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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

The Outer Limits of Galaxy Clusters: Observations to the Virial Radius with Suzaku, XMM,and Chandra

The outskirts of galaxy clusters, near the virial radius, remain relatively unexplored territory and yet are vital to our understanding of cluster growth, structure, and mass. In this presentation, we show the first results from a program to constrain the sate of the outer intra-cluster medium (ICM) in a large sample of galaxy clusters, exploiting the strengths of three complementary X-ray observatories: Suzaku (low, stable background), XMM-Newton (high sensitivity),and Chandra (good spatial resolution). By carefully combining observations from the cluster core to beyond r200, we are able to identify and reduce systematic uncertainties that would impede our spatial and spectral analysis using a single telescope. Our sample comprises nine clusters at z is approximately 0.1-0.2 fully covered in azimuth to beyond r200, and our analysis indicates that the ICM is not in hydrostatic equilibrium in the cluster outskirts, where we see clear azimuthal variations in temperature and surface brightness. In one of the clusters, we are able to measure the diffuse X-ray emission well beyond r200, and we find that the entropy profile and the gas fraction are consistent with expectations from theory and numerical simulations. These results stand in contrast to recent studies which point to gas clumping in the outskirts; the extent to which differences of cluster environment or instrumental effects factor in this difference remains unclear. From a broader perspective, this project will produce a sizeable fiducial data set for detailed comparison with high-resolution numerical simulations.

Miller, Eric D.↗
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 OSTI2024

Impact of Synthetic Parameters on Structure and Electrochemistry of High-Entropy Layered Oxide LiNi 0.2 Co 0.2 Mn 0.2 Al 0.2 Fe 0.2 O 2

Here, this study explored a high-entropy layered oxide (HELO), LiNi 0.2 Co 0.2 Mn 0.2 Al 0.2 Fe 0.2 O 2 , prepared by coprecipitation followed by heat treatment. Coprecipitation yielded a kinetically favored product, and the subsequent heat treatment under various temperatures and times allowed tuning the material toward more thermodynamically favored structures. Refinement of X-ray powder diffraction (XRD) revealed that after 450 °C treatment, ~12% of the Ni 2+ in the structure was located within the lithium cation layer. After heat treatment at 600, 700, and 800 °C, there was a continued decrease of Ni 2+ in the lithium cation layer to 9.3, 6.3, and 3.7%, respectively. Longer heat treatment times at 800 °C decreased the level to 2.5%. Electrochemical behavior was evaluated by cyclic voltammetry, galvanostatic cycling, rate capability testing, and electrochemical impedance spectroscopy, where increased loaded voltage, functional capacity, rate capability, and decreased impedance were observed for samples treated at higher temperatures with lower cation mixing. X-ray absorption near edge spectroscopy (XANES) data indicated that the redox activity of Ni and Co was dominant in the electrochemistry, while the participation of Mn or Fe was minimal. The level of Ni oxidation state change between charge and discharge related to the heat treatment temperature where the samples with high cation mixing showed lower capacity consistent with some Ni 3+ in the transition metal layer inaccessible for electron transfer. Longer heat treatment times at 800 °C did not continue to provide a benefit in electrochemical function even with some additional reduction in cation mixing.

25 ENERGY STORAGE↗
DOE OSTI2024

Selective laser sintering and spark plasma sintering of (Zr,Nb,Ta,Ti,W)C compositionally complex carbide ceramics

