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A lack of coherent plans to analyze, manage, and understand data threatens the various opportunities offered by new neuro-technologies. High-performance computing will allow exploratory analysis of massive datasets stored in standardized formats, hosted in open repositories, and integrated with simulations.
Searches for rare nuclear processes, such as neutrinoless double beta-decay and the interactions of WIMP dark matter, are motivating experiments with ever-decreasing levels of radioactive backgrounds. These background reductions are achieved using various techniques, but amongst the most important is minimizing radioactive contamination in the materials from which the experiment is constructed. To this end there have been decades of advances in material sourcing, manufacture and certification, during which researchers have accumulated many thousands of measurements of material radiopurity. Some of these assays are described in publications, others are in databases, but many are still communicated informally. Until this work, there has been no standard format for encoding assay results and no effective, central location for storing them. The aim of this paper is to address these long-standing problems by creating a concise and flexible material assay data format and powerful software application to manipulate it. Finally, a public installation of this software, available at http://www.radiopurity.org, is the largest database of assay results ever compiled and is intended as a long-term repository for the community's data.
High-energy delayed γ-ray spectroscopy is a potential technique for directly assaying spent fuel assemblies and achieving the safeguards goal of quantifying nuclear material inventories for spent fuel handling, interim storage, reprocessing facilities, repository sites, and final disposal. Requirements for the γ-ray detection system, up to ~6 MeV, can be summarized as follows: high efficiency at high γ-ray energies, high energy resolution, good linearity between γ-ray energy and output signal amplitude, ability to operate at very high count rates, and ease of use in industrial environments such as nuclear facilities. High Purity Germanium Detectors (HPGe) are the state of the art and provide excellent energy resolution but are limited in their count rate capability. Lanthanum Bromide (LaBr 3 ) scintillation detectors offer significantly higher count rate capabilities at lower energy resolution. Thus, LaBr 3 detectors may be an effective alternative for nuclear spent-fuel applications, where count-rate capability is a requirement. This paper documents the measured performance of a 2” (length) × 2” (diameter) of LaBr3 scintillation detector system, coupled to a negatively biased PMT and a tapered active high voltage divider, with count-rates up to ~3 Mcps. An experimental methodology was developed that uses the average current from the PMT’s anode and a dual source method to characterize the detector system at specific very high count rate values. Delayed γ-ray spectra were acquired with the LaBr 3 detector system at the Idaho Accelerator Center, Idaho State University, where samples of ~3g of 235 U were irradiated with moderated neutrons from a photo-neutron source. Results of the spectroscopy characterization and analysis of the delayed γ-ray spectra acquired indicate the possible use of LaBr3 scintillation detectors when high count rate capability may outweigh the lower energy resolution.
The Fork detector is widely used by the safeguards inspectorate of the European Atomic Energy Community (EURATOM) and the International Atomic Energy Agency (IAEA) to verify spent nuclear fuel. Fork measurements are routinely performed for safeguards prior to dry storage cask loading. Additionally, spent fuel verification will be required at the facilities where encapsulation is performed for acceptance in the final repositories planned in Sweden and Finland. The use of the Fork detector as a quantitative instrument has not been prevalent due to the complexity of correlating the measured neutron and gamma ray signals with fuel inventories and operator declarations. A spent fuel data analysis module based on the ORIGEN burnup code was recently implemented to provide automated real-time analysis of Fork detector data. This module allows quantitative predictions of expected neutron count rates and gamma units as measured by the Fork detectors using safeguards declarations and available reactor operating data. This study describes field testing of the Fork data analysis module using data acquired from 339 assemblies measured during routine dry cask loading inspection campaigns in Europe. Assemblies include both uranium oxide and mixed-oxide fuel assemblies. More recent measurements of 50 spent fuel assemblies at the Swedish Central Interim Storage Facility for Spent Nuclear Fuel are also analyzed. An evaluation of uncertainties in the Fork measurement data is performed to quantify the ability of the data analysis module to verify operator declarations and to develop quantitative go/no-go criteria for safeguards verification measurements during cask loading or encapsulation operations. The goal of this approach is to provide safeguards inspectors with reliable real-time data analysis tools to rapidly identify discrepancies in operator declarations and to detect potential partial defects in spent fuel assemblies with improved reliability and minimal false positive alarms. Finally, the results are summarized, and sources and magnitudes of uncertainties are identified, and the impact of analysis uncertainties on the ability to confirm operator declarations is quantified.
