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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The Role of Snowmelt Temporal Pattern in Flood Estimation for a Small Snow‐Dominated Basin in the Sierra Nevada

Abstract Prior research confirmed the substantial bias from using precipitation‐based intensity‐duration‐frequency curves (PREC‐IDF) in design flood estimates and proposed next‐generation IDF curves (NG‐IDF) that represent both rainfall and snow processes in runoff generation. This study improves the NG‐IDF technology for a snow‐dominated test basin in the Sierra Nevada. A well‐validated physics‐based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), is used to continuously simulate snowmelt and streamflow that are used as benchmark data sets to systematically assess the NG‐IDF technology. We find that, for the studied small snow‐dominated basin, the use of standard rainfall hyetographs in the NG‐IDF technology leads to substantial underestimation of design floods. Thus, we propose probabilistic hyetographs that can represent unique patterns of events with different underlying mechanisms. For the test basin where flooding events are generated entirely by snowmelt, we develop a hyetograph that characterizes snowmelt temporal patterns, which greatly improves the performance of NG‐IDF technology in design flood estimates. In contrast to the standard rainfall hyetographs characterized by a symmetrically peaked, bell‐shaped curve, the snowmelt hyetograph displays a more rapid rise (i.e., greater intensity) and a distinct diurnal pattern influenced by solar energy input. The results also show that the uncertainty of hyetography plays an important role in design flood estimation and can have important implications for future flood projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantification of grain rotation in bulk nanostructured copper via in situ heating white beam synchrotron X-ray diffraction

Grain rotation during microstructural relaxation under heating is conventionally studied extensively through transmission electron microscopy and simulations. However, there is a shortage in examining grain rotations at larger length and volume scales in bulk materials. It is critical to understand the thermal stability of bulk nanostructured metals, since those enhanced mechanical properties have been well recognized. Here, this study aimed to employ a white beam microdiffraction X-ray technique under in situ heating from 300 K to 1073 K at 12 K/min on a nanostructured copper processed by high-pressure torsion, yielding an initial grain size of ∼260 nm before the heating. Evaluation across a wide range of temperatures reveals transition temperatures associated with microstructural relaxation processes. By tracking separate Laue diffraction peaks stemming from individual grains, changes in their orientations can be estimated and quantified. This approach becomes particularly effective when the number of grains within the probed volume is reasonably small. At temperatures above 940 K, about 7.5 % of Cu grains are rotating at speeds of ∼2 × 10 –3 °/s. The radial direction of the disk specimen is found to be the preferred direction of grain rotation, with the rotation axis along the shear direction. Further analysis found no correlation between peak intensity and peak size, related to grain sizes and deviatoric strain, respectively, with rotation conditions or speed. These findings demonstrate that the diffraction technique utilizing white beam X-rays is a complementary and novel characterization tool for understanding microstructural evolution, especially in grain rotations, of bulk crystalline materials.

Grain orientation↗
DOE OSTI2026

Hydropower potential derived from streamflow extremes for Alaska, USA

Alaska is an expansive region known for its abundant natural resources, including thousands of miles of streams and rivers. These rivers represent potential opportunities for future hydropower development that could provide reliable energy supply for local communities. There is limited long-term high temporal resolution streamflow data available for the region, making data-driven estimates of potential hydropower and its variability across the state challenging. This study provides a novel data-driven approach for hydropower capacity estimation across Alaska. We use supervised machine learning to develop a relationship between the daily and peak flow duration curves in order to augment the size of our dataset from 44 sites to 67 sites. We perform a stochastic hydropower estimation across the 67 sites and identify approximately 1000 MW of total potential hydropower capacity distributed across these sites. Our study provides the first step towards more comprehensive hydropower estimation for this critical region, highlighting the need for future work integrating high-resolution spatial data, community needs, and economic constraints in estimates of potential hydropower development in Alaska.

