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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1984

A numerical analysis of transient planetary waves and the vertical structure in a meso-strato-troposphere model, part 1.4A

The structure of unstable planetary waves is computed by a quasi-geostrophic model extending from the surface up to 80 km by means of eigenvalue-eigenfunction techniques in spherical coordinates. Three kinds of unstable modes of distinct phase speeds and vertical structures are identified in the winter climate state: (1) the deep Green mode with its maximum amplitude in the stratosphere; (2) the deep Charney mode with its maximum amplitude in the troposphere: and (3) the shallow Charney mode which is largely confined to the troposphere. Both the Green mode and the deep Charney mode are characterized by very slow phase speeds. They are mainly supported by upward wave energy fluxes, but the local baroclinic energy conversion within the stratosphere also contributes in supporting these deep modes. The mesosphere and the troposphere are dynamically independent in the summer season decoupled by the deep stratospheric easterly. The summer mesosphere supports the easterly unstable waves 1-4. Waves 3 and 4 are identified with the observed mesospheric 2-day wave and 1.7-day wave, respectively.

Zhang, K. S.↗
NASA NTRS1990

Broadband shock-associated noise of moderately imperfectly expanded supersonic jets

Recently a stochastic model theory of broadband shock-associated noise from supersonic jets was developed. The theory is capable of predicting the near and far field noise spectra of slightly imperfectly expanded jets. In this paper, the noise prediction formulas are extended to the moderately imperfectly expanded Mach number range. It is found that the shock noise intensity depends only on the amplitude of the quasi-periodic shock cell structure and not on the strong first shock. This makes it possible to extend the noise prediction formulas by simply modifying the shock cell strength in the original formulas. Here this step is implemented semiempirically, and a set of shock noise prediction formulas are developed.

Tam, C. K. W.↗
NASA NTRS1991

Laboratory studies in ultraviolet solar physics

The research activity comprised the measurement of basic atomic processes and parameters which relate directly to the interpretation of solar ultraviolet observations and to the development of comprehensive models of the component structures of the solar atmosphere. The research was specifically directed towards providing the relevant atomic data needed to perform and to improve solar diagnostic techniques which probe active and quiet portions of the solar chromosphere, the transition zone, the inner corona, and the solar wind acceleration regions of the extended corona. The accuracy with which the physical conditions in these structures can be determined depends directly on the accuracy and completeness of the atomic and molecular data. These laboratory data are used to support the analysis programs of past and current solar observations (e.g., the Orbiting solar Observatories, the Solar Maximum Mission, the Skylab Apollo Telescope Mount, and the Naval Research Laboratory's rocket-borne High Resolution Telescope and Spectrograph). In addition, we attempted to anticipate the needs of future space-borne solar studies such as from the joint ESA/NASA Solar and Heliospheric Observatory (SOHO) spacecraft. Our laboratory activities stressed two categories of study: (1) the measurement of absolute rate coefficients for dielectronic recombination and electron impact excitation; and (2) the measurement of atomic transition probabilities for solar density diagnostics. A brief summary of the research activity is provided.

Parkinson, W. H.↗
DOE OSTI2025

Integrated structural model of the palladin–actin complex using XL ‐ MS , docking, NMR , and SAXS

Abstract Palladin is an actin‐binding protein that accelerates actin polymerization and is linked to the metastasis of several types of cancer. Previously, three lysine residues in an immunoglobulin‐like domain of palladin have been identified as essential for actin binding. However, it is still unknown where palladin binds to F‐actin. Evidence that palladin binds to the sides of actin filaments to facilitate branching is supported by our previous study showing that palladin was able to compensate for Arp2/3 in the formation of Listeria actin comet tails. Here, we used chemical crosslinking to covalently link palladin and F‐actin residues based on spatial proximity. Samples were then enzymatically digested, separated by liquid chromatography, and analyzed by tandem mass spectrometry. Peptides containing the crosslinks and specific residues involved were then identified for input to the HADDOCK docking server to model the most likely binding conformation. Small‐angle x‐ray scattering was used to provide further insight into palladin flexibility and the binding interface, and NMR spectra identified potential interactions between palladin's Ig domains. Our final structural model of the F‐actin:palladin complex revealed how palladin interacts with and stabilizes F‐actin at the interface between two actin monomers. Three actin residues that were identified in this study also appear commonly in the actin‐binding interface with other proteins such as myotilin, myosin, and tropomodulin. An accurate structural representation of the complex between palladin and actin extends our understanding of palladin's role in promoting cancer metastasis through the regulation of actin dynamics.

