Results for “nuclear data evaluation” — Page 98

DOE OSTI2020

Expectation-propagation for weak radionuclide identification at radiation portal monitors

We propose a sparsity-promoting Bayesian algorithm capable of identifying radionuclide signatures from weak sources in the presence of a high radiation background. The proposed method is relevant to radiation identification for security applications. In such scenarios, the background typically consists of terrestrial, cosmic, and cosmogenic radiation that may cause false positive responses. We evaluate the new Bayesian approach using gamma-ray data and are able to identify weapons-grade plutonium, masked by naturally-occurring radioactive material (NORM), in a measurement time of a few seconds. We demonstrate this identification capability using organic scintillators (stilbene crystals and EJ-309 liquid scintillators), which do not provide direct, high-resolution, source spectroscopic information. Compared to the EJ-309 detector, the stilbene-based detector exhibits a lower identification error, on average, owing to its better energy resolution. Organic scintillators are used within radiation portal monitors to detect gamma rays emitted from conveyances crossing ports of entry. The described method is therefore applicable to radiation portal monitors deployed in the field and could improve their threat discrimination capability by minimizing “nuisance” alarms produced either by NORM-bearing materials found in shipped cargoes, such as ceramics and fertilizers, or radionuclides in recently treated nuclear medicine patients.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2022

Impact of Increased Latent Generations on Sensitivity Calculations with SCALE

Analyses of cross section sensitivity data from systems with fissile material allow analysts to associate an importance for each material, nuclide, reaction, and neutron energy by simulating real world criticality scenarios. Although criticality safety validation efforts can be guided by the cross-section sensitivity and uncertainty data generated for a particular system, these calculations can often be computationally expensive and sometimes cumbersome without proper guidance. The TSUNAMI suite within the SCALE code package has several methods for generating sensitivity data, including multigroup and continuous energy (CE) capabilities. The release of SCALE 6.3 has three different CE methods for generating cross section sensitivity data: (1) the Iterated Fission Probability (IFP) method with the KENO Monte Carlo transport solver, (2) the IFP method with the Shift Monte Carlo transport solver, and (3) the Contributon-Linked eigenvalue sensitivity/Uncertainty estimation via Tracklength importance CHaracterization (CLUTCH) method with the KENO Monte Carlo transport solver. Although the CLUTCH method has additional parameters for generating sensitivity data files relative to the IFP method, all three methods use latent generations, which are the generations between an event (i.e., fission) and the assessment of importance based on the asymptotic population of progeny neutrons. Increasing the number of latent generations in a calculation leads to increased discrimination of the sensitivity coefficients but at the cost of the increased uncertainty associated with those generated values. Analysts must balance the accuracy of the sensitivity calculations and its uncertainty with the associated computational cost involved in generating the values. This paper discusses the impact of adjusting the latent generation parameter for a range of sensitivity values and how these changes compare with the direct perturbation values obtained from a change of ±0.5% Δ k in both benchmark and safety application models. Two benchmarks from the International Handbook of Evaluated Criticality Safety Benchmark Experiments and the MPC-32 dual purpose canister for spent nuclear fuel are used for analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

GDSA Framework Development and Process Model Integration FY2022

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is disposal system modeling (Sassani et al. 2021). The SFWST Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2022 advances of the Geologic Disposal Safety Assessment (GDSA) performance assessment (PA) development groups of the SFWST Campaign. The common mission of these groups is to develop a geologic disposal system modeling capability for nuclear waste that can be used to assess probabilistically the performance of generic disposal options and generic sites. The modeling capability under development is called GDSA Framework (pa.sandia.gov). GDSA Framework is a coordinated set of codes and databases designed for probabilistically simulating the release and transport of disposed radionuclides from a repository to the biosphere for post-closure performance assessment. Primary components of GDSA Framework include PFLOTRAN to simulate the major features, events, and processes (FEPs) over time, Dakota to propagate uncertainty and analyze sensitivities, meshing codes to define the domain, and various other software for rendering properties, processing data, and visualizing results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Iterative Bragg peak removal on X-ray absorption spectra with automatic intensity correction