Characterization of the energy released from radionuclide decay in nuclear fuel discharged from reactors is essential for the design, safety, and licensing analyses of used nuclear fuel storage, transportation, and repository systems. There are a limited number of decay heat measurements available for commercial used fuel applications. Because decay heat measurements can be expensive or impractical for covering the multitude of existing fuel designs, operating conditions, and specific application purposes, decay heat estimation relies heavily on computer code prediction. Uncertainty evaluation for calculated decay heat is an important aspect when assessing code prediction and a key factor supporting decision making for used fuel applications. While previous studies have largely focused on uncertainties in code predictions due to nuclear data uncertainties, this study discusses uncertainties in calculated decay heat due to uncertainties in assembly modeling parameters as well as in nuclear data. Capabilities in the SCALE nuclear analysis code system were used to quantify the effect on calculated decay heat of uncertainties in nuclear data and selected manufacturing and operation parameters for a typical boiling water reactor (BWR) fuel assembly. Furthermore, the BWR fuel assembly used as the reference case for this study was selected from a set of assemblies for which high-quality decay heat measurements are available, to assess the significance of the results through comparison with calculated and measured decay heat data.
Ensuring the long-term seismic safety of dry storage casks (DSCs) is becoming increasingly critical as these systems evolve from temporary to de facto permanent repositories for spent nuclear fuels. Traditional seismic soil–structure interaction (SSI) assessment methods use one-dimensional deconvolution or simplified boundary conditions to model incident waves. Although computationally appealing, simplifying assumptions may alter the seismic risk by neglecting the full complexity of three-dimensional (3D) wave propagation effects. To address this challenge, this paper introduces a novel high-fidelity computational framework that leverages the Domain Reduction Method (DRM) with perfectly matched layers (PML) to accurately transfer complex, 3D seismic wavefields from regional-scale fault-rupture simulations into local-scale finite element models of DSCs. Using broadband, physics-based ground motions from a generic M w 7.0 strike-slip event, both single-cask and multi-cask configurations were investigated under near- and far-field conditions. Emphasis is placed on capturing complex SSI, spatial variability in the ground motion, and nonlinear phenomena such as cask rocking and sliding. Numerical results demonstrate that near-field conditions, where forward directivity and fling-step effects dominate, lead to significantly higher DSC rocking and sliding. Far-field cases, by contrast, generally exhibit modest responses. Incorporating SSI tends to amplify or alter DSC response spectra and introduce response variability, which underscores the need for site-specific evaluations and robust modeling approaches to ensure the seismic integrity of DSCs in interim spent fuel storage installations.
We present an overview of a software system, Bellerophon, built to support a production-level HPC application called CHIMERA, which simulates core-collapse supernova events at the petascale. Developed over the last four years, Bellerophon enables CHIMERA's geographically dispersed team of collaborators to perform data analysis in near real-time. Its n-tier architecture provides an encapsulated, end-to-end software solution that enables the CHIMERA team to quickly and easily access highly customizable animated and static views of results from anywhere in the world via a web- deliverable, cross-platform desktop application. In addition, Bellerophon addresses software engineering tasks for the CHIMERA team by providing an automated mechanism for performing regression testing on a variety of supercomputing platforms. Elements of the team's workflow management needs are met with software tools that dynamically generate code repository statistics, access important online resources, and monitor the current status of several supercomputing resources.
Abstract not provided
Floodplains, heavily used for water supplies, housing, agriculture, mining, and industry, are important repositories of organic carbon, nutrients, and metal contaminants. The accumulation and release of these species is often mediated by redox processes. By understanding the physicochemical, hydrological, and biogeochemical controls on the distribution and variability of sediment redox conditions we can develop conceptual and numerical models of contaminant transport within floodplains. The Upper Colorado River Basin (UCRB) is impacted by former uranium and vanadium ore processing, resulting in contamination by V, Cr, Mn, As, Se, Mo and U. Previous authors have suggested that sediment redox activity occurring within organic carbon-enriched bodies located below the groundwater level may be regionally important to the maintenance and release of contaminant inventories, particularly uranium. To help assess this hypothesis, vertical distributions of Fe and S redox states and sulfide mineralogy were assessed in sediment cores from three floodplain sites spanning a 250 km transect of the central UCRB. Our results support the hypothesis that organic-enriched reduced sediments are important zones of biogeochemical activity within UCRB floodplains. Furthermore, we found that the presence of organic carbon, together with pore saturation, are the key requirements for maintaining reducing conditions, which were dominated by sulfate-reduction products. Sediment texture was found to be of secondary importance and to moderate the response of the system to external forcing, such as oxidant diffusion. Consequently, fine-grain sediments are relatively resistant to oxidation in comparison to coarser-grained sediments. Exposure to oxidants consumes precipitated sulfides, with a disproportionate loss of mackinawite (FeS) as compared to the more stable pyrite. The accompanying loss of redox buffering capacity creates the potential for release of sequestered radionuclides and metals. Because of their redox reactivity and stores of metals, C, and N, organic-enriched sediments are likely to be important to nutrient and contaminant mobility within UCRB floodplain aquifers.
