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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Why all stars should possess circumstellar temperature inversions

The paper shows that the circumstellar temperature inversions possessed by all stars are the consequence of the 'velocity filtration' process described by Scudder (1992), according to which a stellar envelope is hotter than its underlying layers. The filtration scenario relies on the theoretically predicted and experimentally determined non-Maxwellian velocity distributions of ions and/or electrons in other sampled astrophysical plasmas and the transition region. The most immediate consequence is that the temperature and quasi-neutral plasma density become anticorrelated with increasing radius in a thin transition region, leaving the temperature profile inverted in excess of 10 exp 6 K up into a corona, without depositing a wave of magnetic field energy into the gas above the base of the transition region.

Scudder, Jack D.↗
NASA NTRS1997

Planetary and Primitive Object Strength Measurements and Sampling Apparatus

We present experimental data and a model for the low-velocity (subsonic, 0 - 1000 m/s) penetration of brittle materials by both solid and hollow (i.e., coring) penetrators. The experiments show that penetration is proportional to momentum/frontal area of the penetrator. Because of the buildup of a cap in front of blunt penetrators, the presence or absence of a streamlined or sharp front end usually has a negligible effect for impact into targets with strength. The model accurately predicts the dependence of penetration depth on the various parameters of the target-penetrator system, as well as the qualitative condition of the target material ingested by a corer. In particular, penetration depth is approximately inversely proportional to the static bearing strength of the target. The bulk density of the target material has only a small effect on penetration, whereas friction can be significant, especially at higher impact velocities, for consolidated materials. This trend is reversed for impacts into unconsolidated materials. The present results suggest that the depth of penetration is a good measure of the strength, but not the density, of a consolidated target. Both experiments and model results show that, if passage through the mouth of a coring penetrator requires initially porous target material to be compressed to less than 26% porosity, the sample collected by the corer will be highly fragmented. If the final porosity remains above 26%, then most materials, except cohesionless materials, such as dry sand, will be collected as a compressed slug of material.

Ahrens, Thomas J.↗
NASA NTRS2016

Bone Turnover Does Not Reflect Skeletal Aging in Older Hispanic Men with Type 2 Diabetes

The paradox of fragility fracture in the presence of non-osteoporotic bone mineral density in older patients with type 2 diabetes mellitus (DM2) makes it difficult to clinically predict fracture in this vulnerable group. Serum osteocalcin (OC), a marker of bone turnover, increases with normal skeletal aging indicating risk of fracture. However, OC has been reported to be lower in patients with DM2. An inverse association between higher glycated hemoglobin levels (HbA1c) and lower serum OC in older DM2 patients triggered discussions encouraging further investigation. A key question to be answered is whether changes in glucose metabolism is responsible for bone metabolic changes, ultimately leading to increased risk of fragility fractures in DM2 patients. While these studies were conducted among Caucasian and Asian populations, this has not been studied in Hispanic populations who suffer from a higher prevalence of DM2. The Cameron County Hispanic Cohort (CCHC) in Texas is a homogeneous Hispanic cohort known to have high prevalence of DM2 (30%). Our preliminary data from this cohort reported OC levels lower than the suggested threshold for fragility fracture in post-menopausal women. We further investigated whether bone turnover in older CCHC adults with DM2 show a normal pattern of skeletal aging. Samples and data were obtained from a nested cohort of 68 (21 men and 47 women) Hispanic older adults (≥50 years) who had a diagnosis of DM2. Given high prevalence of uncontrolled DM2 in this cohort, we divided population into two groups: i) poor DM2 control with HbA1c level ≥8 (48% men and 38% women) and ii) good DM2 control with HbA1c level <8). A crosssectional analysis documented associations between serum OC and age adjusted HbA1c levels. There was no direct association between age and OC concentrations in our study. Higher HbA1c was associated with lower serum OC in men (odds ratio -6.5, 95% confidence interval -12.7 to - 0.3, p < 0.04). No significant associations were identified in women. Bone turnover in older Hispanic men with DM2 in our study does not reflect normal pattern of skeletal aging. It is unclear why similar results were not identified in women. We will continue to follow this cohort to investigate longitudinal trend of changes of bone turnover and its relationship with HbA1c in both men and women of this cohort.