This study introduces a novel iterative Bragg peak removal with automatic intensity correction (IBR-AIC) methodology for X-ray absorption spectroscopy (XAS), specifically addressing the challenge of Bragg peak interference in the analysis of crystalline materials. The approach integrates experimental adjustments and sophisticated post-processing, including an iterative algorithm for robust calculation of the scaling factor of the absorption coefficients and efficient elimination of the Bragg peaks, a common obstacle in accurately interpreting XAS data, particularly in crystalline samples. The method was thoroughly evaluated on dilute catalysts and thin films, with fluorescence mode and large-angle rotation. The results underscore the technique's effectiveness, adaptability and substantial potential in improving the precision of XAS data analysis. While demonstrating significant promise, the method does have limitations related to signal-to-noise ratio sensitivity and the necessity for meticulous angle selection during experimentation. Overall, IBR-AIC represents a significant advancement in XAS, offering a pragmatic solution to Bragg peak contamination challenges, thereby expanding the applications of XAS in understanding complex materials under diverse experimental conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Mesoscale modeling to inform Bison models of accident tolerant fuel concepts

U 3 Si 2 and doped UO 2 have been under investigation in recent years as potential accident-tolerant fuel concepts. In this report, lower-length scale studies of these fuel concepts carried out during Fiscal Year 2020 are detailed. U 3 Si 2 is a potential accident-tolerant fuel that shows promise due to its high thermal conductivity and higher uranium density relative to UO 2 . However, its swelling and fission gas release behavior in light water reactor (LWR) conditions is relatively unknown. To provide mechanistic insight and determine parameters for engineering-scale fuel performance modeling of pellet-form U 3 Si 2 , phase-field simulations of the growth, interconnection, and venting of intergranular fission gas bubbles were performed. The fractional coverage of the grain boundary and the fraction of bubble area that is vented were calculated as a function of time. From the simulation data, the fractional grain boundary coverage at saturation, an important parameter needed in engineering-scale modeling of swelling and fission gas release, was determined. Multiple simulations were run to determine the uncertainty in the calculated value. The effect of model assumptions and input parameters that are not well known was evaluated. Simulation results are compared to related theoretical and computational work. Based on the simulation results, a value of 0.60 for the fractional grain boundary coverage at saturation is recommended for U 3 Si 2 fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Modeling and Simulation of Fuel Dispersal During the Loss-of-Coolant Accident

