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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 OSTI2023

Additive manufacturing process parameter determination for a new Fe-C-Cu alloy

As a potential replacement for stainless steel alloys commonly used to print parts with laser powder bed fusion, a new Cu precipitation strengthened ferrous alloy, with composition Fe-0.2C-6Cu (wt%), was recently developed. This material is Co- and Ni-free, printable, has mechanical properties comparable to that of high strength stainless steels (approximately 1300 MPa UTS), and is cost-advantaged relative to existing low-alloy steels for additive manufacturing of parts with complex shapes. To gain traction for broader application, optimal laser powder bed fusion (LPBF) processing conditions to produce nearly defect-free parts without compromising mechanical strength are needed. Here, an optimal processing window based upon laser speeds that achieve minimal porosity was quantified via comparisons of measured melt pool penetration, scan speeds, printed material density and laser beam energy density on a commercial LPBF unit operating at 350 W, 80 μm hatch spacing, and 50 μm layer thickness based upon optimal parameters for 17-4PH. Furthermore, the window is 300 to 500 mm/s to achieve >99.8 % dense Fe-0.2C-6Cu (wt%) specimens. Other defects such as burning, spatter, and lack of fusion were also avoided in this window. The window was validated with in-situ synchrotron X-ray imaging that enabled visualization of the vapor cavity, melting, and solidification during a single laser track scan on a miniature powder bed sample. In addition, in-situ infrared imaging provided temperature fields, cooling rate and solidification range, and confirmed minimal printing defects and spatter within the processing window.

36 MATERIALS SCIENCE↗
DOE OSTI2021

AI-based design of a nuclear reactor core

The authors developed an artificial intelligence (AI)-based algorithm for the design and optimization of a nuclear reactor core based on a flexible geometry and demonstrated a 3× improvement in the selected performance metric: temperature peaking factor. The rapid development of advanced, and specifically, additive manufacturing (3-D printing) and its introduction into advanced nuclear core design through the Transformational Challenge Reactor program have presented the opportunity to explore the arbitrary geometry design of nuclear-heated structures. The primary challenge is that the arbitrary geometry design space is vast and requires the computational evaluation of many candidate designs, and the multiphysics simulation of nuclear systems is very time-intensive. Therefore, the authors developed a machine learning-based multiphysics emulator and evaluated thousands of candidate geometries on Summit, Oak Ridge National Laboratory’s leadership class supercomputer. The results presented in this work demonstrate temperature distribution smoothing in a nuclear reactor core through the manipulation of the geometry, which is traditionally achieved in light water reactors through variable assembly loading in the axial direction and fuel shuffling during refueling in the radial direction. The conclusions discuss the future implications for nuclear systems design with arbitrary geometry and the potential for AI-based autonomous design algorithms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Microstructure Validation of Graph Theory Model-Derived Cooling Rates in the Wire Arc Additive Manufacturing of ER70S-6 Steel

Wire arc additive manufacturing (WAAM) enables high-rate fabrication of large metallic components, but spatial variations in thermal history can lead to microstructural heterogeneity that requires efficient process models to evaluate. This study evaluates whether cooling rates extracted from a graph theory model (GTM)-based thermal simulation are consistent with the microstructural evolution observed in an ER70S-6 WAAM wall. Thermal histories from the model were analyzed at selected build heights, and cooling rates were extracted from the final thermal excursion through the austenite phase field. Microstructures at corresponding locations were characterized using electron backscatter diffraction (EBSD) to quantify grain size distributions, and pearlite interlamellar spacing was used as an additional indicator of cooling behavior. The modeled cooling rates were highest near the substrate and generally decreased with build height, consistent with the observed reduction in the fine grain fraction and the progressive shift in the grain size distribution as build height increased. Pearlite spacing trends also supported the modeled cooling rate variation. These results indicate that GTM-derived thermal histories can be post-processed into metallurgically meaningful cooling rate estimates for WAAM steel builds and linked to dataset specific empirical grain size distribution relationships for process–thermal history–microstructure assessment.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accelerating Additive Manufacturing Process Design for Energy Conversion Materials using In-situ Sensing and Machine Learning

