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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Effect of Gd substitution on the structural, magnetic, and magnetocaloric properties of HoCrO 3

Rare-earth chromites are a novel type of magnetoelectric multiferroics. Here, a Ho 0.33 Gd 0.67 CrO 3 powder sample was synthesized via a citrate route, and the structural properties were characterized by X-ray diffraction, scanning electron microscopy, and the Raman technique. The UV-Visible optical absorbance spectra were also measured in the wavelength range of 200–800 nm. The valence state of Cr was found to be purely 3+ according to the X-ray photoelectron spectroscopy. The temperature-dependent dielectric constant and loss tangent data measured between the frequencies of 1 kHz and 1 MHz show no anomalies around the magnetic transition temperature of the material. The dc magnetization measurements show that the ordering temperature of Cr 3+ ( T N C r ) is 155 K for Ho 0.33 Gd 0.67 CrO 3 , which is larger than 140 K for HoCrO 3 . The positive slope of the Arrott plots from 0 T to 7 T reveals that the antiferromagnetic-paramagnetic phase transition is second-order in nature. At a field of 7 T, the Ho 0.33 Gd 0.67 CrO 3 sample showed a giant magnetocaloric entropy change, -ΔS, of ~23.3 J/kg K at 5 K, and a refrigeration capacity of ~481.2 J/kg, which are much higher than those of pure bulk HoCrO 3 . This renders this material prospective for magnetic refrigeration in the low temperature (<30 K) range.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Unstable vortices in the near region of an internal flow cavity

This paper presents experimental data taken in the forward region of a separated internal free-shear layer produced in an internal cavity flowfield. It has been found that in the region very near the forward restrictor, experimental velocity profiles agree closely with the exact Stuart instability velocity profile with various values of a steepness parameter. Reynolds shear-stress profiles suggest the presence of counter-rotating longitudinal vortices. Spectral analysis by the maximum entropy method of the time samples within the vortices indicates subharmonic and harmonic components of the fundamental frequency, with a weak indication of the fundamental frequency itself.

Isaacson, L. K.↗
NASA NTRS1991

Some practical universal noiseless coding techniques, part 3, module PSl14,K+

The algorithmic definitions, performance characterizations, and application notes for a high-performance adaptive noiseless coding module are provided. Subsets of these algorithms are currently under development in custom very large scale integration (VLSI) at three NASA centers. The generality of coding algorithms recently reported is extended. The module incorporates a powerful adaptive noiseless coder for Standard Data Sources (i.e., sources whose symbols can be represented by uncorrelated non-negative integers, where smaller integers are more likely than the larger ones). Coders can be specified to provide performance close to the data entropy over any desired dynamic range (of entropy) above 0.75 bit/sample. This is accomplished by adaptively choosing the best of many efficient variable-length coding options to use on each short block of data (e.g., 16 samples) All code options used for entropies above 1.5 bits/sample are 'Huffman Equivalent', but they require no table lookups to implement. The coding can be performed directly on data that have been preprocessed to exhibit the characteristics of a standard source. Alternatively, a built-in predictive preprocessor can be used where applicable. This built-in preprocessor includes the familiar 1-D predictor followed by a function that maps the prediction error sequences into the desired standard form. Additionally, an external prediction can be substituted if desired. A broad range of issues dealing with the interface between the coding module and the data systems it might serve are further addressed. These issues include: multidimensional prediction, archival access, sensor noise, rate control, code rate improvements outside the module, and the optimality of certain internal code options.

Rice, Robert F.↗
DOE OSTI2017

The Dynameomics Entropy Dictionary: A Large-Scale Assessment of Conformational Entropy across Protein Fold Space

