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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Slip band characteristics in the presence of grain boundaries in nickel-based superalloy

Shear strain profiles along slip bands in a modified Rolls-Royce nickel superalloy (RR1000) were analyzed for a tensile sample deformed by 2%. The strain increased with distance away from a grain boundary (GB), with maximum shear strain towards the center of the grain, indicating that dislocation nucleation generally occurred in the grain interior. The strain gradients in the neighborhood of the GBs were quantified and generally correlated with rotation about the active slip system line direction. This leads to an ability to determine the active slip system in these regions. The dislocation spacing and pileup stresses were inferred. The dislocation spacing closely follows an Eshelby analytical solution for a single ended pileup of dislocations under an applied stress. The distribution of pileup stress values for GBs of a given misorientation angle follows a log-normal distribution, with no correlation between the pileup stress and the GB misorientation angle. Furthermore, there is no observed correlation between various transmissivity factors and slip band pileup stress. Hence it appears that the obstacle strength of any of the observed GBs is adequate to facilitate the dislocation pileups present in the slip bands. However, slip band transmission does correlate with transmissivity factors, with the current study focusing on the Luster and Morris m’-factor. Observation of strain profiles of transmitted bands indicate dislocation nucleation locations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Extensive nanoprecipitate morphology transformation in a nanostructured ferritic alloy due to extreme thermomechanical processing

Nano-oxide precipitates in a modern nanostructured ferritic alloy were investigated after extreme thermomechanical processing into a thin-walled tube geometry. It was found that the morphology of the precipitates changed from spherical to rod-shaped, with some increasing to aspect ratios of up to 9, despite the precipitate volume fraction (0.3%) and number density (> 10 23 m -3 ) of precipitates remaining unchanged. High-resolution electron microscopy showed that the precipitates likely remained coherent with the Fe-matrix, while atom probe tomography confirmed that the precipitate compositions remained unaffected by the transformation. The morphological change was attributed to the shearable nature of the (Y,Ti,O)-rich precipitates, indicating they should be considered as “soft” obstacles to dislocation motion. The elongation was most pronounced in larger (>5 nm) precipitates, which may be caused by preferential dissolution of the smallest (1–3 nm) precipitates followed by the competition between re-precipitation and solute diffusion to larger precipitates during recovery heat treatments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Improved irradiation resistance of accident-tolerant high-strength FeCrAl alloys with heterogeneous structures

In this work, post–neutron irradiation examination is performed on advanced accident-tolerant fuel (ATF) cladding iron-chromium-aluminum (FeCrAl) alloys with ~10–13at. % Cr, ~10–12 at. % Al, ~1 at. % Mo, and minor alloying elements including Y irradiated to a damage level of 7 displacements per atom (dpa) at irradiation temperatures of 267–282 °C. A compositional dependency of the Cr and Al content is observed on the ratio of sessile and glissile dislocation loops, where the density of a$\langle$100$\rangle$ type loops is somewhat higher than the a/2$\langle$111$\rangle$ type loops. The α' precipitate number density is inversely correlated to the starting Cr concentration of the alloys of interest. The irradiation to a higher dose of 7 dpa results in a higher density of dislocation loops and α' precipitates for the same alloys at a lower irradiation dose, such as 1.8 dpa. In this work, the effect of α' precipitates on the dislocation loop density is discussed, and the presence of α' appears to inhibit the nucleation of loops. Compared with first-generation FeCrAl alloys, these advanced alloys with heterogeneous structure exhibit a lower Cr concentration in α' precipitation at the same dose level; they act as weaker obstacles deviating from the primary hardening contribution from the mature α'. Hence, the overall irradiation-induced hardening decreases; our alloys show improved radiation resistance because of their stronger sink strengths. The results presented in this paper could provide insights for the design and optimization of ATF cladding materials for future fission and space applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Additively manufactured novel Al-Cu-Sc-Zr alloy: Microstructure and mechanical properties

