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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Ground State Properties of the Wide Band Gap Semiconductor Beryllium Sulfide (BeS)

We report the results from self-consistent calculations of electronic, transport, and bulk properties of beryllium sulfide (BeS) in the zinc-blende phase, and employed an ab-initio local density approximation (LDA) potential and the linear combination of atomic orbitals (LCAO). We obtained the ground state properties of zb-BeS with the Bagayoko, Zhao, and Williams (BZW) computational method, as enhanced by Ekuma and Franklin (BZW-EF). Our findings include the electronic energy bands, the total (DOS) and partial (pDOS) densities of states, electron and hole effective masses, the equilibrium lattice constant, and the bulk modulus. The calculated band structure clearly shows that zb-BeS has an indirect energy band gap of 5.436 eV, from Γ to a point between Γ and X, for an experimental lattice constant of 4.863 Å. This is in excellent agreement with the experiment, unlike the findings of more than 15 previous density functional theory (DFT) calculations that did not perform the generalized minimization of the energy functional, required by the second DFT theorem, which is inherent to the implementation of our BZW-EF method.

36 MATERIALS SCIENCE↗
NASA NTRS2024

Characterization of Low-Velocity Impact Damage in Toughened Non-Crimp Fabric Laminates

The damage response of resin-infused composites was investigated using quasi-static indentation (QSI) and instrumented drop-weight impact testing. The test specimens were composed of six layers of non-crimp fabrics where each layer contained +45°, 90°, −45°, and 0° plies. The goals of the testing were to identify damage responses that may differ from traditional thermoset materials and provide damage characterization data to evaluate current damage prediction analyses and aid improvements to current damage models. The damage was characterized using X-ray computed tomography (CT) and ultrasonic testing (UT). Incremental QSI loading was used on a single specimen to investigate damage growth in nine steps at progressively higher loads. Realtime acoustic emission data were used to determine the unloading points. Unlike typical thermoset composites, the force-displacement was smooth and showed no obvious indications of damage. The primary damage consisted of large lines of fiber fracture on the plies near the impact surface and delaminations through the thickness. Nine specimens were impacted at nominal impact energies of 25 ft-lbs, 30 ft-lbs, and 35 ft-lbs. The force histories at each energy followed nearly identical paths. Again, no obvious signs of damage were observed in any of the force histories. The general damage response in the dynamic tests was similar to the QSI-loaded specimens. However, repeat specimens impacted with a given impact energy showed variations in the damage. Some characteristics of the damage response were different than those typically observed in thermoset composites and will need to be evaluated using current analysis methods.

Composite Materials↗
NASA NTRSDate not supplied

Characterization of Low-Velocity Impact Damage in Toughened Non-Crimp Fabric Laminates

The damage response of resin-infused composites was investigated using quasi-static indentation (QSI) and instrumented drop-weight impact testing. The test specimens were composed of six layers of non-crimp fabrics where each layer contained +45°, 90°, −45°, and 0° plies. The goals of the testing were to identify damage responses that may differ from traditional thermoset materials and provide damage characterization data to evaluate current damage prediction analyses and aid improvements to current damage models. The damage was characterized using X-ray computed tomography (CT) and ultrasonic testing (UT). Incremental QSI loading was used on a single specimen to investigate damage growth in nine steps at progressively higher loads. Realtime acoustic emission data were used to determine the unloading points. Unlike typical thermoset composites, the force-displacement was smooth and showed no obvious indications of damage. The primary damage consisted of large lines of fiber fracture on the plies near the impact surface and delaminations through the thickness. Nine specimens were impacted at nominal impact energies of 25 ft-lbs, 30 ft-lbs, and 35 ft-lbs. The force histories at each energy followed nearly identical paths. Again, no obvious signs of damage were observed in any of the force histories. The general damage response in the dynamic tests was similar to the QSI-loaded specimens. However, repeat specimens impacted with a given impact energy showed variations in the damage. Some characteristics of the damage response were different than those typically observed in thermoset composites and will need to be evaluated using current analysis methods.