Molecular dynamics (MD) simulations contain considerable information with regard to the motions and fluctuations of a protein, the magnitude of which can be used to estimate conformational entropy. In this report we survey conformational entropy across protein fold space using the Dynameomics database, which represents the largest existing data set of protein MD simulations for representatives of essentially all known protein folds. We provide an overview of MD-derived entropies accounting for all possible degrees of dihedral freedom on an unprecedented scale. Although different side chains might be expected to impose varying restrictions on the conformational space that the backbone can sample, we found that the backbone entropy and side chain size are not strictly coupled. An outcome of these analyses is the Dynameomics Entropy Dictionary, the contents of which have been compared with entropies derived by other theoretical approaches and experiment. As might be expected, the conformational entropies scale linearly with the number of residues, demonstrating that conformational entropy is an extensive property of proteins. The calculated conformational entropies of folding agree well with previous estimates. Detailed assessment of specific cases identifies deviations in conformational entropy from the average values that highlight how conformational entropy varies with sequence, secondary structure, and tertiary fold. Of interest, α-helices have lower entropy on average than do β-sheets, and both are lower than coil regions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

In Situ High-Temperature Structural Analysis of High-Entropy Rare-Earth Sesquioxides

High-entropy rare-earth (RE) sesquioxides (RE 2 O 3 ) containing five cations in equimolar amounts have been investigated for a variety of applications, but little is known about their polymorphic behavior and coefficient of thermal expansion. Here, in this work, we evaluate the effect of the average ionic radius (AIR) on the polymorphism of high-entropy RE 2 O 3 . Powder samples of compositions 1 (Lu,Y,Ho,Nd,La) 2 O 3 (AIR = 0.938 Å) and 2 (Gd,Eu,Sm,Nd,La) 2 O 3 (AIR = 0.982 Å) were synthesized via a wet chemical method, and bead samples were prepared for aerodynamic levitation by melting the powders in a copper hearth. Structural transitions were monitored upon cooling from the melt to 1000 °C via in situ X-ray diffraction on aerodynamically levitated samples. The phase evolution was liquid, hexagonal H-type, and monoclinic B-type for composition 1 and liquid, cubic X-type, H-type, and B-type for composition 2. Based on their AIR, the general polymorphic transformations of the high-entropy RE 2 O 3 follow the trend of single-RE RE 2 O 3 , but the transition temperatures differ from those of single-RE RE 2 O 3 . The coefficient of thermal expansion values of the B-type phase of compositions 1 and 2 are similar to those of Gd 2 O 3 and previously published high-entropy RE 2 O 3 .

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

The Mean-Field Theory for the Study of the Magnetocaloric Effect of Nd{sub 0.67}Ba{sub 0.33}Mn{sub 0.98}Fe{sub 0.02}O{sub 3} Manganite

In this paper, we exploit the mean-field theory and the Bean-Rodbell model to justify the magnetocaloric effect (MCE) on Nd{sub 0.67}Ba{sub 0.33}Mn{sub 0.98}Fe{sub 0.02}O{sub 3} manganite. The modeling of magnetic properties was investigated. The Bean-Rodbell equation of state leads the system to second-order ferromagnetic (FM)-paramagnetic (PM) phase transition. The expressions describing the magnetic entropy change under the influence of magnetic field have been derived. A large magnetic entropy has been observed in our sample with a peak centered on the Curie temperature. The theoretical calculations are compared with experimental behavior.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Magnetic and Transport Properties of Bi{sub 0.5−x}Pr{sub x}Ca{sub 0.5}MnO{sub 3}(0.0 ≤ x ≤ 0.50) Manganites

In this work, the effect of Pr doping at the Bi site on the critical behavior and magnetocaloric and transport properties of Bi{sub 0.5−x}Pr{sub x}Ca{sub 0.5}MnO{sub 3} (0.0 ≤ x ≤ 0.50) manganite synthesized by sol–gel method is reported. An increase in Curie temperature (T{sub C}) with an increase in Pr content is observed. The samples with x = 0.0–0.40 show first-order phase transition whereas the sample with x = 0.50 shows second-order phase transition near T{sub C}. The estimated values of critical exponents β = 0.75 and γ = 0.82 for the sample with x = 0.50 do not fall into any universality class. The sample with x = 0.50 shows maximum magnetic entropy change (|ΔS{sub m}| = (1.22 J kg{sup −1} K{sup −1}) at T{sub C} in an applied magnetic field of 40 kOe, which is associated with magnetocaloric effect (MCE). All the samples show semiconducting behavior and also follow Mott’s variable range hopping (VRH) model below the charge ordering temperature (T{sub CO}). The changes in magnetic and transport properties are correlated with the changes in the structural parameters of the samples. The results are discussed in view of the effect of Pr doping on the 6s{sup 2} lone pair character of Bi{sup 3+} ions and disorder in the samples.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Deep neural network uncertainty quantification for LArTPC reconstruction