Hydropower↗
DOE OSTI2023

Electrochemical properties of Gd(III) ions in LiCl-KCl-GdCl 3 at 723–1023 K

Electrochemical behavior of Gd(III) ions in molten LiCl-KCl-GdCl 3 was investigated at 723–1023 K via cyclic voltammetry using tungsten as a working electrode, Gd-Bi (mole fraction, x Gd = 0.16) as a reference electrode, and Gd-Bi (x Gd = 0.02) as a counter electrode. A single reduction–oxidation wave was observed, confirming a single-step, 3-electron transfer Gd(III)/Gd transition. The cathodic peak potential exhibited minimal change (<13 mV) over a wide range of scan rates (0.05–0.30 V s –1 ), indicating facile charge transfer kinetics (i.e., a reversible electrode process). A nucleation overpotential associated with solid Gd deposition was observable at low temperatures (T < 823 K). The mass transport properties of Gd(III) ions were estimated using the Berzins and Delahay relation based on diffusion-limiting peak current. The diffusivity values were determined to be D Gd(III) = 0.5–2.7 × 10 –5 cm 2 s –1 at 723–1023 K with an associated activation energy of E a = 33.9 (±1.0) kJ mol –1 . Finally, the two-phase [liquid + GdBi] Gd-Bi alloy reference electrode experienced less than 0.5 mV of drift over 5 days of repeated electrochemical measurements, indicating high stability.

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

Deriving Stable Peak Models to Fit Complex XPS Data From Cu Contaminated Pt Electrocatalysts

X-ray Photoelectron Spectroscopy spectra peak models, designed to partition photoemission signals emanating from different elements or chemical states within an atom, are fitted to data limited to an energy interval over which inelastically scattered photoemission signal can be estimated. While the choice of background approximation and line shapes of components to the peak model requires careful consideration, the energy interval used to define the data to which the peak model is optimized has a significant impact on the final peak model. The relationship between the background intensity and data intensity at the start and end of the energy interval dictates the line shapes used in the peak model. In this work, we devise a method to peak fit a complex overlapping Cu 3p and Pt 4f XPS peak structure to perform the elemental quantification. We first use an Al 2s peak to illustrate how background curves approach data at the limits of the energy interval over which the background is defined, influencing the analysis of XPS spectra. Next, we demonstrate the nature of interactions between specific line shapes (Voigt and pseudo-Voigt profiles) suitable for photoemission peaks and a specific background curve (Shirley) and a peak model is presented that includes components to the peak model that accommodates background intensity during fitting of the peak model to data. The peak model allowed for quantification of the contributions of Pt 4f peaks emanating from the substrate that exhibits strong asymmetry in the presence of the inhomogeneously distributed Cu species, mostly of Lorentzian character.

XPS↗
DOE OSTI2025

The value of integrating a geothermal district heating system into a microgrid

As electrical grids increasingly rely on variable renewable energy, maintaining reliability and cost efficiency becomes more complex. To address these challenges, this study analyzed the integration of geothermal district heating as a grid-responsive thermal resource within a microgrid in Tuttle, Oklahoma. Building energy modeling using EnergyPlus estimated annual district heating demand at 2.9 GWh, with a peak load of 2.8 MW th . Techno-economic analyses were conducted to meet the heating demand under three geothermal scenarios, varying by production depth, flow rate, and thermal output, each supplemented by natural gas peaking boilers. In parallel, equivalent electrical load profiles were developed using typical coefficients of performance (COPs) for air-source heat pumps and electric boilers to establish an electrified baseline scenario. A complete end-use electrical load profile was also developed for the microgrid using Cambium dataset. The modeling results demonstrated reliable and economic operation of the geothermal systems over 30 years, with COPs ranging from 2.6 to 8.9 and the lowest levelized heating cost at $\$$54.6/MWh. Geothermal integration reduced electricity consumption by up to 94.7 % compared to the non-geothermal base case, yielding annual energy savings of up to $\$$803 k. Avoided grid costs ranged from $\$$65 k–$\$$147 k per year, with individual events avoiding up to $\$$4,863 per hour. Grid-responsive operation further reduced wholesale energy costs by 53–56 %. These findings demonstrate geothermal heating, traditionally treated as a non-grid-responsive thermal resource, can be reconfigured to support dynamic grid services, offering a scalable pathway to enhance reliability and reduce costs in renewable-rich microgrids and district heating networks.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Concurrent multi-peak Bragg coherent x-ray diffraction imaging of 3D nanocrystal lattice displacement via global optimization

