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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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Effect of additions of chromium, molybdenum, and tungsten on grain growth of magnesium oxide after high temperature heat treatment
Grain orientation influence on friction properties of tungsten
Stiffness version of finite element method used for numerical analysis of small elementary regions of solid propellant grains
Use of thermally-induced straining for grain refinement in recrystallized tungsten
Lattice and grain boundary self-diffusion coefficients of radioactive nickel 63 into high purity nickel measured over temperature range 650-475 degrees C
Gravitational stresses in circular cylindrical orthotropic viscoelastic solid propellant grain
Random network grain boundary structure in polycrystalline magnesium oxide
Carbonaceous dust and aerosols have been observed in interstellar/circumstellar and planetary environments. Dedicated laboratory experiments have been developed to produce analogs of these solid materials under different experimental conditions (molecular precursors, temperature, pressure, energy source…). These experimental studies are key to investigating the physical and chemical processes that drive the formation of solid particles from gas and solid phase molecular precursors in astrophysical and planetary environments. They also allow the characterization of the physical, optical, and chemical properties of these laboratory-generated analogs, hence providing critical information that can be used as input parameters in models for the analysis and interpretation of observational data. Here, as an example of these laboratory efforts, we will present various studies that combined (1) experiments performed with the NASA Ames COSmIC facility to produce analogs of Titan’s atmospheric aerosols and cosmic grains from gas phase molecular precursors, and (2) the characterization of these analogs with the NASA Ames Optical Constants Facility (OCF) to provide the real and imaginary parts of their refractive indices, n + ik, to the community, from the visible to the far-infrared (0.59–200 µm, 16,950–50 per cm). We will also discuss the importance of the wavelength and spectral range coverage of these optical constants, n and k, for their use in radiative transfer, atmospheric and reflectance models, and present a new effort to develop an Optical Constant database (OCdb) to provide this critical data to the scientific community.
Powerpoint slides for ATR Advanced Materials Program Grain Coarse
Irradiation induced defects on grain boundaries (GBs) can lead to GB strength degradation and cause intergranular cracking in structural materials, affecting the safe operation and service life of nuclear reactors. Establishing the quantitative relationship between the strength and character/microchemistry for individual GB in an effort of integrating experimental measurements and multiscale simulations.
Abstract A major hurdle to the successful deployment of high‐energy‐density lithium metal based batteries is dendrite growth during battery cycling, which raises safety and cycle life concerns. Coating the Li metal anode with a soft polymer layer has been previously shown to be effective in suppressing dendrite growth, leading to uniform lithium deposition even at high current densities. A 3D coarse‐grained molecular model to study the mechanism of dendrite suppression is presented. It is found that the most effective coatings delay or even prevent dendrites from penetrating the polymer layer during deposition. The optimal deposition can be achieved by jointly tuning the polymer stiffness and relaxation time. Higher polymer dielectric permittivity and coating thickness are also effective, but the deposition rate and, therefore, the charging current density is reduced. These findings provide the basis for rational design of soft polymer coatings for stable lithium deposition.
Abstract Here, a comprehensive study on the synthesis, characterization, and reactivity of grain‐boundary (GB)‐rich noble metal nanoparticle (NP) assemblies is presented. A facile and scalable synthesis of Pt, Pd, Au, Ag, and Rh NP assemblies is developed, in which NPs are predominantly connected via Σ3 (111) twin GBs, forming a network. Driven by water electrolysis, the random collisions and oriented attachment of colloidal NPs in solution lead to the formation of Σ3 (111) twin boundaries and some highly mismatched GBs. This synthetic method also provides convenient control over the GB density without altering the crystallite size or GB type by varying the NP collision frequency. The structural characterization reveals the presence of localized tensile strain at the GB sites. The ultrahigh activity of GB‐rich Pt NP assembly toward catalytic hydrogen oxidation in air is demonstrated, enabling room‐temperature catalytic hydrogen sensing for the first time. Finally, density functional theory calculations reveal that the strained Σ3(111) twin boundary facilitates oxygen dissociation, drastically enhancing the hydrogen oxidation rate via the dissociative pathway. This reported large‐scale synthesis of the Σ3 (111) twin GB‐rich structures enables the development of a broad range of high‐performance GB‐rich catalysts.
Hydrogen embrittlement (HE) poses a significant challenge to the durability of materials used in hydrogen production and utilization. Disentangling the competing nanoscale mechanisms driving HE often relies on simulations and electron-transparent sample techniques, limiting experimental insights into hydrogen-induced dislocation behavior in bulk materials. This study employs in situ Bragg coherent X-ray diffraction imaging to track three-dimensional (3D) dislocation and strain field evolution during hydrogen charging in a bulk grain of austenitic 316 stainless steel. Tracking a single dislocation reveals hydrogen-enhanced mobility and relaxation, consistent with dislocation dynamics simulations. Subsequent observations reveal dislocation unpinning and climb processes, likely driven by osmotic forces. Additionally, nanoscale strain analysis around the dislocation core directly measures hydrogen-induced elastic shielding. These findings experimentally validate theoretical predictions and offer mechanistic insights into hydrogen-driven dislocation behavior. The quantified nanoscale phenomena serve as critical inputs for multiscale modeling frameworks to predict bulk material responses and accelerate the development of HE-resistant alloys.