This document is the compilation of the milestone portion to a larger end of project NEUP report. The executive summary of the modeling portion is provided below: In the event of cladding rupture during a postulated LOCA in a pressurized water reactor, fuel particles, along with fission gases, can be expelled into the reactor core from the fractured fuel rod, a phenomenon referred to as fuel dispersal. The initial stage of fuel dispersal is strongly influenced by the high-pressure ejection of fuel fragments, the size and geometry of the ruptured cladding, and the depressurization history of the fuel rod during the postulated LOCA transient. Depending on the location of the burst orifice relative to the quench front, the dispersal event represents an intricate three-phase flow and heat transfer phenomenon, where high-temperature fuel particles carried by the fission gases interact with the coolant within the narrow subchannels of the fuel assemblies, inducing localized phase change. Given the unique multiphysics nature of this phenomena, the current study develops a dedicated computational framework to predict the mass distribution and cooling of dispersing fuel particles, facilitating post-accident assessment and management of the fuel assemblies. Considering the scale of nuclear reactor applications, a continuum three-fluid model is proposed for simulating the transport of solids within the reactor core. With high-temperature fuel fragments within the liquid media, nucleation sites inducing phase changes are dispersed within the flow domain. Coupled with the fact that the transient dispersal event occurs on different time scales than other three-phase flow applications, this study derives a time-averaged three-fluid flow model without losing generality. The assumptions regarding the continuum treatment of the solid phase and the modeling of fuel dispersal behavior are incorporated to simplify the governing equations and derive applicable closure relations. The computational validation of the model was conducted using adiabatic experimental results obtained from ongoing research at Oregon State University, focusing on characterizing fuel dispersal behavior during simulated LOCA conditions. Settlement characteristics of the solids, quantified by the probability distribution of equivalent particles, closely matched the probability density functions reported in experimental studies. The transport of fuel particles within a scaled 5 × 5 lattice of a pressurized-water reactor rod bundle geometry was modeled through a two-fluid Eulerian framework. The required boundary conditions were evaluated from the fuel performance code BISON in a postulated large-break LOCA scenario. The modeling framework considered solid fuel particles as granular matter, interacting with the gaseous dry steam phase and fission gases through the governing interfacial momentum exchange between the participating fluids. The simulation results provided the volume fraction of the solids obtained at the bottom surface of the enclosing tank geometry. Postulated LOCA leading to fuel dispersal phenomena involves the strong coupling between fuel thermomechanics, cladding deformation, thermal-hydraulics, and fuel particle transport. Incorporation of such a strong coupling in numerical simulation is performed by coupling the multiphysics solvers. In the case of fuel dispersal, a strong coupled simulation can be performed by coupling the BISON code for fuel performance, the TRACE code for system-level thermal hydraulics, and fuel particle transport in Multiphysics Object-Oriented Simulation Environment (MOOSE). For such intricate infrastructure, the MOOSE Framework eases the data transfer between codes. The recent version of MOOSE has incorporated the Navier-Stokes module for the fluid flow. An exploratory exercise was done to gain familiarity with finite volume capabilities in the MOOSE framework to incorporate the Spalart-Allmaras (SA) turbulence model. New finite-volume and auxiliary kernels were introduced to assemble the SA transport equation, compute turbulent viscosity, and evaluate wall distance and diagnostic turbulence terms, fully integrated with existing Navier-Stokes modules. A turbulent lid-driven cavity at a Reynolds number of approximately 10,000 is used for verification. MOOSE shows the robust solver convergence and produces the turbulent features. But it underpredicts the velocity profile and turbulent quantities, emphasizing the need to develop improved SA near-wall treatments (e.g., low-Re corrections or wall functions) as a key direction for future work.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRSDate not supplied

Effect of Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for 1st Generation Nuclear Thermal Propulsion Flight Test Systems

Following the first Nuclear Thermal Propulsion (NTP) system test, also known as DRACO, the next NTP system to be developed would be the 1st Generation NTP system. An analysis was conducted to determine the performance of different vehicle configurations utilizing hydrogen (H-NTP), ammonia (A-NTP), and methane (M-NTP) as propellants launched onboard commercial launch vehicles. This was enabled by a quasi-steady-state power balance engine model and vehicle component physics that sized the vehicle system using Master Equipment List (MEL) parameters. The analysis considered configuration cases outlined by a matrix of different mission classes and vehicle configurations that covered the design space of the 1st Generation NTP system to explore various propellant options, engine architectures, and mission scopes. Four mission classes were evaluated which included single burn missions performing maneuvers having a ΔV of 1 km/s and 2.5 km/s followed by 2-burn and 4-burn missions that aimed to exhaust the launch vehicles volume or mass limitations. In all cases, the NTP systems with the lowest thrust class had the longest burn time of which A-NTP and M-NTP systems provided the longest and shortest burn times depending on the launch vehicle used while H-NTP systems tended to cluster together in the middle. Longer burn times could be useful as a testing platform to increase the time for data accumulation. Across the multi-burn cases, H-NTP systems were found to be volume limited while A-NTP and M-NTP systems were mass limited. Both single burn missions showed that A-NTP configurations provided the lowest dry mass given that ammonia had the highest density with comparable performance to M-NTP systems and no requirement for cryocoolers. The results showed that beyond the propellant selection type, the launch vehicle selection, which included Starship, New Glenn, Vulcan, Falcon 9 (recoverable), and Falcon Heavy (recoverable), was a primary driving factor in the test vehicle’s capabilities. Trends were determined based on a set of dimensionless parameters that included the mass ratio of inert mass to initial wetted mass, ratio of specific impulse to burn time, and a dimensionless engine parameter (ratio of impulse to engine momentum). These relationships found the “knees-in-the-curves” that could be a significant point of reference for the designer as they indicate a change in the trend which is located at a specific impulse to burn time ratio of 1, a mass ratio of 0.6, and an engine performance parameter of 4. This study did not attempt to make a recommendation rather provide the tools for the reader to select their own configuration based on their needs.