Abstract In this paper we demonstrated a method to reconstruct vector-valued lattice distortion fields within nanoscale crystals by optimization of a forward model of multi-reflection Bragg coherent diffraction imaging (MR-BCDI) data. The method flexibly accounts for geometric factors that arise when making BCDI measurements, is amenable to efficient inversion with modern optimization toolkits, and allows for globally constraining a single image reconstruction to multiple Bragg peak measurements. This is enabled by a forward model that emulates the multiple Bragg peaks of a MR-BCDI experiment from a single estimate of the 3D crystal sample. We present this forward model, we implement it within the stochastic gradient descent optimization framework, and we demonstrate it with simulated and experimental data of nanocrystals with inhomogeneous internal lattice displacement. We find that utilizing a global optimization approach to MR-BCDI affords a reliable path to convergence of data which is otherwise challenging to reconstruct.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Inferring relative dose-dependent color center populations in proton irradiated thoria single crystals using optical spectroscopy

In this work, we have utilized photoluminescence spectroscopy and optical ellipsometry to characterize the dose-dependence of the photoluminescence emission intensity and changes in optical absorption of thoria single crystals subject to irradiation with energetic protons at room- and elevated-temperatures. The photoluminescence peaks and the optical absorption bands are attributed to creation of new electronic states emerging from defects resulting from displacement damage. These bands are most likely associated with electrons trapped at the oxygen vacancy sites similar to color centers formed in other irradiated oxides and halides. Our experimental observations are supported by a standard density functional theory calculation of the electronic structure in pristine and oxygen vacancy-bearing thoria crystals. The dose-dependence of the intensity of the photoluminescence peaks is used to parameterize a rate theory model that estimates the concentration of color centers in the irradiated crystals. This parameterization provides optimized migration barrier parameters for oxygen interstitials and vacancies that simultaneously capture the optical response of the crystals irradiated at room- and elevated-temperature. These optical spectroscopy techniques offer a promising pathway to characterize the population of color centers formed at the sites of oxygen anion vacancies, particularly in irradiated nuclear fuels, where atomic-level defects cannot be readily imaged using electron microscopy. When combined with other direct and indirect characterization tools, our approach can provide new insight into defect formation and accumulation in energy materials over single atomic to extended length scales.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Real-space atomic dynamics in metallic liquids investigated by inelastic neutron scattering

Understanding the dynamics of liquids at the atomic level remains a major challenge. Even though viscosity is one of the most fundamental properties of liquids, its atomistic origin is not fully elucidated. Through inelastic neutron scattering experiment on levitated metallic liquid droplets, the time-dependent pair correlation function, the Van Hove function, was determined for Z⁢r 50 ⁢C⁢u 50 and Z⁢r 80 ⁢P⁢t 20 liquids at various temperatures. The time for change in local atomic connectivity, τ LC , which is the timescale of atomic bond cutting and forming, is estimated based on the exponential decay of the nearest neighbor peak of the Van Hove function. At high temperatures above the crossover temperature T A , τ LC is equal to the Maxwell relaxation time, τ M =η/G ∞ , where η is the macroscopic shear viscosity and G ∞ is the high-frequency shear modulus. Below T A the ratio of τ M /τ LC increases with decreasing temperature, indicating increased atomic cooperativity as predicted by molecular dynamics simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

An in-situ view cell system for investigating swelling behavior of elastomers upon high-pressure hydrogen exposure