One promising candidate for manufacturing of the bismuth Telluride thermoelectric legs is laser powder bed fusion (LPBF) additive Manufacturing (AM). AM processing parameters highly influence the material properties, however current processing parameter development methods in AM are costly and time consuming. In-situ sensors allow for the capture of physically relevant process information on a layer-by-layer basis and will be used to aide process development. To optimize the AM process for the best thermoelectric performance, process variables, in-situ process sensor data and ex-situ material characterization data are collected. Several different interpretable machine learning (ML) approaches are used, and the performance of each method are assessed. Significant input process variables include laser focus, hatch spacing and laser power. The best performing models are used to determine the manufacturing parameters that maximize the power factor. AM of bismuth telluride material provides the ability to create complex geometries enabling more efficient energy conversion.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership

Energy absorbing efficiency is a key determinant of a structure’s ability to provide mechanical protection and is defined by the amount of energy that can be absorbed prior to stresses increasing to a level that damages the system to be protected. Here, we explore the energy absorbing efficiency of additively manufactured polymer structures by using a self-driving lab (SDL) to perform >25,000 physical experiments on generalized cylindrical shells. We use a human-SDL collaborative approach where experiments are selected from over trillions of candidates in an 11-dimensional parameter space using Bayesian optimization and then automatically performed while the human team monitors progress to periodically modify aspects of the system. The result of this human-SDL campaign is the discovery of a structure with a 75.2% energy absorbing efficiency and a library of experimental data that reveals transferable principles for designing tough structures.

42 ENGINEERING↗
DOE OSTI2024

Exploring laser-material interactions of zirconium carbide under additive manufacturing conditions

Zirconium carbide (ZrC) is an ultra-high temperature ceramic with a melting temperature above 3000°C and a broad range of high temperature applications. Given the high melting and sintering temperatures of pure ZrC, producing near-net shape and fully dense parts remains challenging with conventional techniques. In this study, we investigate the fundamental laser-material interactions of ZrC under laser powder bed fusion (LPBF) additive manufacturing (AM) conditions. Normalized enthalpy, a scaling law term that is used in welding and AM literature for detailing laser-material interactions in metallic alloys, was calculated to determine the predictive capabilities of melt pool features in ZrC. Further, the melt pool quality of laser irradiated ZrC was used to compare LPBF relevant laser parameter combinations of laser power, scan speed, and beam diameter. Laser build parameters that resulted in desirable melt pool morphologies were applied to the fabrication of ZrC coupons using LPBF AM. A custom LPBF system was used to determine hatch spacing and layer height parameters that resulted in a fabricated sample with a density of 85% as measured by Archimedes and a Vicker's microhardness of 20.9 ± 1.9 GPa. This investigation reveals the laser-material interactions of ZrC under AM relevant conditions and is the first step towards LPBF fabrication of ZrC parts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Acoustophoretic Additive Manufacturing for Scalable 3D Battery Electrodes

This project focused on investigating two acoustic-based processing methods: a nozzle-based printhead and a chamber that map to two different battery electrode architectures: (1) a line-pattern electrode and (2) a grid-pattern electrode. These two parallel manufacturing and electrode geometry explorations were proposed for the project to understand the process space of acoustic-based manufacturing methods to fabricate patterned battery electrodes. This two-path exploration also allowed us to de-risk the overall project and not rely on a single process for creating patterned electrodes. This project consisted of six high-level tasks aimed at transitioning the concept of acoustic focusing for battery electrodes from a technology readiness level (TRL) of 1 to 3 by project conclusion. Overall, we believe we have developed a practical and high-impact processing method that is chemistry agnostic and suitable for large-area fabrication of both 3D LIBs and other functional material systems where structuring on the scale of tens of microns has the potential to break conventional bulk material trade-offs in performance. In the case of batteries for electric vehicles, structuring 3D LIBs with our acoustic process breaks traditional energy and power trade-offs observed with conventional flat battery packs.