Composite Materials↗
DOE OSTI2021

MOSAIC: An Effective FFT-based Numerical Method to Assess Aging Properties of Concrete

As the nuclear fleet in the United States ages and subsequent license renewal applications grow, the prediction of concrete durability at extended operation becomes more important. To address this issue, a Fast-Fourier Transform (FFT) method is utilized to simulate aging-related degradation of concrete within the Microstructure Oriented Scientific Analysis of Irradiated Concrete (MOSAIC) software. MOSAIC utilizes compositional phase maps to simulate damage from radiation-induced volumetric expansion (RIVE), applied force, creep, and thermal expansion. This compositional detail allows each mineral in the microstructure to be assigned specific material properties, allowing the simulation to be as accurate and representative as possible. The principal goal of MOSAIC is to simulate the effects of nonlinear aging mechanisms occurring in nuclear concrete on the macroscopic mechanical properties, using only the aggregate microstructure compositional information as a starting point. Here, several realistic example simulations are shown to demonstrate the utility and uniqueness of the MOSAIC software.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Uncertainty quantification for neural network potential foundation models

Abstract For neural network potentials (NNPs) to gain widespread use, researchers must be able to trust model outputs. However, the blackbox nature of neural networks and their inherent stochasticity are often deterrents, especially for foundation models trained over broad swaths of chemical space. Uncertainty information provided at the time of prediction can help reduce aversion to NNPs. In this work, we detail two uncertainty quantification (UQ) methods. Readout ensembling, by finetuning the readout layers of an ensemble of foundation models, provides information about model uncertainty, while quantile regression, by replacing point predictions with distributional predictions, provides information about uncertainty within the underlying training data. We demonstrate our approach with the MACE-MP-0 model, applying UQ to the foundation model and a series of finetuned models. The uncertainties produced by the readout ensemble and quantile methods are demonstrated to be distinct measures by which the quality of the NNP output can be judged.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Stable diagonal stripes in the t-J model at $\bar{n}$ h = 1/8 doping from fPEPS calculations

We investigate the two-dimensional t–J model at a hole doping of $\bar{n}$ h= 1 / 8 using recently developed high accuracy fermionic projected entangled pair states method. By applying the stochastic gradient descent method combined with the Monte Carlo sampling technique, we obtain the ground state hole energy Ehole = -1.621 for J/t = 0.4. We show that the ground state has stable diagonal stripes instead of vertical stripes with a width of 4 unit cells, and stripe filling ρ l = 0.5. We further show that the long-range superconductivity order is suppressed at this point.

36 MATERIALS SCIENCE↗
DOE OSTI2023

On the convexity of phase-field fracture formulations: Analytical study and comparison of various degradation functions

Efficient and accurate fracture modeling is of great importance in applications where catastrophic outcomes under extreme scenarios are possible. The phase-field (PF) approach to fracture received significant attention over the past decade, due to its capability to capture complicated fracture patterns (e.g., crack merging and branching). Specifically, crack initiation and propagation are modeled via minimization of the total energy functional, which is regularized with the aid of a phase field. Despite the promising results and modeling capabilities of the PF method in many applications, the solution of fracture problems remains computationally challenging mainly due to the non-convexity of the total energy functional with respect to the combined unknown (phase field and displacement) fields. Understanding the effects of their coupling on convexity is crucial in order to address frequently encountered hurdles in fracture modeling (e.g., inefficient solvers and non-physical crack nucleation). In this paper, we develop convexity criteria for a wide class of PF fracture formulations. For this class of formulations, the second variation of the total energy functional is expressed in terms of Hessian matrices (evaluated at individual material points). Depending on the choice of geometric crack functions and degradation functions, we classify the formulations into three categories and analytically study each one separately. To study the sign of the second variation, we derive inequalities which are satisfied at material points when the Hessian matrix is locally positive semi-definite. These inequalities provide objective criteria for comparing degradation functions. Finally, the applicability of the proposed convexity criteria is demonstrated in the context of a one-dimensional problem, solved using a conventional monolithic solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Charged point defect benchmark of Hybrid and GGA-PBE

