Material Compositions and Porosity: Implications for Kinetic Impactors
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A method for removing a contaminant from a fluid is provided. A positively charged layered double hydroxide is combined with a negatively charged clay material to form adsorption material. The adsorption material is contacted with an aqueous solution containing the contaminant to adsorb the contaminant onto the adsorption material.
The International Criticality Safety Benchmark Evaluation Project (ICSBEP) was started in 1992 by the United States Department of Energy and later in 1995 became an international project with contributions from 22 countries. The project is now organized by the OECD (Organisation for Economic Co-operation and Development) Nuclear Energy Agency (NEA). In its most recent iteration, the ICSBEP handbook contains over five thousand evaluations of critical, near-critical, and subcritical experiments conducted in facilities all around the world. These benchmarks serve as valuable information for criticality safety engineers who can use them to validate calculation techniques and establish minimum subcritical margins for operations with fissionable materials. The benchmarks in the handbook are categorized by their fissile material composition, material form (oxide, solution, or metal), and fission energy spectra. This is especially useful for those looking for benchmarks similar to a system they are working on to compare methods and identify trends. The ICSBEP Handbook Uncertainty Guide is document outlining recommended practices and methods for determining uncertainties in these benchmarks. Quantifying these uncertainties thoroughly is crucial as it allows a higher degree of confidence that data used from them is valid and relevant. The guide stresses the importance of a thorough and well documented uncertainty analysis when evaluating an experiment. All measured values of a system, whether they be dimensions or material compositions, have a certain amount of uncertainty associated with them and can be analyzed one by one to determine their effects on the system. Many evaluated benchmarks in the handbook present this in detail, however some do not, mostly earlier evaluations performed in the 1990’s and early 2000’s. Recently at Los Alamos National Laboratory (LANL), the Nuclear Criticality Safety Division (NCSD) of LANL has been validating MCNP6.2 ® input files of criticality benchmarks for use by Whisper, a criticality safety code developed at LANL. This effort is also part of the OECD NEA Working Party on International Nuclear Data Evaluation Co-operation (WPEC) Subgroup 45, also known as Validation of Nuclear Data Libraries (VaNDaL). The goal of VaNDaL is to compile a set of validated simulation inputs for use in validating nuclear data and simulation codes. As part of these efforts, one of the benchmarks reviewed was the MIX SOL-THERM-003 ICSBEP benchmark. This paper provides an independent uncertainty analysis of this benchmark experiment.
All levels of the unique hierarchical structure of bone, consisting of collagen and hydroxyapatite crystals at the nanoscale to osteon/lamellae structures at the microscale, contribute to its characteristic toughness and material properties. Elements of bone's density and size contribute to bone quantity (or bone mass), whereas elements of bone's material composition, material properties, internal structure, and organization describe bone quality. Furthermore, bone quantity and quality can be degraded by factors such as aging, disease, treatments, and irradiation, compromising its ability to resist fracture and sustain loading. Accessing the morphology and architecture of bone at the microscale to quantify microstructural features and assess the degree of mineralization and path of crack propagation in bone provides crucial information on how these factors are influencing bone quantity and quality. Synchrotron radiation micro-computed tomography (SRμCT) was first used to assess bone structure at the end of the 1990's. One of the main advantages of the technique is that it enables accurate three-dimensional (3D), non-destructive quantification of structure while traditional histomorphometry on histological sections is inherantly destructive to the sample and two-dimensional (2D). Additionally, SRμCT uses monochromatic, high-flux X-ray beams to provide high-resolution and high-contrast imaging of bone samples. This allows the quantification of small microstructural features (e.g. osteocyte lacunae, canals, trabeculae, microcracks) and direct gray value compositional mapping (e.g. mineral quantification, cement lines) with greater speed and fidelity than lab-based micro-computed tomography. In this article, we review how SRμCT has been applied to bone research to elucidate the mechanisms by which bone aging, disease, and other factors affect bone fragility and resistance to fracture.
