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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach

High entropy alloys (HEAs) are a series of novel materials that demonstrate many exceptional mechanical properties. To understand the origin of these attractive properties, it is important to investigate the thermodynamics and elucidate the evolution of various chemical phases. In this work, we introduce a machine learning approach to construct the effective Hamiltonian and study the thermodynamics of HEAs through canonical Monte Carlo simulation. The main characteristic of our method is to use pairwise interactions between atoms as the machine learning features and systematically improve the representativeness of the dataset using active learning from the Monte Carlo samples. We find this method produces highly robust and accurate effective Hamiltonians that give less than 0.1 mRy test error for all the three refractory HEAs: MoNbTaW, MoNbTaVW, and MoNbTaTiW. Using replica exchange to speed up the MC simulation, we calculated the specific heats and short-range order parameters in a wide range of temperatures. For all the studied materials, we find there are two major order-disorder transitions occurring respectively at T 1 and T 2 , where T 1 is near room temperature but T 2 is much higher. Further, we demonstrate that the transition at T 1 is caused by W and Nb while the one at T 2 is caused by the other elements. By comparing with experiments, the results demonstrate that the order-disorder transitions have a profound impact on the strength and ductility of HEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

On the quantification and efficient propagation of imprecise probabilities with copula dependence

This paper addresses the problem of quantification and propagation of uncertainties associated with dependence modeling when data for characterizing probability models are limited. Practically, the system inputs are often assumed to be mutually independent or correlated by a multivariate Gaussian distribution. However, this subjective assumption may introduce bias in the response estimate if the real dependence structure deviates from this assumption. In this work, we overcome this limitation by introducing a flexible copula dependence model to capture complex dependencies. Here, a hierarchical Bayesian multimodel approach is proposed to quantify uncertainty in dependence model-form and model parameters that result from small data sets. This approach begins by identifying, through Bayesian multimodel inference, a set of candidate marginal models and their corresponding model probabilities, and then estimating the uncertainty in the copula-based dependence structure, which is conditional on the marginals and their parameters. The overall uncertainties integrating marginals and copulas are probabilistically represented by an ensemble of multivariate candidate densities. A novel importance sampling reweighting approach is proposed to efficiently propagate the overall uncertainties through a computational model. Through an example studying the influence of constituent properties on the out-of-plane properties of transversely isotropic E-glass fiber composites, we show that the composite property with copula-based dependence model converges to the true estimate as data set size increases, while an independence or arbitrary Gaussian correlation assumption leads to a biased estimate.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Establishing metrics to quantify spatial similarity in spherical and red blood cell distributions

As computational power increases and systems with millions of red blood cells can be simulated, it is important to note that varying spatial distributions of cells may affect simulation outcomes. Since a single simulation may not represent the ensemble behavior, many different configurations may need to be sampled to adequately assess the entire collection of potential cell arrangements. In order to determine both the number of distributions needed and which ones to run, we must first establish methods to identify well-generated, randomly placed cell distributions and to quantify distinct cell configurations. We utilize metrics to assess (1) the presence of any underlying structure to the initial cell distribution and (2) similarity between cell configurations. We propose the use of the radial distribution function to identify long-range structure in a cell configuration and apply it to a randomly distributed and structured set of red blood cells. To quantify spatial similarity between two configurations, we make use of the Jaccard index, and characterize sets of red blood cell and sphere initializations. As an extension to our work submitted to the International Conference on Computational Science, we significantly increase our data set size from 72 to 1048 cells, include a similar set of studies using spheres, compare the effects of varying sphere size, and utilize the Jaccard index distribution to probe sets of extremely similar configurations. Our results show that the radial distribution function can be used as a metric to determine long-range structure in both distributions of spheres and RBCs. We determine that the ideal case of spheres within a cube versus bi-concave shaped cells within a cylinder affects the shape of the Jaccard index distributions, as well as the range of Jaccard values, showing that both the shape of particle and the domain may play a role. Furthermore, we also find that the distribution is able to capture very similar configurations through Jaccard index values greater than 95% when appending several nearly identical configurations into the data set.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Associating damage nucleation and distribution with grain boundary characteristics in Ta

Here, the effect of grain boundary (GB) characteristics (as represented by misorientation angle) on its dynamic response is investigated by sampling a large set of 185 GBs in Ta bi-crystals. These GBs encompass both ordered and disordered structures – typically referred to as metastable structures, in order to reflect the diversity in the local atomic structure observed in realistic GBs. The results reveal four distinct regimes in damage distribution and failure modes as a function of misorientation angle. The above trends also correlate with the variation in the corresponding void nucleation stress.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Crystallization↗
DOE OSTI2021