We evaluate uncertainty quantification (UQ) methods for deep learning applied to liquid argon time projection chamber (LArTPC) physics analysis tasks. As deep learning applications enter widespread usage among physics data analysis, neural networks with reliable estimates of prediction uncertainty and robust performance against overconfidence and out-of-distribution (OOD) samples are critical for their full deployment in analyzing experimental data. While numerous UQ methods have been tested on simple datasets, performance evaluations for more complex tasks and datasets are scarce. Here we assess the application of selected deep learning UQ methods on the task of particle classification using the PiLArNet monte carlo 3D LArTPC point cloud dataset. We observe that UQ methods not only allow for better rejection of prediction mistakes and OOD detection, but also generally achieve higher overall accuracy across different task settings. We assess the precision of uncertainty quantification using different evaluation metrics, such as distributional separation of prediction entropy across correctly and incorrectly identified samples, receiver operating characteristic curves (ROCs), and expected calibration error from observed empirical accuracy. We conclude that ensembling methods can obtain well calibrated classification probabilities and generally perform better than other existing methods in deep learning UQ literature.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

An Entropy-Maximization Approach to Automated Training Set Generation for Interatomic Potentials

Machine learning-based interatomic potentials are currently garnering a lot of attention as they strive to achieve the accuracy of electronic structure methods at the computational cost of empirical potentials. Given their generic functional forms, the transferability of these potentials is highly dependent on the quality of the training set, the generation of which can be highly labor-intensive. Good training sets should at once contain a very diverse set of configurations while avoiding redundancies that incur cost without providing benefits. We formalize these requirements in a local entropy-maximization framework and propose an automated sampling scheme to sample from this objective function. We show that this approach generates much more diverse training sets than unbiased sampling and is competitive with hand-crafted training sets.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS2018

Uncertainty in Heart Rate Complexity Metrics Caused by R-Peak Perturbations

Heart rate complexity (HRC) is a proven metric for gaining insight into human stress and physiological deterioration. To calculate HRC, the detection of the exact instance of when the heart beats, the R-peak, is necessary. Electrocardiogram (ECG) signals can often be corrupted by environmental noise (e.g., from electromagnetic interference, movement artifacts), which can potentially alter the HRC measurement, producing erroneous inputs which feed into decision support models. Current literature has only investigated how HRC is affected by noise when R-peak detection errors occur (false positives and false negatives). However, the numerical methods used to calculate HRC are also sensitive to the specific location of the fiducial point of the R-peak. This raises many questions regarding how this fiducial point is altered by noise, the resulting impact on the measured HRC, and how we can account for noisy HRC measures as inputs into our decision models. This work uses Monte Carlo simulations to systematically add white and pink noise at different permutations of signal-to-noise ratios (SNRs), time segments, sampling rates, and HRC measurements to characterize the influence of noise on the HRC measure by altering the fiducial point of the R-peak. Using the generated information from these simulations provides improved decision processes for system design which address key concerns such as permutation entropy being a more precise, reliable, less biased, and more sensitive measurement for HRC than sample and approximate entropy.

Napoli, Nicholas J.↗
DOE OSTI2023

Studies on the structure and the magnetic properties of high-entropy spinel oxide (MgMnFeCoNi)Al 2 O 4