The transition to hydrogen as a clean and efficient energy carrier is impeded by challenges in the compatibility of hydrogen with materials used within hydrogen infrastructure. Elastomers, crucial in sealing components, often exhibit premature failures in high-pressure hydrogen environments due to excessive swelling. This study employs an innovative in-situ view cell system to assess the swelling behavior of hydrogenated nitrile butadiene rubber (HNBR) under various hydrogen conditions. The system, designed to withstand pressures up to 96.5 MPa, incorporates Digital Image Correlation (DIC) for strain measurements and volume estimation. Results reveal non-linear volume increases during depressurization, challenging conventional assumptions. Furthermore, investigations into peak hydrogen pressures and pressure-holding scenarios during decompression highlight complex swelling trends. The introduction of a novel computer vision (CV) method enhances precision in volume estimation, overcoming DIC limitations. The study provides insights into mitigating elastomer swelling, crucial for developing robust materials to support future hydrogen-driven energy systems.

Elastomer↗
DOE OSTI2023

Hours-long Near-UV/Optical Emission from Mildly Relativistic Outflows in Black Hole–Neutron Star Mergers

The ongoing LIGO–Virgo–KAGRA observing run O4 provides an opportunity to discover new multimessenger events, including binary neutron star (BNS) mergers such as GW170817 and the highly anticipated first detection of a multimessenger black hole–neutron star (BH–NS) merger. While BNS mergers were predicted to exhibit early optical emission from mildly relativistic outflows, it has remained uncertain whether the BH–NS merger ejecta provides the conditions for similar signals to emerge. We present the first modeling of early near-ultraviolet/optical emission from mildly relativistic outflows in BH–NS mergers. Adopting optimal binary properties, a mass ratio of q = 2, and a rapidly rotating BH, we utilize numerical relativity and general relativistic magnetohydrodynamic (GRMHD) simulations to follow the binary's evolution from premerger to homologous expansion. We use an M1 neutrino transport GRMHD simulation to self-consistently estimate the opacity distribution in the outflows and find a bright near-ultraviolet/optical signal that emerges due to jet-powered cocoon cooling emission, outshining the kilonova emission at early time. The signal peaks at an absolute magnitude of ~–15 a few hours after the merger, longer than previous estimates, which did not consider the first principles–based jet launching. By late 2024, the Rubin Observatory will have the capability to track the entire signal evolution or detect its peak up to distances of ≳1 Gpc. In 2026, ULTRASAT will conduct all-sky surveys within minutes, detecting some of these events within ~200 Mpc. The BH–NS mergers with higher mass ratios or lower BH spins would produce shorter and fainter signals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Flux and estimated spectra from a low-intensity laser-driven X-ray source

Laser-driven X-rays as probes for high-energy-density physics spans an extremely large parameter space with laser intensities varying by 8 orders of magnitude. We have built and characterized a soft X-ray source driven by a modest intensity laser of 4 × 10 13 W/cm 2 . Emitted X-rays were measured by diamond radiation detectors and a filtered soft X-ray camera. A material-dependence study on Al, Ti, stainless steel alloy 304, Fe, Cu and Sn targets indicated 5-μm-thick Cu foils produced the highest X-ray yield. X-ray emission in the laser direction and emission in the reverse direction depend strongly on the foil material and the thickness due to the opacity and hydrodynamic disassembly time. The time-varying X-ray signals are used to measure the material thinning rate and is found to be ~1.5 μm/ns for the materials tested implying thermal temperature around 0.6 eV. The X-ray spectra from Cu targets peaks at ~2 keV with no emission >4 keV and was estimated using images with eight different foil filters. One-dimensional hydrodynamic and spectral calculations using HELIOS-CR provide qualitative agreement with experimental results. Modest intensity lasers can be an excellent source for nanosecond bursts of soft X-rays.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Statistical Correlation of Heliostat Pointing Deviation With Wind

