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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A mechanism for reduced compression in indirectly driven layered capsule implosions

High-yield implosions on the National Ignition Facility rely on maintaining low entropy in the deuterium–tritium fuel, quantified by its adiabat, in order to efficiently couple energy to the hot spot through high compression of the fuel layer. We present very-high-resolution xRAGE simulation results that study the impacts of interfacial mixing and the jetting of materials due to surface defects, defects on internal interfaces, voids, and engineering features on fuel layer compression. Defects and voids are typically neglected in implosion simulations due to their small size and three-dimensional geometry. Our results showed that supersonic jets of material arise through weak spots in the shell at peak implosion velocity that prevent uniform compression of the fuel layer even when they do not introduce contaminant into the hot spot. This occurs despite maintaining low fuel entropy, since the formation of the weak spots involves nonradial displacement of fuel mass. In contrast, simulations show that fuel–ablator mixing due to interfacial instabilities has a much smaller impact on compression. We show that defects on interior interfaces of plastic capsules decrease compression by 15% to 25% and interfacial mixing between the ablator and fuel decreases compression by less than 1% for implosions with plastic or high-density carbon (HDC) ablators. For low adiabat implosions, the impact of jetting seeded by the support tent can also decrease the compression by 25%. We demonstrate that the inclusion of interior defects in simulations can explain the inferred compression in two fielded plastic capsule implosions and that the inclusion of voids, for which available characterization has large uncertainties, in simulations of HDC capsule implosions has a qualitatively consistent impact. This mechanism offers a potential explanation for persistently overestimated fuel compression in design simulations of layered implosions on the National Ignition Facility.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2018

Solar Wind Implantation into Lunar Regolith II: Monte Carlo Simulations of Hydrogen Retention in a Surface with Defects and the Hydrogen (H, H2) Exosphere

Recently, the near-infrared observations of the OH veneer on the lunar surface by the Moon Mineralogy Mapper (M3) have been refined to constrain the OH content to 500-750 parts per million (ppm). The observations indicate diurnal variations in OH up to 200 ppm possibly linked to warmer surface temperatures at low latitude. We examine the M3 observations using a statistical mechanics approach to model the diffusion of implanted H in the lunar regolith. We present results from Monte Carlo simulations of the diffusion of implanted solar wind H atoms and the subsequently derived H and H2 exospheres.

Exosphere↗
NASA NTRS2007

Development of Natural Flaw Samples for Evaluating Nondestructive Testing Methods for Foam Thermal Protection Systems

Low density polyurethane foam has been an important insulation material for space launch vehicles for several decades. The potential for damage from foam breaking away from the NASA External Tank was not realized until the foam impacts on the Columbia Orbiter vehicle caused damage to its Leading Edge thermal protection systems (TPS). Development of improved inspection techniques on the foam TPS is necessary to prevent similar occurrences in the future. Foamed panels with drilled holes for volumetric flaws and Teflon inserts to simulate debonded conditions have been used to evaluate and calibrate nondestructive testing (NDT) methods. Unfortunately the symmetric edges and dissimilar materials used in the preparation of these simulated flaws provide an artificially large signal while very little signal is generated from the actual defects themselves. In other words, the same signal are not generated from the artificial defects in the foam test panels as produced when inspecting natural defect in the ET foam TPS. A project to create more realistic voids similar to what actually occurs during manufacturing operations was began in order to improve detection of critical voids during inspections. This presentation describes approaches taken to create more natural voids in foam TPS in order to provide a more realistic evaluation of what the NDT methods can detect. These flaw creation techniques were developed with both sprayed foam and poured foam used for insulation on the External Tank. Test panels with simulated defects have been used to evaluate NDT methods for the inspection of the External Tank. A comparison of images between natural flaws and machined flaws generated from backscatter x-ray radiography, x-ray laminography, terahertz imaging and millimeter wave imaging show significant differences in identifying defect regions.