Abstract Two advanced manufacturing processes, spark plasma sintering (SPS) and selective laser sintering (SLS), have been developed for synthesis of (Zr,Nb,Ta,Ti,W)C compositionally complex carbide (CCC) via reactive sintering of a powder mixture of constitute monocarbides. X‐ray diffraction analysis confirmed that the single‐phase CCC can be formed by both SPS and SLS. While a homogenous microstructure with uniform metal element distributions was developed during SPS, three‐layer microstructures with a thin TiC‐rich layer and two TaC‐rich layers along with a TiO 2 ‐rich surface layer containing W nanoparticles were formed during SLS. In addition, cellular structures with W, Zr, and Ti element segregation and dislocations on cell boundaries were observed in the SLS‐CCC sample, indicating the effect of nonequilibrium conditions on microstructure formation during laser melting followed by rapid cooling and solidification process. Compared to the SPS‐CCC sample, the SLS‐CCC showed enhanced hardness and reduced thermal conductivity, which may be related to their unique cellular structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accelerating the discovery of low-energy structure configurations: A computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A High-Entropy Oxide as High-Activity Electrocatalyst for Water Oxidation

High-entropy materials are an emerging pathway in the development of high-activity (electro)catalysts because of the inherent tunability and coexistence of multiple potential active sites, which may lead to earth-abundant catalyst materials for energy-efficient electrochemical energy storage. In this report, we identify how the multication composition in high-entropy perovskite oxides (HEO) contributes to high catalytic activity for the oxygen evolution reaction (OER), i.e., the key kinetically limiting half-reaction in several electrochemical energy conversion technologies, including green hydrogen generation. We compare the activity of the (001) facet of LaCr 0.2 Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 O 3-δ with the parent compounds (single B-site in the ABO 3 perovskite). While the single B-site perovskites roughly follow the expected volcano-type activity trends, the HEO clearly outperforms all of its parent compounds with 17 to 680 times higher currents at a fixed overpotential. As all samples were grown as an epitaxial layer, our results indicate an intrinsic composition–function relationship, avoiding the effects of complex geometries or unknown surface composition. In-depth X-ray photoemission studies reveal a synergistic effect of simultaneous oxidation and reduction of different transition metal cations during the adsorption of reaction intermediates. The surprisingly high OER activity demonstrates that HEOs are a highly attractive, earth-abundant material class for high-activity OER electrocatalysts, possibly allowing the activity to be fine-tuned beyond the scaling limits of mono- or bimetallic oxides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Self-Organization of Phase Clusters in Homogeneously Disordered Polymer Composite Materials

The surface morphologies of homogeneously disordered polymer composite films and coatings prepared using mixtures of components with limited or negligible mutual solubility are studied by electron microscopy. We elaborate on the characteristic self-organization of phase clusters in our samples, in which one phase gives rise to the structural scaffold of the composite, an internal percolation cluster of particles. The cluster cavities filled with discrete particle aggregates of the other phase are characterized by a relatively narrow Gaussian-like size distribution. This topological structure arises if composites are prepared by observing specific concentration conditions or using structure modifiers that affect particle aggregation. Self-organization of phase clusters minimizes the entropy production when the components are mixed and gives rise to local ordering in spatial alternation of particle aggregates. The local order transforms into cellular disorder as the length scale increases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images

Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By changing the cross-entropy weights and using augmentation, we demonstrate a generally improved adjusted F1-score over using the originally trained TrailMap model within our test datasets.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Planning Bias: Planning as a Source of Sampling Bias

Many data-driven planning methods are trained on data generated by planners. It is well known that many statistical learning methods are sensitive to sampling bias, and yet there has been little or no attention to planning as a sampling method and its role in introducing sampling bias into planner-generated training data. Recently, it has been demonstrated that A**,* in the presence of problems with variable heuristic error, prefers some solutions over other equally cost-optimal solutions. But, as we discuss in this paper, mitigation may not be as simple as resolving arbitrary tie-breaking by sampling from ties uniformly at random. In this paper, we formalize an intuition of planning bias. We focus on problems which output a single solution. Diverse planning only complicates the problem by generalizing it to bias in the set of sets; we show how it is subject to bias in the single solution. We make some useful observations about deterministic algorithms in contrast to non-deterministic algorithms. We explain how information entropy may be a good way to measure planning bias, and discuss some issues in evaluating practical approaches to measurement. We address the intuition that uniform random tiebreaking should mitigate bias; and sketch a novel approach to constructing an appropriate random distribution for duplicate detection during forward search for unbiased A*. Finally, we suggest directions for future work.