This work was carried out as part of the Heliostat Consortium (HelioCon) Field Deployment subtask with the aim to develop a reduced order model framework for correlating wind speed and pointing deviation of a heliostat facet. There are only sparse field measurements of heliostat pointing deviations and accompanying wind conditions published in the literature. Heliostat test standards, such as IEC 62862-4-3, propose a suite of tests including laser pointing repeatability at wind speeds below 4 m/s, and provide technical requirements for heliostat slope and tracking deviations in coarse average wind speed bins of 4 m/s, 6 m/s, and 8 m/s. In addressing the gap of the variation of heliostat pointing deviation with wind speed, field measurements of laser pointing on a grid target and wind conditions were analyzed in this study at the Third-Party Metrology Platform at the National Laboratory of the Rockies (NLR) Flatirons Campus. Horizontal pointing deviations were found to follow a logarithmic relationship with peak wind speed, whereas vertical pointing deviations follow an exponential relationship with peak wind speed. Both horizontal and vertical pointing deviations also follow a second order polynomial relationship, as expected from the proportionality of elastic loads and deformations with the square of wind speed. The results indicate that heliostat facet pointing deviations in the vertical direction increase at a faster rate than in the horizontal direction with increasing wind speed over the tested range, however these are dependent on the heliostat structural design. Next steps are recommended for additional field measurements to confirm a linear relationship of pointing deviation with applied moment on a heliostat facet, and to distinguish between gravity-induced and wind-induced pointing deviations at different elevation angles. The derived correlations in the preliminary analysis in this report serve as a case study for heliostat developers and plant operators to estimate the wind-induced pointing deviations and their variation with peak gust wind speed. Next steps in future work would recommend higher resolution and longer duration datasets for different elevation angles and wind directions to reduce uncertainties and variance of collected laser beam spot data and their correlations with bin-averaged wind speed.

17 WIND ENERGY↗
DOE OSTI2025

Precision Local Burnup Assessment Through Dynamic Peak Fitting in Atom Probe Tomography for Depleted, Enriched, and Irradiated Metallic and Ceramic Fuels

Abstract Burnup estimation in nuclear fuels is vital for evaluating fuel performance, transportation, and safe fuel storage. Accurate assessments of burnup from service period and spent fuels involve tracking the consumption of fissile isotopes of uranium (U) offering a direct insight into energy changes within the fuels especially for thermal spectrum reactors. In current approach, mass spectroscopic technique in atom probe tomography (APT) is utilized for accurate quantification of U isotopes. Quantification of U peaks in mass spectrum is performed on asymmetric shapes due to delayed signals, known as thermal tails, particularly for poorly conducting samples analyzed in laser mode. In this study, we introduce a novel quantification tool for isotopic analysis from APT datasets by developing a fitting algorithm based on shapes of the peaks. A MATLAB-based dynamic peak fitting toolbox is developed and designed to adapt to various peak shapes, ensuring accurate quantification of U isotopes. The effectiveness of this approach is demonstrated in standard Ni-Cr sample, depleted and enriched U samples, and U-based fuels with different burnup levels. The viability of this approach for isotopic quantification is demonstrated on both metallic and ceramic fuels.

Burnup↗
DOE OSTI2026

Deep Learning Reconstruction of Daily Soil CO 2 Efflux Reveals Biogeochemical Insights and Reduces Annual Estimate Uncertainty Despite Limited Daily Predictability