Abstract High fidelity three‐dimensional (3‐D) numerical simulations of commercial‐scale coal gasifiers are very time consuming and expensive. This article proposes a reduced‐order modeling approach that uses quasi one‐dimensional (1‐D) CFD–DEM simulation results to serve as an accurate initial condition for the 3‐D simulation, which significantly shortens the physical time from several hours to minutes to achieve steady states. The 3‐D simulation employs a previously validated coarse‐grained CFD‐DEM approach, which further accelerates the simulation. Both 1‐D and 3‐D simulations adopt a particle shrinkage scheme to correctly account for the particle volume change due to chemical reaction and thus provides an accurate bed height. Comparison with available experimental measurements of the bed temperature and synthesis gas composition is used to validate the simulation. The final syngas composition and flowrates are strongly affected by the fuel or gasification agent rate and process operating conditions as expected. This approach is then used to optimize two important operating conditions (air and steam flows) to achieve specific gasifier performance goals for the production of Fischer–Tropsch synthesis gas and production of fuel gas for gas turbines.
Delayed silking relative to pollen shed, measured as the anthesis–silking interval (ASI, the period between pollen shed and silking), is a good indicator of response to abiotic stresses in maize ( Zea mays L.). This research was conducted to investigate how ASI is affected by nitrogen (N) and water availability and to assess the utility of ASI to indirectly predict grain yield (GY) under contrasting water and N treatments. Two experiments were conducted in Hancock, WI, in 2018 and 2019. One experiment (Diverse hybrids) included 302 hybrids resulting from the cross of diverse inbred lines by a single tester evaluated at four different treatment levels resulting from combining nonlimited and low N with nonlimited and low water treatments. The second experiment (NSS FAC) included a set of 408 hybrids derived from the cross of biparental doubled-haploid lines from 13 factorial populations and evaluated under nonlimited and low N treatments. Anthesis and silk time in growing degree days, and GY (Mg ha -1 ) were measured. Genomic prediction was assessed using a genomic best linear unbiased prediction model, and predictive ability was calculated as the correlation between genomic predictions and adjusted means in the different treatments. Predictive ability ranged from .15 to .49 for NSS FAC and from .06 to .51 for Diverse hybrids across traits and treatments. The ASI was a good indicator of stress and showed higher heritability than GY in the limited treatments for both experiments; however, it did not improve yield predictability.
Owing to their far-from-dilute compositions, multi-principal element alloys (MPEAs) can exhibit unique combinations of engineering properties. As nearly all MPEAs are polycrystalline aggregates, it is necessary to understand the interactions of various elemental species with grain boundaries (GBs). This is of particular importance in extreme environments, such as radiation and elevated temperatures, where such interactions have implications on the properties of MPEAs. Herein, we employ atomistic simulations to generate a series of [001] asymmetric tilt GBs in a model CoCrFeMnNi MPEA and quantify solute interactions and segregation to these boundaries. We employ the Warren-Cowley order parameters to investigate the interplay between GB segregation and chemical short-range order (SRO). At temperatures above 800 K, simulation results reveal the segregation of Cr and Mn to CoCrFeMnNi GBs and show weak dependence of boundary solute excess on GB geometry, at least for the boundaries explored in this work. At temperatures in the range of 673–800 K, formation of domains rich in Cr is observed at GBs in agreement with experimental observations. Quantitative analysis shows that solute excess of various alloying elements decreases rapidly with the increase in temperature in the range of 1000–1200 K. Furthermore, we show that GB regions exhibit SRO characteristics that are distinct from the bulk crystals, leading to spatial variations in SRO. In broad terms, our study highlights the need to account for GB interactions with alloying elements when designing advanced MPEAs with novel chemistries.
Swine and poultry meat production are two important sectors in the U.S. economy. They play an important role in environmental sustainability because they contribute to the greenhouse gas (GHG) emissions as the result of agricultural and production activities. To improve the sustain ability of swine and poultry meat production, it is important to understand the environmental effects and identify the key drivers of the production activities. In this work, we presented an environmental assessment of the production of swine (i.e., pork) and poultry (i.e., broiler chicken). We conducted a life-cycle analysis of formulating animal feeds using soybean meal, corn, distiller-dried grains with solubles (DDGS), and synthetic amino acids as candidate ingredients to produce swine and poultry. We evaluated GHG emissions, fossil fuel consumption, and water consumption from formulating and utilizing a variety of animal feeds based on these ingredients for swine and poultry production, using an expanded version of the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET (R)) model. With pork and broiler chicken as the finished products, the functional unit was defined as one kg of live-weight animal at the farm gate. Feed production was the major contributor to the life-cycle GHG emissions (88 % and 91 % of the total GHG emissions for swine and poultry production, respectively) and fossil fuel consumption (79 % and 84 % of the total fossil fuel consumption for swine and poultry process, respectively). Among the four ingredient types, amino acids had the biggest GHG emission footprint; however, DDGS had the largest effect on increasing GHG emissions of swine and poultry production.
Segmentation of scanning electron microscopy (SEM) images is critical yet time-consuming for geological analyses, as it needs to differentiate the boundaries for different mineral objects to facilitate subsequent analyses, such as porosity calculation. Recently, various machine learning methods, especially convolutional neural networks (CNNs), have been explored to segment SEM images of fine-grained shale samples. However, we found that general CNNs do not yield optimal performance due to insufficient training data and imbalanced objects in SEM images. This work has revised the U-Net architecture, a popular approach for biomedical image analyses, by incorporating a loss function that addresses the imbalance issue. Furthermore, we used the ensemble learning method to train multiple models and combined the results to improve the overall performance of segmentation. We prepared 2162 sub-images from raw SEM images in our experiments and divided them into training, validation, and testing datasets. The overall results show that our method improves the average Intersection over Union (IOU) of mineral objects from 0.49 to 0.58, compared to the original U-Net model. Our method can clearly distinguish each object from others with boundaries, even in highly imbalanced images. Training our models takes less than three minutes using a single GPU, while manual labeling can take up to three hours for each image. Furthermore, the method helps geoscientists gain insights quickly and effectively by building neural network models from a small dataset of SEM images.