Inverse design of two-dimensional materials with invertible neural networks

The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task. To tackle this problem, we propose an inverse design framework (MatDesINNe) utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property. This approach can be used to generate materials candidates for a designated property, thereby satisfying the highly sought-after goal of inverse design. We then apply this framework to the task of band gap engineering in two-dimensional materials, starting with MoS 2 . Within the design space encompassing six degrees of freedom in applied tensile, compressive and shear strain plus an external electric field, we show the framework can generate novel, high fidelity, and diverse candidates with near-chemical accuracy. We extend this generative capability further to provide insights regarding metal-insulator transition in MoS 2 which are important for memristive neuromorphic applications, among others. This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Scale translation yields insights into gas adsorption under nanoconfinement

This work describes a scale-translating simulation framework to investigate gas adsorption behavior in nanoconfined pores. The framework combines molecular simulations (MSs), equation of state (EoS), and lattice Boltzmann (LB) simulations. MSs reveal the physics of methane adsorption in nano-sized pores, where input values of fugacity coefficients are optimized based on EoS predictions. Then, an LB free-energy model, which incorporates a viral EoS, upscales intermolecular forces and estimates adsorption behavior via a proposed fluid–wall interaction model. Armed with the values of the LB interaction parameter as a function of pressure, the LB model is used to predict fluid behavior in irregular nanopores, and the results are validated against reference MS data. The LB model is then used to study adsorption behavior at a continuum scale in representative organic shale nanopores based on finely characterized Vaca Muerta shale samples. Furthermore, the results show that methane adsorption could significantly increase contained fluids by 10%–25% in pores smaller than 20 nm. However, in larger pores (40 nm to 90 nm), adsorption's impact diminishes to 2%–3%, suggesting sorption's negligible role beyond a 40 nm pore size.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

On the measurement of S diff splitting caused by lowermost mantle anisotropy

Seismic anisotropy has been detected at many depths of the Earth, including its upper layers, the lowermost mantle and the inner core. While upper mantle seismic anisotropy is relatively straightforward to resolve, lowermost mantle anisotropy has proven to be more complicated to measure. Due to their long, horizontal ray paths along the core–mantle boundary (CMB), S waves diffracted along the CMB (S diff ) are potentially strongly influenced by lowermost mantle anisotropy. S diff waves can be recorded over a large epicentral distance range and thus sample the lowermost mantle everywhere around the globe. S diff therefore represents a promising phase for studying lowermost mantle anisotropy; however, previous studies have pointed out some difficulties with the interpretation of differential SH diff –SV diff traveltimes in terms of seismic anisotropy. Here, we provide a new, comprehensive assessment of the usability of S diff waves to infer lowermost mantle anisotropy. Using both axisymmetric and fully 3-D global wavefield simulations, we show that there are cases in which S diff can reliably detect and characterize deep mantle anisotropy when measuring traditional splitting parameters (as opposed to differential traveltimes). First, we analyze isotropic effects on S diff polarizations, including the influence of realistic velocity structure (such as 3-D velocity heterogeneity and ultra-low velocity zones), the character of the lowermost mantle velocity gradient, mantle attenuation structure, and Earth’s Coriolis force. Secondly, we evaluate effects of seismic anisotropy in both the upper and the lowermost mantle on SH diff waves. In particular, we investigate how SH diff waves are split by seismic anisotropy in the upper mantle near the source and how this anisotropic signature propagates to the receiver for a variety of lowermost mantle models. We demonstrate that, in particular and predictable cases, anisotropy leads to S diff splitting that can be clearly distinguished from other waveform effects. Here, these results enable us to lay out a strategy for the analysis of S diff splitting due to anisotropy at the base of the mantle, which includes steps to help avoid potential pitfalls, with attention paid to the initial polarization of S diff and the influence of source-side anisotropy. We demonstrate our S diff splitting method using three earthquakes that occurred beneath the Celebes Sea, measured at many transportable array stations at a suitable epicentral distance. We resolve consistent and well-constrained S diff splitting parameters due to lowermost mantle anisotropy beneath the northeastern Pacific Ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Vadose Zone Flow and Transport Parameters Data Package for the Hanford Site Composite Analysis and Cumulative Impact Evaluation