Sargent, Rachel [Department of Chemistry and Bioch↗
DOE OSTI2022

Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing

Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the δ ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Selective hydrogenation of CO 2 and CO over potassium promoted Co/ZSM-5

The utilization of CO 2 as a C 1 feedstock for synthesis of value-added chemicals and fuels could both mitigate the negative effects associated with increasing CO 2 emissions and decrease dependence on fossil fuels as part of a future circular carbon economy. Co-based catalysts have been well-developed for Fischer-Tropsch synthesis (FTS), but replacing the CO reactant with CO 2 (CO 2 -FTS) typically results in low selectivity toward desirable light olefins. To better understand the structure-property relationships of Co-based catalysts, and extend promising FTS results to CO 2 -FTS, we have studied the effect of a potassium promoter and acidic properties of ZSM-5 on catalytic performance. The selectivity of FTS and CO 2 -FTS is shown to be a strong function of Si/Al ratio in co-impregnated catalysts, with findings supported by in situ XAFS and FTIR, demonstrating light olefin selectivity can be tuned by Si/Al ratio and the method of introducing the K promoter.

36 MATERIALS SCIENCE↗
DOE OSTI2021

CFM-ID 4.0: More Accurate ESI MS/MS Spectral Prediction and Compound Identification

In the field of metabolomics, mass spectrometry (MS) is the method most commonly used for identifying and annotating metabolites. As this typically involves matching a given MS spectrum against an experimentally acquired reference spectral library, this approach is limited by the coverage and size of such libraries (which typically number in the thousands). These experimental libraries can be greatly extended by predicting the MS spectra of known chemical structures (which number in the millions) to create computational reference spectral libraries. To facilitate the generation of predicted spectral reference libraries we developed CFM-ID, a computer program that can accurately predict ESI-MS/MS spectrum for a given compound structure. CFM-ID is one of the best-performing methods for compound-to-mass-spectrum prediction, and also one of the top tools for in silico mass-spectrum-to-compound identification. This work improves CFM-ID’s ability to predict ESI-MS/MS spectra from compounds by: (1) learning parameters from features based on the molecular topology, (2) adding a new approach to ring cleavage that models such cleavage as a sequence of simple chemical bond dissociations and (3) expanding its hand-written rule-based predictor to cover more chemical classes, including acylcarnitines, acylcholines, flavonols, flavones, flavanones, and flavonoid glycosides. We demonstrate that this new version of CFM-ID (version 4.0) is significantly more accurate than previous CFM-ID versions, in terms of both EI-MS/MS spectral prediction and compound identification. CFM-ID 4.0 is available at http://cfmid4.wishartlab.com/ as a webservice and docker images can be downloaded at https://hub.docker.com/r/wishartlab/cfmid

Wang, Fei↗
DOE OSTI2022

Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis

We show that "single-atom” catalysts (SACs) have demonstrated excellent activity and selectivity in challenging chemical transformations such as photocatalytic CO 2 reduction. For heterogeneous photocatalytic SAC systems, it is essential to obtain sufficient information of their structure at the atomic level in order to understand reaction mechanisms. In this work, a SAC was prepared by grafting a molecular cobalt catalyst on a light-absorbing carbon nitride surface. Due to the sensitivity of the X-ray absorption near edge structure (XANES) spectra to subtle variances in the Co SAC structure in reaction conditions, different machine learning (ML) methods, including principal component analysis, K-means clustering, and neural network (NN), were utilized for in situ Co XANES data analysis. As a result, we obtained quantitative structural information of the SAC nearest atomic environment thereby extending the NN-XANES approach previously demonstrated for nanoparticles and size-selective clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Efficient Langevin dynamics for “noisy” forces

