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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Phase Field Dislocation Dynamics (PFDD) version 2.x

This disclosure is for version 2.x of a mesoscale model called Phase Field Dislocation Dynamics (PFDD). PFDD is used for investigating deformation in nanoscale (grain sizes of ~300 nm and less) materials, such as metals and alloys. This approach models the motion and interaction of individual defects, namely dislocations, in the material using scalar-valued phase field variables, also called order parameters. The system is evolved through energy minimization thus the model calculates the total energy density in terms of the phase field variables. The energy minimization is completed using the Ginzburg-Landau equation, and is implemented with explicit time integration. The total system energy can be comprised of several terms, including the strain energy (which describes dislocation-dislocation interactions), the energy due to an applied stress (dislocation interactions with the applied stress), and a core/lattice (perfect dislocations) or generalized stacking fault (partial dislocations) energy (described the dislocation core structure). The latter term in particular may vary based on the crystal structure being modeled and is typically informed using lower length scale (e.g., atomistic) approaches, although no such (atomistic) calculations are completed within the PFDD algorithm. This basic formulation was previously reviewed by Los Alamos National Laboratory and released under license number C17113. This previously reviewed version we will henceforth refer to as PFDD v1.0. PFDD v1.0 consisted of 2 codes (one parallel and one serial) plus input files, all written in the C language. This new disclosure is addressing the next versions of the PFDD, versions 2.x. There have been several enhancements of PFDD v1.0, which are described here and included in the attached code, which we will refer to as PFDD v2.0. There are also several new features described here that are either planned or already in process and are expected to be subsequent releases, i.e., v2.1, v2.2, ...v2.x.

Hunter, Abigail↗
DOE OSTI2023

A Micro-scale Numerical Investigation of Internal and Interfacial Void Defects in Adhesive on Failure Behavior of Adhesively-Bonded Materials with Rough Surfaces

This paper studied the effects of air void defects on the failure behavior of adhesively-bonded materials under global shear via micro-scale computational modeling. The numerical results indicated that interfacial void defects can largely facilitate interfacial debonding of a weaker adherend/adhesive interface under shear. However, this is not the case for a stronger adherend/adhesive interface, showing the reduction on the nominal shear strength of an adhesive joint is mainly due to internal void defects. The reduced bonding performance due to voids can be improved by leveraging an appropriate surface roughness. This preliminary investigation is a first step to better understand the micro-mechanics of interfacial failure in the adhesion of a roughened/patterned adherend via surface modification(s) and an adhesive, and also shows the importance of minimizing interfacial void defects in particular at a weaker adherend/adhesive interface via different techniques.

Qiao, Yao↗
NASA NTRS1989

Metastable defects in Be-doped Al(x)Ga(1-x)As

Deep-level transient spectroscopy has been used to study metastable defects in Be-doped Al(x)Ga(1-x)As grown by molecular-beam epitaxy. The metastability manifests itself by the appearance of different spectra depending upon whether the sample is cooled from a high temperature with zero bias or a reverse bias applied to it. The defects are found in concentrations of 10 to the 15th/cu cm in a sample doped with 10 to the 18th Be/cu cm and in much lower concentrations in a 10 to the 17th Be/cu cm sample. Isochronal annealing experiments indicate that the defect is multistable and that it is best modeled as a mobile interstitial which can reside at several sites near acceptor. The activation energies for these defects are between 0.2 and 0.5 eV above the valence band.