An in-depth understanding of microstructure and resultant properties is paramount in the design of a novel alloy system, especially for additive manufacturing (AM). The present investigation aims to characterize a prototypical AM Al alloy with great potential for structural applications. An Al-1.5Cu-0.8Sc-0.4Zr alloy designed using integrated computational material engineering was printed using the laser powder bed fusion AM process. This novel alloy shows promising combination of strength and ductility in as-built and peak-aged conditions. This improvement in the tensile properties is attributed to the presence of both coherent L1 2 Al 3 Sc/Al 3 (Sc,Zr) precipitates and Cu-rich regions. The microstructures were studied via extensive microscopy at different length scales using X-ray microscopy, scanning electron microscopy, and transmission electron microscopy. Fractography revealed that the columnar grain boundaries in as-built condition allow easy slip transfer as compared to the equiaxed grains, with the apex of the melt pool acting as the crack nucleation site. Furthermore, the peak aged condition resulted in improved strength while marginally sacrificing ductility due to precipitates decorating dislocations, grain boundaries and melt pool boundaries thus acting as obstacles to slip transfer.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mechanistic in situ insights into the formation, structural and catalytic aspects of the La 2 NiO 4 intermediate phase in the dry reforming of methane over Ni-based perovskite catalysts

In this work, we focus on the stability and bulk/surface structural properties of the Ruddlesden-Popper phase La 2 NiO 4 and their consequences for dry reforming of methane (DRM) activity. Fuelled by the appearance as a crucial intermediate during in situ decomposition of highly DRM-active LaNiO3 perovskite structures, we show that La 2 NiO 4 can be equally in situ decomposed into a Ni/La 2 O 3 phase offering CO 2 capture and release necessary for DRM activity, albeit at much higher temperatures compared to LaNiO 3 . Decomposition in hydrogen also leads to an active Ni/La 2 O 3 phase. In situ X-ray diffraction during DRM operation reveals considerable coking and encapsulation of exsolved Ni, yielding much smaller Ni crystallites compared to on LaNiO 3 , where coking is virtually absent. Additionally, generalizing the importance of intermediate Ruddlesden-Popper phases, the in situ decomposition of La-based perovskite structures yields several obstacles due to the high stability of both the parent perovskite and the Ruddlesden-Popper structures and the occurrence of parasitic structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A highly oxygen reduction reaction active and CO 2 durable high-entropy cathode for solid oxide fuel cells

One big obstacle for the oxygen reduction reaction (ORR) electrode in solid oxide fuel cells (SOFCs) is the poor reaction activity and fast degradations caused by CO 2 poisoning. Here, in this study, we report our design of an active A-site Ca-rich high-entropy Pr 0.1875 Ba 0.1875 Sr 0.1875 La 0.1875 Ca 0.25 CoO 3-δ (PBSLC 25 C) electrode, guided by the O p-band theory. Here, when applied as a cathode in solid oxide fuel cells (SOFCs), it demonstrates high ORR activity and excellent CO 2 tolerance under realistic operating conditions. Ni-YSZ-based anode-supported cells with PBSLC 25 C cathodes demonstrate excellent peak power densities of 1.14 W cm -2 , 1.04 W cm -2 , and 0.77 W cm -2 in the air with 1%, 5%, and 10% CO 2 , respectively, at 750 °C. The engineered high-entropy PBSLC 25 C effectively diminishes the CO 2 poisoning effect and maintains active surfaces for fast oxygen exchange, as confirmed by the cell durability test in air containing CO 2 (5 and 10 vol%), Raman spectroscopy, and density functional theory calculations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

A scalable crosslinked fiberglass-aerogel thermal insulation composite

Ceramic aerogels are attractive for thermal insulation due to its lightweight and low thermal conductivity, while it is indispensable to address the obstacle of its brittleness nature and large-scale manufacturing for building efficiency applications. The economical manufacturing of large-scale elastic thermal insulation aerogels has remained challenging. Herein, we report a universal manufacturing of the fiberglass reinforced aerogel composites where the architectured three-dimensional fiber networks with the cross-linked silica aerogel exhibit superinsulation and mechanical elasticity. These remarkable performances are obtained through in-situ cross-linking reaction between molecular assembly induced aerogel and fiberglass under ambient pressure drying environment, in which the interfacial interactions are verified from its infrared spectra. The resulting ultralight aerogel composite sheet exhibits a density of 0.1 g cm -3 , large strain recovery (>50%), and low thermal conductivity (0.0253 W m –1 K –1 ). Here, the thermal insulation aerogel composites shed light on the super-elastic aerogel manufacturing with scalability for energy saving building applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Nitrogen-doped graphene nanomaterials for electrochemical catalysis/reactions: A review on chemical structures and stability

