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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The Coastal Carbon Library and Atlas: Open source soil data and tools supporting blue carbon research and policy

Abstract Quantifying carbon fluxes into and out of coastal soils is critical to meeting greenhouse gas reduction and coastal resiliency goals. Numerous ‘blue carbon’ studies have generated, or benefitted from, synthetic datasets. However, the community those efforts inspired does not have a centralized, standardized database of disaggregated data used to estimate carbon stocks and fluxes. In this paper, we describe a data structure designed to standardize data reporting, maximize reuse, and maintain a chain of credit from synthesis to original source. We introduce version 1.0.0. of the Coastal Carbon Library, a global database of 6723 soil profiles representing blue carbon‐storing systems including marshes, mangroves, tidal freshwater forests, and seagrasses. We also present the Coastal Carbon Atlas, an R‐shiny application that can be used to visualize, query, and download portions of the Coastal Carbon Library. The majority (4815) of entries in the database can be used for carbon stock assessments without the need for interpolating missing soil variables, 533 are available for estimating carbon burial rate, and 326 are useful for fitting dynamic soil formation models. Organic matter density significantly varied by habitat with tidal freshwater forests having the highest density, and seagrasses having the lowest. Future work could involve expansion of the synthesis to include more deep stock assessments, increasing the representation of data outside of the U.S., and increasing the amount of data available for mangroves and seagrasses, especially carbon burial rate data. We present proposed best practices for blue carbon data including an emphasis on disaggregation, data publication, dataset documentation, and use of standardized vocabulary and templates whenever appropriate. To conclude, the Coastal Carbon Library and Atlas serve as a general example of a grassroots F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data effort demonstrating how data producers can coordinate to develop tools relevant to policy and decision‐making.

Holmquist, James R.↗
DOE OSTI2023

2023 Status Report - Fiberboard Properties and Degradation Rates for Storage of 9975 Shipping Package in KAC

Thermal, mechanical, and physical properties have been measured on fiberboard samples aging for up to 18 years at accelerated conditions. The aging environments include elevated temperature up to 250 ºF (the maximum allowed service temperature for fiberboard in 9975 packages) and elevated humidity. Accelerated aging results have been analyzed and used to build aging models. Correlations relating several properties (thermal conductivity, energy absorption, weight, dimensions, and density) to their rate of change in potential storage environments have been developed. Combined with an estimate of the actual conditions the fiberboard experiences in K-Area Complex (KAC), these models allow development of service life predictions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

2024 Status Report - Fiberboard Properties and Degradation Rates for Storage of 9975 Shipping Package in KAC

Fiberboard assemblies in the 9975 package were identified as susceptible to aging and degradation under long-term storage conditions. Thermal, mechanical, and physical properties have been measured on fiberboard samples aging for up to 19 years at accelerated conditions. The aging environments include elevated temperature up to 250 ºF (the maximum allowed service temperature for fiberboard in 9975 packages) and elevated humidity. Accelerated aging results have been analyzed and used to build aging models. Correlations relating several properties (thermal conductivity, energy absorption, weight, dimensions, and density) to their rate of change in potential storage environments have been developed. Combined with an estimate of the actual conditions the fiberboard experiences in K-Area Complex (KAC), these models allow development of service life predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Impact of Self-shielding Minihalos on the Lyα Forest at High Redshift

Abstract Dense gas in minihalos with masses of 10 6 −10 8 M ⊙ can shield themselves from reionization for ∼100 Myr after being exposed to the UV background. These self-shielded systems, often unresolved in cosmological simulations, can introduce strong absorption in quasar spectra. This paper is the first systematic study on the impact of these systems on the Ly α forest. We first derive the H i column density profile of photoevaporating minihalos by conducting 1D radiation–hydrodynamics simulations. We utilize these results to estimate the Ly α opacity from minihalos in a large-scale simulation that cannot resolve self-shielding. When the ionization rate of the background radiation is 0.03 × 10 −12 s −1 , as expected near the end of reionization at z ∼ 5.5, we find that the incidence rate of damped Ly α absorbers increases by a factor of ∼2−4 compared to at z = 4.5. The Ly α flux is, on average, suppressed by ∼3% of its mean due to minihalos. The absorption features enhance the 1D power spectrum up to ∼5% at k ∼ 0.1 h Mpc −1 (or 10 −3 km −1 s), which is comparable to the enhancement caused by inhomogeneous reionization. The flux is particularly suppressed in the vicinity of large halos along the line-of-sight direction at separations of up to 10 h −1 Mpc at r ⊥ ≲ 2 h −1 Mpc. However, these effects become much smaller for higher ionizing rates (≳0.3 × 10 −12 s −1 ) expected in the post-reionization Universe. Our findings highlight the need to consider minihalo absorption when interpreting the Ly α forest at z ≳ 5.5. Moreover, the sensitivity of these quantities to the ionizing background intensity can be exploited to constrain the intensity itself.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Reduced Atmospheres of Post-impact Worlds: The Early Earth