Workman, Gary L.↗
DOE OSTI2021

Capture efficiency and bias from the defect dynamics near grain boundaries in BCC Fe using mesoscale simulations

The capture efficiency describes the capability of a sink, such as a grain boundary (GB), dislocation, and void, to absorb point defects (PDs). The bias defines the difference in capture efficiency between the absorption of a vacancy and dumbbell at a sink. Complete kinetic information on PDs, including diffusion barriers and diffusion orientations, as well as accurate saddle points, are needed to determine the capture efficiency and bias at a sink accurately, which is computationally demanding. Here, in the present study, the Self-Evolving Atomistic Kinetic Monte Carlo (SEAKMC) method was used to investigate the defect dynamics of PDs near different types of grain boundaries (GBs) (with both $\langle$100$\rangle$ and $\langle$110$\rangle$ families) accurately in body-centered cubic (BCC) iron (Fe). The capture efficiency, sink strength, and bias factor of different types of GBs were determined in Fe, which, different from traditional rate theory estimation, showed a distinct capture efficiency, sink strength, and bias in different GBs. The results demonstrate a strong positive correlation between the capture efficiency and the GB strain width, instead of the GB misorientation, GB energy, or GB-PD binding energy, which have been investigated previously. This work provides valuable insight into the radiation-induced microstructural evolution of GBs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

From Femtoseconds to Gigaseconds: The SolDeg Platform for the Performance Degradation Analysis of Silicon Heterojunction Solar Cells

Heterojunction Si solar cells exhibit notable performance degradation. Here, we modeled this degradation by electronic defects getting generated by thermal activation across energy barriers over time. To analyze the physics of this degradation, we developed the SolDeg platform to simulate the dynamics of electronic defect generation. First, femtosecond molecular dynamics simulations were performed to create a-Si/c-Si stacks, using the machine learning-based Gaussian approximation potential. Second, we created shocked clusters by a cluster blaster method. Third, the shocked clusters were analyzed to identify which of them supported electronic defects. Fourth, the distribution of energy barriers that control the generation of these electronic defects was determined. Fifth, an accelerated Monte Carlo method was developed to simulate the thermally activated time-dependent defect generation across the barriers. Our main conclusions are as follows. (1) The degradation of a-Si/c-Si heterojunction solar cells via defect generation is controlled by a broad distribution of energy barriers. (2) We developed the SolDeg platform to track the microscopic dynamics of defect generation across this wide barrier distribution and determined the time-dependent defect density N(t) from femtoseconds to gigaseconds, over 24 orders of magnitude in time. (3) We have shown that a stretched exponential analytical form can successfully describe the defect generation N(t) over at least 10 orders of magnitude in time. (4) We found that in relative terms, Voc degrades at a rate of 0.2%/year over the first year, slowing with advancing time. (5) We developed the time correspondence curve to calibrate and validate the accelerated testing of solar cells. We found a compellingly simple scaling relationship between accelerated and normal times t normal ∝ t accel T(accel)/T(normal) . (6) We also carried out experimental studies of defect generation in a-Si:H/c-Si stacks. We found a relatively high degradation rate at early times that slowed considerably at longer time scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗
NASA NTRS1999

Damage Tolerance of Large Shell Structures

Progressive damage and fracture of large shell structures is investigated. A computer model is used for the assessment of structural response, progressive fracture resistance, and defect/damage tolerance characteristics. Critical locations of a stiffened conical shell segment are identified. Defective and defect-free computer models are simulated to evaluate structural damage/defect tolerance. Safe pressurization levels are assessed for the retention of structural integrity at the presence of damage/ defects. Damage initiation, growth, accumulation, and propagation to fracture are included in the simulations. Damage propagation and burst pressures for defective and defect-free shells are compared to evaluate damage tolerance. Design implications with regard to defect and damage tolerance of a large steel pressure vessel are examined.