The electronic structure of the carbon-based nanomaterials can be modulated by doping heteroatoms into them. When nitrogen is doped into the graphene structure with different bonding configurations, it changes the material's electronic properties in a variety of ways. Because of the tuned electronic properties, nitrogen-doped graphene (N-G) is applicable in electrochemical systems as catalyst. Despite having tremendous prospects, a holistic view of the structural and functional properties of N-G is still unclear. Moreover, to our knowledge, significant findings on the properties of N-G are not well documented yet, which creates an obstacle to the further improvement of this nanomaterial. Keeping our focus on the catalytic activities, in this paper, we presented an in-detailed review of the overall chemical structure and functional properties of N-G nanomaterials. Starting from the structural properties of major precursor materials for N-G synthesis, we reviewed the physical features and its dependence on the synthesis parameters. Also, a detailed study has been conducted on the key nitrogen functional groups' structural properties, favorable formation environment, influence on the electronic structure of N-G, and role in the catalytic activity. Finally, current progress on the stability of N-G nanocatalysts is reported with an insight into the degradation mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Nonthermal hydrogen plasma-enabled ambient, fast lignin hydrogenolysis to valuable chemicals and bio-oils

The reduction of fossil fuel resources and the ongoing surge in global energy demand have captured the interest of researchers worldwide, prompting a focus on developing renewable energy sources. For this reason, biomass conversion has emerged as a crucial pathway for renewable fuel production. Lignin, constituting 10–35% of woody biomass, represents a significant and largely untapped sustainable feedstock. Despite the potential of lignin, a substantial portion of this lignocellulosic residue remains unused, with approximately 60% considered waste. This study addresses the challenge of underutilized lignin by introducing an innovative approach to its hydrogenolysis. Despite their potential, existing hydrogenolysis methods face obstacles such as complexity, high cost, and the need for high temperatures or pressures. Herein we report a noncatalytic nonthermal hydrogen plasma method for lignin hydrogenolysis, conducted under ambient temperature and pressure conditions. Our method proves to be highly effective in breaking lignin bonds, achieving complete conversion, and generating valuable gaseous and bio-oil products including methane and aromatic dimers and monomers obtained from guaiacyl and syringyl units within the lignin structure. Our results showed an increase in gaseous products, especially methane, and aromatic monomer yields, as well as a reduction in total bio-oil and biochar yields and lignin functional groups by increasing reaction time, input power, and H2 partial pressure. This research confirms the considerable promise of utilizing noncatalytic nonthermal hydrogen plasma-assisted hydrogenolysis as an effective technique for producing gaseous and liquid fuels from lignin.

Pishva, Parsa↗
DOE OSTI2020

D614G Spike Mutation Increases SARS CoV-2 Susceptibility to Neutralization

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein acquired a D614G mutation early in the pandemic that confers greater infectivity and is now the globally dominant form. To determine whether D614G might also mediate neutralization escape that could compromise vaccine efficacy, sera from spike-immunized mice, nonhuman primates, and humans were evaluated for neutralization of pseudoviruses bearing either D614 or G614 spike. In all cases, the G614 pseudovirus was moderately more susceptible to neutralization. The G614 pseudovirus also was more susceptible to neutralization by receptor-binding domain (RBD) monoclonal antibodies and convalescent sera from people infected with either form of the virus. Here, negative stain electron microscopy revealed a higher percentage of the 1-RBD “up” conformation in the G614 spike, suggesting increased epitope exposure as a mechanism of enhanced vulnerability to neutralization. Based on these findings, the D614G mutation is not expected to be an obstacle for current vaccine development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Exploring stages of resilience maturity as communities confront climate risks