Magno, R.↗
DOE OSTI2024

LPBF Processability of NiTiHf Alloys: Systematic Modeling and Single-Track Studies

Research into the processability of NiTiHf high-temperature shape memory alloys (HTSMAs) via laser powder bed fusion (LPBF) is limited; nevertheless, these alloys show promise for applications in extreme environments. This study aims to address this limitation by investigating the printability of four NiTiHf alloys with varying Hf content (1, 2, 15, and 20 at. %) to assess their suitability for LPBF applications. Solidification cracking is one of the main limiting factors in LPBF processes, which occurs during the final stage of solidification. To investigate the effect of alloy composition on printability, this study focuses on this defect via a combination of computational modeling and experimental validation. To this end, solidification cracking susceptibility is calculated as Kou’s index and Scheil–Gulliver model, implemented in Thermo-Calc/2022a software. An innovative powder-free experimental method through laser remelting was conducted on bare NiTiHf ingots to validate the parameter impacts of the LPBF process. The result is the processability window with no cracking likelihood under diverse LPBF conditions, including laser power and scan speed. This comprehensive investigation enhances our understanding of the processability challenges and opportunities for NiTiHf HTSMAs in advanced engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Implications of point defect accumulation on UO 2 thermal conductivity and fission gas release under accelerated fuel irradiation

Evaluation of thermal properties is a crucial factor for nuclear fuel performance. During reactor operation, the accumulation of fission products and irradiation-induced lattice defects are responsible for degradation in thermal conductivity. Consequently, it affects fuel temperature and fission gas release (FGR) among other Multiphysics processes important for economics and safety analysis. We analyze the implications of point defects (PD) accumulation described using a rate theory (RT) Model on lattice thermal conductivity of UO 2 . Here, we demonstrate that fission rate-dependent point defect concentrations have the largest impact on in-pile thermal conductivity in the periphery of light water reactor fuels below a temperature threshold governed by the migration barrier of defects. Our analysis provides a mechanistic description of this phenomena which current fuel performance codes treat empirically. The reduction of thermal conductivity in the low -temperature rim region acts as additional thermal resistance and leads to a temperature notably larger than suggested by Lucuta thermal conductivity correlation. These effects are anticipated to have notable impacts when fuels are exposed to accelerated radiation. The impact of such point defect-informed treatment of thermal conductivity on fuel performance is evaluated by a detailed analysis of fission gas behavior and its release. We consider several models capturing different stages of fission gas bubble evolution and fission gas release (FGR). Finally, a new fission rate-dependent correction to the Lucuta correlation is proposed. The results show a significant reduction in thermal conductivity at the fuels’ periphery and an increase in fuel centerline temperature specifically at low burnups. Ultimately a modified LC shows a higher FGR compared to the original LC, while the acceleration process results in a reduction in overall FGR.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Defect evolution in Ni and solid-solution alloys of NiFe and NiFeCoCr under ion irradiation at 16 and 300 K

Single-phase concentrated solid-solution alloys (SP-CSAs) have shown unique chemical complexity at the levels of electrons and atoms, and their defect evolution is expected to be different from conventional dilute alloys. Single crystals of Ni, NiFe and NiFeCoCr are chosen as model systems to understand the chemical complexity on defect formation and damage accumulation in SP-CSAs under ion irradiation. The high-quality crystals were irradiated at 16 and 300 K to different ion fluences, to form irradiated region with little to heavy damages. Additionally, the ion-induced damage was determined using Rutherford backscattering spectrometry technique along a channeling direction (RBS/C) and the level of lattice damage in irradiated Ni and SP-CSAs was quantified from Monte Carlo (MC) simulations. The results are interpreted using the Multi Step Damage Accumulation model to reveal material damage accumulation kinetics. Key findings of the study are that in case of room temperature irradiations the damage level measured for complex alloys at the highest irradiation fluence of 2 × 10 15 cm -2 (~3 dpa) is significantly higher than that obtained for pure nickel samples and suggest two-step damage accumulation process with a defect transformation taking place at a fluence of about 1.5 × 10 15 cm -2 . Furthermore, structural and damage kinetic differences clearly imply that, with increasing degree of chemical complexity and high solid-solution strengthening effects from Ni to NiFe and to NiFeCoCr, the enhanced lattice stiffness resists to randomization of atomic configurations and inhibits the growth of extended defects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
DOE OSTI2023