This paper describes Open-Instance Power System Library (OpenIPSL) version 2.0.0 and its most recent enhancements. This new version brings upgrades that include more robust models that are better documented, and example systems that illustrate certain functionalities to users. Repository and library documentations have been enhanced and expanded, and the library is now released under a new license. Changes are meant to foster user and developer communities, while providing more attractive frameworks for collaborative work to be carried out with the library.
Polycrystalline materials are made of many small crystals separated by grain boundaries (GBs), whose atomic structure strongly influences material properties. Because the structure of a GB determines its properties, the optimal structure must be known in order to determine those impacts. There are many ways of placing atoms in the GB region, but the optimal structure is defined as the one that gives the lowest value of a target property (typically energy). GB structure optimization has been successfully demonstrated using stochastic and evolutionary methods, but no reusable, community-maintained open-source workflow has been developed. GBOpt (Grain Boundary Optimization) is an open-source Python package that creates that workflow, where we have presently implemented two approaches: Markov Chain Monte Carlo, and genetic algorithm based on elite selection. We demonstrate this capability by successfully reproducing the known optimal structures of a specific GB in two materials, and point interested readers to the GitHub repository for additional examples, including optimization for different properties. Both of the implemented approaches recovered the known structures, with the genetic algorithm approach finding the optimal structure faster on average.
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The synthetic biology toolkit for baker's yeast, Saccharomyces cerevisiae, includes extensive genome engineering toolkits and parts repositories. However, with the increasing complexity of engineering tasks and versatile applications of this model eukaryote, there is a continued interest to expand and diversify the rational engineering capabilities in this chassis by FAIR (findable, accessible, interoperable, and reproducible) compliance. In this study, we designed and characterised 41 synthetic guide RNA sequences to expand the CRISPR-based genome engineering capabilities for easy and efficient replacement of genomically encoded elements. Moreover, we characterize in high temporal resolution 20 native promoters and 18 terminators using fluorescein and LUDOX CL-X as references for GFP expression and OD600 measurements, respectively. Additionally, all data and reported analysis is provided in a publicly accessible jupyter notebook providing a tool for researchers with low-coding skills to further explore the generated data as well as a template for researchers to write their own scripts. We expect the data, parts, and databases associated with this study to support a FAIR-compliant resource for further advancing the engineering of yeasts.
Plutonium (Pu) in the subsurface environment can transport in different oxidation states as an aqueous solute or as colloidal particles. The transport behavior of Pu is affected by the relative abundances of these species and can be difficult to predict when they simultaneously exist. This study investigates the concurrent transport of Pu intrinsic colloids, Pu(IV) (aq) and Pu(V-VI) (aq) through a combination of controlled experiments and semi-analytical dual-porosity transport modeling. Pu transport experiments were conducted in a fractured granite at high and low flow rates to elucidate sorption processes and their scaling behavior. In the experiments, Pu(IV) (aq) was the least mobile of the Pu species, Pu(V-VI) (aq) had intermediate mobility, and the colloidal Pu, which consisted mainly of precipitated and/or hydrolyzed Pu(IV), was the most mobile. The semi-analytical modeling revealed that the sorption of each Pu species was rate-limited, as the sorption could not be described by assuming local equilibrium in the experiments. The model was able to describe the sorption of the different Pu species that occurring either on fracture surfaces, in the pores of the rock matrix, or simultaneously in both locations. While equally good fits to the data could be achieved using any of these assumptions, a fracture-dominated process was considered to be the most plausible because it provided the most reasonable estimates of sorption rate constants. Importantly, a key result of this work is that the sorption rate constant of all Pu species tends to decrease with increasing time scales, which implies that Pu will tend to be more mobile at longer time scales than observations at shorter time scales suggest. This result has important implications for predicting the environmental impacts of Pu in the safety assessments of geologic repositories for radioactive waste disposal, and we explore potential mechanistic bases for upscaling the sorption rate constants to time and distance scales that cannot be practically evaluated in experiments.
