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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

How Far Is Quasar UV/Optical Variability from a Damped Random Walk at Low Frequency?

Studies have shown that UV/optical light curves of quasars can be described using the prevalent damped random walk (DRW) model, also known as the Ornstein–Uhlenbeck process. A white noise power spectral density (PSD) is expected at low frequency in this model; however, a direct observational constraint to the low-frequency PSD slope is difficult due to the limited lengths of the light curves available. Meanwhile, quasars show scatter in their DRW parameters that is too large to be attributed to uncertainties in the measurements and dependence on the variation of known physical factors. In this work we present simulations showing that, if the low-frequency PSD deviates from the DRW, the red noise leakage can naturally produce large scatter in the variation parameters measured from simulated light curves. The steeper the low-frequency PSD slope, the larger scatter we expect. Based on observations of SDSS Stripe 82 quasars, we find that the low-frequency PSD slope should be no steeper than −1.3. The actual slope could be flatter, which consequently requires that the quasar variabilities should be influenced by other unknown factors. We speculate that the magnetic field and/or metallicity could be such additional factors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗
DOE OSTI2017

High temperature gasification of high heating-rate chars using a flat-flame reactor

The increasing interest in gasification and oxy-fuel combustion of biomass has heightened the need for a detailed understanding of char gasification in industrially relevant environments (i.e., high temperature and high-heating rate). Despite innumerable studies previously conducted on gasification of biomass, very few have focused on such conditions. Consequently, in this study the high-temperature gasification behaviors of biomass-derived chars were investigated using non-intrusive techniques. Two biomass chars produced at a heating rate of approximately 10 4 K/s were subjected to two gasification environments and one oxidation environment in an entrained flow reactor equipped with an optical particle-sizing pyrometer. A coal char produced from a common U.S. low sulfur subbituminous coal was also studied for comparison. Both char and surrounding gas temperatures were precisely measured along the centerline of the furnace. Despite differences in the physical and chemical properties of the biomass chars, they exhibited rather similar reaction temperatures under all investigated conditions. On the other hand, a slightly lower particle temperature was observed in the case of coal char gasification, suggesting a higher gasification reactivity for the coal char. A comprehensive numerical model was applied to aid the understanding of the conversion of the investigated chars under gasification atmospheres. In addition, a sensitivity analysis was performed on the influence of four parameters (gas temperature, char diameter, char density, and steam concentration) on the carbon conversion rate. Here, the results demonstrate that the gas temperature is the most important single variable influencing the gasification rate.

09 BIOMASS FUELS↗
DOE OSTI2022

Atomistically-informed modeling of point defect clustering and evolution in irradiated ThO 2

A cluster dynamics (CD) model has been developed to investigate the nucleation and growth of point defect clusters, i.e., interstitial prismatic loops and nanoscale and sub-nanoscale voids, in ThO 2 during irradiation by energetic particles. The model considers cluster off-stoichiometry due to the asymmetry of point defect generation on the O and Th sublattices under irradiation, as well as the point defect diffusivities and the defect binding energies to clusters. The energies were established using detailed molecular dynamics simulations considering the statistical variability of cluster configuration. A high-order adaptive time-integration has been used to solve the model. The predicted loop density and their average size is in good agreement with reported experimental observations for proton irradiated ThO 2 at 600°C. In conclusion, the model did not predict void evolution due to the sluggish kinetics of cation vacancies, explaining the absence of voids in proton irradiated ThO 2 (and other oxides) at relatively low temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The relative influence of climate and housing development on current and projected future fire patterns and structure loss across three California landscapes