Direct link between disorder and magnetoresistance in topological semimetals

The extent to which disorder influences the properties of topological semimetals is relevant to the understanding of topological states and their use in practical applications. Using molecular beam epitaxy, we achieve systematic control of point defect concentrations in the prototypical Dirac semimetal Cd 3 As 2 to gain insight into the role of disorder on electron transport behavior. Using the guiding center diffusion model for linear magnetoresistance, we extract point defect densities as a function of deposition conditions. We find that reducing cadmium defect concentrations by an order of magnitude results in an 2x increase in the magnetoresistance from 450% to 900%. This finding yields important information in the quest to identify the origin of linear magnetoresistance in a wider range of materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Feasibility Assessment of Using Electrical Impedance Tomography for Damage Localization in Graphite Microreactor Components

Microreactors have great potential to decrease capital costs and construction timelines, reducing the barriers to implementing advanced nuclear reactor technologies. However, the lower power output of these microreactors introduces economic challenges if the operation and maintenance costs cannot be reduced sufficiently. Many microreactor concepts use graphite materials for in-core moderator and structural purposes, which will require periodic inspection or, ideally, in situ structural health monitoring. This work describes an initial evaluation of the feasibility of leveraging graphite’s semiconducting properties to perform electrical impedance tomography (EIT) for defect localization. First, a study was performed to identify the best methods for bonding electrical contacts to minimize the effects of contact resistance that interfere with impedance measurements of the graphite. After determining the best bonding approach, electrodes were attached to multiple graphite components with varying geometries (e.g., simple block and more representative microreactor hexagonal block). In parallel, finite element analysis approaches were developed and implemented to analyze the impact of defects and contact resistances and to inform an iterative inverse model for reconstructing the conductivity distribution. The results of this study show that defect localization in graphite components is possible using EIT if a sufficient number of electrodes (to improve spatial resolution) can be bonded using a technique with very low contact resistance and those contact resistances remain stable during reactor operation. If the reactor core and vessel design do not allow electrodes to be bonded during reactor operation, then it may be possible to detach/reattach electrodes between reactor operation and outages if a suitable mechanical connection that offers repeatable contact resistance can be identified. Alternatively, future work could focus on identifying contacts that can survive the harsh reactor operating environment so that contacts do not need to be removed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Impacts of PTL coating gaps on cell performance for PEM water electrolyzer

The anode porous transport layer (PTL) is a critical component of polymer electrolyte membrane (PEM) water electrolyzers, providing electrical conduction and water/oxygen transport to the anode catalyst layer. Platinum group metal (PGM) coatings are applied to titanium PTLs to prevent oxidation and preserve conductivity. To implement PEM water electrolysis on a large scale, it is essential to minimize the cost of mass-manufactured cell components while ensuring that defects in materials and components do not adversely affect the performance of the cell. In this study, we created gaps in the Pt coatings that are typically applied to PTLs to understand their effects on cell performance and inform on fabrication tolerances. The PTL coatings were fabricated by sputter deposition of Pt, intentionally leaving uncoated regions in either large patches or thin strips. For either geometry, the cell performance decreased with the increasing fraction of the uncoated regions. However, PTLs containing multiple thin uncoated strips caused a less severe performance effect than PTLs with continuous uncoated regions of the same relative total area. Through analysis of cell performance, impedance, and 3D charge transport modelling, here we demonstrate that both the anode catalyst layer and membrane play a role in distributing current to mitigate the impact of coating defects at a short length scale. These results allow to draw conclusions about the tolerance requirements with regards to PTL coating uniformity in terms of both the size and total area of defects.