This report provides a description of the basis for the development and implementation of a conceptual model for vadose zone flow and transport for the composite analysis (CA) groundwater pathway analysis and the cumulative impact evaluation (CIE). The parameterization for a numerical model is intimately linked to the conceptual model framework. The report describes the basis for the selection of hydraulic and transport parameters for the hydrostratigraphic units (HSUs) identified in the 200 East and 200 West Areas. Whenever data are sparse or unavailable, surrogate hydraulic properties are chosen based on samples collected within the 200 Areas and nearby locations that are representative of sediments characteristic of the HSUs identified elsewhere.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Results from Accelerated Environmental Testing of Barcoded Identifiers for UF 6 Cylinders

This report provides the qualitative and quantitative results from the accelerated environmental testing campaign. A total of 444 samples were subjected to 10 tests that included UV, salt fog, temperature, blowing dust, high pressure and temperature water, and impact exposure. Each sample included a two-dimensional barcode and an alphanumeric string representing the cylinder identification number. When evaluating the results, primary attention was placed on the decodability of the barcodes using a barcode verifier device, since machine scanning a barcode offers the greatest efficiency and accuracy gains when reading cylinder identification numbers during safeguards activities. Contrast was considered the critical test parameter because maintaining contrast under a wide range of environmental conditions is of fundamental importance to decoding the identification number of the barcodes. Additionally, manual readability of the alphanumeric string was considered an important factor in determining whether an inspector or operator could still read the identification number of the cylinder if the barcode proved to be undecodable.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Storage Stability of 50% Biodiesel Blends and Neat Biodiesel Produced in North America

This study investigated aging of two petroleum diesel, seven B100, three B20 blends, and 14 B50 blends representing the full range of fuels in the North American market for 12-weeks at 43 degrees Celsius under the ASTM D4625 accelerated aging protocol. Twelve-weeks under D4625 aging represents approximately 12 months of storage time in clean, dry conditions. Samples were taken every two weeks for measurement of Rancimat induction period (IP), peroxide content, and TAN; and total insoluble were measured at the end of the 12-week test.

09 BIOMASS FUELS↗
DOE OSTI2023

Investigation of the Effect of Atmospheric Plasma Treatment in Nanofiber and Nanocomposite Membranes for Piezoelectric Applications

In this work, we report the effect of steady-state atmospheric plasma (Corona discharge) in nanofibers and nanocomposite membranes for piezoelectric applications. The investigation was performed in PVDF (Poly vinylidene fluoride) nanofibers, CNT (Carbon Nanotubes)-reinforced PVDF nanocomposites, and PAN (Poly acrylonitrile) nanofiber membranes. Steady-state plasma was generated with a high voltage power source with 1 mA discharge current output and 6 kV discharge voltage, and the gap between tip and the material was maintained to be 1 cm. For the fabrication of nanofibers and nanocomposite membranes, an electrospinning method was used. The electrospinning parameters, such as flow rate and voltage, were optimally tuned for obtaining uniform nanofibers and nanomembranes. Along with the plasma treatment, heat treatment above the glass transition temperature was also conducted on the nanofiber membranes. Using a Scanning Electron Microscope (SEM), the morphology of the nanofibers was observed. X-ray Diffraction (XRD) demonstrated the polycrystallinity of the nanofibers. Fourier Transform Infrared Spectroscopy (FTIR) analysis of the PVDF nanofibers shows a peak at 796 cm−1 representing α-phase (C-H rocking) in the control sample which is absent in the treated samples. Raman spectroscopy of PVDF nanofibers identifies a Raman shift from 873 cm−1 to 877 cm−1 (denoting β-phase) for plasma-treated samples only. Electron Paramagnetic Resonance (EPR) concludes that the intensity of the free radicals increases from 1.37 to 1.46 (a.u.) after plasma treatment. Then, sensors were fabricated from the PVDF nanofibers, MWCNT-reinforced PVDF nanofibers, and PAN nanofibers to characterize their piezoelectric properties. The impact test results showed that the atmospheric plasma and heat-treated samples had 86%, 277%, and 92% increases of the d33 value (piezoelectric coefficient) in the case of PVDF nanofibers, MWCNT-reinforced nanofibers, and PAN nanofibers, respectively. It was also observed that the capacitance of the nanofiber membranes has increased due to the plasma treatment.

