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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probing the state of hydrogen in δ − AlOOH at mantle conditions with machine learning potential

Hydrous and nominally anhydrous minerals are a fundamental class of solids of enormous significance to geophysics. They are the water carriers in the deep geological water cycle and impact structural, elastic, plastic, and thermodynamic properties and phase relations in Earth's forming aggregates (rocks). They play a critical role in the geochemical and geophysical processes that shape the planet. Their complexity has prevented predictive calculations of their properties, but progress in materials simulations ushered by machine-learning potentials is transforming this state of affairs. Here, we adopt a hybrid approach that combines deep learning potentials (DPs) with the strongly constrained and appropriately normed meta-generalized gradient approximation functional to simulate a prototypical hydrous system. We illustrate the success of this approach to simulate δ − AlOOH ( δ ) , a phase capable of transporting water down to near the core-mantle boundary of the Earth ( ∼ 2900 km depth and ∼ 135 GPa ) in subducting slabs. A high-throughput sampling of phase space using molecular dynamics simulations with DPs sheds light on the hydrogen-bond behavior and proton diffusion at geophysical conditions. These simulations provide a pathway for a deeper understanding of these crucial components that shape Earth's internal state. Published by the American Physical Society 2024

Luo, Chenxing (ORCID:0000000341166851)↗
DOE OSTI2021

Efficient Active Learning for Gaussian Process Classification by Error Reduction

Active learning sequentially selects the best instance for labeling by optimizing an acquisition function to enhance data/label efficiency. The selection can be either from a discrete instance set (pool-based scenario) or a continuous instance space (query synthesis scenario). In this work, we study both active learning scenarios for Gaussian Process Classification (GPC). The existing active learning strategies that maximize the Estimated Error Reduction (EER) aim at reducing the classification error after training with the new acquired instance in a onestep-look-ahead manner. The computation of EER-based acquisition functions is typically prohibitive as it requires retraining the GPC with every new query. Moreover, as the EER is not smooth, it can not be combined with gradient-based optimization techniques to efficiently explore the continuous instance space for query synthesis. To overcome these critical limitations, we develop computationally efficient algorithms for EER-based active learning with GPC. Further, we derive the joint predictive distribution of label pairs as a one-dimensional integral, as a result of which the computation of the acquisition function avoids retraining the GPC for each query, remarkably reducing the computational overhead. We also derive the gradient chain rule to efficiently calculate the gradient of the acquisition function, which leads to the first query synthesis active learning algorithm implementing EER-based strategies. Our experiments clearly demonstrate the computational efficiency of the proposed algorithms. We also benchmark our algorithms on both synthetic and real-world datasets, which show superior performance in terms of sampling efficiency compared to the existing state-of-the-art algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Near total reflection x-ray photoelectron spectroscopy: quantifying chemistry at solid/liquid and solid/solid interfaces

Near total reflection regime has been widely used in x-ray science, specifically in grazing incidence small angle x-ray scattering and in hard x-ray photoelectron spectroscopy (XPS). In this work, we introduce some practical aspects of using near total reflection (NTR) in ambient pressure XPS and apply this technique to study chemical concentration gradients in a substrate/photoresist system. Experimental data are accompanied by x-ray optical and photoemission simulations to quantitatively probe the photoresist and the interface with the depth accuracy of ~1 nm. Together, our calculations and experiments confirm that NTR XPS is a suitable method to extract information from buried interfaces with highest depth-resolution, which can help address open research questions regarding our understanding of concentration profiles, electrical gradients, and charge transfer phenomena at such interfaces. Furthermore, the presented methodology is especially attractive for solid/liquid interface studies, since it provides all the strengths of a Bragg-reflection standing-wave spectroscopy without the need of an artificial multilayer mirror serving as a standing wave generator, thus dramatically simplifying the sample synthesis.

solid/solid interface↗
DOE OSTI2025

Skyrmion lattice manipulation with electric currents and thermal gradients in MnSi

The skyrmion lattice (SkL) in MnSi was studied using small-angle neutron scattering and under the influence of a radial electric current in a Corbino geometry. In response to the applied current, the SkL undergoes an angular reorientation with respect to the MnSi crystal lattice. The reorientation is nonmonotonic with increasing current, with the SkL rotating first in one direction and then the other. The SkL reorientation was studied at different sample locations and found to depend on the local current density as inferred from a finite-element analysis. Here, the nonmonotonic response indicates the presence of two competing effects on the SkL, most likely due to the presence of both radial electric and thermal currents. Such a scenario is supported by micromagnetic simulations, which show how these effects can act constructively or destructively to drive the SkL rotation, depending on the direction of the electric current. In addition, the simulations also suggest how the direction of the skyrmion flow may affect the SkL orientation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Techno-Economic Performance of Eavor Loop 2.0