Coastal communities are facing increased risks due to climate change, as evidenced by heightened shocks such as hurricanes and wildfires, and stressors including sea-level rise and drought. To confront these risks, many coastal communities are planning and designing their built environments in new ways, with the overall goal of increasing their resilience. However, resilience practices have been widely guided by an event-based timeframe, which may not be sufficient to confront the multiple and overlapping threats expected over the coming decades. To address this concern, this study investigated a long-term perspective on resilience progression, termed resilience maturity. To better understand resilience maturity in practice, this study drew directly on community perspectives, thereby identifying common challenges. Furthermore, variation in resilience maturity across communities provided an opportunity to identify existing successful strategies. Inductive qualitative analysis was used to examine perspectives from 15 interviews with local practitioners across 12 coastal communities. The analysis revealed that communities in early stages of resilience maturity often struggle with local stakeholder alignment and minimal dedicated resilience resources. In contrast, those further along the maturity path often face technology barriers and challenges in aligning regional stakeholders. Few examples of a fully mature resilience culture were found within the communities studied, underscoring systemic barriers to achieving advanced resilience maturity. Overall, these findings supported development of a practical framework for classifying community resilience efforts, identifying common obstacles, and informing localized resilience advancement.

Resilience maturity↗
DOE OSTI2020

Topology optimization of multiple deformable bodies in contact with large deformations

Previous works in topology optimization of structures with contact boundary conditions have concentrated on the two-dimensional rigid obstacle problem. This is because the contact analysis of multiple three-dimensional deformable bodies with meshes that are non-matching across the contact interface requires computationally complex contact algorithms beyond the scope of previous optimization investigations. Here, our research is devoted to addressing topology design problems with multiple deformable three-dimensional components in contact using state-of-the-art contact algorithms.

42 ENGINEERING↗
DOE OSTI2025

An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models

Here, we propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, predictive capabilities, and cost reductions of the proposed method.

42 ENGINEERING↗
DOE OSTI2025

A chain stretch-based gradient-enhanced model for damage and fracture in elastomers

Similar to quasi-brittle materials, it has been recently shown that elastomers can exhibit a macroscopically diffuse damage zone that accompanies the fracture process. In this study, we introduce a stretch-based gradient-enhanced damage (GED) model that allows the fracture to localize and also captures the development of a physically diffuse damage zone. This capability contrasts with the paradigm of the phase field method for fracture, where a sharp crack is numerically approximated in a diffuse manner. Capturing fracture localization and diffuse damage in our approach is achieved by considering nonlocal effects that encompass network topology, heterogeneity, and imperfections. These considerations motivate the use of a statistical damage function dependent upon the nonlocal deformation state. From this model, fracture toughness is realized as an output. While GED models have been classically utilized for damage modeling of structural engineering materials (e.g., concrete), they face challenges when trying to capture the cascade from damage to fracture, often leading to damage zone broadening (de Borst and Verhoosel, 2016). This deficiency contributed to the popularity of the phase-field method over the GED model for elastomers and other quasi-brittle materials. Other groups have proceeded with damage-based GED formulations that prove identical to the phase-field method (Lorentz et al., 2012), but these inherit the aforementioned limitations. To address this issue in a thermodynamically consistent framework, we implement two modeling features (a nonlocal driving force bound and a simple relaxation function) specifically designed to capture the evolution of a physically meaningful damage field and the simultaneous localization of fracture, thereby overcoming a longstanding obstacle in the development of these nonlocal strain- or stretch-based approaches. Here, we discuss several numerical examples to understand the features of the approach at the limit of incompressibility, and compare them to the phase-field method as a benchmark for the macroscopic response and fracture energy predictions.

Elastomers↗
DOE OSTI2025

The latent variable proximal point algorithm for variational problems with inequality constraints

The latent variable proximal point (LVPP) algorithm is a framework for solving infinite-dimensional variational problems with pointwise inequality constraints. The algorithm is a saddle point reformulation of the Bregman proximal point algorithm. At the continuous level, the two formulations are equivalent, but the saddle point formulation is more amenable to discretization because it introduces a structure-preserving transformation between a latent function space and the feasible set. Working in this latent space is much more convenient for enforcing inequality constraints than the feasible set, as discretizations can employ general linear combinations of suitable basis functions, and nonlinear solvers can involve general additive updates. LVPP yields numerical methods with observed mesh-independence for obstacle problems, contact, fracture, plasticity, and others besides; in many cases, for the first time. The framework also extends to more complex constraints, providing means to enforce convexity in the Monge–Ampère equation and handling quasi-variational inequalities, where the underlying constraint depends implicitly on the unknown solution. Here, in this paper, we describe the LVPP algorithm in a general form and apply it to ten problems from across mathematics.

