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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Radiological Monitoring Plan for the Oak Ridge Y-12 National Security Complex: Surface Water

DOE Order 458.1 requires that dose estimates consider contributions from all facilities. In the Y-12 Radiological Monitoring Plan (RMP), surface water is monitored at points that reflect individual facilities, as well as at points that reflect the combined contributions of all facilities. This monitoring plan does not consider other potential routes (i.e., airborne releases and food chains). Thus, a complete determination of total effective dose (TED) cannot be made based on this plan alone. The other routes from Y-12, and all routes from other DOE facilities on the Oak Ridge Reservation (e.g., Oak Ridge National Laboratory (ORNL) and The Heritage Center), must be considered in order to satisfy DOE Order 458.1 requirements. Determination of TED from all sites and pathways is done through the use of dose-assessment models and is documented in the Annual Site Environmental Report. This monitoring plan provides adequate monitoring goals for Y-12 surface water releases to provide input of sufficient sensitivity and accuracy to reliably determine the Y-12 surface water component of the TED. The routine radiological monitoring program is designed to monitor effluents at four types of locations: (1) treatment facilities, (2) other point and area source discharges, (3) instream locations, and (4) production building roof run-off. With this sampling and analysis program, data will be obtained on primary point sources as well as on locations that represent the composite of other potential sources. This plan will be reviewed periodically to determine necessary modifications to the sampling frequencies, parameters, and locations. Modifications, if any, will be based on the analysis of the previous data and its effectiveness in satisfying the objectives of this plan. Appendix A contains graphs of the sum of the DCS fractions for locations and frequencies contained in a previous version of this plan. The data was collected from January 2009 through December 2019. Each sample was analyzed, and each result was divided by the appropriate DCS to compute a DCS fraction. These fractions were summed for all isotopes. According to DOE –STD-1196-2011, the annual average of these sums should be below 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Topological analysis of X-ray CT data for the recognition and trending of subtle changes in microstructure under material aging

X-ray computed tomography (CT) is an established non-destructive tool for 3D imaging of multiphasic composites. Numerous applications of X-ray CT in medical diagnosis and materials characterization have been reported, many involving field-specific innovations in the imaging technology itself. Yet, quantitative summarization to link image features to properties of interest has been rare. Here we address this issue by employing state-of-the-art technics in scalar field topology for the summarization of X-ray CT images of an example biphasic system. By varying processing-parameters we create different microstructures, evolve them through accelerated thermal aging, CT-image them pre- and post-aged, and demonstrate the ability of our image summarization method to systematically track process- and age-related changes, which can often be very subtle. A novel aspect of the algorithm involves recognition over multiple resolution levels, which provides deeper insight into the pattern relationship between grain-like features and their neighbors. The method is general, adaptable to diverse image reconstruction methods and materials systems, and particularly useful in applications where practical constraints on the sample-size limits the reliable use of more complex models, e.g., convolutional neural networks.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Crystal structure, magnetism, and specific heat of lightly depleted layered honeycomb oxides, Na$_2$(Ni$_{2-x}$D$_x$)TeO$_6$ ($D$ = Mg, Ga, Co; 0.10 ≤ $x$ ≤ 0.25)

Layered oxides, in which transition metal atoms form a honeycomb lattice, are known for complex magnetic phase diagrams. In this work we study the doped compounds, Na$_2$(Ni$_{2-x}$D$_x$)TeO$_6$, $D$ = Mg, Ga, and Co (0.10 ≤ $x$ ≤ 0.25), to understand how chemical dilution at the nickel site influences the stability of magnetic exchanges within the honeycomb layer of the parent, Na$_2$ Ni$_2$ TeO$_6$. Here, the studied compounds were found to crystallize in $P6_3/mcm$ space group common to P2 type layered oxides. In general, the lattice parameters and the unit cell volume expanded with increased doping. The oxidation states determined from X-ray photoelectron spectroscopy were consistent with bond valence sum estimates, that confirmed the presence of Ni$^{2+}$, Mg$^{2+}$, Ga$^{3+}$, Co$^{2+}$ in our samples. Irrespective of magnetic or non-magnetic doping, the magnetic phase transition temperature ($T_N$) of the doped compounds were reduced from that of Na$_2$ Ni$_2$ TeO$_6$ or Na$_2$ Co$_2$ TeO$_6$, and lay between 21.7 K and 26.8 K. The effective paramagnetic moments extracted from Curie–Weiss analysis were in the range 3.17 μ B to 3.84 μ B and the Weiss temperatures were between $-15$ K and $-25$ K, suggesting antiferromagnetism. Mg- and Ga-doped compounds showed free spin formation, indicated by Curie–Weiss tails in their magnetic susceptibility. Our results suggest emergence of short-range magnetic correlations in Na$_2$(Ni$_{2-x}$D$_x$)TeO$_6$, enhanced due to the depletion of the Ni honeycomb lattice.

