The JAG inertial confinement fusion simulation dataset for multi-modal scientific deep learning
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eProject Builder (ePB) is a secure, web-based system that enables agencies and ESCOs to securely: (1) preserve, track, and access energy project information for the life of the contract (2) quickly generate data and reports for their portfolio of projects (3) develop project scenarios using standardized amortization calculations (4) benchmark new projects against historical project data
Libra is a Python package that extends support of its parent package, SQLAlchemy. Libra’s primary functionality supports the dynamic creation of object-oriented analogs of SQL tables from a variety of user-defined, text-based schema definition formats and provides quality control and analysis tools and methods
Laser damage of optical components can be a limiting factor in scaling the energetics of high-peak and average power laser systems. Specifically for optical coatings, damage under nanosecond pulsed irradiation is initiated by pre-existing defects in the coating layers, including those that cause discontinuities in the structure, like craze lines. Crazing or cracking in a multilayer dielectric optical coating is induced when the overall coating stress is sufficiently tensile, and is an occasionally observed issue when employing more porous deposition techniques like electron-beam evaporation. Here, in this study, electron-beam high-reflectors were fabricated utilizing process parameters that are known to induce crazing based on prior processing history to systematically evaluate the impact of crazing on reflector damage performance for 1064 and 355 nm lasers. The crazing that was observed was apparently nucleated at nodular defects. When the cross-section of these nodules was investigated, it was observed that there were cracks into the fused silica substrate of approximately 5 µm in depth. The craze lines were irradiated with 1064 and 355 nm light at fluences slightly above the onset of damage initiation fluence of the coating. The 1064 nm irradiated sub-apertures exhibit laser damage but with no spatial correlation with the craze line, whereas the 355 nm irradiated area exhibited many damage sites along the craze line. Finite-difference time-domain electric-field simulations were conducted, and ∼2× field amplification in hafnia was observed for the 355 nm wavelength case. The laser damage can be attributed to a slight electric-field intensification coincidental with an area where UV damage-prone precursors are known to occur. The 355 nm laser damage in uncoated fused silica substrates has been previously correlated to initiate through localized UV absorption at the broken silica bonds in the tips of fractures.
The common beamline of the AWAKE experiment at CERN involves the co-propagation of two particle beams: protons with 48 nC bunch charge and 250 ps bunch length, and electrons with up to 600 pC bunch charge and approximately 4 ps bunch length. The existing operational beam position monitors at AWAKE cannot measure the electron bunches whilst the more-intense proton bunches are present, due to their low operating frequency. In order to try to address this challenge, two different types of high-frequency pick-ups were studied, a conical-shaped button pick-up and a Cherenkov diffraction radiation-based pick-up designed to operate at around 30 GHz. Both devices were installed at AWAKE and were connected to two identical read-out systems designed by TRIUMF. This contribution presents and discusses the results obtained from beam-based measurements during the current experimental year.
The AWAKE facility uses novel proton beam-driven plasma wakefields to accelerate electron bunches over 10m of Rubidium plasma. Precise monitoring of 2 diverse beam types necessitates an electron beam position monitor (BPM) working in a frequency regime of tens of GHz. A high frequency conical button-style BPM with a working regime of up to 40 GHz has been investigated as a way to discriminate the electromagnetic fields of 19 MeV, 4 ps electron bunches propagating spatially and temporally together with a 400 GeV, 170 ps proton bunch in the AWAKE common beamline. The sensitivity of the HF BPM to the electron beam position is determined under various beam conditions, with both electrons and protons, and integration with a TRIUMF front-end is discussed.
If there’s one word that describes Matt Kohn’s career, it’s eclectic. What is possibly most astounding is that Matt’s contributions have not been in a single, narrow field, but in two distinct fields that, at least at first glance, are not even that closely related. While Matt is a metamorphic petrologist par excellence making significant contributions to the fields of geo-thermobarometry, metamorphic phase equilibria, metamorphic fluid flow, monazite geochronology, argon geochronology, and metamorphic P-T-t studies and their tectonic implications, he has also pioneered the use of stable isotopes to unravel vertebrate paleodietary patterns and paleoclimate.