Impacts may have had a significant effect on the atmospheric chemistry of the early Earth. Reduced phases in the impactor (e.g., metallic iron) can reduce the planet's H 2 O inventory to produce massive atmospheres rich in H 2 . While previous studies have focused on the interactions between the impactor and atmosphere in such scenarios, we investigate two further effects: (1) the distribution of the impactor's iron inventory during impact between the target interior, target atmosphere, and escaping the target; and (2) interactions between the post-impact atmosphere and the impact-generated melt phase. We find that these two effects can potentially counterbalance each other, with the melt–atmosphere interactions acting to restore reducing power to the atmosphere that was initially accreted by the melt phase. For a ~10 22 kg impactor, when the iron accreted by the melt phase is fully available to reduce this melt, we find an equilibrium atmosphere with H 2 column density ~10 4 moles cm –2 (pH 2 ~ 120 bars, X H2 ~ 0.77), consistent with previous estimates. However, when the iron is not available to reduce the melt (e.g., sinking out in large diameter blobs), we find significantly less H 2 (7 × 10 2 – 5 × 10 3 moles cm –2 , pH 2 ≲ 60 bars, X H2 ≲ 0.41). These lower H 2 abundances are sufficiently high that species important to prebiotic chemistry can form (e.g., NH 3 , HCN), but sufficiently low that the greenhouse heating effects associated with highly reducing atmospheres, which are problematic to such chemistry, are suppressed. The manner in which iron is accreted by the impact-generated melt phase is critical in determining the reducing power of the atmosphere and resolidified melt pool in the aftermath of impact.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Underestimation of multi-decadal global O 2 loss due to an optimal interpolation method

Abstract. The global ocean's oxygen content has declined significantly over the past several decades and is expected to continue decreasing under global warming, with far-reaching impacts on marine ecosystems and biogeochemical cycling. Determining the oxygen trend, its spatial pattern, and uncertainties from observations is fundamental to our understanding of the changing ocean environment. This study uses a suite of CMIP6 Earth system models to evaluate the biases and uncertainties in oxygen distribution and trends due to sampling sparseness. Model outputs are sub-sampled according to the spatial and temporal distribution of the historical shipboard measurements, and the data gaps are filled by a simple optimal interpolation method using Gaussian covariance with a constant e-folding length scale. Sub-sampled results are compared to full model output, revealing the biases in global and basin-wise oxygen content trends. The simple optimal interpolation underestimates the modeled global deoxygenation trends, capturing approximately two-thirds of the full model trends. The North Atlantic and subpolar North Pacific are relatively well sampled, and the simple optimal interpolation is capable of reconstructing more than 80 % of the oxygen trend in the non-eddying CMIP models. In contrast, pronounced biases are found in the equatorial oceans and the Southern Ocean, where the sampling density is relatively low. The application of the simple optimal interpolation method to the historical dataset estimated the global oxygen loss to be 1.5 % over the past 50 years. However, the ratio of the global oxygen trend between the sub-sampled and full model output has increased the estimated loss rate in the range of 1.7 % to 3.1 % over the past 50 years, which partially overlaps with previous studies. The approach taken in this study can provide a framework for the intercomparison of different statistical gap-filling methods to estimate oxygen content trends and their uncertainties due to sampling sparseness.

Ito, Takamitsu (ORCID:000000019873099X)↗
DOE OSTI2021

Towards Efficient Uncertainty estimation in deep learning for robust energy prediction in crystal materials

In material science, recent studies have started to explore the potential of using deep learning to improve property prediction from high-fidelity simulations, e.g, density functional theory (DFT). However, the design spaces are sometimes too large and intractable to sample completely. This results in a critical question that is how to evaluate the confidence and robustness of the prediction. In this paper, we propose an efficient approach to estimate uncertainty in deep learning using a single forward pass and then apply it for robust prediction of the total energy in crystal lattice structures. Our approach is built upon the deep kernel learning (DKL) that originally introduces to leverage the expressiveness of deep neural networks as input with a probabilistic prediction of Gaussian processes (GPs) as output. Existing DKL methods have difficulties in the accuracy of predictive uncertainty, training stability, and scaling to large datasets, which lead to significant barriers in real-world applications. We propose to address these challenges by using an inducing point approximate GP in feature space combined with spectral normalization as a regularization. We finally demonstrate our robust performance on an artificial example and a real-world application from materials chemistry.