08 HYDROGEN↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗
DOE OSTI2025

Trap-assisted Auger-Meitner recombination in GaN p-i-n diodes

Most properties of semiconductor devices are dominated by shallow impurities. However, deep defects often play an important role, for instance, in recombination processes or high field transport. While a variety of techniques are available to assess the density and energy levels of impurities, other properties, such as the recombination mechanisms of the defects, escape observation. We report on the direct measurement of hot electrons generated by trap-assisted Auger-Meitner recombination (TAAR) in GaN p-i-n diodes. By performing electron emission spectroscopy (EES) on diodes with surfaces activated to negative electron affinity by cesium, we observe the expected overflow electrons of p-i-n diodes under low current injection. However, when operating the devices at higher current densities, as low as ∼25 A/c⁢m 2 , we measure the emission of high-energy electrons. At variance with the observed hot electrons in light-emitting diodes (LEDs) using EES, the hot electrons generated in p-i-n diodes at our tested currents cannot be from eeh Auger-Meitner recombination due to the diodes' significantly lower carrier densities compared to those in LEDs. During our measurements, we observe the emission of accumulated electrons with energies ∼0.42 eV, ∼0.99 eV, ∼1.43 eV, and ∼2.32 eV above the conduction-band minimum (CBM) at various bias conditions, suggesting the existence of conduction-band features in GaN at these energies where electrons can be long-lived, such as satellite-valley minima and inflection points. We also measure incompletely relaxed hot electrons approaching energies 1.97 ± 0.13 eV and 2.94 ± 0.13 eV above the CBM, as the diodes are biased to high currents, suggesting at least some of the TAAR partaking defects have an energy level ≳1.97 eV and ≳2.94 eV away from either the conduction or valence band edges. Additionally, at our highest operating currents, we measure hot electrons with energies 3.28 ± 0.13 eV above the CBM, providing direct evidence of TAAR processes involving shallow impurities. This unexpected observation of TAAR in GaN p-i-n diodes spotlights the importance of further studies of defects in GaN and the necessity to incorporate the multi-phonon emission, radiative, and TAAR capture steps of defect-assisted recombination cycles into device modeling. Furthermore, this experiment demonstrates the applicability of the simplest semiconductor structures, p-i-n diodes, as a test bed to study the rich recombination physics of semiconductor materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Dislocation loop evolution in Kr-irradiated ThO 2

Here, the early stage of microstructural evolution of ThO 2 , under krypton irradiation at 600, 800, and 1000°C, was investigated using in situ transmission electron microscopy (TEM). Dislocation loops grew faster, whereas their number density decreased with increasing irradiation temperature. Loop density was found to decrease with ion dose. Interstitial dislocation loops, including Frank loops with Burgers vector of $\textit{a}/3\langle111\rangle$ and perfect loops with Burgers vector of $\textit{a}/2\langle110\rangle$, were determined by traditional TEM and atomic resolution–scanning TEM techniques. Atomistic and mesoscale level modeling are performed to interpret experimental observations. The migration energy barriers of defects in ThO 2 were calculated using density-functional theory. The energetics of different dislocation loop types were studied using molecular dynamics simulations. Loop density and diameter were analyzed using a kinetic rate theory model that considers stoichiometric loop evolution. This analysis reveals that loop growth is governed by the mobility of cation interstitials, whereas loop nucleation is determined by the mobility of anion defects. Lastly, a rate theory model was used to extract the diffusion coefficients of thorium interstitials, oxygen interstitials, and vacancies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1994

Comprehensive stellar population models and the disentanglement of age and metallicity effects