The study of high-entropy materials has attracted enormous interest since they could show new functional properties that are not observed in their related parent phases. Here, we report single crystal growth, structure, thermal transport, and magnetic property studies on a novel high-entropy oxide with the spinel structure (MgMnFeCoNi)Al 2 O 4 . We have successfully grown high-quality single crystals of this high-entropy oxide using the optical floating zone growth technique for the first time. The sample was confirmed to be a phase pure high-entropy oxide using x-ray diffraction and energy-dispersive spectroscopy. Through magnetization measurements, we found (MgMnFeCoNi)Al 2 O 4 exhibits a cluster spin glass state, though the parent phases show either antiferromagnetic ordering or spin glass states. Furthermore, we also found that (MgMnFeCoNi)Al 2 O 4 has much greater thermal expansion than its CoAl 2 O 4 parent compound using high resolution neutron Larmor diffraction. We further investigated the structure of this high-entropy material via Raman spectroscopy and extended x-ray absorption fine structure spectroscopy (EXAFS) measurements. From Raman spectroscopy measurements, we observed (MgMnFeCoNi)Al 2 O 4 to display a combination of the active Raman modes in its parent compounds with the modes shifted and significantly broadened. This result, together with the varying bond lengths probed by EXAFS, reveals severe local lattice distortions in this high-entropy phase. Additionally, we found a substantial decrease in thermal conductivity and suppression of the low temperature thermal conductivity peak in (MgMnFeCoNi)Al 2 O 4 , consistent with the increased lattice defects and strain. These findings advance the understanding of the dependence of thermal expansion and transport on the lattice distortions in high-entropy materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Process window estimation in manufacturing through Entropy-Sigma active learning

In manufacturing, there exist boundary identification problems for defining parameter spaces that meet desired thresholds on outcomes. This paper presents an Entropy-Sigma acquisition function for active learning of the process window/map in manufacturing using a Gaussian Process surrogate. Here, the method is applied to identify the stability boundary for the stability process map in machining using time-domain simulations with a periodic sampling stability metric. Results show that the proposed Entropy-Sigma method significantly outperforms Latin hypercube sampling or grid-based methods. The described method can be applied to identify the process window/map for any manufacturing application using a quantitative process outcome metric.

42 ENGINEERING↗
DOE OSTI2022

Entanglement entropy production in deep inelastic scattering

Deep inelastic scattering (DIS) samples a part of the wave function of a hadron in the vicinity of the light cone. Lipatov constructed a spin chain which describes the amplitude of DIS in leading logarithmic approximation. Kharzeev and Levin proposed the entanglement entropy as an observable in DIS [Phys. Rev. D 95, 114008 (2017)], and suggested a relation between the entanglement entropy and parton distributions. Here we represent the DIS process as a local quench in Lipatov’s spin chain and study the time evolution of the produced entanglement entropy. We show that the resulting entanglement entropy depends on time logarithmically, $\mathcal{S}(t) = 1/3 ln(t/τ)$ with $τ = 1/m for 1/m ≤ t ≤ (mx)^{–1}$, where m is the proton mass and $\textit{x}$ is the Bjorken $\textit{x}$. The central charge c of Lipatov’s spin chain is determined here to be $\textit{c}$ = 1; using the proposed relation between the entanglement entropy and parton distributions, this corresponds to the gluon structure function growing at small $\textit{x}$ as $xG(x) ~ 1/x^{1/3}$.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

EE-SMOTE: An oversampling method in conjunction with information entropy for imbalanced learning

Imbalanced learning attracts great attention in various research fields. Existing literature-reported methodologies in imbalanced learning have shown drawbacks including over-generation or noisy/wrong samples generations. This paper presents EE-SMOTE, an oversampling technique based on information entropy, to support the imbalance classifications. Specifically, we propose a metric, Eigen-Entropy (EE), to identify homogenous samples from minority classes for oversampling technique, specifically, SMOTE to reach data balances for classification. Experiments on public dataset and real-world datasets demonstrate the efficacy and effectiveness of the proposed EE-SMOTE in imbalanced learning.