Planning Scheduling Algorithms↗
DOE OSTI2021

High-throughput ion irradiation of additively manufactured compositionally complex alloys

Several advanced nuclear reactor designs promise efficiency and safety improvements over the current reactor fleet but are limited by the current set of ASME code-qualified materials. Novel alloys including high-entropy alloys (HEAs), and more broadly compositionally complex alloys (CCAs), have shown promising irradiation-tolerance. However, the vast range of alloy compositions adds to an already time-consuming alloy development process. In this study, to accelerate the development of novel alloys for nuclear applications, a high-throughput (HTP) methodology has been employed. Additive manufacturing has been used to produce a compositional array of unary, binary, ternary, and quaternary alloys, including several CCAs, which span the Cr-Fe-Mn-Ni composition space. Additionally, the compositional array was homogenized at 1000°C for 24 hours and each sample was irradiated using 4-MeV Ni2+ ions at room temperature to a peak damage of 50 dpa, as estimated using SRIM, at the University of Wisconsin Ion Beam Laboratory. A custom XY stage was built to accommodate the large compositional array and half of each sample was masked during irradiation enabling both the irradiated and unirradiated properties of each alloy to be characterized side-by-side. Each alloy was characterized using X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and nanoindentation. CALPHAD simulations spanning the entire Cr-Fe-Mn-Ni composition space at 1000°C were performed to compare predicted equilibrium phases with phases identified experimentally from the unirradiated regions of each alloy. Nanoindentation measurements indicate radiation-induced hardening ranging from ~1-1.5 GPa in each of Cr-Fe-Mn-Ni CCAs, which is relatively insensitive to modest changes in alloy composition and comparable to hardening observed in neutron irradiated Cr-Fe-Mn-Ni CCAs in the literature. Overall, a substantial time savings was realized by employing HTP synthesis, irradiation, and characterization in this study compared to conventional techniques, the implications of which are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Experimental Study and Fractal Analysis on the Anisotropic Performance of Explosively Welded Interfaces of 304 Stainless Steel/245 Carbon Steel

The mechanical anisotropy of an explosive welding composite plate made of 304 stainless steel/245 steel was studied through shear experiments performed on explosively welded wavy interfaces along several orientation angles. The results indicated that the strength and the fracture energy of samples significantly varied with the orientation angles. The fracture surfaces of all samples were observed using a scanning electron microscope and through three-dimensional structure microscopy. The periodic features of all the fracture surfaces were clearly shown in different fracture modes. The fractal dimension of the fracture surfaces was calculated based on the fractal geometry by the box-counting method in MATLAB. The cohesive element model was used to analyze the fracture energy according to the physical dependence of the fractal dimension on thermodynamic entropy and interface separation energy. The fracture energy was an exponential function of the fractal dimension value, which was in good agreement with the experimental results. All results were validated for effective use in the application of anisotropy analysis to the welded interface and structural optimization of explosively welded composite plates.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Viscous shock-layer flowfield analysis by an explicit-implicit method

The present work extends the recently reported implicit analogue of MacCormack's earlier widely-used explicit method to external axisymmetric laminar flows with strong entropy gradients. The details of the 'numerics' of the implicit part are provided in a body-oriented coordinate system with a moving outer (shock) boundary during the transient part of the solutions. The limiting values of the Courant number are obtained when the shock boundary is treated explicitly. The solution algorithm outlined includes the treatment of the source term associated with the equations in weak conservation form. From the results obtained for two sample problems, it becomes clear that accuracy of predictions is, indeed, very good at higher values of the Courant number. There is a significant saving in overall computing time, depending on the Courant number used and the flow Reynolds number. These properties combined with the simplicity of programming the implicit analogue may appeal to researchers for using it in the analysis of three-dimensional flow problems.