Climate and land use patterns are expected to change dramatically in the coming century, raising concern about their effects on wildfire patterns and subsequent impacts to human communities. The relative influence of climate versus land use on fires and their impacts, however, remains unclear, particularly given the substantial geographical variability in fire-prone places like California. Here, we developed a modeling framework to compare the importance of climatic and human variables for explaining fire patterns and structure loss for three diverse California landscapes, then projected future large fire and structure loss probability under two different climate (hot-dry or warm-wet) and two different land use (rural or urban residential growth) scenarios. The relative importance of climate and housing pattern varied across regions and according to fire size or whether the model was for large fires or structure loss. The differing strengths of these relationships, in addition to differences in the nature and magnitude of projected climate or land use change, dictated the extent to which large fires or structure loss were projected to change in the future. Despite this variability, housing and human infrastructure were consistently more responsible for explaining fire ignitions and structure loss probability, whereas climate, topography, and fuel variables were more important for explaining large fire patterns. For all study areas, most structure loss occurred in areas with low housing density (from 0.08 to 2.01 units/ha), and expansion of rural residential land use increased structure loss probability in the future. Regardless of future climate scenario, large fire probability was only projected to increase in the northern and interior parts of the state, whereas climate change had no projected impact on fire probability in southern California. Given the variation in fire-climate relationships and land use effects, policy and management decision-making should be customized for specific geographical regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO 2 ) across the Chequamegon Ecosystem-Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became weaker with time as non-climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. Higher CO 2 increased maximum net assimilation parameters but not total gross primary productivity. Stand-scale sites were larger net sinks than the landscape tower. Clustered, long-term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Extended MHD modeling of tearing-driven magnetic relaxation

Discrete relaxation events in reversed-field pinch relevant configurations are investigated numerically with nonlinear extended magnetohydrodynamic modeling, including the Hall term in Ohm’s law and first-order ion finite Larmor radius effects. Our results show variability among relaxation events, where the Hall dynamo effect may help or impede the MHD dynamo effect in relaxing the parallel current density profile. The competitive behavior arises from multi-helicity conditions where the dominant magnetic fluctuation is relatively small. The resulting changes in parallel current density and parallel flow are aligned in the core, consistent with experimental observations. Analysis of simulation results also confirms that force density from fluctuation-induced Reynolds stress arises subsequent to the drive from fluctuation-induced Lorentz force density. Transport of momentum density is found to be dominated by the fluctuation-induced Maxwell stress over most of the cross section with viscous and gyroviscous contributions being large in the edge region. The findings resolve a discrepancy with respect to the relative orientation of current density and flow relaxation, which had not been realized or investigated in Ref. [King et. al. Phys. Plasmas 19, 055905 (2012)], where only the magnitude of flow relaxation is actually consistent with experimental results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Structure of a monolayer of molecular rotors on aqueous subphase from grazing-incidence X-ray diffraction

In situ grazing-incidence X-ray scattering shows here that a monolayer of artificial rod-shaped dipolar molecular rotors produced on the surface of an aqueous subphase in a Langmuir trough has a structure conducive to a 2D ferroelectric phase. The axes of the rotors stand an average of 0.83 nm apart in a triangular grid, perpendicular to the surface within experimental error. They carry 2,3-dichlorophenylene rotators near rod centers, between two decks of interlocked triptycenes installed axially on the rotor axle. The analysis is based first on simultaneous fitting of observed Bragg rods and second on fitting the reflectivity curve with only three adjustable parameters and the calculated rotor electron density, which also revealed the presence of about seven molecules of water near each rotator. Dependent on preparation conditions, a minor and variable amount of a different crystal phase may also be present in the monolayer.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics

The chemical equilibrium between self-ionized and molecular water dictates the acid–base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)–based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid–base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H 2 O) cells. The reversible work to separate the hydroxide and hydronium to a distance S is found to converge for simulation cells containing more than 500 H 2 O, and a distance of ∼ 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Robust and Nonrobust Aspects of Atlantic Meridional Overturning Circulation Variability and Mechanisms in the Community Earth System Model

