Bayesian optimization experiment for trajectory alignment at the low energy RHIC electron cooling system
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Cross sections of Sr86(d,x) reactions leading to the products Rb84m,g, Rb83, and Rb82m were measured by the stacked-sample activation technique up to deuteron energies of 49 MeV. Nuclear model calculations were performed using the codes talys and empire, which combine the statistical, precompound, and direct interaction components. In all cases, the empire results were much higher than the talys calculation. Fairly good agreement was obtained between measured data and the talys calculation after some optimization of the input model parameters. Insight into competition between α-particle and multinucleon emission in the Y88 compound-nucleus system was also gained.
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Diffusion limited oxidation (DLO) as a phenomenon has complicated material lifetime predictions from accelerated aging studies. Many methods to analyze DLO have been developed in recent years. However, an analytical tool for high throughput and low-cost analysis of DLO affected samples remains absent from the literature. Color differences in polymers exposed to oxidative conditions have recently been observed and correlated to increased concentrations of oxidation products. Herein, total color difference was utilized to rapidly quantify DLO in ethylene propylene rubber (EPR) polymeric cable insulation specimens. Insulation specimens were aged at 121 °C, 136 °C, 150 °C, and 165 °C and a 10% ?E*ab difference between the specimen edge and specimen center was used as the threshold for DLO. Based on these specifications DLO was observed in EPR insulation specimen at temperatures of 136 °C and above, with an increasing degree of DLO observed as the time of exposure increased. The method presented in this work represents a quantitative and high-throughput method for the investigation of DLO with a low cost barrier for entry.
Metal additive manufacturing (AM) is a disruptive manufacturing technology that opens the design space for parts outside those possible from traditional manufacturing methods. In order to accelerate industry and R&D needs to certify AM parts, the Exascale Additive Manufacturing project (ExaAM) has developed a suite of exascale-ready computational tools to model the process-to-structure-to-properties (PSP) relationship for additively manufactured metal components. One such tool is an uncertainty quantification (UQ) pipeline to quantify the effect that uncertainty in processing conditions has on local mechanical responses. We present an overview of this pipeline and its required simulation and workflow codes. Using the Oak Ridge National Laboratory’s (ORNL) exascale computer, Frontier, we utilize this pipeline to cross multiple length and time scales to predict the local mechanical response of a location within a complex AM bridge part, AMB2018-01 produced by the National Institute of Standards and Technology (NIST) as part of their 2018 AM-Bench test series. Our results are then compared to experimental mechanical tests of parts from the NIST build to quantify the error in the ExaAM UQ workflow.
A method based on separation of volatilized molten salt (MS) components, including by mass, is being developed to extract fission products (FPs) from operating molten-salt reactors. The initial application of this method is for accelerator-driven subcritical reactors fueled by (fluorinated molten salt) spent nuclear fuel (UNF) from any past, present, or future reactor. The actinides remain in the subcritical reactor to produce profitable energy and be transmuted while the extracted FPs can be buried without long-lived actinides such that a geologic repository is not necessarily needed to close the nuclear fuel cycle. By removing neutron-absorbing FPs and operating sub-critically, where the restrictive link between operation and criticality is broken, it is possible to envision complete burnup of the UNF fuel. The game-changing feature of continuously processing the molten salt inside the reactor while the reactor operates eliminates the need for a separate reprocessing plant. This feature also simultaneously improves the neutronics of the reactor, increasing the burnup of the fuel and extending its useful life for generating energy. Nuclear nonproliferation and need for geologic repositories are addressed by keeping actinides inside the reactor containment until they are consumed. (authors)