75 - CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY A↗
DOE OSTI2024

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models

Purpose To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.Methods We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.Results The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.Conclusions Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach.

artificial neural networks↗
DOE OSTI2022

Universal signal scaling in microwave impedance microscopy

Microwave impedance microscopy (MIM) is an emerging scanning probe technique that measures the local complex dielectric function using near-field microwave. Although it has made significant impacts in diverse fields, a systematic, quantitative understanding of the signal's dependence on various important design parameters is lacking. Here, we show that for a wide range of MIM implementations, given a complex tip-sample admittance change ΔΥ, the MIM signal—the amplified change in the reflected microwave amplitude—is –G · ΔΥ/2Υ 0 · η 2 · V in , where η is the ratio of the microwave voltage at the probe to the incident microwave amplitude, Y o is the system admittance, and G is the total voltage gain. For linear circuits, η is determined by the circuit design and does not depend on V in . We show that the maximum achievable signal for different designs scales with η 2 or η when limited by input power or sample perturbation, respectively. Furthermore, this universal scaling provides guidance on diverse design goals, including maximizing narrow-band signal for imaging and balancing bandwidth and signal strength for spectroscopy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A novel statistical methodology for quantifying the spatial arrangements of axons in peripheral nerves

A thorough understanding of the neuroanatomy of peripheral nerves is required for a better insight into their function and the development of neuromodulation tools and strategies. In biophysical modeling, it is commonly assumed that the complex spatial arrangement of myelinated and unmyelinated axons in peripheral nerves is random, however, in reality the axonal organization is inhomogeneous and anisotropic. Present quantitative neuroanatomy methods analyze peripheral nerves in terms of the number of axons and the morphometric characteristics of the axons, such as area and diameter. In this study, we employed spatial statistics and point process models to describe the spatial arrangement of axons and Sinkhorn distances to compute the similarities between these arrangements (in terms of first- and second-order statistics) in various vagus and pelvic nerve cross-sections. We utilized high-resolution transmission electron microscopy (TEM) images that have been segmented using a custom-built high-throughput deep learning system based on a highly modified U-Net architecture. Our findings show a novel and innovative approach to quantifying similarities between spatial point patterns using metrics derived from the solution to the optimal transport problem. We also present a generalizable pipeline for quantitative analysis of peripheral nerve architecture. Our data demonstrate differences between male- and female-originating samples and similarities between the pelvic and abdominal vagus nerves.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗
DOE OSTI2023

Multiscale graph neural network autoencoders for interpretable scientific machine learning

The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes. This is accomplished here with the development of a novel graph neural network (GNN) autoencoding architecture with demonstrations on complex fluid flow applications. To address the first goal of interpretability, the GNN autoencoder achieves reduction in the number nodes in the encoding stage through an adaptive graph reduction procedure. Further, this reduction procedure essentially amounts to flowfieldconditioned node sampling and sensor identification, and produces interpretable latent graph representations tailored to the flowfield reconstruction task in the form of so-called masked fields. These masked fields allow the user to (a) visualize where in physical space a given latent graph is active, and (b) interpret the time-evolution of the latent graph connectivity in accordance with the time-evolution of unsteady flow features (e.g. recirculation zones, shear layers) in the domain. To address the goal of unstructured mesh compatibility, the autoencoding architecture utilizes a series of multi-scale message passing (MMP) layers, each of which models information exchange among node neighborhoods at various lengthscales. The MMP layer, which augments standard single-scale message passing with learnable coarsening operations, allows the decoder to more efficiently reconstruct the flowfield from the identified regions in the masked fields. Analysis of latent graphs produced by the autoencoder for various model settings are conducted using unstructured snapshot data sourced from large-eddy simulations in a backward-facing step (BFS) flow configuration with an OpenFOAM-based flow solver at high Reynolds numbers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Enhanced red luminescence quantum yield from Gd3+/Eu3+: CaLa2ZnO5 phosphor spheres for photonic applications