This project evaluated techno-economic performance for a sample Eavor-Loop 2.0 design for electricity production and direct-use heating. The Eavor-Loop 2.0 design investigated is a 7.5-km deep closed-loop geothermal system consisting of 12 laterals for a total of more than 90 km of downhole well and lateral length. Both a high geothermal gradient scenario of 60 degrees C/km and a low geothermal gradient scenario of 30 degrees C/km were considered. With pure water injected at 60 degrees C and 80 kg/s, reservoir simulations with the Slender-Body Theory simulator indicate average production temperatures over a 30-year lifetime of ~125 degrees C and ~210 degrees C for the low and high geothermal gradient scenario, respectively. These correspond to heat production of ~22 M Wth and ~51 M Wth, respectively. Using IPSEpro simulations, we find average power production of ~2.2 M We and ~8.6 M We, respectively, for a subcritical organic Rankine cycle power plant with air-cooled condensers. Cost estimates indicate the overall capital and levelized costs are dominated by the lateral drilling cost. Obtaining a levelized cost of electricity below $70/M Wh requires a geothermal gradient of 60 degrees C/km, a discount rate below 9%, and lateral drilling cost below $400/m. A well cost model indicates that ~$400/m for the Eavor-Loop 2.0 design investigated can be obtained for a drilling rate of penetration about 40 ft/hr (with bit life of 50 hours), and omitting casing and cement. Traditional (geothermal) well drilling has achieved these drilling rate conditions, including the Utah FORGE project where the rate of penetration has exceeded 50 ft/hr in granite. However, it is unclear if these conditions are still valid for drilling the Eavor-Loop 2.0 laterals (i.e., ~82 km of laterals at 4 to 7.5-km vertical depth with rock temperatures up to 460 degrees C), as such downhole completion has never been developed before. Competitive levelized cost of heat values ($1.2-$8.2/GJ) are calculated, even for the low geothermal gradient scenario (30 degrees C/km) and lateral drilling cost of $600/m.

advanced geothermal system↗
DOE OSTI2021

A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

14 SOLAR ENERGY↗
DOE OSTI2025

Water Dynamics of Superacid Aromatic Proton Exchange Membranes for Fuel Cell Applications

Proton exchange membranes (PEMs) with high conductivity are of critical importance for the development of fuel cells, electrolyzers, and other electrochemical technologies. In this research, poly(1,1,2,2-tetrafluoro-2-phenoxyethane-1-sulfonic acid) (PTPS) with an aromatic polymer main chain and a perfluorinated superacidic polymer side chain was synthesized. The water dynamics of PTPS were characterized across various length scales using a combination of Fourier-transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (NMR) and compared with Nafion, a standard perfluorinated PEM, and sulfonated poly(ether sulfone) (SPES 40), an aromatic PEM without perfluorinated superacid side chains. The T 1 and T 2 relaxation times of water in the samples probed by NMR increase from SPES 40 to PTPS to Nafion, indicating that the local motion of the water molecules becomes faster. This trend corresponds well with the relative fraction of bulk-like water determined using FTIR. At larger length scales, the diffusion coefficient of water was characterized using pulsed-field gradient NMR (PFG-NMR). At a longer diffusion time (Δ = 100 ms), PTPS has a smaller diffusion coefficient compared with both Nafion and SPES 40, due to restricted diffusion, and this effect is also evident in the proton conductivity of the hydrated membranes. From this comparison, it is apparent that the aromatic backbone and side chain type greatly influence the water dynamics in PEMs at various length scales and the water dynamics significantly impact the bulk proton conductivity. These insights will lead to new designs for aromatic PEMs and help to identify bottlenecks in current materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Hyperspectral Detection of the Fluorescence Shift between Chirality-Sorted Empty and Water-Filled Single-Wall Carbon Nanotube Enantiomers