In this project, combinatorial synthesis and testing methods were combined with high-throughput materials theory calculations to greatly accelerate the discovery of thermodynamically suitable candidates for green hydrogen production via a two-stage solar thermochemical water splitting (STCH) process. Over the course of the project, more than 8000 quinary and higher oxide compositions were computationally screened for STCH viability, and detailed stability calculations were performed for more than 30 of the most promising identified compositional archetypes. As a result, three new STCH capable compositional families were discovered and experimentally verified. The first, Ce x Sr 2-x MnO 4 (CSM), represents the first known Ruddlesden-Popper compound to show STCH activity, and thus demonstrates that perovskite-related structures may hold promise for this application. The second family, Sr 1-x Ce x MnO 3 (SCM), is the simple perovskite sister-analog to CSM. Sr 0.7 Ce 0.3 MnO 3 (SCM30), a member of this compositional family, was found to produce the highest hydrogen yields of any compound tested in this project, exceeding the end of project milestone target of > 150 μmol H 2 /gram oxide at a reduction temperature of 1350 °C, although only at steam-to-hydrogen ratios greater than 1000:1. Finally, we proved that a third novel Sr-and Mn-containing family, Sr 1-x Ca x Ti 1-y Mn y O 3 (SCTM), which was identified by Materials Project tools, also splits water. The behavior of the SCTM system was found to be similar to the previously discovered Sr 1-x La x Al 1-y Mn y O 3 (SLMA) family, albeit with lower H 2 yields. Across the three thrusts of the project (computational, combinatorial, and bulk testing), five journal articles were published. As part of Program End Analysis and Data Dissemination, relevant data used for the publications was uploaded to the HydroGEN Data Hub for public access, and in certain cases, results were added to public materials databases.
This project assembles a strong U.S. industry and national laboratory team to complete scope of work. Kairos Power (KP), headquartered in Alameda, CA, is the leader of this effort and has built an internal team of highly competent engineers and managers with extensive combined experience in nuclear power, conventional power, product development, and licensing. INL, ANL, and LANL bring unique capabilities in advanced reactor R&D and licensing. The project funding source is the result of FOA No. 0001817, U. S. Industry Opportunities for Advanced Nuclear Technology Development. There has been on-going work and this Access Cooperative Research and Development Agreement (CRADA) will cover the remaining work scope of FOA 0001817. KP is implementing innovative strategies that can reduce the cost and accelerate the initial demonstration of the Kairos Power Fluoride-salt-cooled, High-temperature Reactor (KP-FHR) to meet the needs of the U.S. electricity market by 2030. Licensing of the KP-FHR could be significantly accelerated using advanced computing methods with sufficient predictive capabilities to be able to extrapolate potential response of the structure in different scenarios. However, currently used computational methods heavily rely on empirical fits and cannot be used for extrapolation. The scope focuses on improving the modeling capability in the NEAMS Grizzly structural mechanics code and the NEAMS BISON fuel performance code and the NEAMS SAM systems analysis code. This work leverages the expertise and know-how gathered in three DOE National Laboratories – INL, ANL, and LANL.
Spiking Neural Networks (SNNs) are brain-inspired computing models incorporating unique temporal dynamics and event-driven processing. Rich dynamics in both space and time offer great challenges and opportunities for efficient processing of sparse spatiotemporal data compared with conventional artificial neural networks (ANNs). Under this context, the goal of this project is to develop spiking neural network based neuromorphic computing to enable energy-efficient real-time learning and processing of spatiotemporal data. This report summarizes the key results on network architecture design, training methods, and SNN hardware acceleration achieved under this project, demonstrating the promise of spiking neural networks.
Introduction: 43 Sc and 44g Sc are both positron-emitting radioisotopes of scandium with suitable half-lives and favorable positron energies for clinical positron emission tomography (PET) imaging. Irradiation of isotopically enriched calcium targets has higher cross sections compared to titanium targets and higher radionuclidic purity and cross sections than natural calcium targets for reaction routes possible on small cyclotrons capable of accelerating protons and deuterons. Methods: In this work, we investigate the following production routes via proton and deuteron bombardment on CaCO 3 and CaO target materials: 42 Ca(d,n) 43 Sc, 43 Ca(p,n) 43 Sc, 43 Ca(d,n) 44g Sc, 44 Ca(p,n) 44g Sc, and 44 Ca(p,2n) 43 Sc. Radiochemical isolation of the produced radioscandium was performed with extraction chromatography using branched DGA resin and apparent molar activity was measured with the chelator DOTA. The imaging performance of 43 Sc and 44g Sc was compared with 18 F, 68 Ga, and 64 Cu on two clinical PET/CT scanners. Discussion: The results of this work demonstrate that proton and deuteron bombardment of isotopically enriched CaO targets produce high yield and high radionuclidic purity 43 Sc and 44g Sc. Laboratory capabilities, circumstances, and budgets are likely to dictate which reaction route and radioisotope of scandium is chosen.