The Advanced Test Reactor Fresh Fuel Shipping Container (ATR FFSC) is a rectangular stainless steel container used for shipping radioactive material. The container is described in the ATR FFSC Safety Analysis Report (SAR). Per the ATR FFSC SAR, the ATR FFSC is designated a Type AF-96 packaging per the definition of 10 CFR §71.4, and was originally designed to transport high enriched uranium (HEU) reactor fuel elements for the Advanced Test Reactor (ATR), the Advanced Test Reactor Critical (ATRC) facility, the Massachusetts Institute of Technology Reactor (MITR), and the University of Missouri Research Reactor (MURR). The Department of Energy, National Nuclear Security Administration’s (NNSA), Office of Material Management and Minimization (M3) is working with the Idaho National Laboratory (INL) to develop and qualify new low enriched uranium (LEU) fuels and technologies for use in the ATR, ATRC, MITR, and MURR reactors. The LEU fuel elements will weigh significantly more than the current HEU designs and, combined with their associated Fuel Handling Enclosures for packaging, some configurations will exceed the 50 lbf used in the ATR FFSC qualifying drop tests. There are LEU versions of MITR, MURR, and ATR fuel elements. However, for this evaluation, drop analysis of the ATR FFSC with only the heavier ATR Low Enrichment (LOWE) fuel element is considered in this evaluation because the LOWE fuel element is the heaviest of the considered LEU fuel elements. The ATR HEU fuel element and the ATR LOWE fuel element are identical in every design aspect except for the fuel meat inside the 19 fuel plates. The LEU fuel meats are made using a U-10Mo high-density foil rather than uranium dispersed in aluminum in the HEU fuel elements. The high density of the uranium in the LEU fuel meat increases the LOWE fuel element weight to just under 44 lbf (versus the 22.1 lbf weight of the tested ATR HEU fuel element). ATR fuel elements are placed in a thin-gauge aluminum weldment called a "Fuel Handling Enclosure" during packaging. The Fuel Handling Enclosure is used to cover and protect the element during loading and unloading operations. The ATR Fuel Handling Enclosure weighs about 15 lbf per the drawings in the ATR FFSC SAR and the weight is accounted for in this evaluation. Transporting the heavier LEU fuel elements require evaluation of two issues. The first is the effect of the increased mass of the LEU fuel elements on the survivability of the ATR FFSC package following the requisite drop qualifications. The second is the effect of the increased mass of the fuel plates on the fuel element during the same drops. The ATR FFSC containing an ATR HEU fuel element in an ATR Fuel Handling Enclosure was physically dropped multiple times to qualify the container as a Type AF-96 package. The ATR FFSC SAR describes the drop tests performed with an actual ATR HEU fuel element weighing 22.1 lbf contained in a 14.3 lbf Fuel Handling Enclosure for a total payload of 36.4 lbf. Those drop tests showed that the ATR FFSC maintained containment of the ATR HEU fuel element and the fuel element was not significantly damaged. (Containment herein is not defined as a leak tight but is retention of the radioactive contents.) The purpose of the evaluation is to analytically show that, for a similar set of tests, the ATR FFSC maintains containment of the heavier ATR LOWE fuel element and to assess the damage to the fuel element during the drops. The approach was to create finite element analysis (FEA) models that produce the same results as the physical drops. Those models were then used as the benchmarks for the follow-on analyses using the heavier contents. FEA models of the drops of ATR FFSC using up to a 115 lbf fuel element were run and evaluated. Likewise, drops of a LOWE fuel element weighing 44 lbf in the ATR FFSC were run and evaluated. It is important to note that this report was done at the quality level necessary to be included in a nuclear facility safety basis. However, it is not the intent of this report to conclude the suitability of the ATR FFSC for transporting the heavier payloads. This report only describes the results of the FEA as related to the required drop scenarios. Incorporation of the FEA into the safety basis will be evaluated by the ATR FFSC design authority. The physical drop tests of the HEU fuel element and FEA drop analysis for the LOWE fuel element showed noteworthy damage to the fuel plates. An aluminum protective block was conceived to mitigate the damage. The concept requires the blocks to be placed in the fuel element between the end boxes and fuel plates. Additional FEA drop analyses were performed using the protective block. The addition of the blocks is primarily intended to mitigate the damage to the LOWE fuel element fuel plates. However, FEA drop analyses of the ATR HEU fuel element with the blocks were also performed and included for information.