Minnetyan, L.↗
DOE OSTI2022

Computational investigation of hydrogen-induced phonon changes in carbon fiber

Optical vibrational spectroscopy has shown promise as a noninvasive means of monitoring the mechanical properties of carbon fiber (CF), which is increasingly used for industrial and consumer purposes. However, interpretation of optical vibrational spectra for solid materials is inferential, particularly when defects are present. Because inelastic neutron scattering (INS) spectroscopy is not subject to selection rules, the full vibrational spectra can be measured. And, identifying correlations between INS features and tensile properties can assist in the interpretation of spectra from more commonly used optical vibrational spectroscopic techniques, such as Raman and infrared (IR) spectroscopy. Recent INS experiments on high-performance commercial carbon fibers showed features near 900 and 1100 cm –1 in addition to a broad feature near 3000 cm –1 that increased in intensity with decreasing tensile strength. These features were assigned to hydrogen defects. Here, we use density functional theory to simulate the INS spectra of several hydrogen defect geometries in graphite as a model for carbon fiber structure units, confirming the experimental assignment of these peaks to hydrogen modes and providing insights into the structure and lattice dynamics of the defects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Strain-Induced Growth of Twisted Bilayers during the Coalescence of Monolayer MoS 2 Crystals

Tailoring the grain boundaries (GBs) and twist angles between two-dimensional (2D) crystals are two crucial synthetic challenges to deterministically enable envisioned applications such as moiré excitons, emerging magnetism, or single-photon emission. Here, we reveal how twisted 2D bilayers can be synthesized from the collision and coalescence of two growing monolayer MoS 2 crystals during chemical vapor deposition. The twisted bilayer (TB) moiré angles are found to preserve the misorientation angle (θ) of the colliding crystals. The shapes of the TB regions are rationalized by a kink propagation model that predicts the GB formed by the coalescing crystals. Optical spectroscopy measurements reveal a θ-dependent long-range strain in crystals with stitched grain boundaries and a sharp (θ > 20°) threshold for the appearance of TBs, which relieves this strain. Reactive molecular dynamics simulations explain this strain from the continued growth of the crystals during coalescence due to the insertion of atoms at unsaturated defects along the GB, a process that self-terminates when the defects become saturated. The simulations also reproduce atomic-resolution electron microscopy observations of faceting along the GB, which is shown to arise from the growth-induced long-range strain. These facets align with the axes of the colliding crystals to provide favorable nucleation sites for second-layer growth of a TB with twist angles that preserve the misorientation angle θ. This interplay between strain generation and aligned nucleation provides a synthetic pathway for the growth of TBs with deterministic angles.

2D materials↗
DOE OSTI2025

Enabling Next Generation Reaction Injection Molding (RIM) for Lightweight Structures

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this project, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. This optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption. This research has been performed in collaborations with McClarin Composites. The research outcome has been submitted to the Journal of Manufacturing Processes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Atomistic Modeling of Irradiation-Induced Defects and Clusters in Additively-Manufactured Austenitic Stainless Steel

Understanding the progression of irradiation-induced defects in real materials and alloys requires insight into the interaction between point defects, defect clusters, and the existing microstructure. This is particularly true when it comes to additively manufactured (AM) materials with complicated microstructures and residual stresses. In this work, we employ molecular dynamics (MD) to simulate the creation of point defects via primary radiation damage modeling and point defect interactions with extended defects such as voids and dislocations using simplified but representative alloy compositions. We analyze the effect of alloying elements on the formation energies of the defect clusters and measure interactions such as sink strengths and binding energies. This study provides insight into the unique behaviors of irradiation damage in AM materials and information necessary to bridge the gap in length and time scales to connect the atomistic behavior of defects to longer length scale simulations such as cluster dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Molecular dynamics studies of lattice defect effects on tritium diffusion in zirconium