Governments are increasingly turning toward public–private partnerships to bring industry support to improving public assets or services. Here, we describe a unique public–private collaboration where a government entity has developed mechanisms to support public and private sector advancement and commercialization of monitoring technologies for marine renewable energy. These support mechanisms include access to a range of skilled personnel and test facilities that promote rapid innovation, prove reliability, and inspire creativity in technology development as innovations move from concept to practice. The ability to iteratively test hardware and software components, sensors, and systems can accelerate adoption of new methods and instrumentation designs. As a case study, we present the development of passive acoustic monitoring technologies customized for operation in energetic waves and currents. We discuss the value of testing different systems together, under the same conditions, as well as the progression through different test locations. The outcome is multiple, complementary monitoring technologies that are well suited to addressing an area of high environmental uncertainty and reducing barriers to responsible deployment of low-carbon energy conversion systems, creating solutions for the future.
ImpactX is the next generation of the IMPACT-Z code. It is a s-based simulation code for modeling intense beams in particle accelerators using symplectic tracking methods and includes collective effects. It is multi-node parallel and supports modern compute hardware such as GPUs, modern algorithms such as mesh-refinement and realistic geometries (embedded boundaries).
This specification outlines the fabrication requirements for kernel compact uranium monocarbide (UC) fuel to be fabricated at General Atomics (GA) and is already issued in EDMS and is going thru LRS so that it can be shared with the Vendor. The UC fuel will be irradiated in the Advanced Test Reactor (ATR) using the Irradiation System for High-Throughput Acquisition (ISHA-1). The primary objective of the ISHA-1 capsule design is to deliver to Idaho National Lab (INL) a semi-universal drop-in capsule that can facilitate the irradiation of both fissile and structural materials in a variety of ATR positions. This irradiation experiment will support the need for development of Accelerated Fuel Qualification (AFQ) methods.
Thermochemical properties play a key role in modeling and simulation of several key phenomena in nuclear reactors. There has been an increasing interest in incorporating CALPHAD-based formulations in multiphysics simulations including for Molten Salt Reactors where knowledge of phase evolution of the salt and the chemical potentials of various elements are of utmost importance in source term analyses and redox control. However, the size of such simulations is often limited by the high computational cost of full thermodynamic equilibrium calculations. This work discusses the current efforts aimed at accelerating thermochemical equilibrium calculations for multiphysics simulations performed using the open-source finite element / finite volume code Multiphysics Object Oriented Simulation Environment (MOOSE) [1]. While several methods have been proposed for accelerating phase equilibrium calculations [2], most focus on relatively small systems and often rely on a- priori knowledge of the state-space of the system. Nuclear materials, however, are often multi-component systems owing to the evolution of composition under irradiation and an approach based on a-priori mapping of phase diagram is often not enough. This work is aimed at demonstrating an on-the-fly surrogate modeling framework that uses active learning to reduce the number of full equilibrium calculations that must be performed. By combining with efficient coupling approaches, the surrogate framework helps in reducing the computational cost of thermodynamic equilibrium informed multiphysics simulations of nuclear materials. The performance is benchmarked against full coupling with the thermochemistry library Thermochimica [3]. This work uses a machine learning based approach for constructing surrogate models to predict the stable phases in a multicomponent system. The surrogates were constructed using neural networks and Gaussian process classification. In this work, we compare the relative performance of the two methods. We also demonstrate the use of caching previous calculations by interpolating the values from nearest neighbors. References [1] Lindsay, A.D., et al. "2.0 – MOOSE: Enabling massively parallel multiphysics simulation", SoftwareX, 20 (2022): 101202. [2] Roos, W.A. and Zietsman J.H. "Accelerating complex chemical equilibrium calculations – A Review", Calphad, 77 (2022): 102380. [3] Piro, M.H.A., et al. "The thermochemistry library Thermochimica", Computational Materials Science, 67 (2013): 266-272.
