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Proceedings for the Workshop on Applied Nuclear Data Activities 2025

The 2025 Workshop for Applied Nuclear Data Activities (WANDA) covered four topic areas in nuclear data: Nuclear Data and Deterrence, Nuclear Data Prioritization for Fusion, High-Assay Low-Enriched Uranium and Novel Moderators for Advanced Reactors, and Data Preservation and Data Workflows. The intention of this workshop is to connect different communities that are invested in nuclear data and have their own unique sets of needs for the purposes of sharing information, fostering collaboration in areas of shared interest, and leveraging synergistic capabilities. The attendance of federal program managers at these workshops is essential in creating awareness of the needs of their respective communities and in providing information to better guide funding investments. In each of these topical sessions, a general description of the nuclear data needs and/or capabilities was presented, along with discussions of existing capabilities that could be leveraged, potential synergistic needs or resources, and challenges that must be overcome for the application space to progress. There are many synergistic nuclear data needs among these application spaces. The discussion largely focused on increasing the accuracy of the nuclear data and better quantifying the data uncertainties that have the greatest impact on applications. The full-day session on data processing and data workflows was by nature intended to be synergistic and applicable to all technical sessions at WANDA. A notable common theme that was highlighted across all sessions was the need for accelerated delivery of nuclear data products across complex and time-consuming workflows.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Proceedings for the Workshop on Applied Nuclear Data Activities 2024

The Workshop for Applied Nuclear Data Activities (WANDA) is designed to increase communication among nuclear data (ND) users in multidisciplinary federal programs, ND producers, ND funders, and other ND experts. It also presents an opportunity to cross-pollinate ideas as well as introduce ND gaps identified by federal programs to ND experts and ND capabilities to the various federal ND users. WANDA 2024 included five technical sessions, three of which focused on Fusion Energy Sciences (FES)—FES Fusion Neutronics, FES Tritium Production, and FES Material Damage—and two stand-alone sessions—Isotopes and Targetry for Nuclear Data and Uncertainty Quantification. The FES sessions successfully brought new voices to the WANDA discussions, expanding the application space in which nuclear data are critical. FES programs need accurate nuclear data with realistic uncertainty quantification to properly estimate, for example, shielding, activation, tritium production, helium production, structural material integrity, and superconducting magnet operation. This includes a variety of projectile (neutrons, photons, charged particles) and target atoms. One of the action items common to all the FES sessions was a need to perform sensitivity studies to identify the prioritization of nuclear data needs. The Isotopes and Targetry session highlighted the many capabilities available to produce high-quality targets for nuclear data measurements, including 3D printing with spherical powders, combustion synthesis coupled with spin coating & electrospraying, inkjet printing, and isotopic doping. These new methods open doors for more accurate measurement, but it was also stressed that sample characterization following any method of fabrication is of the highest importance to accurately interpret nuclear data measurement results that used that sample. The Uncertainty Quantification (UQ) session was broken into two categories: nuclear data uncertainty quantification and the use of that uncertainty quantification. Thematic to the UQ session was the loss of information when going from nuclear data measurement, to evaluation, to evaluated file, and finally to neutron transport calculations. Current evaluated ND libraries typically only contain covariances, which assume that the probability distributions are Gaussian. Beyond being a simplified assumption for many evaluations, this can lead to negative values on many observables when attempting to sample the covariance. The covariance format, however, is very efficient in that a simple set of linear equations can transform uncertainty from parameters or cross sections to the application of interest. Focused collaboration is needed between nuclear data evaluators and nuclear data users to ensure that needs are being met.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Final Report for the Workshop for Applied Nuclear Data Activities

The Workshop for Applied Nuclear Data Activities (WANDA) was held at the Elliot School of International Affairs at George Washington University from 22 to 25 January 2019. The purpose of WANDA was to bring subject matter experts from the national laboratories, universities and industry together with government program managers and their advisors to develop collaborative plans of action (e.g., roadmaps ) to address outstanding issues in nuclear data that effect applications ranging from nuclear energy to national security and nonproliferation to isotope production. WANDA was the latest in a series of collaborative, cross-programmatic workshops focused on nuclear data needs for applications that started in 2015 with the Nuclear Data Needs and Capabilities for Applications Workshop (NDNCA) and continuing with the Nuclear Data Roadmapping Enhancement Workshop (NDREW) in 2018 focused on nuclear data needs for applications. NDREW and WANDA played an important role in providing guidance for the recently created Nuclear Data Interagency Working Group (NDIAWG) comprised of program managers from a panoply government agencies. The NDIAWG issued two Funding Opportunity Announcements (FOA) in FY2017 and FY2018 which are supporting collaborative efforts to address high-priority nuclear data needs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Global Solar Activity Data Portal for Studying 3D Dynamics and Activity of the Sun