Tritium diffusion in α-Zr containing point defects such as vacancies or self-interstitial atoms (SIAs) is simulated using molecular dynamics. Point defects rapidly aggregate to form extended defects, such as 3D nanoclusters and Frank loops. The geometry of extended defects is affected by the presence of tritium. At low temperature and in the absence of tritium, vacancies aggregate to form stacking fault pyramids. Addition of tritium at these temperatures promotes aggregation of vacancies to form 3D nanoclusters, within which the tritium concentration can be sufficiently high to suggest that these defects may serve as nucleation sites for hydride precipitation. Trapping of tritium in vacancy nanocluster reduces the calculated bulk diffusivity by an amount proportional to the vacancy concentration. At high temperature, vacancy clusters change shape to form planar basal dislocation loops, which bind tritium less strongly, leading to a sharp reduction in the fraction of trapped tritium and a corresponding increase in tritium diffusivity at high temperature. In contrast, SIAs increase tritium diffusion through α-Zr. Analysis of atomic trajectories shows that tritium does not interact directly with SIAs. In conclusion, diffusion enhancement is instead related to expansion of the lattice.

36 MATERIALS SCIENCE↗
DOE OSTI2025

First-Principles Simulations Correlating X-ray Absorption Spectroscopy Features to Point Defects in h -BN

Hexagonal boron nitride (h-BN) is a promising material for a range of emerging applications in electronics, quantum information technology, and energy storage. Soft X-ray absorption spectroscopy (XAS) is powerful to reveal atomic details of BN, especially in the presence of defects. However, correlating XAS spectral features with specific defect types remains elusive. In this Letter, we report B K-edge XAS measurements of sputter-deposited turbostratic h-BN films and use a combination of first-principles spectroscopic simulations and analysis of detailed electronic structure and local charge transfer characteristics to elucidate their unique spectroscopic features. Our results show that the two main defect-related peaks, between the main π* resonances of h-BN and B2O3, as typically observed in BN films deposited by energetic condensation or bombarded with energetic ions, are associated with electronic states of H-passivated B atoms bonded to one and two oxygen impurity atoms, respectively. These conclusions hold significant implications for applications relying on defect-mediated properties of h-BN.

chemical structure↗
DOE OSTI2021

Stress-dominated growth of two-dimensional materials on nonplanar substrates

Curved features are ubiquitous on solid surfaces, but the effect of surface curvatures on growth of two-dimensional (2D) materials has not yet been established. Using a newly developed method based on the Metropolis algorithm and taking graphene as a prototype, we find that a curved feature on substrates can result in a variety of topological defects in 2D materials. As the feature's size increases by just nanometers, the defects can vary from adatoms, dislocation pairs, and grain boundary scars to long-range grain boundaries, in contrast to previously reported defect-free modes of rigid colloidal crystals growing on spheres. We identify an important role of curvature-induced lattice stress in lowering the growth rate over the curved features and driving a plastic instability in the materials. When the feature's size increases to several nanometers, the stress effect is compromised by an enhanced effect of geodesic curvature, yielding long-range grain boundaries as a result of increased local growth rate on the feature with respect to that on flat regions. Here we further provide a ‘phase diagram’ of defects that helps to guide a rational choice of geometrical parameters of features towards the growth of high-quality 2D materials as well as controllable creation of topological defects.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Simulation-driven design optimization of reaction injection molding (RIM) process for polydicyclopentadiene (pDCPD): Minimizing cycle time, defects, and warpage

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this study, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. In conclusion, this optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption.

42 ENGINEERING↗
DOE OSTI2025

Atoms-to-devices models for post-irradiation annealing kinetics in III-V semiconductors

Understanding atomic displacement damage and propagating this understanding into validated defect-aware radiation damage models is a critical step toward achieving predictive, atoms-to-systems assessment of radiation vulnerabilities of microelectronics. These models are essential for evaluating the performance and reliability of nuclear stockpile components or space-based electronics (satellites). The goal of this project was to bridge a fundamental science gap between defect spectroscopies and atomistic modeling of radiation-induced defects on the one hand, and simulations of device response on the other hand, to develop predictive defect-aware damage models for high-fidelity device simulations of radiation-damaged III-V semiconductors.

36 MATERIALS SCIENCE↗