25 ENERGY STORAGE↗
DOE OSTI2022

Evaluating the intrinsic resistance to balling of alloys: A High-throughput physics-informed and data-enabled approach

To date, the vast majority of work on metal additive manufacturing (AM) has been framed in terms of the need to tune processing conditions for a particular AM technology in order to print conventional alloys, oftentimes developed for fabrication methods other than AM. This approach overlooks the fact that historically, many engineering alloy system has been designed with a particular processing route in mind, e.g., ingot metallurgy, powder metallurgy, rapid quenching, etc. There are thus significant opportunities to design alloys specifically for AM. A key challenge is that alloy design requires performance metrics that can be optimized by exploring the alloy chemistry space. Here, we present a study in which we examine how intrinsic thermophysical properties can be used to estimate performance metrics related to the behavior of a solidifying metal droplet under AM-relevant conditions. By identifying these intrinsic properties, it is possible to directly incorporate ‘intrinsic printability’, specifically ‘intrinsic resistance to balling’, into AM-focused alloy design.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Tardigrade (NA-22 Quarterly Report)

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. The project is 24 months into the 36-month plan. We are continuing effort on 3 main technical thrusts: material design algorithm development (transitioning into full 3D design), multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In the last quarter, we have focused on developing the code for 3D Topology Optimization, building data sets for 2D autoencoder training, and finishing lens housing engineering for the microbolometer based test sensor. We are exploring 2 potential multi-material AM technologies, a polymer-based technique in development at LLNL and a metal SLS system produced by Aconity, called the AeroSint deposition head.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tardigrade (Quarterly Report Q1 FY2022)

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. The project is 27 months into the 36-month plan. We are continuing effort on 3 main technical thrusts: material design algorithm development (transitioning into full 3D design), multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In the last quarter, we have focused on developing the code for 3D Topology Optimization, building data sets for 2D autoencoder training, and finishing lens housing engineering for the microbolometer based test sensor. We are exploring 2 potential multi-material AM technologies, a polymer-based technique in development at LLNL and a metal SLS system produced by Aconity, called the AeroSint deposition head.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Performance of Windows in Walls With Continuous Insulation

Window openings in walls are a significant contributor to poor thermal performance because of thermal bridging through the framing members (e.g., studs, joists, plates, bracing) and because windows lack the thermal properties of insulation. Window installation guidance for walls with continuous insulation (CI) is critical for continued market growth of this energy efficiency technology. This research project offers window manufacturers a starting point and a potential path toward developing installation instructions for windows over CI. The objectives of the research include evaluating the common method for installing windows in walls with CI, as well as establishing acceptance criteria for evaluating the performance of windows installed in walls with and without CI. The research measures: 1. The performance characteristics (e.g., water management, structural integrity) of windows in walls without CI. 2. The performance of different thicknesses and types of CI used in walls. 3. The performance of different types of window assemblies (e.g., double-hung windows, mulled double-hung windows, mulled casement windows, and slider windows) installed over CI. 4. The performance of window flange types (e.g., rigid mounting and less robust flanges) installed over CI. 5. Installing windows over CI using baseline installation instructions versus window manufacturer installation instructions. The project’s sequential testing protocol consists of the following: • A water penetration resistance testing adapted from two ASTM standards: E331 (uniform static air pressure in four steps) and E547 (cyclic static air pressure) • A temperature cycling adapted from ASTM E2264 Method B (convective hot air) • A service condition wind loading test adapted from ASTM E330 • A six-month vertical displacement observation phase prior to the structural performance testing • A final water penetration resistance test after vertical displacement observation • A structural performance test adapted from ASTM E330. Key research findings include: • The criterion for passing a water penetration resistance test is that there is no water overflowing at the interior face of the studs. If there is any bubbling or slight pooling of water at the sill, then it must recede after the pressure is removed. Excessive leakage and/or water leaking to the interior face of the framing around the window constitutes a failure. • All single double-hung windows installed directly to lumber or over oriented strand board passed all test protocols. • For most wall specimens, the test results showed that the use of foam sheathing did not affect the performance of the window for water leakage. • All wall specimens underwent temperature cycling. The results indicated that temperature cycling had little to no effect on windows installed over foam sheathing. • For wall specimens that underwent six-month vertical displacement monitoring, the results showed that windows installed over foam sheathing do not sag over time. • The single-hung and double-hung windows installed using window manufacturer installation instructions passed the structural performance test, compared to failures observed in windows that were installed using generic installation instructions. The generic and manufacturer installation methods differed on the following construction details: type of fasteners, fastening patterns on the flanges, and window shimming details. • Additional testing is required to determine methods to improve structural pressure performance of slider windows. Potential solutions that would require additional testing may include fastener spacing, different types of fasteners, masonry window clips, construction adhesive, foam sealant, stronger window flange material, and/or straps.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.