The design and production of Nb 3 Sn-based dipole and quadrupole magnets is critical for the realization of the High-Luminosity Large Hadron Collider (HL-LHC) at the European Organization for Nuclear Research (CERN). Nb 3 Sn superconducting coils are aimed at enhancing the bending and focusing strengths of accelerator magnets for HL-LHC and beyond. Due to the brittle nature of Nb 3 Sn, the coil fabrication steps are very challenging and require very careful QA/QC. Flaws in the Nb 3 Sn filaments may lead to quenches, and eventually, performance limitation below nominal during magnet testing. A novel inspection method, including advanced non-destructive and destructive techniques, was developed to explore the root-causes of quenches occurring in performance-limiting coils. The most relevant results obtained for MQXF coils through this innovative inspection method are presented. This approach allows for precise assessment of the physical events associated to the quenches experienced b y magnet coils, mainly occurring under the form of damaged strands with transversely broken sub-elements. Coil-slice preparation, micro-optical observations of transverse and longitudinal cross-sections, and a deep etching technique of copper will be illustrated in the present work, with a focus on the results achieved for a CERN coil from a non-conforming quadrupole magnet prototype, and two coils fabricated in the US, in the framework of the Accelerator Upgrade Project (AUP) collaboration, from two different non-conforming quadrupole magnets, respectively. The results obtained through the proposed inspection method will be illustrated.
In this work, we show the first experiment of a transverse laser interference for electron injection into the laser plasma accelerators. Simulations show such an injection is different from previous methods, as electrons are trapped into later acceleration buckets other than the leading ones. With optimal plasma tapering, the dephasing limit of such unprecedented electron beams could be potentially increased by an order of magnitude. In simulations, the interference drives a relativistic plasma grating, which triggers the splitting of relativistic-intensity laser pulses and wakefield. Consequently, spatially dual electron beams are accelerated, as also confirmed by the experiment.
We simulate the dynamics, including laser cooling, of three-dimensional (3-D) ion crystals confined in a Penning trap using a newly developed molecular dynamics-like code. The numerical integration of the ions’ equations of motion is accelerated using the fast multipole method to calculate the Coulomb interaction between ions, which allows us to efficiently study large ion crystals with thousands of ions. In particular, we show that the simulation time scales linearly with ion number, rather than with the square of the ion number. By treating the ions’ absorption of photons as a Poisson process, we simulate individual photon scattering events to study laser cooling of 3-D ellipsoidal ion crystals. Initial simulations suggest that these crystals can be efficiently cooled to ultracold temperatures, aided by the mixing of the easily cooled axial motional modes with the low frequency planar modes. In our simulations of a spherical crystal of 1000 ions, the planar kinetic energy is cooled to several millikelvin in a few milliseconds while the axial kinetic energy and total potential energy are cooled even further. This suggests that 3-D ion crystals could be well suited as platforms for future quantum science experiments.
Spatial properties of electron beams are the essential parameters needed in particle physics and accelerator research. Beam characterization using optical detection methods through the interaction between photons and electrons such as Compton scattering, or electro-optic sampling of the electric field of electrons have been extensively explored and successfully implemented as non-invasive diagnostics at various accelerator facilities. However, such methods often suffer from inherently low sensitivity. Here we present the study of a new type of electron beam diagnostic device for direct optical imaging of electron beams at various energy levels. The concept is based on the extremely high sensitivity of atoms, prepared in a specific "dark superposition" quantum state, to the external electric or magnetic perturbations induced by the passing charged particle, which allows atoms to change polarization of the probe light or absorb probe light and to fluoresce, enabling direct 3D imaging of the charged particles with high resolution. We report our recent experiment results and the design effort on a compact apparatus intended to be tested with the relativistic electron beams at Jefferson Laboratory.