Soil CO 2 efflux is commonly measured monthly or seasonally, leaving daily dynamics poorly resolved and contributing to global estimation uncertainty. We trained a single Long Short-Term Memory (LSTM) model to predict daily soil CO 2 efflux across 82 globally distributed sites in COSORE, with 0.2%–46.9% daily data coverage from 2003 to 2020. Despite using far fewer sites than are typically used to train a single deep learning model, with observations biased toward temperate mesic sites, the LSTM model performed well at approximately one-third of sites, reconstructed nearly 2 decades of daily efflux, and outperformed commonly used approaches for estimating daily efflux when applied to the same data set. Performance was weakest at pronounced peaks and troughs and at non-temperate sites with <1.5 years of observations and irregular data patterns. Nevertheless, annual efflux from reconstructed daily data had <40% error even at underperforming sites, substantially improving estimates derived from monthly and seasonal sampling (maximum errors of 95% and 136%, respectively). Temperature sensitivity (Q 10 ) estimated from reconstructed daily predictions closely matched estimates from daily observations, whereas Q 10 values derived from monthly or seasonal observations deviated substantially, suggesting that coarse temporal sampling may contribute to uncertainty in reported Q 10 values. Consistent daily reconstructions further enabled trend analyses for well-performing, predominantly temperate sites and showed increasing soil CO 2 efflux at most sites from 2003 to 2020, with more variable summer trends. Despite limitations, these results demonstrate the potential of LSTM models to reconstruct daily soil CO 2 efflux and reduce estimation uncertainties from sparse observations.

Smykalov, Valerie [Pennsylvania State University, ↗
DOE OSTI2022

Valuing Residential Energy Efficiency: Analysis for a Prototypical Southeastern Utility [Slides]

The increasing amount of variable renewable energy resources and shifts towards more end-use and vehicle electrification suggests profound changes to power system planning and operation. Specifically, renewable energy is expected to shift net peak demand from late afternoon to early evening and end-use electrification may significantly increase winter peak demand. Residential energy efficiency is likely to align well with these shifts as it tends to produce savings in the early evening (e.g., from lighting measures) and coincident with heating loads (e.g., from envelope and space conditioning measures). Despite the opportunity to decrease system costs and emissions, residential energy efficiency is often limited by static valuation methods and its economic potential is considerably less than its technical potential. Using hourly residential energy efficiency characterizations, utility program cost data, and a capacity expansion model, we estimate the benefits of residential energy efficiency for a prototypical, summer-peaking utility in the Southeastern region. We first establish the cost-effective residential energy efficiency portfolio through “competition” with supply-side resources in a forward-looking capacity expansion model. Importantly, we then evaluate several scenarios intended to drive an increasing amount of cost-effective residential energy efficiency through measure cost reductions, increased customer adoption, policy goals (e.g., carbon price), and delivery of an integrated package of measures. The results quantify total system cost and emissions reductions, fossil-fuel plant retirements, and peak demand reductions. Results suggest the design and prioritization of policies and programs to access the untapped amount of cost-effective residential energy efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Reviews and syntheses: Opportunities for robust use of peak intensities from high-resolution mass spectrometry in organic matter studies

Abstract. Earth's biogeochemical cycles are intimately tied to the biotic and abiotic processing of organic matter (OM). Spatial and temporal variations in OM chemistry are often studied using direct infusion, high-resolution Fourier transform mass spectrometry (FTMS). An increasingly common approach is to use ecological metrics (e.g., within-sample diversity) to summarize high-dimensional FTMS data, notably Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). However, problems can arise when FTMS peak-intensity data are used in a way that is analogous to abundances in ecological analyses (e.g., species abundance distributions). Using peak-intensity data in this way requires the assumption that intensities act as direct proxies for concentrations. Here, we show that comparisons of the same peak across samples (within-peak) may carry information regarding variations in relative concentration, but comparing different peaks (between-peak) within or between samples does not. We further developed a simulation model to study the quantitative implications of using peak intensities to compute ecological metrics (e.g., intensity-weighted mean properties and diversity) that rely on information about both within-peak and between-peak shifts in relative abundance. We found that, despite analytical limitations in linking concentration to intensity, ecological metrics often perform well in terms of providing robust qualitative inferences and sometimes quantitatively accurate estimates of diversity and mean molecular characteristics. We conclude with recommendations for the robust use of peak intensities for natural organic matter studies. A primary recommendation is the use and extension of the simulation model to provide objective guidance on the degree to which conceptual and quantitative inferences can be made for a given analysis of a given dataset. Broad use of this approach can help ensure rigorous scientific outcomes from the use of FTMS peak intensities in environmental applications.

54 ENVIRONMENTAL SCIENCES↗