Highlights: • Ravishing red emission is observed from Gd{sup 3+}/Eu{sup 3+}: CaLa{sub 2}ZnO{sub 5} phosphor spheres. • Red emission of Eu{sup 3+} was significantly enhanced by co-doping with Gd{sup 3+}. • Effective sensitization of Gd{sup 3+} ions and enhanced quantum yield have been elucidated. • Energy transfer from Gd{sup 3+} to Eu{sup 3+} was demonstrated by several fluorescence dynamics. • CIE coordinates are very close to National Television Standard Committee (NTSC). - Abstract: Novel bright red emission was obtained from co-doped Gd{sup 3+}/Eu{sup 3+}: CaLa{sub 2}ZnO{sub 5} (CLZO) phosphor spheres. We have successfully synthesized co-doped Gd{sup 3+}/Eu{sup 3+}: CLZO phosphor spheres by citrate sol-gel method. Structural and morphological analysis was carried out by XRD and FE-SEM. EDS and XPS studies were employed to analyze the presence of elements and their ionic states respectively. The complex formation studies were carried out by the FTIR spectral study. The photoluminescence spectral profiles confirm the dazzling red emission has been observed at 626 nm ({sup 5}D{sub 0} → {sup 7}F{sub 2}) from the Eu{sup 3+}: CLZO phosphors under the excitation of 467 nm. Upon increasing the Eu{sup 3+} concentration, the photoluminescence performance has also increased remarkably. The optimized concentration of the Eu{sup 3+} was noticed at 12 mol%. Upon co-doping with Gd{sup 3+} ions to Eu{sup 3+}: CLZO phosphors, the red emission pertaining to Eu{sup 3+} was appreciably enhanced through energy transfer from Gd{sup 3+} to Eu{sup 3+} ions. The energy transfer mechanism was substantiated by several fluorescent methods such as lifetime decay dynamics and quantum efficiencies. Commission International de I’Echairage (CIE) 1931 chromaticity coordinates (x–y) calculated for singly doped and co-doped CLZO samples. The CIE coordinates were found to be (0.6644, 0.3355) and (0.6688, 0.3311) respectively, which were good consistent with the standard values of NTSC surprisingly. Correlated color temperature (CCT) value was evaluated for optimised co-doped sample and it was found to be between 2000–5000 K which could be more preferable red component for warm white light applications. The lifetime and quantum efficiency of the optimized co-doped sample have been evaluated as 1.102 ms and 69% respectively. The ravishing red emission of the Gd{sup 3+}/Eu{sup 3+}: CLZO phosphor spheres which suggests that these phosphor materials could be promising candidates for red luminescent optical devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Structural Insights into the Vapochromic Behavior of Pt- and Pd-Based Compounds

Anionic complexes having vapochromic behavior are investigated: [K(H 2 O)][M(ppy)(CN) 2 ], [K(H 2 O)][M(bzq)(CN) 2 ], and [Li(H 2 O) n ][Pt(bzq)(CN) 2 ], where ppy = 2-phenylpyridinate, bzq = 7,8-benzoquinolate, and M = Pt(II) or Pd(II). These hydrated potassium/lithium salts exhibit a change in color upon being heated to 380 K, and they transform back into the original color upon absorption of water molecules from the environment. The challenging characterization of their structure in the vapochromic transition has been carried out by combining several experimental techniques, despite the availability of partially ordered and/or impure crystalline material. Room-temperature single-crystal and powder X-ray diffraction investigation revealed that [K(H 2 O)][Pt(ppy)(CN) 2 ] crystallizes in the Pbca space group and is isostructural to [K(H 2 O)][Pd(ppy)(CN) 2 ]. Variable-temperature powder X-ray diffraction allowed the color transition to be related to changes in the diffraction pattern and the decrease in sample crystallinity. Water loss, monitored by thermogravimetric analysis, occurs in two stages, well separated for potassium Pt compounds and strongly overlapped for potassium Pd compounds. Furthermore, the local structure of potassium compounds was monitored by in situ pair distribution function (PDF) measurements, which highlighted changes in the intermolecular distances due to a rearrangement of the crystal packing upon vapochromic transition.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Discerning Deception: An Empirically-Driven Agent-Based Model of Expert Evaluation of Scientific Content