Efficient Boltzmann-sampling using first-principles methods is challenging for extended systems due to the steep scaling of electronic structure methods with the system size. Additionally, stochastic approaches provide a gentler system-size dependency at the cost of introducing “noisy” forces, which could limit the efficiency of the sampling. When the forces are deterministic, the first-order Langevin dynamics (FOLD) offers efficient sampling by combining a well-chosen preconditioning matrix S with a time-step-bias-mitigating propagator [G. Mazzola and S. Sorella, Phys. Rev. Lett. 118, 015703 (2017)]. However, when forces are noisy, S is set equal to the force-covariance matrix, a procedure that severely limits the efficiency and the stability of the sampling. Here, we develop a new, general, optimal, and stable sampling approach for FOLD under noisy forces. We apply it for silicon nanocrystals treated with stochastic density functional theory and show efficiency improvements by an order-of-magnitude.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings

We extend the DeePMD neural network architecture to predict electronic structure properties necessary to perform non-adiabatic dynamics simulations. While learning the excited state energies and forces follows a straightforward extension of the DeePMD approach for ground-state energies and forces, how to learn the map between the non-adiabatic coupling vectors (NACV) and the local chemical environment descriptors of DeePMD is less trivial. Most implementations of machine-learning-based non-adiabatic dynamics inherently approximate the NACVs, with an underlying assumption that the energy-difference-scaled NACVs are conservative fields. We overcome this approximation, implementing the method recently introduced by Richardson [J. Chem. Phys. 158, 011102 (2023)], which learns the symmetric dyad of the energy-difference-scaled NACV. Furthermore, the efficiency and accuracy of our neural network architecture are demonstrated through the example of the methaniminium cation CH 2 NH 2 + .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Impact of Radial Reflector Fidelity on Neutronics and Vessel Fluence Simulations

The Consortium for Advanced Simulation of Light Water Reactors is developing the Virtual Environment for Reactor Applications (VERA), and the MPACT code, which is the primary deterministic neutron transport solver in VERA, provides sub-pin level flux and power distributions as part of full-scale cycle depletion and analysis. In such calculations, an important aspect is the radial reflector treatment. To improve the fidelity of the radial reflector treatment, MPACT was extended to approximate the modeling of the reactor’s structural components such as the core shroud, barrel, neutron pads, and vessel. This work explores several modeling configurations with varying levels of fidelity and computational burden and assesses the importance of modeling fidelity on the eigenvalue and pin power distribution. Two two-dimensional (2-D) problems were analyzed to assess the impact on eigenvalue and pin power distributions with low-fidelity, coarse square cell reflector representations: (1) a Watts Bar Nuclear Plant Unit 1 (WBN1) quarter-core slice with depletion and (2) an AP1000 quarter-core slice. In this work, the analyses showed that the effect on eigenvalue is fairly small, but the effect on pin power is more pronounced, especially locally in the assemblies closest to the periphery, where the maximum pin power difference is nearly 3.5% in the AP1000 case. Two additional 2-D problems were used to assess the comparison between the low-fidelity coarse square cell treatment and a high-fidelity geometric representation that uses subpin material specification: (1) the same WBN1 quarter-core slice and (2) a representative model of the NuScale small modular reactor (SMR), which features a solid reflector design with moderator holes. These results demonstrate that even a coarse, low-fidelity representation adequately captures the necessary simulation characteristics. Last, these capabilities were applied to the 2-D WBN1 quarter-core depletion to assess the impact on vessel fluence using VeraShift. From adjoint calculations, pins along the periphery were observed to be of highest importance for fluence calculation, so the impact of the reflector representation in MPACT could theoretically substantially affect the predicted result. However, it was observed that the change in pin powers along the periphery minimally impacts the maximum vessel fluence with a difference within the statistical uncertainty but provides terrific insight on the sensitivity of the peripheral pins.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Microstructural evolution of nanotwinned Al-Zr alloy with significant 9R phase

Aluminum (Al) alloys have a multitude of applications, notably in the automotive, aerospace and coating industries, yet they exhibit significantly lower mechanical strength than conventional steels. Nanotwins drastically improve mechanical strength while retaining ductility. However, the high SFE of Al largely prevents twinning. Here, we synthesize Al-Zr alloy films containing an abundance of incoherent twin boundaries and 9R phases. These alloys exhibit an extended solid solubility of Zr, retaining a columnar nanotwinned structure across all compositions. These films reach a hardness up to 4.2 GPa with 10 at% Zr and demonstrate the capacity for producing strong Al alloys with nanotwins.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Condensation of collective polar vortex modes

