire states of California and Nevada in the United States. Comparisons between hourly surface observations and WRF simulations of air temperature, relative humidity and wind speeds show mean absolute errors on the order of (1.6-2.0 C), (10 %) and 1.2–1.5 m s -1 , respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Growth mechanisms of large, faceted crystals grown from solutions

Here, the paper describes optical techniques and methods used to study growth mechanisms and phenomena occurring on multiple faces of real bulk crystals grown from aqueous and organic solutions. Using examples of ionic KDP and molecular trans-stilbene crystals, it shows how in-situ and ex-situ surface observations can be applied to identify growth structures characteristic for the dislocation, two-dimensional (2D), and 3D cluster deposition mechanisms. Results are analyzed and applied to understanding of defects origins and methods for their elimination in practical growth of large, high optical quality crystals.

2D growth mechanism↗
DOE OSTI2022

Using the Black Carbon Particle Mixing State to Characterize the Lifecycle of Biomass Burning Aerosols

The lifecycle of black carbon (BC)-containing particles from biomass burns is examined using aircraft and surface observations of BC mixing state for plume ages from ~15 minutes to 10 days. Because BC is non-volatile and chemically inert, changes in the mixing state of BC-containing particles are driven solely by changes in particle coating, which is mainly secondary organic aerosol (SOA). The coating mass initially increases rapidly (k = 0.84 hr-1), then remains growth relatively constant for 1 to 2 days as plume dilution no longer supports further growth, then decreases slowly until only ~30 % of the maximum coating mass remains after 10 days, (kloss = 0.011 hr-1). The mass ratio of coating-to-core for a BC-containing particle with a 100 nm mass- equivalent diameter BC core reaches a maximum of ~20 after a few hours and drops to ~5 after 10 days of aging. The initial increase in coating mass can be used to determine SOA formation rates. The slow loss of coating material, not captured in global models, comprises the dominant fraction of the lifecycle of these particles. Coating-to-core mass ratios of BC particles in the stratosphere are much greater than those in the free troposphere indicating a different lifecycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ligand-Induced Size-Dependent Circular Dichroism in Quantum Dots

Recent experiments have probed the chiral properties of semiconductor nanocrystal (NC) quantum dots (QDs), but understanding the circular dichroism line shape, excitonic features, and chirality induction mechanism remains a challenge. We propose an atomistic pseudopotential method to model chiral ligand passivated QDs, computing circular dichroism (CD) spectra for CdSe QDs (2.6-3.8 nm). We find strong agreement between calculated and measured line shapes, predicting consistent bisignate line shapes with decreasing CD magnitude as size increases. Further, our analysis reveals the origin of bisignate line shapes, arising from nondegenerate excitons with opposing angular momenta. We also explore the impact of chiral ligand orientation on QD surfaces, observing changes in the optical activity magnitude and sign. This orientation sensitivity offers the means to distinguish ordered from disordered ligand configurations, facilitating the study of order-disorder transitions at ligand-QD interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Adhesively Bonded Copper and Polyimide for Flexible Printed Circuit Boards: A Data-Driven Degradation Study Under Accelerated Aging

The long-term durability of polyimide laminated adhesive based flexible printed circuit boards (PCBs) is critical to the microelectronics industry. The interface of the adhesive layer binding the polyimide and copper layers is particularly vulnerable since decreased bonding strength can lead to deadhesion, performance loss, and premature device failure. Here, in this study, adhesive-bonded polyimide and copper samples are subjected to 1000 h of damp heat exposure (85 °C/85%RH) to evaluate the impact of moisture and copper inclusion on the adhesive bonding performance. Significant copper diffusion through the adhesive layer is observed, which, combined with moisture uptake, is found to substantially weaken interfacial adhesion in copper-containing samples, thereby reducing device lifetime. Observed surface and bulk material changes are correlated to adhesion strength using the network structural equation modeling (netSEM) approach, resulting in a robust predictive model of performance degradation based on measurable materials properties.

Chemistry↗
DOE OSTI2023

Estimating Permafrost Distribution Using Co‐Located Temperature and Electrical Resistivity Measurements

Abstract Assessing the lateral and vertical extent of permafrost is critical to understanding the fate of Arctic ecosystems under climate change. Yet, direct measurements of permafrost distribution and temperature are often limited to a small number of borehole locations. Here, we assess the use of co‐located shallow temperature and electrical resistivity tomography (ERT) measurements to estimate at high‐resolution the distribution of permafrost in three watersheds underlain by discontinuous permafrost. Synthetic modeling shows that co‐located temperature and ERT measurements allow for supervised classification schemes that provide 60% higher accuracy compared to unsupervised methods. Linking resistivity and size of the identified permafrost bodies to surface observations, we show that tall vegetation (>0.5 m) and gentle slopes (<15°) are related to warmer and smaller permafrost bodies, and a more frequent occurrence of taliks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Association Between Cloud Droplet Number over the Summer Southern Ocean and Air Mass History

The cloud properties and governing processes in Southern Ocean marine boundary layer clouds have emerged as a central issue in understanding the Earth's climate sensitivity. While our understanding of Southern Ocean cloud feedbacks have evolved in the most recent climate model intercomparison, the background properties of simulated summertime clouds in the Southern Ocean are not consistent with measurements due to known biases in simulating cloud condensation nuclei concentrations. This paper presents several case studies collected during the Capricorn 2 and Marcus campaigns held aboard Australian research vessels in the Austral Summer of 2018. Combining the surface–observed cases with MODIS data along forward and backward air mass trajectories, we demonstrate the evolution of cloud properties with time. These cases are consistent with multi–year statistics showing that long trajectories of air masses over the Antarctic ice sheet are critical to creating high droplet number clouds in the high latitude summer Southern Ocean. We speculate that secondary aerosol production via the oxidation of biogenically derived aerosol precursor gasses over the high actinic flux region of the high latitude ice sheets is fundamental to maintaining relatively high droplet numbers in Southern Ocean clouds during Summer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Lake Biogeochemistry Model for Global Methane Emissions: Model Development, Site‐Level Validation, and Global Applicability