Propellant↗

NASA NTRSDate not supplied

Effect of Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for 1st Generation Nuclear Thermal Propulsion Flight Test Systems

Following the first Nuclear Thermal Propulsion (NTP) system test, also known as DRACO, the next NTP system to be developed would be the 1st Generation NTP system. An analysis was conducted to determine the performance of different vehicle configurations utilizing hydrogen (H-NTP), ammonia (A-NTP), and methane (M-NTP) as propellants launched onboard commercial launch vehicles. This was enabled by a quasi-steady-state power balance engine model and vehicle component physics that sized the vehicle system using Master Equipment List (MEL) parameters. The analysis considered configuration cases outlined by a matrix of different mission classes and vehicle configurations that covered the design space of the 1st Generation NTP system to explore various propellant options, engine architectures, and mission scopes. Four mission classes were evaluated which included single burn missions performing maneuvers having a ΔV of 1 km/s and 2.5 km/s followed by 2-burn and 4-burn missions that aimed to exhaust the launch vehicles volume or mass limitations. In all cases, the NTP systems with the lowest thrust class had the longest burn time of which A-NTP and M-NTP systems provided the longest and shortest burn times depending on the launch vehicle used while H-NTP systems tended to cluster together in the middle. Longer burn times could be useful as a testing platform to increase the time for data accumulation. Across the multi-burn cases, H-NTP systems were found to be volume limited while A-NTP and M-NTP systems were mass limited. Both single burn missions showed that A-NTP configurations provided the lowest dry mass given that ammonia had the highest density with comparable performance to M-NTP systems and no requirement for cryocoolers. The results showed that beyond the propellant selection type, the launch vehicle selection, which included Starship, New Glenn, Vulcan, Falcon 9 (recoverable), and Falcon Heavy (recoverable), was a primary driving factor in the test vehicle’s capabilities. Trends were determined based on a set of dimensionless parameters that included the mass ratio of inert mass to initial wetted mass, ratio of specific impulse to burn time, and a dimensionless engine parameter (ratio of impulse to engine momentum). These relationships found the “knees-in-the-curves” that could be a significant point of reference for the designer as they indicate a change in the trend which is located at a specific impulse to burn time ratio of 1, a mass ratio of 0.6, and an engine performance parameter of 4. This study did not attempt to make a recommendation rather provide the tools for the reader to select their own configuration based on their needs.

Propellant↗

NASA NTRSDate not supplied

Effect of Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for 1st Generation Nuclear Thermal Propulsion Flight Test Systems

Following the first Nuclear Thermal Propulsion (NTP) system test, also known as DRACO, the next NTP system to be developed would be the 1st Generation NTP system. An analysis was conducted to determine the performance of different vehicle configurations utilizing hydrogen (H-NTP), ammonia (A-NTP), and methane (M-NTP) as propellants launched onboard commercial launch vehicles. This was enabled by a quasi-steady-state power balance engine model and vehicle component physics that sized the vehicle system using Master Equipment List (MEL) parameters. The analysis considered configuration cases outlined by a matrix of different mission classes and vehicle configurations that covered the design space of the 1st Generation NTP system to explore various propellant options, engine architectures, and mission scopes. Four mission classes were evaluated which included single burn missions performing maneuvers having a ΔV of 1 km/s and 2.5 km/s followed by 2-burn and 4-burn missions that aimed to exhaust the launch vehicles volume or mass limitations. In all cases, the NTP systems with the lowest thrust class had the longest burn time of which A-NTP and M-NTP systems provided the longest and shortest burn times depending on the launch vehicle used while H-NTP systems tended to cluster together in the middle. Longer burn times could be useful as a testing platform to increase the time for data accumulation. Across the multi-burn cases, H-NTP systems were found to be volume limited while A-NTP and M-NTP systems were mass limited. Both single burn missions showed that A-NTP configurations provided the lowest dry mass given that ammonia had the highest density with comparable performance to M-NTP systems and no requirement for cryocoolers. The results showed that beyond the propellant selection type, the launch vehicle selection, which included Starship, New Glenn, Vulcan, Falcon 9 (recoverable), and Falcon Heavy (recoverable), was a primary driving factor in the test vehicle’s capabilities. Trends were determined based on a set of dimensionless parameters that included the mass ratio of inert mass to initial wetted mass, ratio of specific impulse to burn time, and a dimensionless engine parameter (ratio of impulse to engine momentum). These relationships found the “knees-in-the-curves” that could be a significant point of reference for the designer as they indicate a change in the trend which is located at a specific impulse to burn time ratio of 1, a mass ratio of 0.6, and an engine performance parameter of 4. This study did not attempt to make a recommendation rather provide the tools for the reader to select their own configuration based on their needs.