Abstract Robust and nonrobust aspects of Atlantic meridional overturning circulation (AMOC) variability and mechanisms are analyzed in several 600-yr simulations with the Community Earth System Model. The simulations consist of a set of cases where a few loosely constrained ocean model parameter values are changed, a pair of cases where round-off level perturbations are applied to the initial atmospheric temperature field, and a millennium-scale integration. The time scales of variability differ among the cases with the dominant periods ranging from decadal to centennial. These dominant periods are not stationary in time, indicating that a robust characterization of AMOC temporal variability requires long, multimillennium-scale simulations. A robust aspect is that positive anomalies of the Labrador Sea (LS) upper-ocean density and boundary layer depth and the positive phase of the North Atlantic Oscillation lead AMOC strengthening by 2–3 years. Respective contributions of temperature and salinity to these density anomalies vary across the simulations, but in a majority of the cases temperature contributions dominate. Following an AMOC intensification, all cases show that advection of warm and salty waters into the LS region results in near-neutral density anomalies. Analysis of the LS heat budget indicates that temperature acts to increase density in all cases prior to an AMOC intensification, primarily due to losses by sensible and latent heat fluxes. The accompanying salt budget analysis reveals that the salt contribution to density anomalies varies across the cases, taking both positive and negative values.

Danabasoglu, Gokhan↗
DOE OSTI2019

Genetic variation of biomass recalcitrance in a natural Salix viminalis (L.) population

Background Salix spp. are high-productivity crops potentially used for lignocellulosic biofuels such as bioethanol. In general, pretreatment is needed to facilitate the enzymatic depolymerization process. Biomass resistance to degradation, i.e., biomass recalcitrance , is a trait which can be assessed by measuring the sugar released after combined pretreatment and enzymatic hydrolysis. We have examined genetic parameters of enzymatic sugar release and other traits related to biorefinery use in a population of 286 natural Salix viminalis clones. Moreover, we have evaluated phenotypic and genetic correlations between these traits and performed a genomewide association mapping analysis using a set of 19,411 markers. Results Sugar release (glucose and xylose) after pretreatment and enzymatic saccharification proved highly variable with large genetic and phenotypic variations, and chip heritability estimates (h 2 ) of 0.23-0.29. Lignin syringyl/guaiacyl (S/G) ratio and wood density were the most heritable traits (h 2 = 0.42 and 0.59, respectively). Sugar release traits were positively correlated, phenotypically and genetically, with biomass yield and lignin S/G ratio. Association mapping revealed seven marker-trait associations below a suggestive significance threshold, including one marker associated with glucose release. Conclusions We identified lignin S/G ratio and shoot diameter as heritable traits that could be relatively easily evaluated by breeders, making them suitable proxy traits for developing low-recalcitrance varieties. One marker below the implied threshold for marker associations was identified for sugar release, meriting further investigation while also highlighting the difficulties in employing genomewide association mapping for complex traits.

09 BIOMASS FUELS↗
DOE OSTI2023

Weak phylogenetic and habitat effects on root trait variation of 218 Neotropical tree species

Tropical forests harbor a large diversity of closely related tree species that can thrive across habitats. This biodiversity has been found to correspond to large functional diversity in aboveground traits, and likely also relates to belowground trait variation. Globally, root trait (co-)variation is driven by different belowground resource strategies of species, environmental variation, and phylogeny; however, these patterns mostly reflect observations from temperate biomes and remain unconfirmed in tropical trees. We examine phylogenetic and environmental effects on root trait (co-)variation of trees across habitats in an Amazonian rainforest. Roots of 218 tree species from ten dominant families were sampled across three major habitats near Manaus, Brazil. We quantified five morphological and architectural root traits to (i) investigate how they reflected different resource strategies across species, (ii) compare them between families and superorders to test phylogenetic effects, and (iii) compare them between habitats to determine environmental effects on root trait expressions and variability. Root traits discriminated species along a tradeoff between root diameter and root branching and, secondly, due to variation in root tissue density. Our results further show weak phylogenetic effects on tropical tree root variation, for example, families from the same superorder showed large divergence in their root traits, while those from different superorders often overlapped in their root morphology and architecture. Root traits differed significantly between habitats but habitat type had only little effect on overall root trait variation. Our work suggests that the dimensions and drivers that underlie (co-)variation in tropical root traits may differ from global patterns defined by mostly temperate datasets. Due to (a)biotic environmental differences, different root trait dimensions may underlie the belowground functional diversity in (Neo)tropical forests, and we found little evidence for the strong phylogenetic conservatism observed in root traits in temperate biomes. We highlight important avenues for future research on tropical roots in order to determine the degree of, and shifts in functional diversity belowground as communities and environments change in tropical forests.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Operational-based annual energy production uncertainty: are its components actually uncorrelated?