Biochemistry & Molecular Biology↗
DOE OSTI2022

Plant-level performance and degradation of 31 GW DC of utility-scale PV in the United States

In this updated study, which samples 50% more capacity than the original and adds two additional years of operating history, we assess the performance of a fleet of 631 utility-scale PV plants totaling 31.0 GW DC (23.6 GW AC ) of capacity that achieved commercial operations in the United States from 2007-2018 and that have operated for at least two full calendar years. We use detailed information on individual plant characteristics, in conjunction with modeled irradiance data, to model expected or “ideal” capacity factors in each full calendar year of each plant’s operating history. A comparison of ideal versus actual first-year capacity factors finds that this fleet has modestly underperformed initial expectations (as modeled) on average, though perhaps due as much to modeling issues as to actual underperformance. We then analyze fleet-wide performance degradation in subsequent years by employing a “fixed effects” regression model to statistically isolate the impact of age on plant performance. The resulting average fleet-wide degradation rate of -1.2%/year (±0.1%) represents a slight improvement (seemingly driven by the oldest plants in our sample) over the -1.3%/year (±0.2%) found in our original study, yet is still of greater magnitude than is commonly found. We emphasize, however, that these fleet-wide estimates reflect both recoverable and unrecoverable degradation across the entire plant, and so will naturally be of greater magnitude than module- or cell-level studies, and/or studies that focus only on unrecoverable degradation. Moreover, when focusing on a sub-sample of newer and larger plants with higher DC:AC ratios—i.e., plants that more-closely resemble what is being built today—we find a more moderate sample-wide average performance decline of -0.7%/year (±0.4%), which is more in line with other estimates from the recent literature.

14 SOLAR ENERGY↗
DOE OSTI2022

Atmospheric Brown Carbon on the Tibetan Plateau: Regional Differences in Chemical Composition and Light Absorption Properties

In this work, fine particulate matter (PM 2.5 ) filter samples were collected from two high-altitude remote sites located in the southern (QOMS) and northern (WLG) regions of the Tibetan Plateau (TP) to explore the regional differences in brown carbon (BrC) properties. Chemical differences in BrC composition representative of these two areas were inferred from molecular-level analysis of the PM 2.5 samples using high-performance liquid chromatography coupled with photodiode array and high-resolution mass spectrometry detectors. The results show that more polar BrC chromophores were abundant in QOMS samples, while contributions from polar and less polar chromophores were comparable in WLG samples. A higher mass absorption coefficient of BrC was observed at QOMS than at WLG. Strong BrC chromophores in QOMS samples were identified as oxygenated aromatics and nitrophenol compounds, while organosulfate compounds were found in the WLG sample. The results of this study indicate regional differences in BrC chromophores and provide insights into their sources and chemical processes, which should be considered for predictive understanding and modeling of the radiative forcing of aerosol in the TP area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A viscoelastic model for seismic attenuation using fractal mechanical networks

SUMMARY Seismic attenuation (quantified by the quality factor Q) has a significant impact on the seismic waveforms, especially in the fluid-saturated rocks. This dissipative process can be phenomenologically represented by viscoelastic models. Previous seismological studies show that the Q value of Earth media exhibits a nearly frequency-independent behaviour (often referred to as constant-Q in literature) in the seismic frequency range. Such attenuation can be described by the mathematical Kjartansson constant-Q model, which lacks of a physical representation in the viscoelastic sense. Inspired by the fractal nature of the pore fluid distribution in patchy-saturated rocks, here we propose two fractal mechanical network (FMN) models, that is, a fractal tree model and a quasi-fractal ladder model, to phenomenologically represent the frequency-independent Q behaviour. As with the classic viscoelastic models, the FMN models are composed of mechanical elements (spring and dashpots) arranged in different hierarchical patterns. A particular parametrization of each model can produce the same complex modulus as in the Kjartansson model, which leads to the constant-Q. Applying the theory to several typical rock samples, we find that the seismic attenuation signature of these rocks can be accurately represented by either one of the FMN models. Besides, we demonstrate that the ladder model in particular exhibits the realistic multiscale fractal structure of the saturated rocks. Therefore, the FMN models as a proxy could provide a new way to estimate the microscopic rock structure property from macroscopic seismic attenuation observation.