The main problems with understanding and predicting solar activity are tightly linked to limitations in describing the global evolution of the Sun from the deep interior to the corona. Because of the complexity of interactions in a wide range of dynamical, turbulent, and spatial scales and dramatic changes in thermodynamic and magnetic field conditions, only physics-based models can provide essential background to generate reliable solar activity forecasts. However, performing accurate model calibration and estimating uncertainties is often challenging due to the unavailability of a long time series of observations. To mitigate these limitations, we have developed the Global Solar Activity (GSA) Data Portal, which enables convenient access to a variety of modern and historical data. The portal supports a dynamic visualization for 1D time series (such as the sunspot number and solar irradiance) and quick-look visualization for 2D datasets (e.g., synoptic magnetograms, the solar internal rotation, and flows). The GSA portal includes a search engine that enables data retrieval from user-specified data sources and time intervals. In this presentation, we will discuss the current and upcoming capabilities of the data portal and its potential applications for space weather studies.

SMD↗

Development of the Self-Powered Extravehicular Mobility Unit Extravehicular Activity Data Recorder

The Self-Powered Extravehicular Mobility Unit (EMU) Extravehicular Activity (EVA) Data Recorder (SPEEDR) is a field-programmable gate array (FPGA)-based device designed to collect high-rate EMU Primary Life Support Subsystem (PLSS) data for download at a later time. During EVA, the existing EMU PLSS data downlink capability is one data packet every 2 minutes and is subject to bad packets or loss of signal. Higher-rate PLSS data is generated by the Enhanced Caution and Warning System but is not normally captured or distributed. Access to higher-rate data will increase the capability of EMU anomaly resolution team to pinpoint issues remotely, saving crew time by reducing required call-down Q&A and on-orbit diagnostic activities. With no Space Shuttle flights post Fiscal Year 2011 (FY11), and potentially limited down-mass capability, the ISS crew and ground support personnel will have to be capable of on-orbit operations to maintain, diagnose, repair, and return to service EMU hardware, possibly through 2028. Collecting high-rate EMU PLSS data during both intravehicular activity (IVA) and EVA operations will provide trending analysis for life extension and/or predictive performance. The SPEEDR concept has generated interest as a tool/technology that could be used for other International Space Station subsystems or future exploration-class space suits where hardware reliability/availability is critical and low/variable bandwidth may require store then forward methodology. Preliminary work in FY11 produced a functional prototype consisting of an FPGA evaluation board, custom memory/interface circuit board, and custom software. The SPEEDR concept includes a stand-alone battery that is recharged by a computer Universal Serial Bus (USB) port while data are being downloaded.

Bernard, Craig↗

Nuclear Data Activities Supporting MCNP [Slides]

Nuclear Data Team at LANL is working to improve nuclear data availability. These efforts include an updated, modernized, and improved website and a new, dynamic tool for examining what ACE data is on your machine.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

BOREAS TGB-12 Rn-222 Activity Data over the NSA

The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites to estimate the rates of carbon accumulation and turnover in each of the major vegetation types. Sampling strategies for soils were designed to take advantage of local fire chronosequences, so that the accumulation of carbon in regrowing mosses could be determined. All the data are used to: (1) calculate the inventory of carbon and nitrogen in moss and mineral soil layers at NSA sites, (2) determine the rates of input and turnover (using both accumulation since the last stand-killing fire and radiocarbon data), and (3) link changes in soil respiration rate to shifts in the C-14 content of soil CO2 to determine the average "age" respired CO2 . These Rn-222 activity data were collected from 15-Nov-1993 to 16-Aug-1994 over the NSA sites. They are useful for determining the rate of gas exchange between soil and the overlying atmosphere. The data in this data set are stored in tabular ASCII files.

Trumbore, Susan↗

Description of Data Archiving Activities

Data restoration and archiving activities for this project have resulted in the restoration of 100% of the original Mariner 9 raw data set as well as many of the secondary analysis data sets. These data sets have been submitted to the Planetary Data System (PDS) Atmospheric Node, long with their PDS labels and descriptive metadata.