This work describes the development of a micromechanical-based constitutive model accounting for the effect of internal expansion on the residual elasticity of a Hashin composite material. This material is made of spherical inclusions that are subjected to gradual swelling within a quasi-brittle matrix. The main focus of this work is to describe and analyze the material mechanical response, with an additional focus on the internal swelling’s effect on the stress–strain response and residual elasticity. Microstructural features and parameters of major importance for the mechanical responses were identified. The innovative characteristics of the proposed approach are summarized as follows: (1) a full determination of the physics of a complete-damage problem throughout the whole process of inclusion swelling with upscaling techniques, which transfers the microcrack-related properties from the lower scale to upper scale; and (2) an evolution of the mechanical fields and the corresponding residual elasticity for various inclusion swelling levels. Concerning the matrix–inclusion composite, it was hypothesized that only the matrix was susceptible to cracking, with varied degrees of damage, whereas the inclusions behave elastically and the elastic modulus of the expanding inclusions remains constant. It is to emphasize that the gradual swelling of inclusions is modeled by an increasing strain in the current micromechanical-based constitutive model, and the microcracks are represented by a set of randomly oriented penny-shaped microcracks with identical radii (namely, closed cracks). The main contribution of the current research is to establish the exact mathematical solutions for the mechanical fields (stress, strain) caused by the swelling of the inclusions (mechanical loading) and derive the effective residual elasticity of a composite (structural response) subject to internal expansion and quasi-brittle damage. Based on the assumption of closed cracks that could be extended to open cracks in the upcoming work, the results proposed by the present paper help to understand the non-linear mechanical behavior of quasi-brittle materials subject to microcracking and provide a theoretical framework to be used as academical benchmark for numerical simulations.
The impact of material chemical composition on microbial growth on building materials remains relatively poorly understood. We investigate the influence of the chemical composition of material extractives on microbial growth and community dynamics on 30 different wood species that were naturally inoculated, wetted, and held at high humidity for several weeks. Microbial growth was assessed by visual assessment and molecular sequencing. Unwetted material powders and microbial swab samples were analyzed using reverse phase liquid chromatography with tandem mass spectrometry. Different wood species demonstrated varying susceptibility to microbial growth after 3 weeks and visible coverage and fungal qPCR concentrations were correlated (R 2 = 0.55). Aspergillaceae was most abundant across all samples; Meruliaceae was more prevalent on 8 materials with the highest visible microbial growth. A larger and more diverse set of compounds was detected from the wood shavings compared to the microbial swabs, indicating a complex and heterogeneous chemical composition within wood types. Several individual compounds putatively identified in wood samples showed statistically significant, near-monotonic associations with microbial growth, including C 11 H 16 O 4 , C 18 H 34 O 4 , and C 6 H 15 NO. A pilot experiment confirmed the inhibitory effects of dosing a sample of wood materials with varying concentrations of liquid C 6 H 15 NO (assuming it presented as Diethylethanolamine).
Fiber-reinforced ceramic matrix composites have many desirable properties for high-temperature nuclear applications, including excellent thermal and mechanical properties and reasonable to outstanding radiation resistance. Over the last 20 years, the use of ceramic composite materials has already expanded in many commercial nonnuclear industries as fabrication and application technologies mature. The new ASME design and construction rules under Section III, Subsection HH, Subpart B lay out the requirements and criteria for materials, design, machining and installation, inspection, examination, testing, and the marking procedure for ceramic composite core components, which is similar to the established graphite code under Section III, Subsection HH, Subpart A. Moreover, the general requirements listed in Section III, Subsection HA, Subpart B are also expanded to include ceramic composite materials. The code rules rely heavily on the development and publication of standards for composite specification, classification, and testing of mechanical, thermal, and other properties. These test methods are developed in the American Society for Testing and Materials Committee C28 on Advanced Ceramics with a current focus on ceramic composite tubes. Details of the composites code, design methodology, and similarities to the graphite code, as well as guidance for the development of specifications for ceramic composites for nuclear application and recent standard developments, are discussed. The next step is to "close the gap" to support licensing aspects by validating the code with benchmarking data.