Vidyaratne, Lasitha S.↗
DOE OSTI2025

Laser Powder Bed Fusion Additive Manufacture Nb1Zr Development

Next generation fission and fusion nuclear reactors require materials that can withstand operating temperatures greater than 500 °C, neutron irradiation doses of up to 200 displacements per atom (dpa), and potentially corrosive coolants such as the alkali liquid metals sodium, lithium, and NaK (Na33K eutectic alloy). Refractory alloys, such as Nb1Zr (Nb-1wt%Zr) and Molybdenum alloy TZM (Mo-0.5wt%Ti-0.08wt%Zr) have been traditionally considered viable candidates for advanced fission and fusion reactor concepts. However, it is relatively difficult to generate complex geometries of interest from these alloys using traditional manufacturing methods. In addition, there needs to be a concentrated effort to address refractory metal challenges at elevated temperature operation. In order to generate complex geometries of interest, modern manufacturing techniques are considered to increase the technological readiness level (TRL), cost-effectiveness, and schedule savings. This work focused on the continued development of laser powder bed fusion (L-PBF) additive manufacturing (AM) to improve both design flexibility, evaluate microstructure and properties, and ultimately accelerate the TRL and qualification of these processes and alloys for components to potentially be put into service. Niobium alloy Nb1Zr was identified through a down-selection process outlined in previous reports as a candidate to develop in L-PBF AM. Historically, Nb1Zr had been explored for high temperature fast spectrum fission reactors for both terrestrial and space applications. Molybdenum alloy TZM has also been considered for these reactor concepts due to exceptional high-temperature strength, creep resistance, and stability under irradiation. L-PBF AM of TZM has previously been investigated at LANL under the Microreactor program, NASA, ORNL, and in academia. However, due to the crack prone nature of TZM, L-PBF AM of TZM resulted in significant microcracking and additional development is required to pursue viable maturation. Other AM methods have been found to be more successful in printing TZM, and those alternatives approaches are discussed in this effort. The efforts detailed in this report focused on continued development of Nb1Zr through L-PBF and development of TZM via L-PBF and electron powder bed fusion (E-PBF). The objective of this work was to further the development of these AM techniques for the chosen refractory alloys, elucidating and addressing associated challenges through characterization of several demonstration builds. At LANL, Nb1Zr builds were completed using an EOS M290 and M400 machines, and a refractory alloy-dedicated L-PBF system, the Xact Metal XM200G, was installed. The XM200G primary purpose was to do the Nb1Zr parameter development process; however, due to difficulties associated with the machine installation and qualification process, it was decided to pivot development to the larger M400 and M290 machines. Although the supply of Nb1Zr powder was limited, it was sufficient to generate sub-scale metallographic specimens for the purpose of parameter development. This was first accomplished on the EOS M400 then the M290 due to machine schedule availability. Further development of TZM has been initiated at the University of Texas El Paso (UTEP) under contract with LANL to use both a heated build envelope L-PBF machine and E-PBF machine that have been found in the literature to mitigate microcracking. UTEP was provided with TZM powder and build plates to support parallel TZM parameter development across both machines. As part of the contract, UTEP will also be conducting microstructural characterization once optimized process parameters have been identified. The optimized process parameters for each machine will be used to generate a series of metallographic, mechanical, and surface finish specimens for subsequent characterization and testing. In the next section, we provide a detailed discussion of the methodology used for investigating the feasibility of leveraging these alloys for use in advanced reactor applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification

The previously established ExaCA software for performance portable alloy grain structure simulation has been updated to better represent the solidification behavior during complex alloy processing conditions, such as those encountered during metal additive manufacturing (AM), and for improved performance and scalability. Here, an extension to the time–temperature history input data format and the core ExaCA algorithm to include an arbitrary number of melting and solidification events yielded improved prediction of texture for various melt pool geometries, expanding the range of AM-relevant conditions that can be accurately simulated. Improved heat transport process simulation coupling, including the creation of large raster datasets from single track time–temperature history data and in-memory coupling with the new, performance portable finite difference code Finch, were also demonstrated in example studies on the effect of multilayer AM microstructure predictions on hatch spacing and cell size, respectively. Additional new features are detailed and demonstrated, including the ability to perform simulations using various interfacial response function forms, execute simulations on state-of-the-art hardware, improved usability through post-processing versatility, and improved strong and weak scaling performance. The performance, physics, and versatility improvements demonstrated here will further enable large-scale studies on AM process–microstructure relationships that were not previously possible. Furthermore, the usability improvements and ability to run coupled AM process–microstructure simulations using the Finch-ExaCA workflow will facilitate broader use of this open-source software by the computational materials community.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Cell invasion during competitive growth of polycrystalline solidification patterns

Abstract Spatially extended cellular and dendritic array structures forming during solidification processes such as casting, welding, or additive manufacturing are generally polycrystalline. Both the array structure within each grain and the larger scale grain structure determine the performance of many structural alloys. How those two structures coevolve during solidification remains poorly understood. By in situ observations of microgravity alloy solidification experiments onboard the International Space Station, we have discovered that individual cells from one grain can unexpectedly invade a nearby grain of different misorientation, either as a solitary cell or as rows of cells. This invasion process causes grains to interpenetrate each other and hence grain boundaries to adopt highly convoluted shapes. Those observations are reproduced by phase-field simulations further demonstrating that invasion occurs for a wide range of misorientations. Those results fundamentally change the traditional conceptualization of grains as distinct regions embedded in three-dimensional space.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A lightweight Fe–Mn–Al–C austenitic steel with ultra-high strength and ductility fabricated via laser powder bed fusion

Lightweight Fe–Mn–Al–C steels have become a topic of significant interest for the defense and automotive industries. These alloys can maintain high strength and ductility while also reducing weight in structural applications. Conventionally processed Fe–Mn–Al–C austenitic steels with high Al content (~9 wt%) demonstrate greater than 1.5 GPa strength with 35% elongation. Several recent studies have demonstrated success in fabricating steel parts using laser powder bed fusion (L-PBF) additive manufacturing (AM), which can generate near-net-shape components with complex geometries and is capable of local microstructural control. However, studies on L-PBF processing of Fe–Mn–Al–C alloys have focused on low Al content (<5 wt%) compositional regimes representing alloys that undergo transformation-induced plasticity (TRIP) and twinning-induced plasticity (TWIP). Here, in this study, we present the effects of L-PBF processing on the microstructure and mechanical properties of an Fe–30Mn–9Al–1Si-0.5Mo-0.9C austenitic steel. A process optimization framework is employed to determine an ideal L-PBF processing space that will result in >99% density parts. Implementing this framework resulted in near-fully dense specimens fabricated over a broad range of process parameters. Additionally, two bi-directional scan rotation strategies (90° and 67°) were applied to understand their effects on texture and anisotropy in this material. As-printed specimens displayed considerable work-hardening characteristics with average strengths of up to 1.3 GPa and 36% elongation in the build direction. However, solidification microcracks oriented in the build direction resulted in anisotropy in tensile strength and ductility resulting in average strengths of 1.1 GPa and 20% elongation perpendicular to the build direction. The successful L-PBF fabrication of Fe–30Mn–9Al–1Si-0.5Mo-0.9C presented here is expected to open new avenues for weight reduction in structural applications with a high degree of control over part topology.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of 18 O Exchange on Neutron Emission Rates of Aging 238 PuO 2 LWRHUs