Calculations of annual energy production (AEP) from a wind power plant – whether based on preconstruction or operational data – are critical for wind plant financial transactions. The uncertainty in the AEP calculation is especially important in quantifying risk and is a key factor in determining financing terms. A popular industry practice is to assume that different uncertainty components within an AEP calculation are uncorrelated and can therefore be combined as the sum of their squares. We assess the practical validity of this assumption for operational-based uncertainty by performing operational AEP estimates for more than 470 wind plants in the United States, mostly in simple terrain. We apply a Monte Carlo approach to quantify uncertainty in five categories: revenue meter data, wind speed data, regression relationship between density-corrected wind speed (from reanalysis data) and measured wind power, length of long-term-correction data set, and future interannual variability. We identify correlations between categories by comparing the results across all 470 wind plants. We observe a positive correlation between interannual variability and the linearized long-term correction; a negative correlation between wind resource interannual variability and linear regression; and a positive correlation between reference wind speed uncertainty and linear regression. Then, we contrast total operational AEP uncertainty values calculated by omitting and considering correlations between the uncertainty components. We quantify that ignoring these correlations leads to an underestimation of total AEP uncertainty of, on average, 0.1% and as large as 0.5% for specific sites. Although these are not large increases, these would still impact wind plant financing rates; further, we expect these values to increase for wind plants in complex terrain. Based on these results, we conclude that correlations between the identified uncertainty components should be considered when computing the total AEP uncertainty.

17 WIND ENERGY↗
DOE OSTI2017

Second-Order Uncertainty Analysis for Radiation Shielding Problems Using MCNP

Standard uncertainty propagation is based on a first-order Taylor expansion. This approximation is usually justified by assuming that 'uncertainties are very small numbers compared with the values of their corresponding quantities'. Furthermore, the second derivatives needed for a second-order analysis are not usually available, and a second-order analysis requires more knowledge regarding the distribution of the random variables. For radiation shielding problems, the MCNP6 Monte Carlo code computes first and second derivatives of a response with respect to nuclide density or reaction cross section. This paper explores the application of these derivatives to second-order uncertainty analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Close Companions around Young Stars

Multiplicity is a fundamental property that is set early during stellar lifetimes, and it is a stringent probe of the physics of star formation. The distribution of close companions around young stars is still poorly constrained by observations. We present an analysis of stellar multiplicity derived from Apache Point Observatory Galactic Evolution Experiment-2 spectra obtained in targeted observations of nearby star-forming regions. This is the largest homogeneously observed sample of high-resolution spectra of young stars. We developed an autonomous method to identify double-lined spectroscopic binaries (SB2s). Out of 5007 sources spanning the mass range of ∼0.05–1.5 M {sub ⊙}, we find 399 binaries, including both radial velocity (RV) variables and SB2s. The mass ratio distribution of SB2s is consistent with being uniform for q < 0.95 with an excess of twins for q > 0.95. The period distribution is consistent with what has been observed in close binaries (<10 au) in the evolved populations. Three systems are found to have q ∼ 0.1, with a companion located within the brown dwarf desert. There are no strong trends in the multiplicity fraction as a function of cluster age from 1 to 100 Myr. There is a weak dependence on stellar density, with companions being most numerous at Σ{sub *} ∼ 30 stars/pc{sup −2} and decreasing in more diffuse regions. Finally, disk-bearing sources are deficient in SB2s (but not RV variables) by a factor of ∼2; this deficit is recovered by the systems without disks. This may indicate a quick dispersal of disk material in short-period equal-mass systems that is less effective in binaries with lower q.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Control-oriented core-SOL-divertor model to address integrated burn and divertor control challenges in ITER