Single-wall carbon nanotubes (SWCNTs) have extraordinary electronic and optical properties that depend strongly on their exact chiral structure and their interaction with their inner and outer environment. The fluorescence (PL) of semiconducting SWCNTs, for instance, will shift depending on the molecules with which the SWCNT’s hollow core is filled. These interaction-induced shifts are challenging to resolve on the ensemble level in samples containing a mixture of different filling contents due to the relatively large inhomogeneous line width of the ensemble SWCNT PL compared to the size of these shifts. To circumvent this inhomogeneous broadening, single-tube spectroscopy and hyperspectral imaging are often applied, which until now required time-consuming statistical studies. Here, we present hyperspectral PL microscopy combined with automated SWCNT segmenting based on either principal component analysis or a convolutional neural network, capable of both spatially and spectrally resolving the PL along the length of many individual SWCNTs at the same time and automatically fitting peak positions and line widths of individual SWCNTs. The methodology is demonstrated by accurately determining the emission shifts and line widths of thousands of left- and right-handed empty and water-filled SWCNTs coated with a chiral surfactant, resulting in four statistical distributions which cannot be resolved in ensemble spectroscopy of unsorted samples. The results demonstrate a robust method to quickly probe ensemble properties with single-enantiomer spectral resolution. Moreover, it promises to be an absolute quantitative method to characterize the relative abundances of SWCNTs with different handedness or filling content in macroscopic samples, simply by counting individual species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Kent Island Boundary Layer Field Campaign Report

Understanding the mechanisms governing the urban atmospheric environment is critical for informing urban populations about the impacts of climate change and associated mitigation and adaptation measures. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) was conducted to study the interactions among the earth’s surface, the atmospheric boundary layer (ABL), aerosols, atmospheric composition, clouds, radiation, and precipitation at each experimental site, and to examine how the spatial gradients across the region interact to create the climate conditions in Baltimore. In this activity we performed weekly soundings, alternating between day and night from Kent Island, to get low-frequency, long-term observations of ABL/lower tropospheric thermodynamic structure. However, during the CoURAGE intensive operational period (IOP), we performed high-frequency sampling (2x/day), at times coinciding with CoURAGE IOP soundings at the main site. The short-term, high-frequency sounding information that was launched at 12Z and 18Z was to complement the longer-term data set.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Nb3Sn Superconducting Radiofrequency Cavity Fabricated by Sputter Deposition from a Stoichiometric Target

Nb$_3$Sn is a promising coating material for superconducting radiofrequency cavities in next-generation accelerators due to its higher superconducting transition temperature (~18.3 K) and superheating field (~400 mT), offering the potential to surpass the intrinsic performance limits of bulk niobium. We developed a sputter coating technique employing a stoichiometric target to fabricate Nb$_3$Sn films on the inner surfaces of niobium cavities. A Nb$_3$Sn tube target, prepared by growing a >20 m thick Nb$_3$Sn coating on the surface of a Nb tube via a Sn vapor diffusion process, was used in a DC cylindrical magnetron sputtering system. Approximately 170 nm thick Nb-Sn films were sputter-coated onto flat Nb substrates positioned to replicate the beam tube and equator regions of a 2.6 GHz cavity, followed by annealing at 950 °C for 3 hours to form Nb$_3$Sn. The composition, morphology, and structure of the annealed Nb$_3$Sn films were examined. Then, a ~1.2 m-thick film was sputter-coated onto the interior of a 2.6 GHz Nb cavity following the procedure developed for the flat samples. The cavity was subsequently annealed under the same conditions used for the flat samples. Cryogenic RF testing of the sputter-coated cavity demonstrated a T$_c$ of approximately 17.8 K, consistent with Nb$_3$Sn layer. Subsequently, the cavity underwent a light Sn recoating followed by a vapor diffusion Nb$_3$Sn coating process, achieving a quality factor of approximately 8.5 × 10$^9$ and an accelerating gradient of up to 11.3 MV/m at 4.2 K.

Shakel, Md Sharifuzzaman [Old Dominion U.; Jeffers↗
DOE OSTI2020

DPM: A deep learning PDE augmentation method with application to large-eddy simulation