The dynamics of extended objects such as domain walls, domain bubbles, vortex structures, etc., can be described by their equations of motion associated with their effective mass and spring constant. Here we analytically derive the equations of motion for the polarization dynamics and elastodynamics for the structural responses of ferroelectric polar vortices, and theoretically extract their effective mass, spring constant, and mode frequencies. We demonstrate two subterahertz phonon modes and predicted their frequencies, both consistent with our recent experimental measurements and phase-field simulations. We show that elastic modulation of the energy function and spring constants leads to a condensation of a collective mode upon a second-order structural transition from symmetric to asymmetric vortices at a critical strain, analogous to the ferroelectric soft phonon mode at a ferroelectric transition. Furthermore, the present work offers a theoretical framework for predicting and manipulating the ultrafast collective dynamics of polar nanostructures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Atomic dynamics in fluids: Normal mode analysis revisited

Developing microscopic understanding of the thermal properties of liquids is challenging due to their strong dynamic disorder, which prevents characterization of the atomic degrees of freedom. There have been significant research interests in the past few decades to extend the normal mode analysis for solids to instantaneous structures of liquids. However, the nature of normal modes that arise from these unstable structures is still elusive. Here, in this paper, we explore the instantaneous eigenmodes of dynamical matrices of various Lennard-Jones argon liquid and gas systems at high temperatures and show that the normal modes can be interpreted as an interpolation of T →∞ (gas) and T=0 (solid) mode descriptions. We find that normal modes become increasingly collisional and translational, recovering atomistic gaslike behavior rather than vibrational with increase in temperature, suggesting that normal modes in liquids may be described by both solidlike and gaslike modes.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

High-power terahertz pulse generation from bias-free nanoantennas on graded composition InGaAs structures

We present a bias-free photoconductive emitter that uses an array of nanoantennas on an InGaAs layer with a linearly graded Indium composition. The graded InGaAs structure creates a built-in electric field that extends through the entire photoconductive active region, enabling the efficient drift of the photo-generated electrons to the nanoantennas. The nanoantenna geometry is chosen so that surface plasmon waves are excited in response to a 1550 nm optical pump to maximize photo-generated carrier concentration near the nanoantennas, where the built-in electric field strength is maximized. With the combination of the plasmonic enhancement and built-in electric field, high-power terahertz pulses are generated without using any external bias voltage. We demonstrate the generation of terahertz pulses with 860 µW average power at an average optical pump power of 900 mW, exhibiting the highest radiation power compared to previously demonstrated telecommunication-compatible terahertz pulse emitters.

42 ENGINEERING↗
DOE OSTI2024

ORBITAL ANGULAR MOMENTUM BEAMS RESEARCH USING A FREE-ELECTRON LASER OSCILLATOR

Orbital angular momentum (OAM) photon beams are excellent tools for non-contact optical manipulation of mat- ter in a broad photon energy range. A free-electron laser (FEL) oscillator is well-suited for studying OAM beams with various features including a wide spectral coverage, wavelength tunability, two-color lasing, polarization control, etc. Here, we report the first experimental demonstration of superposed OAM beams from an oscillator FEL. Lasing at around 458 nm, we have generated superposed OAM beams up to the fourth order as a superposition of two pure OAM modes with opposite helicities. These generated beams have a good beam quality, a high degree of circular polarization, and high power. Using external rf modulation with frequen- cies from 1 to 30 Hz, we also developed a pulsed mode operation of the OAM beams with a highly reproducible temporal structure. FEL operation showcased in this work can be extended to higher photon energies, e.g. using a future x-ray FEL oscillator. The operation of such an OAM FEL also paves the way for the generation of OAM gamma-ray beams via Compton scattering.

Liu, P.↗
DOE OSTI2022

Tearing parameter failure integration with the multilevel solver.

The tearing parameter criterion and material softening failure method currently used in the multilinear elastic-plastic constitutive model was added as an option to modular failure capabilities. The modular failure implementation was integrated with the multilevel solver for multi-element simulations. Currently, this implementation is only available to the J 2 plasticity model due to the formulation of the material softening approach. The implementation compared well with multilinear elastic-plastic model results for a uniaxial tension test, a simple shear test, and a representative structural problem. Necessary generalizations of the failure method to extend it as a modular option for all plasticity models are highlighted.

36 MATERIALS SCIENCE↗