Highlights: • 4996 mRNAs and 2942 circRNAs were identified differentially expressed in platelets of AS patients. • Several platelet-derived immune mediators were upregulated to regulate the interaction between platelets and immune cells. • Two downregulated circRNA were identified, and the corresponding circRNA-miRNA-mRNA regulatory network was constructed. • mRNAs and circRNAs found herein could be used as diagnostic biomarkers or new therapeutic targets. Ankylosing spondylitis (AS) is a chronic inflammatory disease significantly decreasing the quality of life. Platelets play an important and active role in the development of AS. Accumulating evidence demonstrated platelets contain diverse RNA repository inherited from megakaryocytes or microvesicles. Platelet RNAs are dynamically affected by pathological conditions and could be used as diagnostic or prognostic biomarkers. However, the role of the platelet RNAs in AS is elusive. In this study, we compared mRNA and circRNA profiles in platelets between AS patients and healthy controls using RNA sequencing and bioinformatic analysis, and found 4996 mRNAs and 2942 circRNAs were differently expressed. The significantly over-expressed mRNAs in AS patients are involved in platelet activity, gap junction, focal adhesion, rap1 and toll and Imd signaling pathway. The previous identified platelet-derived immune mediators such as P2Y1, P2Y12, PF4, GPIbα, CD40L, ICAM2, CCL5 (RANTES), TGF-β (TGF-β1 and TGF-β2) and PDGF (PDGFB and PDGFA) are also included in these over expressed mRNAs, implying these factors may trigger inflammatory cascades and promote the development of AS. Additionally, we found two down-regulated circRNA (circPTPN22 and circFCHSD2) from the intersection analyses of platelets and spinal ligament tissues of AS patients. The circRNA-miRNA-mRNA regulatory network of these two circRNAs was constructed, and the target mRNAs were enriched in Th17 cell differentiation, inflammatory bowel disease, cell adhesion molecules, cytokine-cytokine receptor interaction, Jak-STAT and Wnt signaling pathway, all these pathways participate in the bone remodeling and pro/anti-inflammatory immune regulation in AS. Then, qRT-PCR was performed to validate the expression of selected key mRNAs and circRNAs and the results demonstrated that the expression levels of P2Y12, GPIbα, circPTPN22 and circFCHSD2 were consistent with the sequencing analysis. In addition, the high expression of five predicted miRNAs interacting with circPTPN22 and circFCHSD2 were also detected in AS by qRT-PCR. Taken together, our study presents a comprehensive overview of mRNAs and circRNAs in platelets in AS patients and offers new insight into the mechanisms of platelet involving in the pathogenesis of AS. The mRNAs and circRNAs identified in this study may serve as candidates for diagnosis and targeted treatment of AS.
The goal of this paper is to present a set of measurements from a benchmark structure containing two bolted joints to support future efforts to predict the damping due to the joints and to model nonlinear coupling between the first two elastic modes. Bolted joints introduce nonlinearities in structures, typically causing a softening in the natural frequency and an increase in damping because of frictional slip between the contact interfaces within the joint. These nonlinearities pose significant challenges when characterizing the response of the structure under a large range of load amplitudes, especially when the modal responses become coupled, causing the effective damping and natural frequency to not only depend on the excitation amplitude of the targeted mode, but also the relative amplitudes of other modes. In this work, two nominally identical benchmark structures, known in some prior works as the S4 beam, are tested to characterize their nonlinear properties for the first two elastic modes. Detailed surface measurements are presented and validated through finite element analysis and reveal distinct contact interactions between the two sets of beams. The free-free test structures are excited with an impact hammer and the transient response is analyzed to extract the damping and frequency backbone curves. A range of impact amplitudes and drive points are used to isolate a single mode or to excite both modes simultaneously. Differences in the nonlinear response correlate with the relative strength of the modes that are excited, allowing one to characterize mode coupling. Each of the beams shows different nonlinear properties for each mode, which is attributed to the different contact pressure distributions between the parts, although the mode coupling relationship is found to be consistent between the two. Lastly, the test data key finding are presented in this paper and the supporting data is available on a public repository for interested researchers.
Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.
In 2020, NASA will launch out into the first step of what may be an ambitious, multi-mission campaign with the goal to bring back for analysis in laboratories here to Earth a collection of rock, regolith, and atmosphere samples from Mars. It is the main objective of this endeavor to determine whether or not Mars was ever host to ancient microbial life. Finding evidence for extinct (or extant) life on Mars has the potential to fundamentally alter our view of the origin and evolution of life. It would provide proof for Earth being just one amongst others regarding the question of biological activity in the Solar System. Furthermore, returned Martian samples would provide a rich repository of information on the history of planetary accretion and differentiation, geochronology, atmospheric evolution, and the paleo-environmental history of Mars, adding tremendous additional value to such a mission beyond the issue of extraterrestrial life. Here the potential knowledge to be obtained by such a collection of samples will equal or exceed that of the Apollo lunar sample archive, which even today yields new insights on the history of the Solar System, over 40 years after they were returned to Earth.