A framework is introduced that leverages known physics to reduce overfitting in machine learning for scientific applications. The partial differential equation (PDE) that expresses the physics is augmented with a neural network that uses available data to learn a description of the corresponding unknown or unrepresented physics. Training within this combined system corrects for missing, unknown, or erroneously represented physics, including discretization errors associated with the PDE's numerical solution. For optimization of the network within the PDE, an adjoint PDE is solved to provide high-dimensional gradients, and a stochastic adjoint method (SAM) further accelerates training. Additionally, the approach is demonstrated for large-eddy simulation (LES) of turbulence. High-fidelity direct numerical simulations (DNS) of decaying isotropic turbulence provide the training data used to learn sub-filter-scale closures for the filtered Navier–Stokes equations. Out-of-sample comparisons show that the deep learning PDE method outperforms widely-used models, even for filter sizes so large that they become qualitatively incorrect. It also significantly outperforms the same neural network when a priori trained based on simple data mismatch, not accounting for the full PDE. Measures of discretization errors, which are well-known to be consequential in LES, point to the importance of the unified training formulation's design, which without modification corrects for them. For comparable accuracy, simulation runtime is significantly reduced. A relaxation of the typical discrete enforcement of the divergence-free constraint in the solver is also successful, instead allowing the DPM to approximately enforce incompressibility physics. Since the training loss function is not restricted to correspond directly to the closure to be learned, training can incorporate diverse data, including experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A molecular dynamics simulation study of thermal conductivity anisotropy in β-octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (β-HMX)

Molecular dynamics (MD) simulations were used to predict the thermal conductivity of β-octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (β-HMX) along directions normal to the (011), (110), and (010) crystal planes. These directions were selected based on the measured morphological importance of the corresponding crystal surfaces. A reverse non-equilibrium MD approach was used wherein a constant heat flux is imposed along a prescribed direction and the resulting steady-state temperature gradient determined. The coefficient of thermal conductivity λ is the quotient of heat flux and temperature gradient (i.e. Fourier's law). Finite-size effects and sensitivity to imposed heat flux were investigated. The results reveal a modest dependence of the conductivity on crystal orientation, significant finite-size effects, and low sensitivity to imposed flux so long as the Fourier's law analysis is limited to the spatial interval in the simulation cell for which the temperature gradient is constant. Infinite-length thermal conductivities were estimated by linear regression of λ -1 (L) versus reciprocal cell length L -1 for each direction. The predicted values are systematically larger, but within a factor of two, than most published experimental determinations, the latter of which were obtained for pressed-powder or composite samples rather than oriented single crystals.

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

Defect And Damage Characterization Of Additively Manufactured Titanium Alloy Ti-5553 Using Traditional Computed Tomography Volume Segmentation And Machine Learning Algorithms

The mechanical response of a component is affected by defects, such as porosity, arising from the laser powder bed fusion (LPBF) fabrication process. Thus, it is important to develop accurate and efficient inspection methods for identifying porosity. In this work, porosity identified in an X-ray computed tomography (XCT) volume of a Ti-5553 coupon was compared to pores identified in a serial sectioned volume that represented the ground truth. The porosity of the XCT scan was identified using contrast-based, ISO-based, and machine learning (ML) methods for segmentation. Large inherent porosity was easy to identify, but the ISO thresholding still struggled due to the intensity gradient resulting from both the beam hardening in XCT and the uneven lighting of the serial sectioning panels. Further, the results show that ML-based methods were better suited for identifying small pores and reducing the amount of false positives. Additionally, high strain-rate impact testing was done on some of the XCT samples as well as post-mortem XCT inspection, and the same suite of segmentation and quantification tools were used to identify the large spallation cavities. The comparison of porosity pre- and post-mortem provides insight on the influence of the LPBF porosity on the formation of spall cavities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Neutron Characterization of 3D Printed Gas Turbine Alloy With Internal Cooling Structure

The objective of this research work is to investigate the residual strains induced by the presence of a exit hole and internal hollow geometry present in a 3D printed IN718 similar to that seen in a gas turbine blade. The specimens were manufactured with an internal hollow structure that ended with an exit hole, and fabricated by direct metal laser sintering additive manufacturing technique. Using this technique, metal powder was melted by a laser source and rapidly cooled to form the part layer by layer. Rapid heating and cooling generate large thermal gradients and residual stresses in the 3D printed material. To be aligned with industry standards, specimens were heat-treated to relieve most of the residual stresses as well as to investigate whether IN718 still preserved a nonhomogeneous stress state after the thermal treatment. Roughness and microstructure analysis was performed to understand the heat-treated sample properties. To investigate the volumetric residual strain, the neutron diffraction technique was used with a definite neutron wavelength. Bragg’s law was used to determine lattice spacing. Interplanar lattice spacing and stress-free state lattice spacings were measured to calculate the strains. Residual strains were measured in the x and y direction to understand the impact of the presence of a exit hole and internal hollow geometry. The results of volumetric residual strain in the x and y directions showed no significant variations in the distribution of the strain due to the presence of a hollow structure as well as the exit hole. Overall, no significant stress concentration was observed in the heat treated sample. Sample geometry, instrumentation for neutron diffraction, and material properties are discussed in detail in this paper.