Gupta, R. N.↗
NASA NTRS1983

A viscous shock-layer flowfield analysis by an explicit-implicit method

An implicit analogue of a widely used explicit method to external axisymmetric laminar flows with strong entropy gradients is extended. The details of the "numerics" of the implicit part are provided in a body oriented coordinate system with a moving outer (shock) boundary during the transient part of the solutions. The limiting values of the Courant number are obtained when the shock boundary is treated explicitly. The solution algorithm outlined includes the treatment of the source term associated with the equations in weak conservation form. From the results obtained for two sample problems, it becomes clear that accuracy of predictions is, indeed, very good at higher values of the Courant number. There is a significant saving in overall computing time, depending on the Courant number used and the flow Reynolds number. These properties combined with the simplicity of programming the implicit analog may appeal to researchers for using it in the analysis of 3-D flow problems.

Gupta, R. M.↗
DOE OSTI2024

Phase stability of a eutectic high entropy alloy under extremes of pressures and temperatures

Additively manufactured high-entropy alloys are of interest because of their unique combination of high yield strength and large ductility achieved with far-from-equilibrium crystalline phases and micro/nanostructure morphology. We report on the phase transformation and thermal equation of state of the eutectic high-entropy alloy (EHEA) Al 18 Co 20 Cr 10 Fe 10 Ni 40 W 2 , produced by laser powder-bed fusion (L-PBF). The EHEA was studied in a large-volume Paris–Edinburgh cell using energy-dispersive x-ray diffraction to a pressure of 5.5 GPa and a temperature of 1723 K. Static compression studies in diamond anvil cells using angle-dispersive x-ray diffraction extended the high-pressure structural data to 317 GPa at ambient temperature. The initial dual-phase nanolamellar face-centered cubic (FCC) and body-centered cubic (BCC) structure of Al 18 Co 20 Cr 10 Fe 10 Ni 40 W 2 transforms into a single FCC phase under high pressure, with the BCC-to-FCC phase transformation completing at 9 ± 2 GPa. The FCC phase remained stable up to the highest pressure of 317 GPa. The measured thermal equation of state for the FCC phase of Al 18 Co 20 Cr 10 Fe 10 Ni 40 W 2 is presented up to 5.5 GPa and 1473 K. We observed melting of the EHEA at 1698 ± 25 K at a pressure of 5.5 GPa, and the recrystallized sample shows an increased fraction of the CsCl-type (B2) phase at ambient conditions following release from the high-pressure high-temperature state. The BCC-to-FCC phase transition completion pressure is correlated with the nanolamellae thickness of the BCC layer in this diffusion-less transformation at ambient temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploring the First High-Entropy Thin Film Libraries: Composition Spread-Controlled Crystalline Structure

Thin films of two types of high-entropy oxides (HEOs) have been deposited on 76.2 mm Si wafers using combinatorial sputter deposition. In one type of the oxides, (MgZnMnCoNi)O x , all the metals have a stable divalent oxidation state and similar cationic radii. In the second type of oxides, (CrFeMnCoNi)O x , the metals are more diverse in the atomic radius and valence state, and have good solubility in their sub-binary and ternary oxide systems. Therefore, the resulting HEO thin films were characterized using several high-throughput analytical techniques. The microstructure, composition, and electrical conductivity obtained on defined grid maps were obtained for the first time across large compositional ranges. The crystalline structure of the films was observed as a function of the metallic elements in the composition spreads, that is, the Mn and Zn in (MgZnMnCoNi)O x and Mn and Ni in (CrFeMnCoNi)O x . The (MgZnMnCoNi)O x sample was observed to form two-phase structures, except single spinel structure was found in (MgZnMnCoNi)O x over a range of Mn > 12 at. % and Zn < 44 at. %, while (CrFeMnCoNi)O x was always observed to form two-phase structures. Composition-controlled crystalline structure is not only experimentally demonstrated but also supported by density function theory calculation.