The Lightweight Radioisotope Heater Unit (LWRHU) provides about 1 Watt of thermal power to critical electrical and mechanical systems in the extreme temperatures of space, maintaining the operating temperature of the spacecraft’s scientific and general operating equipment. Los Alamos National Laboratory (LANL) designed and tested the LWRHU in the 1970s through early 1980s. The first production campaign at LANL was in the early 1980s in support of the Galileo mission in which over 100 LWRHUs were manufactured. In the mid-1990s, almost 200 LWRHUs were manufactured in support of the Cassini mission. After the primary Cassini manufacturing campaign, the Department of Energy (DOE) approved the fabrication of more than 10 additional LWRHUs as spares in 1998. The LWRHU assembly contains the following components: a fuel pellet, a vented capsule, a pyrolytic graphite insulator, and a fine-weave pierced fabric (FWPF) graphite aeroshell. The fuel pellet is a hot-pressed 238 PuO 2 cylindrical pellet that is sintered to create a ceramic pellet. The pellet is encapsulated in a platinum-rhodium encapsulation. The welded fueled clad is placed inside a pyrolytic graphite thermal insulation to protect the fuel from the heat produced in reentry events. The exterior layer of the LWRHU assembly is the FWPF aeroshell. Before sintering, PuO 2 granules are heated in a furnace at temperatures greater than or equal to 700°C under an enriched 16 O atmosphere in order to reduce trace quantities of 18 O. Because 18 O undergoes an (α,n) reaction, 18 O needs to be removed as much as possible to meet radiation emission requirements set by the Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), as well as other agencies. The relative abundances of the oxygen isotopes are indicated by neutron emission rate (NER) measurements. LWRHUs are vented with a sintered platinum frit vent (George 1986). During extraterrestrial operation, the vent in the cladding allows helium from the decay of plutonium to escape and relieves pressure from the system while preventing the solids from escaping (Rinehart 1996, Tate 1982 & 1985). This vent is protected during manufacture by a platinum-30 rhodium cover. The capsule vent is activated by milling a 0.025-inch diameter hole through the protective cover to a nominal depth of 0.015 inches, generally immediately before the LWRHU is loaded into the aeroshell (George 1986). Prior to long-term storage, LWRHUs are vented and thus potentially allow infiltration of 18 O from the ambient atmosphere. Although NER decreases with age due to the reduction in emitted alpha particles, it might be expected that NER reduction is mitigated by the exchange of 18 O. However, this is not necessarily the case and no trend was observed relating NER to age other than the natural decay of 238 Pu (Mulford 2021). The most probable and dominant factor for determining specific NER was manufacturing discrepancies between individual units. It is important to understand the lack of observed 16 O- 18 O exchange and to compare LWRHU pellets as much as possible.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Microstructural Pattern Formation during Far-from-Equilibrium Alloy Solidification

We introduce a new phase-field formulation of rapid alloy solidification that quantitatively in- corporates non-equilibrium effects at the solid-liquid interface over a very wide range of interface velocities. Simulations identify a new dynamical instability of dendrite tip growth driven by solute trapping at velocities approaching the absolute stability limit. They also reproduce the formation of the widely observed banded microstructures, revealing how this instability triggers transitions between dendritic and microsegregation-free solidification. Predicted band spacings agree quantita- tively with observations in rapidly solidified Al-Cu thin films.

36 MATERIALS SCIENCE↗