Geochemistry & Geophysics↗
DOE OSTI2020

NDE Technology Engineering Program for Hanford DST Non-Visual Volumetric Inspection Technology: Phase II RAVIS Radiation Tolerance Test Report

This test report provides the results of radiation tolerance robustness testing that was performed on samples of robotic components and an ultrasonic guided wave air-slot sensor that represent components/sub-systems of the Robotic Air-slot Volumetric Inspection System (RAVIS) that has been engineered for volumetric inspection of Hanford tank bottom plates via under-tank refractory pad air-slots. The specific components tested for 1) functionality during active irradiation and 2) tolerance to cumulative radiation dose (until failure or upon reaching a cumulative dose test limit) were: • four samples each of a printed circuit board (PCB) and direct current (DC) motor, which are robotic components, and • 26 ultrasonic piezoelectric elements (samples) inside an air-slot sensor. The robotic components are part of the RAVIS air-slot inspection crawler drive control system that is responsible for remote communication with and actuation of the air-slot inspection crawler. The failure of either of these components during under-tank deployment would require manual retrieval via the crawler’s tether, which risks damage to the robot/refractory/tank. Preemptive replacement of the components at appropriately conservative dose/time intervals informed by failure dose would reduce the likelihood of under-tank failure. The components were included in radiation tolerance testing to quantify their failure doses to inform replacement intervals. The air-slot sensor is responsible for collecting ultrasonic inspection data (scan images) for the tank bottom plates during under-tank deployment. Compromised signal quality due to elevated noise levels caused by gamma radiation would compromise inspection performance. The air-slot sensor was included in radiation tolerance testing to quantify the impact of active irradiation on sensor signal quality. The irradiation and in-situ functional tests of the PCBs, DC motors and air-slot sensor took place in June and July 2020 at the Pacific Northwest National Laboratory. Testing was performed at a gamma dose rate near 300 rad/hr., which, in the absence of under-tank dose rate data, has been conservatively estimated to be the upper-bound dose rate beneath the primary tanks at Hanford. Irradiation took place at elevated temperatures of 150-200°F to determine failure doses that reflect the compounding effects of gamma radiation and heat. The test results revealed: • The DC motors can tolerate being actively irradiated at the high dose rate at 200°F and can tolerate a cumulative dose of 300,000 rad, that which would be incurred after 5 years of service at the 300 rad/hr dose rate. The component therefore meets minimum and preferred radiation tolerance and lifecycle requirements for robotic components. • The air-slot sensor can tolerate being actively irradiated at the high dose rate at 150°F and can tolerate a cumulative dose of 60,000 rad, that which would be incurred after 1 year of service at the 300 rad/hr dose rate. The sensor therefore meets minimum radiation tolerance and lifecycle requirements. • The PCB can tolerate being actively irradiated at the high dose rate, but can only tolerate a cumulative dose of 19,000 rad at 150-200°F. The PCB does not meet minimum radiation tolerance and lifecycle requirements; however, because the component is considered replaceable, it can be replaced before a cumulative dose of 19,000 rad is reached, determined through either monitoring with a dosimeter or scheduled time intervals that are calculated based on conservative estimates of under-tank dose rates.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Improved Manufacturability and Throughput of Ultra- Transparent, Super-Insulating Aerogels

AeroShield Materials produces a novel silica aerogel material with exemplary thermal performance and unprecedented optical clarity, offering the potential for super-insulating fenestration and insulated glass to reduce thermal losses in the built environment by billions of dollars every year. One of the most significant challenges facing AeroShield today is the total amount of time that is required to produce large monolithic aerogel samples. This overall process can require as much as 144 hours total, which can significantly hinder scale-up to an economically viable product. The purpose of this Phase 1 research was to continue development of our novel aerogel manufacturing process to reduce material processing time by up to 10x, greatly improving product throughput and reducing cost. AeroShield’s manufacturing process can be divided into 4 major stages, each with their own distinct set of parameters and time requirements. Under this Phase 1 award, AeroShield was able to identify and optimize a number of these parameters, including molding materials, solvent rinse conditions, critical point drying time, and annealing conditions. AeroShield also performed thorough analyses on how these optimized parameters affected important final characteristics of the gels, including optical clarity, thermal conductivity, and dimensional stability. This campaign culminated in the production of laboratory scale aerogel samples using significantly lower process times of both 36 and 18 total hours, which represent Phase 1 Target and Stretch goals. In order to achieve widespread market adoption, monolithic sheets of the aerogel material must be made to industry-standard sizes (8’ x 12’) at a cost that provides 5-7 year or less breakeven energy savings for consumers (<$2 sq/ft). The work performed under this Phase 1 award shows that time and materials required to make aerogel samples, which make up a significant portion of their overall cost, can be greatly reduced without sacrificing quality. AeroShield plans to use these optimized processes to produce larger, product-relevant sized aerogels in order to achieve target final material costs.

Wilke, Kyle↗