Data used by the publication "High-throughput calculations of charged point defect properties with semi-local density functional theory - performance benchmarks for materials screening applications." This work presented an in-depth benchmark analysis of automated, semi-local point defect calculations with a-posteriori corrections, compared to 245 “gold standard” hybrid calculations previously published. We considered three different a-posteriori correction sets for semi-local calculations, implemented in a fully automated workflow, and consider the qualitative and quantitative differences for four different categories of defect information: thermodynamic transition levels, formation energies, fermi levels, and dopability limits. We highlighted the type of qualitative information about point defect properties that can be extracted from high-throughput calculations based on semi-local DFT methods, while also demonstrating the limits of quantitative accuracy that can be achieved by these approaches.

Broberg, Danny↗
NASA NTRS1978

The practical impact of elastohydrodynamic lubrication

Elastohydrodynamic lubrication has had its most significant impact on, among all the types of concentrated contact mechanisms, rolling element bearings. EHL technology, through its inclusion in computer codes, now provides us with more effective methods for optimizing bearing design and for predicting bearing life, power loss, temperature and dynamic behavior. Bearing life prediction has advanced to a much more sophisticated level as compared to the calculation of fatigue life based on Lundberg-Palmgren theory. Application of elastohydrodynamics to gearing has, more or less, been limited to the calculation of pitch point film thicknesses. Techniques for calculating film thicknesses over the entire range of tooth meshes for arbitrarily shaped gear teeth (noninvolute, spur, helical, etc.) need to be developed. Elastomer seals with both unidirectional and reciprocating motion offer a fruitful application for the elastohydrodynamics of low modulus materials.

Anderson, W. J.↗
NASA NTRS2008

Mounting system for optical frequency reference cavities

A technique for reducing the vibration sensitivity of laser-stabilizing optical reference cavities is based upon an improved design and mounting method for the cavity, wherein the cavity is mounted vertically. It is suspended at one plane, around the spacer cylinder, equidistant from the mirror ends of the cavity. The suspension element is a collar of an extremely low thermal expansion coefficient material, which surrounds the spacer cylinder and contacts it uniformly. Once the collar has been properly located, it is cemented in place so that the spacer cylinder is uniformly supported and does not have to be squeezed at all. The collar also includes a number of cavities partially bored into its lower flat surface, around the axial bore. These cavities are support points, into which mounting base pins will be inserted. Hence the collar is supported at a minimum of three points.

Notcutt, Mark↗
NASA NTRS2001

The Effect of Stochastic Perturbation of Fuel Distribution on the Criticality of a One Speed Reactor and the Development of Multi-Material Multinomial Line Statistics

The effect of random fuel redistribution on the eigenvalue of a one-speed reactor is investigated. An ensemble of such reactors that are identical to a homogeneous reference critical reactor except for the fissile isotope density distribution is constructed such that it meets a set of well-posed redistribution requirements. The average eigenvalue, , is evaluated when the total fissile loading per ensemble element, or realization, is conserved. The perturbation is proven to increase the reactor criticality on average when it is uniformly distributed. The various causes of the change in reactivity, and their relative effects are identified and ranked. From this, a path towards identifying the causes. and relative effects of reactivity fluctuations for the energy dependent problem is pointed to. The perturbation method of using multinomial distributions for representing the perturbed reactor is developed. This method has some advantages that can be of use in other stochastic problems. Finally, some of the features of this perturbation problem are related to other techniques that have been used for addressing similar problems.