Raju, Nandhini↗
DOE OSTI2021

Equatorial Electronic Structure in the Uranyl Ion: Cs 2 UO 2 Cl 4 and Cs 2 UO 2 Br 4

Electric field gradient (EFG) tensors in the equatorial plane of the linear UO 2 2+ ion have been measured by nuclear magnetic resonance (NMR) and nuclear quadrupole resonance (NQR) experiments and computed by relativistic Kohn–Sham methods with and without environment embedding for Cs 2 UO 2 Cl 4 and Cs 2 UO 2 Br 4 . This approach expands the possibilities for probing the electronic structure in uranyl complexes beyond the strongly covalent U–O bonds. The combined analyses find that one of the two largest principal EFG tensor components at the halogen sites points along the U–X bond (X = Cl, Br), and the second is parallel to the UO 2 2+ ion; in Cs 2 UO 2 Cl 4 , the components are nearly equal in magnitude, whereas in Cs 2 UO 2 Br 4 , due to short-range bromide–cesium interactions, the equatorial component is dominant for one pair of Br sites and the axial component is larger for the second pair. Additionally, the directions and relative magnitudes of the field gradient principal axes are found to be sensitive to the σ and π electron donation by the ligands and the model of the environment. Chlorine-35 NQR spectra of 235 U-depleted and 235 U-enriched Cs 2 UO 2 Cl 4 exhibited no uranium-isotope-dependent shift, but the resonance of the depleted sample displayed a 58% broader line width.

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

Dynamics of Bottlebrush Polymers in Solution by Neutron Spin Echo Spectroscopy

In this work, we present dynamical and morphological studies on poly(dimethylsiloxane) (PDMS)-based bottlebrush polymers in solution. A combination of small-angle neutron scattering (SANS), pulsed field gradient nuclear magnetic resonance (PFG NMR), and neutron spin echo (NSE) spectroscopy was used to identify structural changes inherent in changing the side-chain length while keeping the backbone constant. These morphological changes are mirrored in the diffusion coefficient determined by PFG NMR, resulting in two coefficients for the elongated and one for the more spherical sample. Faster motions are tracked by NSE using the advantage of time- and length-scale resolutions with the diffusion coefficient predetermined by PFG NMR. Hereby, the sample with short side chains relaxes like wormlike micelles considering the longitudinal direction, whereas the dynamics along the radial extension shows a much stronger Q-dependence as known from any theory. The dynamical behavior within the blob region, covered with the more spherical sample, follows the predictions of Zimm as well as of Zilman and Granek (ZG).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sulfuric Acid New Particle Formation Field Campaign Report

This campaign aimed to measure gaseous sulfuric acid concentrations at the surface level using a newly developed sulfuric acid dimethylamine-reactive condensation particle counter (SAD-RCPC). Sulfuric acid is a key nucleation precursor and is the likely driving compound for new particle formation at the Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) observatory. The SAD-RCPC will be deployed in the future at ARM SGP (AFC010010: "Vertically Resolved Nucleation Precursors at SGP") scheduled for 2024. The campaign was in conjunction with Eleanor Browne's campaign (AFC010075: Boundary-layer gradients in new particle formation) and took place from 05/7/23 to 5/21/23. The measured sulfuric acid concentrations by the SAD-RCPC will be compared to concentrations measured by Browne's chemical ionization mass spectrometer (CIMS). The SAD-RCPC consisted of a glass flow reactor coupled to a 1-nm diethylene glycol condensation particle counter (DEG CPC). Sampled air was mixed with a high concentration of dimethylamine vapor (~1 ppbv). Sulfuric acid within the sampled gas nucleated with dimethylamine to form detectable 1-nm particles. The measured particle concentration was converted to sulfuric acid concentration using nucleation reaction kinetics. The pulse height distribution of particles detected by the DEG CPC was observed using a multi-channel analyzer. Large pre-existing particles were separated from the freshly formed particles in the SAD-RCPC from the pulse height distribution (PHD). During the short campaign, seven new particle formation events were observed. The SAD-RCPC detected an increase in sulfuric acid concentration, but this also coincided with an increase in pre-existing <3 nm particle concentrations. In addition, Browne's CIMS detect sulfuric acid concentrations in the range of 10 5 -10 6 cm -3 .

54 ENVIRONMENTAL SCIENCES↗