Lanthanum strontium cobaltite (LSCo) is considered as a good candidate cathode contact material for solid oxide fuel cells, due to high electrical conductivity. However, LSCo has a very large coefficient of thermal expansion (CTE) than the cells and metallic interconnects. As a result, poor mechanical stability is expected during thermal cycling. To minimize the CTE mismatch, we investigate a composite approach involving mixing LSCo with an inert material of low CTE, such as mullite at volume fractions from 0.1 to 0.4. Composite’s CTE shows a decreasing trend with increasing mullite volume fractions, and is consistent with model predictions. X-ray powder diffraction analysis of sintered LSCo/mullite composites exhibits no presence of other phases for samples aged for 500h at 800oC, indicating chemical compatibility. Electrical conductivity by a 4-pt method shows a decreasing trend with increasing mullite content. Contact strength of as-sintered and thermally cycled samples show that only the composite with 0.4 volume fraction has a measurable strength; the other composites have no strength. Overall, the composite approach is demonstrated in the LSCo/mullite system to lower the CTE and hence achieve thermal cycle stability. The addition of the inert phase to the LSCo matrix; however, also reduces the electrical conductivity.
Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from a composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating the composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of the composite aerostructure material. The methods developed here are both highly reliable with a top recall value of 98.64% as well as extremely efficient requiring an average of 4 s during the inferencing stage to evaluate new composites. Lastly, we investigate model robustness to concept drift by measuring its performance over time.
Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of composite aerostructure material. Furthermore, the methods developed here are both highly reliable with a top recall value of 98.63% as well as extremely efficient requiring an average of 4 seconds during the inferencing stage to evaluate new composites.
Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data-driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe-based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe-based materials prepared using a simple high-throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m −1 K −2 , which is a 75% improvement from the baseline composite (nominal composition of Ag 2 Se 1 ). In conclusion, the success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.
A reactive matrix infiltration process is described herein, which includes contacting a surface of a preform comprising reinforcement material particles with a molten infiltrant comprising a matrix material, the matrix material comprising an Al—Ce alloy, whereby the infiltrant at least partially fills spaces between the reinforcement material particles by capillary action and reacts with the reinforcement material particles to form a composite material form, the composite material comprising the matrix material, at least one intermetallic phase, and, optionally, reinforcement material particles. A composite material form also is described, which includes a plurality of reinforcement material particles comprising a metal alloy or a ceramic, a matrix material at least partially filling spaces between the reinforcement material particles; and at least one intermetallic phase surrounding at least some of the reinforcement material particles. The reinforcement material particles and intermetallic phase together may form a gradient core-shell structure.
Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.
An electrode material includes a lithium active material composition. The lithium active material composition includes lithium and an active anode material. The lithium active material composition is coated with a lithium ion conducting passivating material, such that the electrode material is lithiated and pre-passivated. An electrode and a battery are also disclosed. Methods of making an electrode material, electrode and battery that are lithiated and pre-passivated are also disclosed.
As inhomogeneous orthotropic materials, composite mechanical properties can be difficult to predict. Even with tight fabrication process controls, there is always some microstructural variation that will affect these properties. As a highly defect-dependent phenomena, mechanical strength is even more subject to variation, especially considering that composites exhibit multiple potential failure modes. The most direct way to determine composite material strength is via experimental testing, maintaining loading and specimen morphology as close as possible to the in situ states. When multiple materials and designs are being considered, this process is often prohibitively costly and slow. However, some approximations and rules of thumb can consistently guide the design process and consideration of alternatives.
Additively manufactured polymer heat exchangers are of recent interest in the thermal sciences due to their lightweight and intricate heat-transfer-enhancing geometrical features. The goal of the present research is to directly encapsulate phase-change material (PCM) into polymer filament for the purpose of 3D printing polymer heat exchangers capable of latent heat thermal energy storage and management. PCMs have the ability to absorb large amounts of latent heat while undergoing a solid-to-liquid phase change at a nearly constant temperature. Encapsulation or containment of PCMs is necessary to prevent leaking since the material continuously cycles between a liquid and a solid state. An organic-based PCM and high-density polyethylene (HDPE) were combined and extruded into a functional composite filament. For the first time, with the aid of a heated enclosure and HDPE build plates, it was demonstrated that these functional composite materials can be printed using fused filament fabrication (FFF). Printing HDPE is known to be difficult; however, based on what was observed in the current study, the PCM and HDPE composite was easier to print than pure HDPE. Thermal properties, such as latent heat of fusion, phase-change temperature, and thermal conductivity, of the composite filament and 3D printed samples were investigated and compared with compression-molded bulk material. Finally, the microstructures of the composite filament and 3D printed samples were visualized, and the basic structures of the HDPE and PCM within both the filament and 3D printed parts were clearly delineated.