Rianon, N.↗
DOE OSTI2020

Extending the Southern Shore of the Island of Inversion to F 28

Detailed spectroscopy of the neutron-unbound nucleus 28 F has been performed for the first time following proton/neutron removal from 29 Ne/ 29 F beams at energies around 230 MeV / nucleon . The invariant-mass spectra were reconstructed for both the 27 F(*) + n and 26 F(*) + 2n coincidences and revealed a series of well-defined resonances. A near-threshold state was observed in both reactions and is identified as the 28 F ground state, with S n ( 28 F) = -199(6) keV, while analysis of the 2n decay channel allowed a considerably improved S n ( 27 F) = 162(60) keV to be deduced. Comparison with shell-model predictions and eikonal-model reaction calculations have allowed spin-parity assignments to be proposed for some of the lower-lying levels of 28 F. Importantly, in the case of the ground state, the reconstructed 27 F + n momentum distribution following neutron removal from 29 F indicates that it arises mainly from the 1p 3/2 neutron intruder configuration. Finally, this demonstrates that the island of inversion around N = 20 includes 28 F, and most probably 29 F, and suggests that 28 O is not doubly magic.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Inverse mapping of properties to composition through generative modeling for designing molten salts

Generative modeling (GM) has been increasingly used for the inverse design and optimization of materials, yet its application to molten salt mixtures remains unexplored despite how a successful approach to the inverse design of molten salts would contribute to efficiently exploiting their customizability and unlocking their advantages in applications, such as energy production and energy storage. This work presents a workflow for the inverse design of molten salts with targeted density values, addressing the challenge of representing these complex mixtures in GM. A dataset of critically evaluated molten salt densities is used to train a variational autoencoder coupled with a predictive deep neural network, which then can be used to generate new molten salt compositions with desired density values. The effectiveness of the approach is demonstrated by designing mixtures with distinct densities and validating the predicted values using ab initio molecular dynamics simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

High-throughput search for magnetic topological materials using spin-orbit spillage, machine learning, and experiments

Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation. Here, we use systematic high-throughput density functional theory calculations to identify magnetic topological materials from the ≈ 40000 three-dimensional materials in the JARVIS-DFT database. First, we screen materials with net magnetic moment > 0.5 μB and spin-orbit spillage > 0.25, resulting in 25 insulating and 564 metallic candidates. The spillage acts as a signature of spin-orbit induced band-inversion. Then, we carry out calculations of Wannier charge centers, Chern numbers, anomalous Hall conductivities, surface bandstructures, and Fermi-surfaces to determine interesting topological characteristics of the screened compounds. We also train machine learning models for predicting the spillage, bandgaps, and magnetic moments of new compounds, to further accelerate the screening process. We experimentally synthesize and characterize a few candidate materials to support our theoretical predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Growth and Saturation of the Electron Drift Instability in a Crossed Field Plasma

The linear growth and nonlinear energy transfer of the electron drift instability (EDI) are experimentally measured in the plume of a low-temperature, Hall effect discharge. A frequency-based bispectral analysis technique applied to fast ion density fluctuation measurements shows a growth rate function that is qualitatively similar to predictions from the linear instability dispersion relation, but an order of magnitude smaller. Calculation of the nonlinear transfer function indicates multiple three-wave interactions between high-frequency resonances of the instability in addition to an inverse energy cascade toward lower-frequency modes. Furthermore, these results are discussed in the context of recent theoretical, numerical, and experimental efforts on the EDI in Hall effect discharges and how the EDI may impact anomalous cross field transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2022