Both human subject experiments and computational, modeling and simulations have been used to study detection of deception. This work aims to combine these two methods by integrating empirically-derived information (from human subject experiments) into agent-based models to generate novel insights into the complex problems of detection of disinformation content. Computational experiments are used to simulate across multiple scenarios for evaluation and decision-making regarding the validity of potentially deceptive scientific documents. Factors influencing the human agent behaviors in the model were identified through a human subject experiment that was conducted to evaluate and characterize decision making related to disinformation discernment. Correlation and regression analyses were used to translate insights from the human subjects experiment to inform the parameterization of agent features and scenario development. Three scenarios were evaluated with the agent-based models to help evaluate the replicability of the simulations (validation analysis) and assess the influence of human agent and document features (sensitivity analyses). A replication of the human participant experiment demonstrated that the agent-based simulations compare favorably to empirical findings. The agent-based modeling was then used to conduct sensitivity analysis on the accuracy of deception detection as a function of document proportions and human agent features. Results indicate that precision values are adversely impacted when the proportion of deceptive documents is lower in the overall sample, whereas recall values are more sensitive to changes in human agent features. These findings indicate important nuances in accuracy evaluations that should be further considered (including consideration of potential alternate metrics) in future agent-based models of disinformation. Additional areas for future exploration include extension of simulations to consider other ways to align the agent-based model design with psychological theory and inclusion of agent-agent interactions, especially as it pertains to sharing of scientific information within an organizational context.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

3′ RNA-seq is superior to standard RNA-seq in cases of sparse data but inferior at identifying toxicity pathways in a model organism

The application of RNA-sequencing has led to numerous breakthroughs related to investigating gene expression levels in complex biological systems. Among these are knowledge of how organisms, such as the vertebrate model organism zebrafish (Danio rerio), respond to toxicant exposure. Recently, the development of 3' RNA-seq has allowed for the determination of gene expression levels with a fraction of the required reads compared to standard RNA-seq. While 3' RNA-seq has many advantages, a comparison to standard RNA-seq has not been performed in the context of whole organism toxicity and sparse data. Here, we examined samples from zebrafish exposed to perfluorobutane sulfonamide (FBSA) with either 3' or standard RNA-seq to determine the advantages of each with regards to the identification of functionally enriched pathways. We found that 3' and standard RNA-seq showed specific advantages when focusing on annotated or unannotated regions of the genome. We also found that standard RNA-seq identified more differentially expressed genes (DEGs), but that this advantage disappeared under conditions of sparse data. We also found that standard RNA-seq had a significant advantage in identifying functionally enriched pathways via analysis of DEG lists but that this advantage was minimal when identifying pathways via gene set enrichment analysis of all genes. These results show that each approach has experimental conditions where they may be advantageous. Our observations can help guide others in the choice of 3' RNA-seq vs standard RNA sequencing to query gene expression levels in a range of biological systems.

3’ RNA-seq↗
DOE OSTI2025

Recovering new viruses from New Mexico soils

Here, we utilized metagenomic and size-filtered virome sequencing to recover 4,157 medium, high, or complete quality viral genomes from soils taken from three high elevation sites in New Mexico, USA. Among recovered viral genomes, 90% were from size-filtered samples, indicating the importance of this enrichment in assessments of complex viromes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Collaborative Research: Improved Efficiency and Coupling of the Radiation Code in the ACME Earth System Model (Final Report)

The complexity of radiative transfer, its importance to the exchange of energy in the climate system, and its high computational cost establishes importance of an accurate and efficient radiative transfer parameterization for climate simulation. Previous work by the proposing team at AER led to the development of the radiation code, RRTMG, which has been widely accepted by the global modeling community as a fast and accurate advancement over the previous generation of radiation codes. It has been in use in the NCAR CESM for many years, and it has been implemented in the initial version the DOE E3SM model. However, its computational cost remains high relative to other components in part due to its complexity and to its inefficient use of modern optimization strategies, and this project helped address this limitation for the code’s application in E3SM. Under other funding, the Investigators of this project led an effort to develop a high-performance broadband radiation code, called RTE+RRTMGP, which is a completely restructured code that will take advantage of modern computational capabilities to enhance its performance while retaining the strengths and accuracy of the original code. Designed to perform over a range of computer architectures, RTE+RRTMGP makes extensive use of Fortran 2003 features to improve both its efficiency and its use of memory. RTE+RRTMGP is expected to be adopted in the next generation of RTE+RRTMGP. This project allowed for advancements to the code’s computational capabilities and adding new and enhanced features. This included revising RTE+RRTMGP to run on GPU processors, analysis of and improvements to the code’s timing, modifying the gas optics in RRTMGP to increase the code’s accuracy, extensive validation of computed fluxes, heating rates and forcings, generating a cloud optical property and vertical sampling capabilities for RTE+RRTMGP, implementing a fast and accurate longwave scattering capability, and groundwork for the inclusion of a capability to specify solar variability. Many of the accomplishment in this project necessitated significant collaboration with the E3SM development team. The result of this project was optimization of a key physical component (radiative transfer calculations) of E3SM, directly supporting E3SM’s overarching global modeling objectives. More broadly, this project provided overall advancements in the use of radiative transfer calculations in atmospheric modeling and simulation, particularly for climate.