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Conventional macroscale experimentation is generally considered to be straightforward with few limitations. Conversely, micro/nanoscale experimentation presents numerous challenges in loading device design, sample preparation and handling, as well as accurate understanding of grain size and local texture effects on recorded measurements. Despite these challenges, nanopillar compression, MEMs based micro-tension, and nanoindentation approaches have been able to provide fundamental contributions to the understanding of material behavior at small lengthscales. However, the overarching shortcoming of these micro/nanoscale experimentation approaches, is the inability to directly translate measurements evaluated at the nm and µm length scales (e.g., hardness) to macroscale tensile material behavior (i.e., elastic modulus, yield strength, and ductility). The objectives of the proposed study are, 1) to establish best practices for obtaining tensile microscale ductility measurements, and 2) to validate methodologies to for comparing microscale ductility measurements to macroscale ductility measurements. In order to achieve these objectives, a multi-lengthscale, multi-temperature testing protocol and simulation framework are executed first on copper as a model material to validate the following approach, and second on reactor grade Zircaloy-2. Experiments are conducted on specimens extracted from the same test piece to ensure nominally identical grain size and texture from specimens to specimen. Motivated by the need to isolate the contribution of size-effects on obtained mechanical property measurements, specimens are manufactured with thicknesses at the micro- (1-10 µm), meso- (10-100's µm), and macroscales (sub-sized ASTM E8). In-situ full-field deformation techniques (scanning electron microscopy (SEM) grid methods and optical DIC) are incorporated into testing at each specimen length-scale to capture plasticity localization and evolution. Experimental testing for all specimens is conducted at both room temperature and elevated temperatures to probe the role of thermal activation on plastic deformation accommodation processes. Simulation efforts focus on examining the mechanical behavior of microscale specimens using a finite element approach with explicitly resolved grain morphologies, and an embedded crystal plasticity model. The cost-efficient implementation method allows for the modeling of a statistically significant number of both real (i.e., digital twin) and generated microstructures to obtain an understanding of the interrelationships between specimen microstructure and geometric variables (grain size, texture, specimen geometry, etc.) on microscale mechanical behavior.
Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.
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This technical brief estimates the expected performance of a solar photovoltaic and energy storage system (PVESS) for providing backup power during short-duration power interruption events, accounting for the unpredictable nature of those events. The analysis relies on the Power Reliability Event Simulation TOol (PRESTO), a publicly available model developed by Berkeley Lab to simulate the occurrence of short-duration power interruption events at the county-level. A separate storage dispatch model is then used to simulate PVESS operation and backup performance for each of the large number of interruption events produced by PRESTO. In performing this simulation, the analysis accounts for how the customer operates its battery on a day-to-day basis—in this case, we assume the battery is cycled each day in response to time-of-use rates—and how that, in turn, impacts the battery’s state of charge at the beginning of each interruption event. The analysis presented here is intended to demonstrate an application of the PRESTO model as well as to illustrate some of the key determinants of PVESS backup power performance during short-duration power interruption events. The analysis focuses initially on a typical single-family home in Maricopa County, Arizona, and includes a limited set of scenarios related to system sizing, backup power configuration, and whether the customer charges its battery storage system from the grid during normal operating conditions. The analysis also presents comparative results for two other counties, in Massachusetts (Middlesex) and California (Los Angeles), illustrating how regional differences in climate, interruption patterns, and retail rate structures can affect PVESS performance as a backup power source. In the conclusions, we highlight a number of other important considerations for evaluating PVESS backup power capabilities.