ADDITIVE MANUFACTURING OF TITANIUM-ZIRCONIUM-MOLYBDENUM ALLOY USING ELECTRON BEAM POWDER BED FUSION
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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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Using first-principle, atomic scale methods to predict thermal carrier (electron and phonon) behavior at nanoscale yields highly resolved thermal properties. Density functional theory simulations can model myriad effects on the transport of phonons and electrons (e.g., phonon and electron coupling, electron correlation, defect presence, carrier scattering), and yield thermal properties for small geometric domains such as thermal and electrical conductivity, or heat capacity. While these thermal properties are accurate in atomistic and nanoscopic systems, they must be scaled up to the microstructural domain to be a useful predictor for experimental basis, as the microscale is where myriad changes and physical phenomenon occur within a material (e.g., grain boundaries, precipitate aggregations, interfaces, defect clusters). My talk will discuss the necessity of thermal property predictions for nuclear fuels applications and more. Additionally I will discuss the necessity in developing multi-physics, multi-scale methods for future applications in predictions for not only nuclear fuels, but other materials applications.
An introduction to the LDRD program at Los Alamos National Laboratory (LANL), the program’s structure and strategic values, the LDRD portfolio management process, and highlights of outstanding accomplishments by LDRD researchers.
As an engineering team within a facilities-driven organization, Engineering Services – Science and Technology Operations (ES-STO), supports Los Alamos National Laboratory (LANL), playing a pivotal role in the U.S. nuclear stockpile mission. This report outlines ES-STO’s contributions through the installation of crucial systems such as HVAC units, compressors, and scientific specialty equipment, as well as providing expert consultation to optimize laboratory operations. ES-STO’s goal is to ensure that LANL's infrastructure and research facilities are aligned with mission-critical needs, supporting both operational efficiency and safety in the nuclear stockpile management and maintenance. This report discusses the installation processes, ongoing consultations, and the significant impact of our efforts on national security objectives.
Los Alamos National Laboratory is a world leader in applying multi-disciplinary science to complex systems within the Complex Natural and Engineered Systems (CNES) challenge areas. Our search for solutions requires science and technology innovation, as well as an integrated experiment, theory, and modeling and simulation approach. Our research and development spans from improving engineered systems such as nuclear weapons and the power grid, to understanding the interface of human and engineered systems from the subsurface to space, to studying how complex natural systems such as disease and climate impact humanity.
Agnew National Security Postdoc Fellows pursue cutting-edge experimental, theoretical, computational science, and engineering research aligned with the national security mission. Metropolis Postdoc Fellows pursue cutting-edge research in the areas of computational and computer science, physics, and engineering. Computer simulation capabilities are developed in support of the stockpile stewardship program together with broader national nuclear security needs. Fellows have access to some of the most powerful supercomputers in the world to perform pioneering research.
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Neutron emission probabilities and half-lives of 37 𝛽-delayed neutron emitters from 75 Ni to 92 Br were measured at the RIKEN Nishina Center in Japan, including 11 one-neutron and 13 two-neutron emission probabilities and six half-lives for the first time that supersede theoretical estimates. These nuclei lie in the path of the weak 𝑟 process occurring in neutrino-driven winds from the accretion disk formed after the merger of two neutron stars synthesizing elements in the 𝐴∼80 abundance peak. The presence of such elements dominates the accompanying kilonova emission over the first few days and have been identified in the AT2017gfo event, associated to the gravitational wave detection GW170817. Abundance calculations based on over 17,000 simulated trajectories describing the evolution of matter properties in the merger outflows show that the new data lead to an increase of 50%–70% in the abundance of Y, Zr, Nb, and Mo. This enhancement is large compared to the scatter of relative abundances observed in old very metal poor stars and thus is significant in the comparison with other possible astrophysical processes contributing to the light-element production. These results underline the importance of including experimental decay data for very neutron-rich 𝛽 -delayed neutron emitters into 𝑟 -process models.
This paper presents an overview into the use of acoustic backscatter systems (ABS) for the in situ characterization of nuclear waste sludge concentration, size and aggregation, highlighting key achievements from the University of Leeds. Previous projects funded through the Engineering and Physical Sciences Research Council (EPSRC) UK and Innovate UK are firstly reviewed and linked to current research within the new TRANSCEND consortium. Detailed are a number of developments at various stages of technology readiness level, from the use of ABS to monitor small-scale sludge settling in columns, to large-scale jet-mixing and waste transfer operations. Current work with Sellafield Ltd will see new technology and world leading analysis methods deployed to measure the consolidation of complex wastes currently undergoing containment, the first time full industrial utilization of these systems has been achieved. (authors)
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High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.