Bi, Sirui↗
DOE OSTI2024

Improved accuracy and robustness of electron density profiles from JET’s X-mode frequency-modulated continuous-wave reflectometers

JET’s frequency-modulated continuous wave (FMCW) reflectometers have been operating well with the current design since 2005, and density profiles have been automatically calculated intershot since then. However, the calculated profiles had long suffered from several shortcomings: poor agreement with other diagnostics, sometimes inappropriately moving radially by several centimeters, elevated levels of radial jitter, and persistent wriggles (strong unphysical oscillations). Here, in this research, several techniques are applied to the reflectometry data analysis, and the shortcomings are significantly improved. Starting with improving the equilibrium reconstruction that estimates the background magnetic field, adding a ripple correction in the reconstructed magnetic field profile, and adding new inner-wall reflection positions estimated through ray-tracing, these changes not only improve the agreement of reconstructed profiles to other diagnostics but also solve density profile wriggles that were present during band transitions. Other smaller but also persistent wriggles were also suppressed by applying a localized correction to the measured beat frequency where persistent oscillations are present. Finally, the burst analysis method, as introduced by Varela et al., has been implemented to extract the beat frequency from stacked spectrograms. Due to the strong suppression of spurious reflections, the radial jitter that sometimes would span several centimeters has been strongly reduced. The stacking of spectrograms has also been shown to be very useful for stacking recurring events, like small gas puff modulations, and extracting transport coefficients that would otherwise be below the noise level.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Thermodynamics of free and bound magnons in graphene

Symmetry-broken electronic phases support neutral collective excitations. For example, monolayer graphene in the quantum Hall regime hosts a nearly ideal ferromagnetic phase at specific filling factors that spontaneously breaks the spin-rotation symmetry. This ferromagnet has been shown to support spin-wave excitations known as magnons that can be electrically generated and detected. Although long-distance magnon propagation has been demonstrated via transport measurements, important thermodynamic properties of such magnon populations—including the magnon chemical potential and density—have not been measured. Here we present local measurements of electron compressibility under the influence of magnons, which reveal a reduction in the gap associated with the ν = 1 quantum Hall state by up to 20%. Combining these measurements with the estimates of temperature, our analysis reveals that the injected magnons bind to electrons and holes to form skyrmions, and it enables the extraction of free magnon density, magnon chemical potential and average skyrmion spin. Our methods provide a means of probing the thermodynamic properties of charge-neutral excitations that are applicable to other symmetry-broken electronic phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Extrapolating Contaminant Effects from Individuals to Populations: A Case Study on Nanoparticle Toxicity to Daphnia Fed Environmentally Relevant Food Levels

Ecological risk assessment (ERA) is charged with assessing the likelihood a chemical will have adverse environmental or ecological effects. When assessing the risk of a potential contaminant to biological organisms, ecologists are most concerned with the sustainability of populations of organisms, rather than protecting every individual. However, ERA most commonly relies on data on the effect of a potential contaminant on individuals because these experiments are more feasible than costly population-level exposures. In this work, we address the challenge of extrapolating these individual-level results to predict population-level effects. Previous per-capita population growth rate estimates calculated from individual-level exposures of Daphnia pulicaria to silver nanoparticles (AgNPs) at different food rations predict a critical daily food requirement for daphnid populations exposed to 200 μg/L AgNPs to avoid extinction. To test this, we exposed daphnid populations to the same AgNP concentration at three different food inputs, with the lowest ration close to the extinction threshold predicted from data on individuals. The two populations with the higher food inputs persisted, and the population with the lowest food input went extinct after 50 days but did persist through two generations. Further, we demonstrate that we can extrapolate between these levels of biological organization by parameterizing an individual-level biomass model with data on individuals’ response to AgNPs and using these parameters to predict the outcome for control and AgNP-exposed populations. Key to successful extrapolation is careful modeling of temporal changes in resource density, driven by both the experimental protocols and feedback from the consumer. The implication for ecotoxicology is that estimates of extinction thresholds based on studies of individuals may be reliable predictors of population outcomes, but only with careful treatment of resource dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Thermal Neutron Scattering Law for Beryllium Hydride and Critical Mass Calculations

The thermal neutron scattering law (TSL) for crystalline beryllium hydride (BeH 2 ) is developed from first-principles ab initio lattice dynamics calculations and the impact of neutron thermalization in this material on critical mass is estimated. BeH 2 has a body-centered orthorhombic crystal structure with 12 molecules per unit cell and a theoretical density of 0.755 g/cm 3 . The vibrational (phonon) densities of states for H and Be bound in BeH 2 are determined using VASP density functional theory and PHONON lattice dynamics calculations. The TSLs for H bound in BeH 2 , H(BeH 2 ), and Be bound in BeH 2 , Be(BeH 2 ), are then evaluated in the incoherent approximation from the calculated H and Be partial phonon density of states using FLASSH. Finally, critical mass as a function of 235 U loading density for bare and reflected BeH 2 moderated spheres is predicted from MC21 Monte Carlo neutron transport calculations using ENDF/B-VIII.0 cross sections and the H(BeH 2 ) and Be(BeH 2 ) TSL evaluations. Comparisons are made to water (H 2 O), polyethylene (CH 2 ), and beryllium oxide (BeO) as moderators. These critical mass predictions are a refinement upon the prior work by Rao and Srinivasan that neglected thermal neutron scattering effects. The minimum critical mass of a BeH 2 moderated assembly is estimated to be 0.207 kg 235 U for a 0.20 m thick BeO reflected sphere and 0.178 kg 235 U for a 0.40 m thick BeO reflected sphere.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