Interpolation as a means of shift selection for multilevel Monte Carlo with lattice displacements

The calculation of disconnected diagram contributions to physical signals is a computationally expensive task in Lattice QCD. To extract the physical signal, the trace of the inverse Lattice Dirac operator, a large sparse matrix, must be stochastically estimated. Because the variance of the stochastic estimator is typically large, variance reduction techniques must be employed. Multilevel Monte Carlo (MLMC) methods reduce the variance of the trace estimator by utilizing a telescoping sequence of estimators. Frequency Splitting is one such method that uses a sequence of inverses of shifted operators to estimate the trace of the inverse of the lattice Dirac operator, however there is no a priori way to select the shifts that minimize the cost of the multilevel trace estimation. We present a sampling and interpolation scheme that is able to predict the variances associated with Frequency Splitting under displacements of the underlying space time lattice. The interpolation scheme is able to predict the variances to high accuracy and therefore choose shifts that correspond to an approximate minimum of the cost for the trace estimation. We show that Frequency Splitting with the chosen shifts displays significant speedups over multigrid deflation

Whyte, Travis↗
DOE OSTI2022

Design-Space Exploration for Inverse-Design of Wind Turbine Blades Using Data-Driven Methods

The state-of-the-practice aerodynamic design methods for wind turbine blades is typically based on Blade-Element Momentum (BEM) theory using a pre-designed frozen family of airfoils. The airfoils are themselves typically designed using panel methods. The design of the next-generation of large flexible rotors will need to capture the non-linear aerodynamics and three-dimensional flow to reduce the levelized cost of wind energy. Data-driven methods for aerodynamic design using data generated by computational fluid dynamics offer an attractive alternative to BEM-based methods that captures the non-linear aerodynamics of the component airfoils as well as the root and tip sections. In this work, we develop and demonstrate a framework to "smartly" explore the relevant design space in combination with an appropriate automated CFD pipeline to evaluate the aerodynamics of each design. The design-space exploration framework uses appropriate perturbations to the airfoil shape and induction profile from a baseline shape in combination with the inverse-design using BEM. The resulting designs are evaluated using an automated CFD pipeline using the in-house CFD solver framework "Mercury". We perform verification and validation to establish the capability of the Mercury framework to predict the aerodynamic performance of wind turbines. The CFD simulation of the perturbed blade shapes are optimized to restart from the converged baseline simulation to reduce the computational time. Finally, we demonstrate the design-space exploration technique for the design of the outboard section and the full rotor using perturbations to the shape and operating conditions of the NREL 5-MW turbine.

aerodynamic design methods↗
NASA NTRS1975

Silica heat shield sizing

The sensitivity of silica heat shield requirements to gap width, tile edge radius, and heat transfer distribution within tile gaps was investigated. A two-dimensional thermal model was modified and used to determine the effect of two dimensional heat transfer distributions at high temperature reusable surface insulation edges on shuttle thermal protection system (TPS) requirements. The sensitivity of TPS requirements to coating thickness, emissivity, substructure thickness, and changes in gap heating for several locations on shuttle was also studied. An inverse solution technique was applied to temperature data obtained in the Ames 20 MW turbulent duct in order to examine the effect of tile edge radius on TPS requirements. The derived heating values were then used to predict TPS requirements. Results show that increasing tile radius reduces TPS requirements.

Ebbesmeyer, L. H.↗
NASA NTRS1991

Infrared reflectance spectra (4-12 micron) of lunar samples

Presented here are infrared reflectance spectra of a typical set of Apollo samples to illustrate spectral character in the mid-infrared (4 to 12 microns) of lunar materials and how the spectra varies among three main forms: soil, breccia, and igneous rocks. Reflectance data, to a close approximation, are the inverse of emission spectra; thus, for a given material the spectral reflectance (R) at any given wavelength is related to emission (E) by 1 - R equals E. Therefore, one can use reflectance spectra of lunar samples to predict how emission spectra of material on the lunar surface will appear to spectrometers on orbiting spacecraft or earthbound telescopes. Spectra were measured in the lab in dry air using a Fourier Transform Infrared spectrometer. Shown here is only the key portion (4 to 12 microns) of each spectrum relating to the principal spectral emission region for sunlit lunar materials and to where the most diagnostic spectral features occur.