Objective. Our objective is to formulate the problem of the magnetoencephalographic (MEG) sensor array design as a well-posed engineering problem of accurately measuring the neuronal magnetic fields. This is in contrast to the traditional approach that formulates the sensor array design problem in terms of neurobiological interpretability the sensor array measurements. Approach. We use the vector spherical harmonics (VSH) formalism to define a figure-of-merit for an MEG sensor array. We start with an observation that, under certain reasonable assumptions, any array of m perfectly noiseless sensors will attain exactly the same performance, regardless of the sensors' locations and orientations (with the exception of a negligible set of singularly bad sensor configurations). We proceed to the conclusion that under the aforementioned assumptions, the only difference between different array configurations is the effect of (sensor) noise on their performance. We then propose a figure-of-merit that quantifies, with a single number, how much the sensor array in question amplifies the sensor noise. Main results. We derive a formula for intuitively meaningful, yet mathematically rigorous figure-of-merit that summarizes how desirable a particular sensor array design is. We demonstrate that this figure-of-merit is well-behaved enough to be used as a cost function for a general-purpose nonlinear optimization methods such as simulated annealing. We also show that sensor array configurations obtained by such optimizations exhibit properties that are typically expected of 'high-quality' MEG sensor arrays, e.g. high channel information capacity. Significance. Our work paves the way toward designing better MEG sensor arrays by isolating the engineering problem of measuring the neuromagnetic fields out of the bigger problem of studying brain function through neuromagnetic measurements.
I am pleased to introduce the second 2021 issue of Physical Sciences Vistas, with a focus on “excellence in mission-focused science, technology, and engineering.” Excellence in mission-focused ST&E within the Physical Sciences Directorate (ALDPS) spans a broad spectrum of accomplishments in nuclear physics and materials science that sustains and enhances LANL’s science base.
The mission of the Nuclear and Chemical Sciences (NACS) Division within the Physical and Life Sciences (PLS) Directorate is to advance scientific understanding, capabilities, and technologies in nuclear and particle physics, radiochemistry, forensic science, and isotope systems to support LLNL’s national security mission. NACS Division personnel conduct a diverse range of research activities in particle physics, nuclear physics, radiation detection, nuclear measurements, chemical and nuclear forensic science, nuclear and radiochemistry, isotope geochemistry, and environmental science. These areas are leveraged to address evolving national security challenges. Scientific research provides the foundation for addressing these challenges, and it is also the principal means of attracting, training, and retaining staff scientists who can deliver solutions across the Laboratory’s mission space. The overarching strategy is to position the NACS Division at the nexus between fundamental nuclear and chemical science research and nuclear security applications. This approach will support efforts to recruit, train, and retain top-flight scientists and engineers who will play a key role in executing the Laboratory’s core nuclear security missions, while also enhancing LLNL’s reputation as a center for innovative scientific research. This document describes the strategic vision that will be used to guide key investments aimed at enabling NACS scientists to lead new efforts and meet future challenges.
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Various Inconel 625 coupons fabricated via Laser Powder Bed Fusion (L-PBF) were neutron irradiated using the inside reflector of the reactor at the University of Missouri Research Reactor (MURR). Effects of build orientation and heat treatment on neutron-induced hardening were investigated by inspecting L-PBF samples built vertically or at a 45º angle in the following heat-treated conditions: as-built (no heat treatment), 700 ºC for 1 hour, 900 ºC for 1 hour, and 1050 ºC for 1 hour. The microhardness results of L-PBF samples before and after neutron irradiation were compared with traditional wrought Inconel 625. All samples underwent an irradiation flux of 6.61 x 1013 neutrons/cm²/s for 310 hours for an estimated damage of 0.012 dpa. Results indicate that as-built L-PBF specimens are less prone to radiation hardening relative to their wrought counterparts. As-printed diagonal specimens were shown to harden by 8% as compared to 1.2% hardening in as-printed vertical specimens.
Corrosion of materials presents a significant challenge for the long-term operation of molten salt reactors. This study aims to identify the most effective techniques for evaluating the corrosion performance of materials in molten salts, with a focus on the effects of galvanic corrosion and dissimilar materials. A reliable testing methodology for assessing material corrosion in molten chloride salts has been successfully developed. The corrosion of Alloy 316H in molten NaCl-MgCl2 salt was found to be significantly accelerated by galvanic corrosion. Additionally, the presence of dissimilar materials resulted in a slight increase in the corrosion rate of Alloy 316H in NaCl-MgCl2 salt. Microstructural characterization was utilized to understand the corrosion behavior of test samples under different conditions. Common trends observed across samples include chromium depletion and iron enrichment near corroded surfaces. Molybdenum enrichment along grain boundaries and corrosion surfaces was also frequently noted.
The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.