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

Application of the ESMACS Binding Free Energy Protocol to a Multi-Binding Site Lactate Dehydogenase A Ligand Dataset

Over the past two decades, the use of fragment-based lead generation has become a common, mature approach to identify tractable starting points in chemical space for the drug discovery process. This approach naturally involves the study of the binding properties of highly heterogeneous ligands. Such datasets challenge computational techniques to provide comparable binding free energy estimates from different binding modes. The performance of a range of statistically robust ensemble-based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), is evaluated. Ligands designed to target two binding pockets in the lactate dehydogenase, a target protein, which vary in size, charge, and binding mode, are studied. When compared to experimental results, excellent statistical rankings are obtained across this highly diverse set of ligands. In addition, three approaches to account for entropic contributions are investigated: 1) normal mode analysis, 2) weighted solvent accessible surface area (WSAS), and 3) variational entropy. Normal mode analysis and WSAS correlate strongly with each other—although the latter is computationally far cheaper—but do not improve rankings. Variational entropy corrects exaggerated discrimination of ligands bound in different pockets but creates three outliers which reduce the quality of the overall ranking.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Silicon nanocrystals in SiN{sub x}/SiO{sub 2} hetero-superlattices: The loss of size control after thermal annealing

Superlattices containing 3 nm thick silicon rich silicon nitride sublayers and 3 nm and 10 nm thick SiO{sub 2} barriers were prepared by plasma enhanced chemical vapor deposition. Despite the as-prepared samples represented a well-kept multilayer structure with smooth interfaces, the high temperature annealing resulted in the total destruction of multilayer structure in the samples containing 3 nm SiO{sub 2} barriers. Energy-filtered transmission electron microscopy images of these samples indicated a silicon nanoclusters formation with sizes of 2.5–12.5 nm, which were randomly distributed within the structure. Although in the sample with 10 nm SiO{sub 2} barriers some fragments of the multilayer structure could be still observed after thermal annealing, nevertheless, the formation of large nanocrystals with diameters up to 10 nm was confirmed by dark field transmission electron microscopy. Thus, in contrast to the previously published results, the expected size control of silicon nanocrystals was lost. According to the FTIR results, the thermal annealing of SiN{sub x}/SiO{sub 2} superlattices led to the formation of silicon nanocrystals in mostly oxynitride matrix. Annealed samples demonstrated a photoluminescence peak at 885 nm related to the luminescence of silicon nanocrystals, as confirmed by time-resolved photoluminescence measurements. The loss of nanocrystals size control is discussed in terms of the migration of oxygen atoms from the SiO{sub 2} barriers into the silicon rich silicon nitride sublayers. A thermodynamic mechanism responsible for this process is proposed. According to this mechanism, the driving force for the oxygen migration is the gain in the configuration entropy related to the relative arrangements of oxygen and nitrogen atoms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Optimal Twirling Depth for Classical Shadows in the Presence of Noise

The classical shadows protocol is an efficient strategy for estimating properties of an unknown state p using a small number of state copies and measurements. In its original form, it involves twirling the state with unitaries from some ensemble and measuring the twirled state in a fixed basis. It was recently shown that for computing local properties, optimal sample complexity (copies of the state required) is remarkably achieved for unitaries drawn from shallow depth circuits composed of local entangling gates, as opposed to purely local (zero depth) or global twirling (infinite depth) ensembles. Here, we consider the sample complexity as a function of the depth of the circuit, in the presence of noise. We find that this noise has important implications for determining the optimal twirling ensemble. Under fairly general conditions, we (i) show that any single-site noise can be accounted for using a depolarizing noise channel with an appropriate damping parameter f, (ii) compute thresholds f th at which optimal twirling reduces to local twirling for Pauli operators, (iii) nth order Renyi entropies (n ≥2), and (iv) provide a meaningful upper bound t max on the optimal circuit depth for any finite noise strength f, which applies to observables and entanglement entropy measurements. In conclusion, these thresholds strongly constrain the search for optimal strategies to implement shadow tomography and are easily tailored to the experimental system at hand.

97 MATHEMATICS AND COMPUTING↗