The real-time regulation of a burning plasma’s temperature and density, or burn control, will be necessary to produce high fusion power in future tokamaks like ITER. This is made more challenging due to the plasma’s nonlinear characteristics and the interdependence between the core-plasma and edge-plasma regions. For example, a raising plasma temperature leads to increasing reactivity and therefore to more alpha-particle heating, which further increases temperature. Furthermore, a raise of the fusion power increases the heat flow through the scrape-off-layer (SOL), which can compromise the integrity of the divertor without proper safeguards. For control design, a model-based approach is attractive because it can directly incorporate the nonlinear, coupled, burning-plasma dynamics into the design. To facilitate this design approach, a control-oriented core-SOL-divertor (CSD) model is presented in this work. In this CSD model, a core-plasma model captures the nonlinear dynamics of the core’s density and temperature, and a SOL-divertor model defines the plasma conditions at the separatrix and divertor including the heat load on the target plates. The core-plasma and SOL-divertor models are coupled through the exchange of various variables. In particular, the SOL-divertor model yields the separatrix temperature and the influx of recycled particles into the core-plasma. Further, these variables influence the power and particle balances captured by the core-plasma model. In return, the core-plasma model determines the intensity of the heat and particles fluxes across the separatrix, and this outflow strongly impacts the SOL-divertor model. Therefore, the power and density of the core-plasma, which can be readily modulated through external heating systems and pellet injection, can be viewed as control knobs for the SOL-divertor region in addition to the gas puffing. In simulations of the CSD model, it is demonstrated how external actuation can be utilized to meet burn control and divertor control objectives simultaneously.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Bayesian Inference with Latent Hamiltonian Neural Networks (L-HNNs)

When sampling for Bayesian inference, one popular approach is to use Hamiltonian Monte Carlo (HMC) and the No-U-Turn Sampler (NUTS). However, HMC and NUTS can require numerous numerical gradients of the target density and can prove slow in practice. We propose Hamiltonian neural networks (HNNs) with HMC and NUTS for solving Bayesian inference problems [1, 2]. Once trained, HNNs do not require gradients of the target density while sampling. Moreover, they satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well suited for use within HMC and NUTS because stationarity can be shown. We also propose an HNN extension called latent HNNs (L-HNNs), which predict latent variable outputs. Compared to HNNs, L-HNNs offer improved expressivity and a reduction in integration errors. Finally, we propose employing L-HNNs in NUTS with an online error monitoring scheme to prevent degeneracy of the sampling in regions of low probability density. We demonstrate L-HNNs in NUTS with online error monitoring by using several example cases involving complex, heavy-tailed, and high local curvature probability densities. Overall, L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. Compared to traditional NUTS, L-HNNs in NUTS with online error monitoring improved the effective sample size (ESS) per gradient by an order of magnitude.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Combined Machine Learning and High-Energy X-ray Diffraction Approach to Understanding Liquid and Amorphous Metal Oxides

Determining the structure-property relations of liquid and amorphous metal oxides is challenging, due to their variable short-range order and polyhedral connectivity. To predict chemically realistic structures, we have developed a Machine Learned, Gaussian Approximation Potential (GAP) for HfO 2 , with a focus on enhanced sampling of the training database and accurate density functional theory calculations. By using training datasets for the GAP model at the level of Density Functional Theory-Strongly Constrained and Appropriately Normed (DFT-SCAN) level of theory, our results show that the topology of both the low viscosity liquid and the amorphous form are dominated by edge-shared chains and small corner-shared rings of polyhedra. This topology is shown to be consistent with the structure of other liquid and amorphous transition metal oxides of variable ion size, such as TiO 2 and ZrO 2 . Current limitations of the ML-GAP modeling method for obtaining glass structures and future perspectives are also discussed.

36 MATERIALS SCIENCE↗