Inequality constraints↗
DOE OSTI2026

Chemical bonding, phase stability and magnetic property in Sm 2 Fe 17 X 3 (X=H, C, N): A first-principles perspective

As a promising alternative to Nd–Fe–B magnets, the critical rare earth free Sm 2 Fe 17 X 3 (X = C, N) exhibits potential for high-performance magnets. However, their poor phase stability remains a major obstacle to developing bulk magnets. We investigated the phase stability and intrinsic magnetic properties of Sm 2 Fe 17 X 3 (X = H, C, N) using first-principles calculations and chemical bond analysis. The formation energies are negative, while the decomposition energies are −1.53, 0.348, and −0.74 eV per formula unit for X = H, C, and N, respectively, which is responsible for the weak thermal stability. Our chemical bond analysis reveals that the bonding asymmetry between Sm–X and Fe–X interactions creates local structural distortions and degrades the phase stability of Sm 2 Fe 17 X 3 . The project Crystal Orbital Hamilton Population (-pCOHP) analysis indicates that the Sm–X bonding remains positive up to the Fermi level, indicating stable bonding interactions. Here, in contrast, the Fe–X bonding becomes negative near the Fermi level, signifying anti-bonding contributions that reduce structural stability. Interstitial atoms X expand the lattice and enhance Fe magnetic moments, but Fe–X bonding suppresses neighboring Fe moments. Electron transfer from Sm to X modifies the valence state of Sm and the crystal field at the site, contributing to enhanced magnetocrystalline anisotropy in Sm 2 Fe 17 X 3 . Among the interstitial elements, carbon and nitrogen—with their larger atomic radius and higher electronegativity—induce greater lattice expansion and form stronger bonds with neighboring Sm and Fe atoms compared to hydrogen. Consequently, Sm 2 Fe 17 X 3 (X = C and N) exhibits better phase stability and significant improvement in magnetic properties.

Chemical bonding↗
DOE OSTI2022

GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation

High fidelity simulations of grain formation in alloys are an indispensable tool for process-to-mechanical-properties characterization. Such simulations, however, can be computationally expensive as they require fine spatial and temporal discretizations. Their cost becomes an obstacle to parametric studies and ensemble runs and ultimately makes downstream tasks like optimal control and uncertainty quantification challenging. To enable such downstream tasks, we introduce GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. Its innovations are (1) an attention mechanism with grain-microstructure-specific transformer architecture; and (2) an overlapping combination of several clones of the network to generalize to grain configurations that are different from those used for training. This design enables GrainNN to predict grain formation for unseen physical parameters, grain number, domain size and geometry. Furthermore, GrainNN not only reconstructs the quantities of interest but also can be pointwise accurate. In our numerical experiments, we use a polycrystalline phase field method to both generate the training data and assess GrainNN. For multiparametric, ensemble simulations with many grains, GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. Additionally, this speedup includes the cost of the phase field simulations for generating training data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multifidelity methods for uncertainty quantification of a nonlocal model for phase changes in materials

This study is devoted to the construction of a multifidelity Monte Carlo (MFMC) method for the uncertainty quantification of a nonlocal, non-mass-conserving Cahn-Hilliard model for phase transitions with an obstacle potential. Here, we are interested in estimating the expected value of an output of interest (OoI) that depends on the solution of the nonlocal Cahn-Hilliard model. As opposed to its local counterpart, the nonlocal model captures sharp interfaces without the need for significant mesh refinement. However, the computational cost of the nonlocal Cahn-Hilliard model is higher than that of its local counterpart with similar mesh refinement, inhibiting its use for outer-loop applications such as uncertainty quantification. The MFMC method augments the desired high-fidelity, high-cost OoI with a set of lower-fidelity, lower-cost OoIs to alleviate the computational burden associated with nonlocality. Most of the computational budget is allocated to sampling the cheap surrogate models to achieve speedup, whereas the high-fidelity model is sparsely sampled to maintain accuracy. For the non-mass-conserving nonlocal Cahn-Hilliard model, the use of the MFMC method results in, for a given computational budget, about an order of magnitude reduction in the mean-squared error of the expected value of the OoI relative to that of the Monte Carlo method.

97 MATHEMATICS AND COMPUTING↗