Simmons, K. E.↗

Data for "Metabolic Engineering Strategies to Produce Medium-Chain Oleochemicals via Acyl-ACP:CoA Transacylase Activity"

Microbial lipid metabolism is an attractive route for producing oleochemicals. The predominant strategy centers on heterologous thioesterases to synthesize desired chain-length fatty acids. To convert acids to oleochemicals (e.g., fatty alcohols, ketones), the narrowed fatty acid pool needs to be reactivated as coenzyme A thioesters at cost of one ATP per reactivation – an expense that could be saved if the acyl-chain was directly transferred from ACP- to CoA-thioester. Here, we demonstrate such an alternative acyl-transferase strategy by heterologous expression of PhaG, an enzyme first identified in Pseudomonads, that transfers 3-hydroxy acyl-chains between acyl-carrier protein and coenzyme A thioester forms for creating polyhydroxyalkanoate monomers. We use it to create a pool of acyl-CoA’s that can be redirected to oleochemical products. Through bioprospecting, mutagenesis, and metabolic engineering, we develop three strains of Escherichia coli capable of producing over 1 g/L of medium-chain free fatty acids, fatty alcohols, and methyl ketones.

Bioproducts↗

Data for Sugar Accumulation Enhancement in Sorghum Stem is Associated with Reduced Reproductive Sink Strength and Increased Phloem Unloading Activity

Sweet sorghum has emerged as a promising source of bioenergy mainly due to its high biomass and high soluble sugar yield in stems. Studies have shown that loss-of-function Dry locus alleles have been selected during sweet sorghum domestication, and decapitation can further boost sugar accumulation in sweet sorghum, indicating that the potential for improving sugar yields is yet to be fully realized. To maximize sugar accumulation, it is essential to gain a better understanding of the mechanism underlying the massive accumulation of soluble sugars in sweet sorghum stems in addition to the Dry locus. We performed a transcriptomic analysis upon decapitation of near-isogenic lines for mutant (d, juicy stems, and green leaf midrib) and functional (D, dry stems and white leaf midrib) alleles at the Dry locus. Our analysis revealed that decapitation suppressed photosynthesis in leaves, but accelerated starch metabolic processes in stems. SbbHLH093 negatively correlates with sugar levels supported by genotypes (DD vs. dd), treatments (control vs. decapitation), and developmental stages post anthesis (3d vs.10d). D locus gene SbNAC074A and other programmed cell death-related genes were down regulated by decapitation, while sugar transporter-encoding gene SbSWEET1A was induced. Both SbSWEET1A and Invertase 5 were detected in phloem companion cells by RNA in situ assay. Loss of the SbbHLH093 homolog, AtbHLH093, in Arabidopsis led to a sugar accumulation increase. This study provides new insights into sugar accumulation enhancement in bioenergy crops, which can be potentially achieved by reducing reproductive sink strength and enhancing phloem unloading.

Transcriptomics↗

Building A Cloud Based Distributed Active Data Archive Center

NASA's Earth Science Data System (ESDS) Program facilitates the implementation of NASA's Earth Science strategic plan, which is committed to the full and open sharing of Earth science data obtained from NASA instruments to all users. The Earth Science Data information System (ESDIS) project manages the Earth Observing System Data and Information System (EOSDIS). Data within EOSDIS are held at Distributed Active Archive Centers (DAACs). One of the key responsibilities of the ESDS Program is to continuously evolve the entire data and information system to maximize returns on the collected NASA data.

Earth Science Informatics↗

Proceedings of the Workshop for Applied Nuclear Data Activities WANDA 2022

On February 28 – March 4, 2022, the Nuclear Data Interagency Working Group (NDIAWG) hosted the 5-day virtual Workshop for Applied Nuclear Data (WANDA2022) to facilitate interagency collaboration on nuclear data for applications. This year’s focus was nuclear data for space applications, but also included photon reactions and transport, reactions on unstable nuclei, and data adjustment topics. The annual WANDA workshops are planned by the Nuclear Data Working Group (NDWG) with the goal of assembling users and producers of nuclear data to provide input to identify and prioritize nuclear data needs and to suggest solutions to address those needs. The workshop consisted of talks by agency program managers, six topic focused road mapping sessions and a review of NDIAWG-funded projects. More than 350 attendees represented national laboratories, universities, and federal agencies, as well as international organizations and industry. The proceedings presented herein summarize the workshop’s content, highlight important outcomes, and document attendees’ recommendations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Analysis, review, and documentation of the activation data from LDEF material

Samples removed from Long Duration Exposure Facility (LDEF-1) are being studied at various laboratories to determine the specific activity(pCi/kg) produced in orbit by exposure to protons and neutrons in near-Earth orbit. These activities are being corrected for efficiency, self-attenuation, and background. The activities and associated gamma-ray spectra are being collected, analyzed, documented and reviewed by faculty and graduate students at Eastern Kentucky University. The currently available activation results have been tabulated and reviewed in this report. Approximately 500 spectra have been accumulated for future archival and analysis. The effect of the changing satellite orbit on the activation is reported herein and was calculated using more recent estimates of the flux of Van Allen belt protons.