Abstract Lakes are important sentinels of climate change and may contribute over 30% of natural methane (CH 4 ) emissions; however, no earth system model (ESM) has represented lake CH 4 dynamics. To fill this gap, we refined a process‐based lake biogeochemical model to simulate global lake CH 4 emissions, including representation of lake bathymetry, oxic methane production (OMP), the effect of water level on ebullition, new non‐linear CH 4 oxidation kinetics, and the coupling of sediment carbon pools with in‐lake primary production and terrigenous carbon loadings. We compiled a lake CH 4 data set for model validation. The model shows promising performance in capturing the seasonal and inter‐annual variabilities of CH 4 emissions at 10 representative lakes for different lake types and the variations in mean annual CH 4 emissions among 106 lakes across the globe. The model reproduces the variations of the observed surface CH 4 diffusion and ebullition along the gradients of lake latitude, depth, and surface area. The results suggest that OMP could play an important role in surface CH 4 diffusion, and its relative importance is higher in less productive and/or deeper lakes. The model performance is improved for capturing CH 4 outgassing events in non‐floodplain lakes and the seasonal variability of CH 4 ebullition in floodplain lakes by representing the effect of water level on ebullition. The model can be integrated into ESMs to constrain global lake CH 4 emissions and climate‐CH 4 feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Revealing CO 2 dissociation pathways at vicinal copper (997) interfaces

Size- and shape-tailored copper (Cu) nanocrystals can offer vicinal planes for facile carbon dioxide (CO 2 ) activation. Despite extensive reactivity benchmarks, a correlation between CO 2 conversion and morphology structure has not yet been established at vicinal Cu interfaces. Herein, ambient pressure scanning tunneling microscopy reveals step-broken Cu nanocluster evolutions on the Cu(997) surface under 1 mbar CO 2 (g). The CO 2 dissociation reaction produces carbon monoxide (CO) adsorbate and atomic oxygen (O) at Cu step-edges, inducing complicated restructuring of the Cu atoms to compensate for increased surface chemical potential energy at ambient pressure. The CO molecules bound at under-coordinated Cu atoms contribute to the reversible Cu clustering with the pressure gap effect, whereas the dissociated oxygen leads to irreversible Cu faceting geometries. Synchrotron-based ambient pressure X-ray photoelectron spectroscopy identifies the chemical binding energy changes in CO-Cu complexes, which proves the characterized real-space evidence for the step-broken Cu nanoclusters under CO(g) environments. Our in situ surface observations provide a more realistic insight into Cu nanocatalyst designs for efficient CO 2 conversion to renewable energy sources during C 1 chemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Spectral function of the $J_1$ – $J_2$ Heisenberg model on the triangular lattice

Spectral probes, such as neutron scattering, are crucial for characterizing excitations in quantum many-body systems and the properties of quantum materials. Among the most elusive phases of matter are quantum spin liquids, which have no long-range order even at zero temperature and host exotic fractionalized excitations with nontrivial statistics. These phases can occur in frustrated quantum magnets, such as the paradigmatic Heisenberg model with nearest- and next-nearest-neighbor exchange interactions on the triangular lattice, the so-called $J_1$ – $J_2$ model. In this work, we compute the spectral function using large-scale matrix product state simulations across the three different phases of this model's phase diagram, including a quantum spin-liquid phase at intermediate $J_2$/$J_1$. Despite a plethora of theoretical and experimental studies, the exact nature of this phase is still contested, with the dominant candidates being a gapped $\mathbb{Z}_2$, a gapless U(1) Dirac, and a spinon Fermi surface quantum spin-liquid state. We find a V-shaped spectrum near the center of the Brillouin zone ($Γ$ point), a key signature of a spinon Fermi surface, observed in prior neutron scattering experiments. However, we find a small gap near the $Γ$ point, ruling out such a phase. Furthermore, we find localized gapless excitations at the corner of the Brillouin zone boundary (K point) and the middle of the edge of the Brillouin zone boundary (M point), ruling out the gapped $\mathbb{Z}_2$ spin-liquid phase. Here, our results imply that the intermediate spin-liquid phase is a gapless $U(1)$ Dirac spin liquid, and provide clear signatures to detect this phase in future neutron scattering experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Deep learning Hamiltonians from disordered image data in quantum materials

The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length- and timescales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimensionality, and disorder—a spatial encoding of the Hamiltonian driving the pattern formation. Image recognition techniques from machine learning are an excellent tool for interpreting information encoded in the spatial relationships in such images. Here, we develop a deep learning framework for using the rich information available in these spatial correlations in order to discover the underlying Hamiltonian driving the patterns. We first vet the method on a known case, scanning near-field optical microscopy on a thin film of V⁢O 2 . We then apply our trained convolutional neural network architecture to new optical microscope images of a different V⁢O 2 film as it goes through the metal-insulator transition. We find that a two-dimensional Hamiltonian with both interactions and random field disorder is required to explain the intricate, fractal intertwining of metal and insulator domains during the transition. This detailed knowledge about the underlying Hamiltonian paves the way for using the model to control the pattern formation via, e.g., tailored hysteresis protocols. Finally, we also introduce a distribution-based confidence measure on the results of a multilabel classifier, which does not rely on adversarial training. In addition, we propose a machine-learning-based criterion for diagnosing a physical system's proximity to criticality.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