The construction of detailed models for intermediate and old stellar populations is described. Input parameters include metallicity (-2 less than (Fe/H) less than 0.5), single-burst age (between 1.5 and 17 Gyr), and initial mass function (IMF) exponent. Quantities output include broadband magnitudes, spectral energy distributions, surface brightness fluctuation magnitudes, and a suite of 21 absorption feature indices. The models are checked against a wide variety of available observations. Examinations of model output yield the following conclusions. (1) If the percentage change delta age/delta Z approximately equals 3/2 for two populations, they will appear almost identical in most indices. A few indices break this degeneracy by being either more abundance sensitive (Fe4668, Fe5015, Fe5709, and Fe5782) or more age sensitive (G4300, H beta, and presumably higher order Balmer lines) than usual. (2) Present uncertainties in stellar evolution are of the same magnitude as the effects of IMF and Y in the indices studied. (3) Changes in abundance ratios (like (Mg/Fe)) are predicted to be readily apparent in the spectra of old stellar populations. (4) The I-band flux of a stellar population is predicted to be nearly independent of metallicity and only modestly sensitive to age. The I band is therefore recommended for standard candle work or studies of M/L in galaxies. Other conclusions stem from this work. (1) Intercomparison of models and observations of two TiO indices seem to indicate variation of the (V/Ti) ratio among galaxies, but it is not clear how this observation ties into the standard picture of chemical enrichment. (2) Current estimates of (Fe/H) for the most metal-rich globulars that are based on integrated indices are probably slightly too high. (3) Colors of population models from different authors exhibit a substantial range. At solar metallicity and 13 Gyr, this range corresponds to an age error of roughly +/- 7 Gyr. Model colors from different authors applied in a differential sense have smaller uncertainties. (4) In the present models the dominant error for colors is probably the transformation from stellar atmospheric parameters to stellar colors. (5) Stellar B - V is difficult to model, and current spreads among different authors can reach 0.2 mag. (6) If known defects in the stellar flux library are corrected, the population model colors of this work in passbands redder than U would be accurate to roughly 0.03 mag in an absolute sense. These corrections are not made in the tables of model output.

Worthey, Guy↗
DOE OSTI2025

Simulated effect of defect volume and location on very high cycle fatigue of laser beam powder bed fused AlSi10Mg

This study quantifies the interaction between volumetric defect location and size on the very high cycle fatigue (VHCF) of laser beam powder bed fused (LB-PBF) AlSi10Mg. Crystal plasticity finite element method (CPFEM) simulations were used to investigate the effects of defect location and size on the driving force for crack initiation. The CPFEM model was calibrated against uniaxial and cyclic experimental data of LB-PBF AlSi10Mg. Defect characteristics were informed by experimental data from the specimens produced in various geometries to create realistic representative volume elements (RVEs) with equivalent volume fractions of defects. By embedding defects of varying sizes and locations within the RVEs, fatigue indicator parameters (FIPs) were calculated to analyze the impact of defects’ characteristics on fatigue performance. Different combinations of defect volume and locations were generated for various microstructure instantiations, providing insight into extreme value fatigue responses. Larger defect volumes located on free surfaces consistently generated the highest FIPs, suggesting defect size and boundary proximity intensify stress concentration effects. RVEs with multiple smaller defects produced lower FIPs than those with single large critical defects. These findings underscore the critical role of defect characteristics on fatigue life, providing a foundation for future predictive modeling in fatigue-sensitive AM applications.

AlSi10Mg↗
DOE OSTI2022

Carrier capture and emission by substitutional carbon impurities in GaN vertical diodes

A model was developed for the operation of a GaN pn junction vertical diode which includes rate equations for carrier capture and thermally activated emission by substitutional carbon impurities and carrier generation by ionizing radiation. The model was used to simulate the effect of ionizing radiation on the charge state of carbon. These simulations predict that with no applied bias, carbon is negatively charged in the n-doped layer, thereby compensating n-doping as experimentally observed in diodes grown by metal-organic chemical vapor deposition. With reverse bias, carbon remains negative in the depletion region, i.e., compensation persists in the absence of ionization but is neutralized by exposure to ionizing radiation. This increases charge density in the depletion region, decreases the depletion width, and increases the capacitance. The predicted increase in capacitance was experimentally observed using a pulsed 70 keV electron beam as the source of ionization. In additional confirming experiments, the carbon charge-state conversion was accomplished by photoionization using sub-bandgap light or by the capture of holes under forward bias.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