Jahshan, S. N.↗
DOE OSTI2026

Grid Operator Analytics and Assessment Tools for Inverter- Based Resources Dominated Grid (GOAAT-IBR) Project Update

This presentation provides an update on the OPTIMA GOAAT project, with emphasis on the cloud-native data platform developed in-house to ingest, manage, and operationalize high-resolution power system data. Since our last NASPI presentation, accessible via OSTI ID #2671437, the project team advanced the design and deployment of a scalable architecture capable of handling both synchronized and non-synchronized streams, including PMU, point-on-wave (POW), COMTRADE, and SCADA data. These materials review the project status, recent progress, and key lessons learned. The core of the presentation examines the architecture and engineering of our cloud-native ingestion and data management platform. We then explain how pipelines were designed to collect, normalize, time-align, store, and serve heterogeneous data at scale. We will discuss design choices such as data models, streaming versus batch ingestion, storage tiers, and interoperability with analytics applications. Practical experiences with cloud-native technologies were shared during the event, including benefits, limitations, and integration challenges in a utility environment, along with methods used to improve performance, reduce latency, and optimize resource usage. The presentation also showcases user interface designs and visualization tools that convert raw measurements and analytics results into intuitive, actionable insights for operators and engineers. During the presentation examples were provided demonstrating how visualization, event views, and summarized analytics enhance situational awareness and support operational decision-making. These use cases illustrate how a well-designed data infrastructure can bridge the gap between high-volume measurements and practical grid operations.

Aminifar, Farrokh↗
DOE OSTI2022

Estimates of Quantum Tunneling Effects for Hydrogen Diffusion in PuO 2

We detail the estimation of activation energies and quantum nuclear vibrational tunneling effects for hydrogen diffusion in PuO 2 based on Density Functional Theory calculations and a quantum double well approximation. We find that results are relatively insensitive to choice of exchange correlation functional. In addition, the representation of spin in the system and use of an extended Hubbard U correction has only a small effect on hydrogen point defect formation energies when the PuO 2 lattice is held fixed at the experimental density. We then compute approximate activation energies for transitions between hydrogen interstitial sites seeded by a semi-empirical quantum model and determine the quantum tunneling enhancement relative to classical kinetic rates. Our model indicates that diffusion rates in H/PuO 2 systems could be enhanced by more than one order of magnitude at ambient conditions and that these effects persist at high temperature. The method we propose here can be used as a fast screening tool for assessing possible quantum nuclear vibrational effects in any number of condensed phase materials and surfaces, where hydrogen hopping tends to follow well defined minimum energy pathways.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1990

Development of impact design methods for ceramic gas turbine components

Impact damage prediction methods are being developed to aid in the design of ceramic gas turbine engine components with improved impact resistance. Two impact damage modes were characterized: local, near the impact site, and structural, usually fast fracture away from the impact site. Local damage to Si3N4 impacted by Si3N4 spherical projectiles consists of ring and/or radial cracks around the impact point. In a mechanistic model being developed, impact damage is characterized as microcrack nucleation and propagation. The extent of damage is measured as volume fraction of microcracks. Model capability is demonstrated by simulating late impact tests. Structural failure is caused by tensile stress during impact exceeding material strength. The EPIC3 code was successfully used to predict blade structural failures in different size particle impacts on radial and axial blades.

Song, J.↗
NASA NTRS1990

Effects of off-axis radiation on reflective concentrating systems for space power

Off-axis incident radiation, resulting from misalignment of a concentrator relative to incident solar flux, can potentially result in high flux concentrations at positions other than the design focal point. It is suggested that the impact of these concentrations should be considered when choosing materials and thermal control strategies for structures adjacent to the concentrator. A numerical (ray tracing) analysis of the effects of off-axis radiation on the proposed solar concentrator for the Space Station Freedom is performed. The magnitude and position of the reflected flux peaks are identified for a matrix of off-axis angles and target distances. The principles of geometric optics are presented to illustrate that the results of the numerical study are justified, and these results are explained in terms of these basic optical principles. A method that allows prediction of the reflected flux field for a generalized parabolic reflective concentrating system is presented.

Campbell, J. S.↗
NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗
DOE OSTI2021

Decoding defect statistics from diffractograms via machine learning

Abstract Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.

36 MATERIALS SCIENCE↗