Huang, Jiajing↗
DOE OSTI2018

Tuning martensitic transitions in (MnNiSi) 0.65 (Fe 2 Ge) 0.35 through heat treatment and hydrostatic pressure

A first-order magneto-structural transition from a ferromagnetic orthorhombic TiNiSi-type martensite phase to a paramagnetic hexagonal Ni2In-type austenite phase was observed in (MnNiSi) 0.65 (Fe2Ge) 0.35 . In this work, we demonstrate that the first-order magneto-structural transition temperature for a given composition is tunable over a wide temperature range through heat treatment and hydrostatic pressure. The first-order transition temperature was reduced by over 100 K as the annealing temperature went from 600 to 900 °C, and this first-order transition was converted to second order when the sample was annealed at 1000 °C. The maximum magnetic-induced isothermal entropy change with μ0ΔH=7 T reaches -58 J/kg K for the sample annealed at 600 °C, and the relative cooling power reaches 558 J/kg for the sample annealed at 700 °C. Similar to the influence of annealing temperatures, the first-order martensitic transition temperatures were reduced as the application of hydrostatic pressure increased until they were converted to second order. Our results suggest that the (MnNiSi) 0.65 (Fe2Ge) 0.35 system is a promising platform for tuning magneto-structural transitions and the associated magnetocaloric effects. Furthermore, a similar heat treatment methodology or application of hydrostatic pressure can be applied to MnNiSi-based shape memory alloys to tailor their working transition temperatures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Artificial intelligence based analysis of nanoindentation load–displacement data using a genetic algorithm

In this work, we developed an automated tool, Nanoindentation Neo package for the analysis of nanoindentation load–displacement curves using a Genetic Algorithm (GA) applied to the Oliver-Pharr method (Oliver et al.,1992). For some materials, such as polycrystalline isotropic graphites, Least Squares Fitting (LSF) of the unload curve can produce unrealistic fit parameters. These graphites exhibit sharply peaked unloading curves not easily fit using the LSF, which tends to overestimate the indenter tip geometry parameter. To tackle this problem, we extended our general materials characterization tool Neo for EXAFS analysis (Terry et al., 2021) to fit nanoindentation data. Nanoindentation Neo automatically processes and analyzes nanoindentation data with minimal user input while producing meaningful fit parameters. GA, a robust metaheuristic method, begins with a population of temporary solutions using model parameters called chromosomes; from these we evaluate a fitness value for each solution, and select the best solutions to mix with random solutions producing the next generation. A mutation operator then modifies existing solutions by random perturbations, and the optimal solution is selected. We tested the GA method using Silica and Al reference standards. We fit samples of graphite and a high entropy alloy (HEA) consisting of BCC and FCC phases.

42 ENGINEERING↗
DOE OSTI2017

Plastic dynamics of the Al 0.5 CoCrCuFeNi high entropy alloy at cryogenic temperatures: Jerky flow, stair-like fluctuation, scaling behavior, and non-chaotic state

This study reports on the plastic behavior of the Al 0.5 CoCrCuFeNi high-entropy alloy at cryogenic temperatures. The samples are uniaxially compressed at 4.2 K, 7.5 K, and 9 K. A jerky evolution of stress and stair-like fluctuation of strain are observed during plastic deformation. A scaling relationship is detected between the released elastic energy and strain-jump sizes. Furthermore, the dynamical evolution of serrations is characterized by the largest Lyapunov exponent. The largest Lyapunov exponents of the serrations at the three temperatures are all negative, which implies that the dynamical regime is non-chaotic. This trend reflects an ordered slip process, and this ordered slip process exhibits a more disordered slip process, as the temperature decreases from 9 K to 4.2 K or 7.5 K.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Identifying geochemical hot moments and their controls on a contaminated river floodplain system using wavelet and entropy approaches, Environmental modelling & software: Dataset

We used entropy and wavelet techniques to identify temporal variability in geochemical constituents and their controls along three transects within a contaminated floodplain system near Rifle CO. The objectives of this study were to: (1) identify hot moments of conservative and redox sensitive species as short time-periods with disproportionally high levels of concentration, and (ii) associate the transport and/or biogeochemical factors controlling this temporal variability. This data package includes tables from Arora et al. 2016a: Identifying geochemical hot moments and their controls on a contaminated river floodplain system using wavelet and entropy approaches, Environmental modelling & software, 85, 27-41. In particular, files include sampling frequency for geochemical data at the Rifle site, and results of entropy analysis.

54 ENVIRONMENTAL SCIENCES↗