Propellant↗

DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗

DOE OSTI2026

Modern insights into the mechanisms of neptunium oxalate decomposition

Neptunium oxalate (Np(C 2 O 4 ) 2 ·6H 2 O) is a historically relevant solid phase used in nuclear processing as a precursor for neptunium dioxide (NpO 2 ). Although Np oxalate has been synthesized and used for NpO 2 production for decades, the thermal decomposition mechanism of this phase remains poorly understood and has not been evaluated in over 30 years. Conflicting reports in historical literature suggest either a direct conversion from anhydrous oxalate to NpO 2 or a decomposition that includes the formation of Np carbonate or oxidized Np intermediate phases. In this work, we reexamine the decomposition pathway of Np(C 2 O 4 ) 2 ·6H 2 O using thermal analysis coupled with evolved gas analysis and temperature-dependent Raman spectroscopy to elucidate decomposition mechanisms and intermediate phases using modern analytical techniques. Thermal analysis revealed a three-stage decomposition process, including dehydration below 200 °C, oxalate breakdown between 170 and 370 °C, and NpO 2 formation by 500 °C. However, an unidentified plateau in the thermal data was observed during measurements. Raman spectroscopy confirmed the stages of decomposition, and in the analysis of potential intermediate phases, no carbonate phases or Np 2 O 5 were identified. Raman data suggest that residual oxalate or nonstoichiometric oxide are present during decomposition before pure NpO 2 is formed. These findings clarify aspects of the Np oxalate decomposition mechanism and address longstanding discrepancies in the literature, with a specific focus on Np-specific materials chemistry.

Lawson, Kathryn M. [Oak Ridge National Laboratory ↗

NASA NTRS2015

Practical Applications of Cosmic Ray Science: Spacecraft, Aircraft, Ground-Based Computation and Control Systems, Exploration, and Human Health and Safety

In this presentation a review of galactic cosmic ray (GCR) effects on microelectronic systems and human health and safety is given. The methods used to evaluate and mitigate unwanted cosmic ray effects in ground-based, atmospheric flight, and space flight environments are also reviewed. However not all GCR effects are undesirable. We will also briefly review how observation and analysis of GCR interactions with planetary atmospheres and surfaces and reveal important compositional and geophysical data on earth and elsewhere. About 1000 GCR particles enter every square meter of Earth’s upper atmosphere every second, roughly the same number striking every square meter of the International Space Station (ISS) and every other low- Earth orbit spacecraft. GCR particles are high energy ionized atomic nuclei (90% protons, 9% alpha particles, 1% heavier nuclei) traveling very close to the speed of light. The GCR particle flux is even higher in interplanetary space because the geomagnetic field provides some limited magnetic shielding. Collisions of GCR particles with atomic nuclei in planetary atmospheres and/or regolith as well as spacecraft materials produce nuclear reactions and energetic/highly penetrating secondary particle showers. Three twentieth century technology developments have driven an ongoing evolution of basic cosmic ray science into a set of practical engineering tools needed to design, test, and verify the safety and reliability of modern complex technological systems and assess effects on human health and safety effects. The key technology developments are: 1) high altitude commercial and military aircraft; 2) manned and unmanned spacecraft; and 3) increasingly complex and sensitive solid state micro-electronics systems. Space and geophysical exploration needs drove the development of the instruments and analytical tools needed to recover compositional and structural data from GCR induced nuclear reactions and secondary particle showers. Finally, the possible role of GCR secondary particle showers in addressing an important homeland security problem, finding nuclear contraband and weapons, will be briefly reviewed.