Laird, C. E.↗

Software Displays Data on Active Regions of the Sun

The Solar Active Region Display System is a computer program that generates, in near real time, a graphical display of parameters indicative of the spatial and temporal variations of activity on the Sun. These parameters include histories and distributions of solar flares, active region growth, coronal mass ejections, size, and magnetic configuration. By presenting solar-activity data in graphical form, this program accelerates, facilitates, and partly automates what had previously been a time-consuming mental process of interpretation of solar-activity data presented in tabular and textual formats. Intended for original use in predicting space weather in order to minimize the exposure of astronauts to ionizing radiation, the program might also be useful on Earth for predicting solar-wind-induced ionospheric effects, electric currents, and potentials that could affect radio-communication systems, navigation systems, pipelines, and long electric-power lines. Raw data for the display are obtained automatically from the Space Environment Center (SEC) of the National Oceanic and Atmospheric Administration (NOAA). Other data must be obtained from the NOAA SEC by verbal communication and entered manually. The Solar Active Region Display System automatically accounts for the latitude dependence of the rate of rotation of the Sun, by use of a mathematical model that is corrected with NOAA SEC active-region position data once every 24 hours. The display includes the date, time, and an image of the Sun in H light overlaid with latitude and longitude coordinate lines, dots that mark locations of active regions identified by NOAA, identifying numbers assigned by NOAA to such regions, and solar-region visual summary (SRVS) indicators associated with some of the active regions. Each SRVS indicator is a small pie chart containing five equal sectors, each of which is color-coded to provide a semiquantitative indication of the degree of hazard posed by one aspect of the activity at the indicated location. The five aspects in question are the history of solar flares, the history of coronal mass ejections, the growth or decay of activity, the overall size, and the magnetic configuration. Mouse-clicking on an active-region-marking dot, SRVS indicator, or NOAA region number causes the program to generate a solar-region summary table (SRT) for the active region in question. The SRT contains additional quantitative and qualitative data, beyond those contained in the SRVS: These data include the solar coordinates of the region, the area of the region and its change in area during the past 24 hours, the change in the number of sunspots in the region during the past 24 hours, the magnetic configuration, and the types, dates, and times of the most recent flare and coronal mass ejection.

Golightly, Mike↗

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

97 MATHEMATICS AND COMPUTING↗

Collection, analysis, and archival of LDEF activation data

Samples intentionally placed aboard the LDEF and samples obtained from the LDEF structure were studied at NASA Marshall and seven national labs to determine the radioactivity produced in orbit. The gamma ray spectra from these studies have provided information concerning the type and quantity of radioactive nuclei produced by various activating particles. The gamma ray spectra, the resulting activation, and the experimental arrangements are being collected at Marshall and Eastern Kentucky Univ. for review, further analysis, and future archival. An overview of this process and the type of information that will be available for future reference is given. This information includes the samples studied, the location of the samples on LDEF, the amount and type of covering material, the types of detector systems, the format of the gamma ray spectra, and the corrections for geometry, self-absorption, detector efficiency, and background needed to obtain accurate specific activations (activation per kilogram) of material. Plans are given as to the archival of the data for such future reference and how other scientific investigators or spacecraft designers can access the data.

Laird, C. E.↗

Mesoscopic approach to modeling electric vehicle fleets based upon driving activity data to investigate recharge strategies’ impact on grid loads

This paper presents a mesoscopic model to populate a set of sub-fleets of battery electric vehicles (BEVs) that simulates how grid power demand changes with varied mixes of different charging strategies. The proposed model considers real driving activities of vehicles based on the National Household Travel Survey (NHTS) and various charging strategies while parked (at home and in other locations). To be easily scaled to any fleet size, a mesoscopic model is implemented, where BEVs with the same physical properties and driving schedules and clustered together into sub-fleets. The degree of granularity is explored for stable results. Monte Carlo simulations were run to demonstrate the potential of this model's use in grid loads under various charging strategies. Furthermore, an example cost function was optimized to demonstrate finding optimal allocations of charging strategies.

Electric Vehicles, Grid Integration, Monte Carlo S↗