ReaxFF molecular dynamics simulations of electrolyte–water systems at supercritical temperature

We have performed ReaxFF molecular dynamics simulations of alkali metal–chlorine pairs in different water densities at supercritical temperature (700 K) to elucidate the structural and dynamical properties of the system. The radial distribution function and the angular distribution function explain the inter-ionic structural and orientational arrangements of atoms during the simulation. The coordination number of water molecules in the solvation shell of ions increases with an increase in the radius of ions. We find that the self-diffusion coefficient of metal ions increases with a decrease in density under supercritical conditions due to the formation of voids within the system. The hydrogen bond dynamics has been interpreted by the residence time distribution of various ions, which shows Li+ having the highest water retaining capability. The void distribution within the system has been analyzed by using the Voronoi polyhedra algorithm providing an estimation of void formation within the system at high temperatures. We observe the formation of salt clusters of Na+ and K+ at low densities due to the loss of dielectric constants of ions. The diffusion of ions gets altered dramatically due to the formation of voids and nucleation of ions in the system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Parameter space for magnetization effects in high-energy-density plasmas

Magnetic fields are well known to affect the evolution of fluids via the J × B force, where J is the current density and B is the magnetic field. This force leads to the influence of magnetic fields on hydrodynamics (magnetohydrodynamics). Magnetic fields are often neglected in modeling of high-energy-density plasmas, since J × B is very small compared with the plasma pressure gradients. However, many experiments lie in a separate part of parameter space where the plasma is indirectly affected via magnetization of the heat flux and charged particle transport. This is true even for initially unmagnetized plasmas, since misaligned density and temperature gradients can self-generate magnetic fields. By comparing terms in the induction equation, we go on to estimate the regions of parameter space where these self-generated fields are strong enough to affect the hydrodynamics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Benchmark Data Set of Crystalline Organic Semiconductors

This work reports a Benchmark Data set of Crystalline Organic Semiconductors to test calculations of the structural and electronic properties of these materials in the solid state. The data set contains 67 crystals consisting of mostly rigid molecules with a single dominant conformer, covering the majority of known structural types. The experimental crystal structure is available for the entire data set, whereas zero-temperature unit cell volume can be reliably estimated for a subset of 28 crystals. Using this subset, we benchmark r 2 SCAN-D3 and PBE-D3 density functionals. Then, for the entire data set, we benchmark approximate density functional theory (DFT) methods, including GFN1-xTB and DFTB3(3ob-3-1), with various dispersion corrections against r 2 SCAN-D3. Our results show that r 2 SCAN-D3 geometries are accurate within a few percent, which is comparable to the statistical uncertainty of experimental data at a fixed temperature, but the unit cell volume is systematically underestimated by 2% on average. The several times faster PBE-D3 provides an unbiased estimate of the volume for all systems except for molecules with highly polar bonds, for which the volume is substantially overestimated in correlation with the underestimation of atomic charges. Considered approximate DFT methods are orders of magnitude faster and provide qualitatively correct but overcompressed crystal structures unless the dispersion corrections are fitted by unit cell volume.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2022

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. Further, we show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Information Content of the Parity-Violating Asymmetry in Pb 208

The parity-violating asymmetry A PV in 208 Pb, recently measured by the PREX-2 Collaboration, is studied using modern relativistic (covariant) and nonrelativistic energy density functionals. We first assess the theoretical uncertainty on A PV which is intrinsic to the adopted approach. To this end, we use quantified functionals that are able to accommodate our previous knowledge on nuclear observables such as binding energies, charge radii, and the dipole polarizability α D of 208 Pb. We then add the quantified value of A PV together with α D to our calibration dataset to optimize new functionals. Based on these results, we predict a neutron skin thickness in 208 Pb r skin = 0.19 ± 0.02 fm and the symmetry-energy slope L = 54 ± 8 MeV. These values are consistent with other estimates based on astrophysical data and are significantly lower than those recently reported using a particular set of relativistic energy density functionals. Here, we also make a prediction for the A PV value in 48 Ca that will be soon available from the CREX measurement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