Koontz, Steve↗

DOE OSTI2021

Oxidation behaviors of matrix-grade graphite during water vapor ingress accidents for high temperature gas-cooled reactors

Water ingress into cores of high temperature gas-cooled reactors (HTGRs) can cause serious safety problems in graphitic components, especially for matrix graphite, which is more vulnerable than nuclear graphite. Here in this study, oxidation behaviors of a matrix-grade graphite, ARB-B1, were investigated under simulated water ingress accidental conditions. The oxidation tests were conducted in water vapor/helium mixed atmospheres at elevated temperatures up to 1200 °C. Oxidation rates and activation energy were evaluated by mass loss measurements. The activation energy was 121.5 kJ/mol in the considered temperature range, indicating only one oxidation regime-in-pore diffusion assisted by cracks. The cracks originated from the fabrication process, acting as penetration paths for the oxidant, played an important role in the oxidation behaviors. Ungraphitized binder oxidation occurred at all temperatures, while apparent oxidation of filler particles started from 1100 °C. Moreover, oxidation effects on compressive properties and hardness of the matrix graphite were discussed in terms of oxidized microstructures. This first detailed study on matrix graphite oxidation in water vapor provides important kinetics knowledge and microstructural and mechanical data for accidental scenarios of HTGRs as well as for development of new matrix graphite materials.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Application of Principal Component Analysis for the Monitoring of the Aging Process of Nuclear Electrical Cable Insulation

To ensure the safe operation of nuclear power plants (NPPs), it is critical to understand how NPP electrical cable insulation will degrade under different service environments. In this study, various nondestructive examination methods were selected to evaluate degradation of electrical cable insulation after aging. Elongation at break, indenter modulus, relaxation constant, mass change, total color difference, and carbonyl index were collected stepwise on cross-linked polyethylene (XLPE) cable insulations after predetermined exposure intervals. Three different insulation aging scenarios were investigated: 1) simultaneous irradiation and heating at 150°C, 2) heating at 150°C followed by corresponding times of irradiation at ambient temperature, and 3) irradiation at ambient temperature followed by heating at 150°C for the same duration. A constant dose rate of 300 Gy/hr was selected with a total gamma irradiation absorbed dose up to 320 kGy. To gain insight into the long-term performance of NPP electrical cable insulation, principal component analysis (PCA), a data-driven approach, was utilized to identify key indicators of cable insulation degradation. By reducing the dimensionality of the data while retaining degradation information, PCA was used to highlight the changes in the measured properties under gamma irradiation according to total absorbed dose and the different aging scenarios.

Li, Donghui↗

DOE OSTI2024

Considerations for Introducing Artificial Intelligence into Nuclear Power Plants

Advanced computational tools and techniques such as artificial intelligence and machine learning (AI/ML) can transform the nuclear power industry. This is necessary given that the economic viability of the existing fleet is in jeopardy and its labor-centric approach to operations and maintenance. Currently, AI/ML research is being undertaken for reactor system design and analysis including fault and accident prognosis, nuclear risk analysis such as plant safety and security evaluation, and plant operations and maintenance including predictive maintenance. Applications include both existing and advanced reactor technologies with the aim of improving operational and business efficiencies. Most every aspect of the organization can benefit, from instrumentation and control, to work planning, to human-machine interactions and business management. AI/ML in nuclear can simplify complex problems and produce more effective decision-making. Nonetheless, careful consideration must be given to the implementation of an AI/ML initiative. The aims of this research are to 1) review barriers to AI/ML adoption within the nuclear power industry, and 2) suggest potential solutions. These barriers are organized along five distinct categories (Figure 1) that are interconnected. The first are historical barriers that track the industry’s development over the decades including worldwide nuclear events that shaped public perceptions. The resulting federal scrutiny and intense safety culture that emerged are discussed. Technical barriers to AI/ML adoption are considerable, and include data privacy concerns, data governance, and the current lack of AI/ML expert knowledge at the plants. The main business case barrier remains cost, but an absence of an industry-wide vision and wide-scale adoption also produces reluctance. Stakeholder readiness is reviewed with special attention given to regulatory readiness. The 5-year strategic plan for AI readiness recently published by the U.S. Nuclear Regulatory Commission is highlighted. Last, adoption barriers at the user level are addressed including the importance of user experience and explainable AI. The AI adoption barriers described here are inter-related and ideally should be addressed in a holistic fashion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Evaluation of The NSUF Reactor Activation and Damage (RAD) Calculator: Assessing the Accuracy of the Activation Calculator