58 GEOSCIENCES↗
DOE OSTI2019

A probabilistic perspective on thermodynamic parameter uncertainties: Understanding aqueous speciation of mercury

Speciation plays an important role in determining the fate and transport of metals in terrestrial surface and subsurface systems. Equilibrium speciation modeling in aqueous systems relies on thermodynamic constants (log K values) of complexes, which are subject to uncertainties. In this study, using Monte Carlo (MC) simulations with Latin hypercube sampling (LHS) we systematically analyze the propagation of thermodynamic constant uncertainty through speciation modeling of an inorganic mercury-sulfide-chloride-water system. We find that seemingly small variances of the input log K normal distributions can lead to output species concentrations spanning multiple orders of magnitude, with highly skewed probability distributions. When equilibrium with mineral metacinnabar (β-HgS(s)) is neglected, the relative uncertainty of each output species is strongly positively correlated with the skewness of its concentration probability distribution, i.e., the lowest uncertainty occurs when the species concentration probability distribution is the most negatively skewed as its concentration approaches the total element concentration limit. The highest uncertainty in the identity of dominant species is located around species equivalence points. For cases where the mineral equilibrium is included, we derive analytical probability density functions for the log concentrations of all major species in the system. The mineral log K uncertainty is found to be an important contributor to all output concentration uncertainties. The analysis of combined effects of both pH and total sulfide concentration on output concentration uncertainties shows that high concentration uncertainties occur under highly sulfidic alkaline conditions and low uncertainties at low pH and sulfide concentrations. Finally, an analysis as presented here can distinguish between conditions that require a full uncertainty analysis and those for which the classical deterministic speciation modeling suffices.

58 GEOSCIENCES↗
DOE OSTI2018

Low Solution Temperature Heat Treatment of AlSi{sub 9}Cu{sub 3}(Fe) High-Pressure Die-Casting Actual Automotive Components

Usually, high-pressure die-casting (HPDC) components cannot be heat-treated at high temperature without the occurrence of surface blisters, which are unacceptable for surface finish and may reduce the mechanical properties. In this context, the purpose of the present paper was to analyze the effectiveness of special low solution temperature T6 heat treatment in overcoming this limit for HPDC AlSi{sub 9}Cu{sub 3} alloy. Very low solution temperatures (< 450 °C, followed by 165 °C aging) to prevent the occurrence of blisters were combined with commonly used times (from 1 to 16 h) ensuring the feasibility of industrial application. Treatments were conducted on samples extracted from actual castings to evaluate the typical defects encountered in common production. Properties were analyzed by means of visual inspection, microstructural observations, image analysis, hardness, tensile tests and fractography. The results showed that it is possible to use solubilization temperatures below 450 °C for several hours in a T6 treatment to give strengthening without relevant blistering in AlSi{sub 9}Cu{sub 3} alloy. The optimum match of properties was provided by a solution treatment at 430 °C for 4 h followed by an aging at 165 °C for 8 h, which gave a yield increase of ~ 50 MPa, an increase in ductility and the best Quality Index value.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ceramic Composite Experimental Testing Status

Over recent years, ceramic matrix materials such as SiC–SiC and C–C have been gaining interest for use in fusion reactors, light water reactors (LWRs), and high-temperature reactors (HTRs). These materials are good candidates to operate in very high temperature and moderate to high radiation environments. The evaluation of composite materials, in general, is challenging because of variations in precursor materials, variations in the fabrication process across fabricators, and the wide range of potential fiber architectures, to name a few. However, the need to evaluate neutron-irradiated properties adds another layer of complexity, which includes cost, timeline, and specimen size limitations (often associated with irradiation testing). A qualification methodology for the use of ceramic composites is provided in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code Section III-5-HHB. The methodology is supported by ASTM International (ASTM) guides, which provide a pathway to accomplish this effort. Part of the qualification strategy is for the designer to collect material property data on environmental conditions representative of its design envelope. These data include irradiation effects. This report presents an experimental study and test campaign developed to partially address this gap by providing initial mechanical and physical property data required for design. A variety of different materials using different manufacturing techniques are considered as part of this campaign. The test plan suggests performing a screening or partial irradiation study to assist the designer during the material selection process. The designer can then perform a more comprehensive qualification study if the material performance is promising. This work focuses on the status of the specimen preparations (machining of samples), the current test methods and failure analysis as well as the preparation of irradiation vehicles for the irradiation campaign. The irradiation will be performed at Oak Ridge National Laboratory (ORNL) in the High Flux Isotope Reactor (HFIR) and at Idaho National Laboratory (INL) in the Advanced Test Reactor (ATR).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