Nash, Douglas B.↗
NASA NTRS1992

Expected level of self-Compton scattering in radio loud quasars

Radio-loud quasars usually contain parsec-scale nonthermal jets. The most compact emission region ('the core'), and perhaps some of the moving 'knots', are expected to be efficient producers of inverse Compton scattered X-rays and gamma-rays since many of the synchrotron photons will upscatter before escaping. Through multifrequency flux density observations and Very Long Baseline Interferometry (VLBI) measurements of angular sizes, one can predict the flux density of this self-Compton high-energy emission. It is not always the case that the brightest synchrotron sources are also the brightest X-ray and gamma-ray sources. Perhaps a better predictor of high-energy brightness is the ratio of hard X-ray to high-frequency radio emission. Using the synchrotron self-Compton relations, we predict the gamma-ray fluxes of several sources we expect to be detected by the Energetic Gamma Ray Experiment Telescope (EGRET). More accurate predictions will be made when we complete a program of contemporaneous radio-submillimeter and X-ray observations during the course of the EGRET all-sky survey.

Bloom, Steven D.↗
NASA NTRS2022

Design of Materials with Alchemite

Machine learning models that establish the relationships between materials processing and properties can enable inverse design of materials through active learning. Alchemite is a commercial software that can perform inverse materials design on sparse data. Here we evaluate Alchemite’s performance on a dataset of shape memory alloys and a dataset of heat exchangers compared to baseline random forest models. Alchemite had higher accuracy when making predictions on sparse data and was more accurate or nearly as accurate as random forests on complete datasets while also quantifying uncertainty. The software was also used to suggest processing steps and design parameters to optimize properties and performance; however, physical validation of the suggested design parameters was beyond the scope of this work. Several useful design insights were gained about the impact of the design parameters on properties and performance including the importance of dopant choice and amount for shape memory alloys and the importance of height and weight on the thermal resistance of heat exchangers.

Machine learning↗
NASA NTRS2012

All-Sky Observational Evidence for An Inverse Correlation Between Dust Temperature and Emissivity Spectral Index

We show that a one-component variable-emissivity-spectral-index model (the free- model) provides more physically motivated estimates of dust temperature at the Galactic polar caps than one- or two-component fixed-emissivity-spectral-index models (fixed- models) for interstellar dust thermal emission at far-infrared and millimeter wavelengths. For the comparison we have fit all-sky one-component dust models with fixed or variable emissivity spectral index to a new and improved version of the 210-channel dust spectra from the COBE-FIRAS, the 100-240 micrometer maps from the COBE-DIRBE and the 94 GHz dust map from the WMAP. The best model, the free-alpha model, is well constrained by data at 60-3000 GHz over 86 per cent of the total sky area. It predicts dust temperature (T(sub dust)) to be 13.7-22.7 (plus or minus 1.3) K, the emissivity spectral index (alpha) to be 1.2-3.1 (plus or minus 0.3) and the optical depth (tau) to range 0.6-46 x 10(exp -5) with a 23 per cent uncertainty. Using these estimates, we present all-sky evidence for an inverse correlation between the emissivity spectral index and dust temperature, which fits the relation alpha = 1/(delta + omega (raised dot) T(sub dust) with delta = -.0.510 plus or minus 0.011 and omega = 0.059 plus or minus 0.001. This best model will be useful to cosmic microwave background experiments for removing foreground dust contamination and it can serve as an all-sky extended-frequency reference for future higher resolution dust models.

Liang, Z.↗
NASA NTRS2024

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Yang Zhang↗