Nuclear Science User Facilities developed an activation calculator as part of the Combined Material Experiment Toolbox project. The activation calculator estimates the radionuclide concentration in an irradiated sample to indicate when and where the samples may best be examined to only help with scoping the experiment. To verify the accuracy of the calculator, the results from the specific gamma dose rate [mrem/hr/g] at various cooldown times were compared to equivalent results generated in ORIGEN. Most elements were tested as a sample composed solely of that element. These samples were exposed to thirteen different lengths of irradiation in all reactor positions included in the calculator. For elements of interest (i.e., ones currently included in the Nuclear Fuels and Materials Library), 83% of the test cases fell within the allowed tolerance for this scoping tool of ±50%. The overall error is logarithmic with irradiation length and is almost completely independent of reactor position. The primary source of error is missing nuclear data and reactions. NSUF believes that the RAD Calculator can be deployed to the user community because it is designed to provide estimates to aid researchers in planning experiments, and users are cautioned about the limitations of the calculator.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Neutrino event selection in the MicroBooNE liquid argon time projection chamber using Wire-Cell 3D imaging, clustering, and charge-light matching

An accurate and efficient event reconstruction is required to realize the full scientific capability of liquid argon time projection chambers (LArTPCs). The current and future neutrino experiments that rely on massive LArTPCs create a need for new ideas and reconstruction approaches. Wire-Cell, proposed in recent years, is a novel tomographic event reconstruction method for LArTPCs. The Wire-Cell 3D imaging approach capitalizes on charge, sparsity, time, and geometry information to reconstruct a topology-agnostic 3D image of the ionization electrons prior to pattern recognition. A second novel method, the many-to-many charge-light matching, then pairs the TPC charge activity to the detected scintillation light signal, thus enabling a powerful rejection of cosmic-ray muons in the MicroBooNE detector. A robust processing of the scintillation light signal and an appropriate clustering of the reconstructed 3D image are fundamental to this technique. In this paper, we describe the principles and algorithms of these techniques and their successful application in the MicroBooNE experiment. A quantitative evaluation of the performance of these techniques is presented. Using these techniques, a 95% efficient pre-selection of neutrino charged-current events is achieved with a 30-fold reduction of non-beam-coincident cosmic-ray muons, and about 80% of the selected neutrino charged-current events are reconstructed with at least 70% completeness and 80% purity.

3D imaging↗

DOE OSTI2021

Evaluation of Apex Alpha with LabWare-LIMS Data Reduction for Alpha Analyses

Savannah River Site's Environmental Bioassay Laboratory is migrating its laboratory information management system (LIMS) from SQL-LIMS to Oracle Labware-LIMS (LW-LIMS) systems to align with the Department of Energy's cyber security policies. Concurrently with the LIMS upgrade, the current VMS based Alpha Measurement System (AMS) software is being replaced with Apex Alpha software to reduce other cyber vulnerabilities, streamline procedural production aspects of data handling, and improve outdated reduction processes to align the program with requirements outlined in ANSI N13.30. The primary technical changes which are being implemented are: - Reagent contributions are added in data reduction, - Moving average background equivalent activity are used for gross signal corrections, - Measured uncertainty of both are included in the decision level and propagated uncertainty, - Tracer levels are increased for precision and accuracy improvements, and - Reports are modified for compliance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