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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Updated Application of Frequency of Detection Methods for the INL Site Ambient Air Monitoring Network

This report presents a quantitative assessment of the current INL Site air monitoring network using frequency of detection (FD) methods. The first assessment of the INL network was performed in 2015 and made recommendations for improving the network. As a result of changes made in response to the recommendations and the addition of new source locations, the network was modified and reassessed in 2017. Since 2017, administration of the air sampling program has been consolidated under one contractor, which resulted in additional changes to the network (sampler numbers and locations) and changes in radionuclide detection levels. As a result of these changes and others, an updated assessment of the INL Site ambient air monitoring network was performed. The same two exposure scenarios used in previous assessments were used for this assessment: a resident scenario and a shepherd/rancher scenario. The resident was assumed to be continuously present at their residence/business/farm operation outside the INL Site boundary while the shepherd/rancher was assumed to be present 24-hours at the nearest INL grazing allotment boundary in each of the 22.5-degree sectors along the sector centerline from each source. Updates to both the resident and shepherd/rancher receptor locations were included. Other changes include updates to flow rates for stack sources, expansion of the list of important radionuclides based on the most recent National Emission Standards for Hazardous Pollutants (NESHAPs) analysis, and updated dose coefficients. The assessment was conducted to determine whether the current INL monitoring network is capable of detecting releases of important radionuclides from INL Site sources that have the potential to exceed a conservative dose threshold for the two exposure scenarios. The assessment revealed that for the resident scenario, the current network meets the desired performance objective (FD = 95%) for all radionuclides and sources except for Cl-36 from the TRA-770 stack (94.4%). For the shepherd/rancher scenario, the FD performance objective is met for all radionuclides and sources except tritium from MFC-774 and TAN 679 (91% for both). An investigation of reported emissions for the past three years revealed that Cl 36 is not emitted from TRA-770, and routine tritium emissions from MFC-774 and TAN-679 are very small and the sources are likely incapable of emitting enough tritium to cause a release that should be detectable by the monitoring network. This assessment is based on a conservative dose threshold. This coupled with fact that the FD for Cl 36 is only slightly less than the performance objective and Cl-36 is not emitted from TRA-770, modifying the network (i.e. adding another sampler, increasing sampler flow rate, moving samplers) to meet the 95% performance objective for this radionuclide/source/receptor scenario is not warranted. Similarly, because tritium emissions from MFC-774 and TAN-679 are very small and these two sources are likely incapable of causing a dose due to tritium release that should be detectable by the network, modifications to increase tritium detection for these sources is also unwarranted at this time. However, if it is required to meet the performance objective for tritium for all sources and receptor scenarios, additional analysis determined the FD could be raised from 91% to > 99% for both sources by adding two tritium samplers to the network.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

A Comparison of Groundwater Sampling Technologies, Including Passive Diffusion Sampling, for Radionuclide Contamination - 20205

Using traditional high-flow purge methods for long-term water quality monitoring of deep groundwater wells can be expensive, affect contaminant migration, and produce excessive volumes of discharge water that can be difficult to manage. The use of low-flow pumping methods and depth discrete bailers (DDBs) can reduce the cost of sampling deep groundwater wells. In general, using different pumping methods to obtain reproducible and representative groundwater can be challenging. Passive diffusion samplers (PDSs) have successfully been used in long-term monitoring for volatile organic compounds, major ions, and trace-elements, but application has been limited for stable and radioactive isotopes. Beginning in 2018, the United States Geological Survey (USGS) conducted three sampling events to test the ability of PDSs and DDBs to obtain reproducible and representative groundwater samples. The sampled well is completed in a regional, permeable carbonate aquifer and is one in a set of wells that have historically been used for tritium tracer testing. All samples were obtained at 180 m (590 ft) below land surface (bls) in a 13.97 cm (5.5 inch) uncased well that has a total depth of 202 m (662 ft) bls. The first sampling event deployed a regenerated cellulose dialysis membrane (RCDM) PDS for 14 days with deionized water as the blank. The second sampling event deployed a RCDM for 27 days also with deionized water as the blank. The third sampling event deployed a Dual Membrane (DM) PDS for 65 days using a blank of tritium-dead carbonate water. The DM PDS was used to assess the effect of longer term deployment on tritium concentrations and address whether or not the PDSs reached equilibrium with ambient groundwater. The day after each of the three passive samplers were retrieved a DDB was used to obtain discrete non-integrated groundwater samples. For each DDB sampling day, the bailer was lowered into the well 10 consecutive times to determine if the water chemistry changed from the first to the last bailed sample. Quality assurance samples including blanks and duplicates were obtained during all three sampling events. All blank waters had tritium concentrations less than 21±33 pCi/L. Major ion (e.g. calcium, chloride, sodium, and sulfate) results were compared between all samples obtained with RCDM and DDB. Major ion concentrations showed a coefficient of variation of less than 6% between all RCDM and DDB samples; however, the coefficient of variation between the different deployment times and the two different methods for trace-element concentrations was much larger, particularly for manganese (82%), lead (41%), and zinc (33%). Stable isotope values were compared between the RCDM and DDB samples. The DDB sample results all fell within analytical uncertainty and were considered representative of the formation groundwater. The stable isotope values from the RCDM samples indicated that a longer deployment time was necessary to gain equilibrium and to obtain representative groundwater samples. Tritium results from groundwater samples obtained from the RCDM, DM, and DDB indicate that groundwaters obtained with PDSs produced tritium concentrations 2 to 3 times higher (between 1,426 and 3,060±87 pCi/L) than groundwaters obtained with a DDB (479 to 1,219±51 pCi/L). The longer the passive diffusion samplers were deployed, the higher the tritium concentration, suggesting that equilibrium with tritium was not reached within a 27-day deployment. DDB samples showed tritium results declining from the first to the last bailed sample for all three sampling events. This research suggests that tritium results from groundwater samples obtained from PDSs are more reproducible than samples obtained from DDBs. Also, PDSs likely do not accumulate isotopes of water but rather equilibrate with the ambient groundwater. On the other hand, both PDSs and DDBs were able to provide representative groundwater samples for major ions and have the potential to produce representative groundwater samples for stable isotopes of water (δD and δ{sup 18}O). Utilizing the most effective method for sampling groundwater is important for long-term groundwater monitoring. Quantifying the ability of passive diffusion groundwater samplers to obtain representative radioactive chemistry samples will help determine if these methods can be a useful alternative to pumping and purging wells for long-term monitoring. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey

Recent cosmological analyses rely on the ability to accurately sample from high-dimensional posterior distributions. A variety of algorithms have been applied in the field, but justification of the particular sampler choice and settings is often lacking. Here, we investigate three such samplers to motivate and validate the algorithm and settings used for the Dark Energy Survey (DES) analyses of the first 3 yr (Y3) of data from combined measurements of weak lensing and galaxy clustering. We employ the full DES Year 1 likelihood alongside a much faster approximate likelihood, which enables us to assess the outcomes from each sampler choice and demonstrate the robustness of our full results. We find that the ellipsoidal nested sampling algorithm multinest reports inconsistent estimates of the Bayesian evidence and somewhat narrower parameter credible intervals than the sliced nested sampling implemented in polychord. We compare the findings from multinest and polychord with parameter inference from the Metropolis–Hastings algorithm, finding good agreement. We determine that polychord provides a good balance of speed and robustness for posterior and evidence estimation, and recommend different settings for testing purposes and final chains for analyses with DES Y3 data. Our methodology can readily be reproduced to obtain suitable sampler settings for future surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Salt Sampling FY21 Technical Report

The goal of the salt sampling program at Argonne is to develop and deploy automated molten salt sampling approaches to enable high-precision in-process salt sample analysis to improve the timeliness of sampling-based accountancy measurements. Tools currently under development in support of this goal include (1) a modular vacuum sampler with an accompanying sample handling method for coupling vacuum sampling with high-precision at-line sample analysis, (2) a pneumatic sample generator that enables high-throughput sample analysis to improve the precision of existing analytical techniques, and (3) a windowless flow cell to enable on-line optical analysis of molten salt in a sampling loop. Compared to point sampling approaches (i.e., dip probes), vacuum sampling systems and on-line sampling loops facilitate access to a larger cross-section of a process fluid. This is known to improve the characterization of the process fluid by producing more representative samples and by enabling the analysis of a larger cross section of the fluid. A vacuum sampling approach for molten salts eliminates the risk of dross contamination of samples and avoids the use of moving parts in the salt. In FY21, two methods for integrating a vacuum sampler with a pneumatic sample generator were tested. These included direct fluidic coupling and coupling using a solid salt transfer mechanism. Solid salt transfer was ultimately selected over fluidic coupling, primarily to enable the transport of samples over longer distances to support automated at-line integration with high-precision techniques (such as microcalorimetry) that cannot withstand the extreme conditions near an electrorefining process. To facilitate rapid solid salt coupling, new mechanisms were developed for rapidly charging and discharging salt sample tubes at the vacuum sampler and pneumatic sample generator, respectively. While the charging mechanism will be deployed in FY22, the tube transfer method and discharge mechanism were tested in FY21. These were deployed at one of Argonne’s engineering-scale electrorefiners to implement at-line high-throughput pneumatic micro-sample generation capabilities. The method was used to generate precise uranium- and lanthanide-bearing electrorefiner micro-samples with the specific dimensions requested by researchers at Los Alamos National Laboratory for use in testing their novel microcalorimeter x-ray techniques. The solid salt transfer mechanism proved not only to be an effective means of integrating the precision sample generator with vacuum sampling, but also improved the performance of the sampler generator. Because the modular sampling approach described here eliminates the need for new high-radiation sample handling capabilities, salt-wetted seals, salt-wetted moving parts, and heated transfer lines outside the electrorefiner, it will address most of the remaining technical challenges for the at-line deployment of high-precision analytical techniques. This will enable significant reductions in the time delay for sampling-based accountancy measurements by eliminating the need for manual off-line sample processing and analysis. On-line optical analysis of molten salt in a sampling loop would provide complementary information to at-line and in-situ techniques. In FY21, an open-aperture molten salt gravity flow cell with windowless optical access to flowing salt was successfully demonstrated. Future work should include the refinement and performance testing of the on-line and at-line sampling tools, integration of additional analysis techniques, stakeholder outreach and collaboration, evaluation of the integrated methods, and analyses to determine how the various tools might fit into an integrated safeguards monitoring system of unattended near real time monitoring tools.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Collection, release, and detection of analytes with polymer composite sampling materials

A unique fiber core sampler composition, related systems, and techniques for designing, making, and using the same are described. The sampler is used to interface with existing field instrumentation, such as Ion Mobility Spectrometer (IMS) equipment. Desired sampler characteristics include its: stiffness/flexibility; thermal mass and conductivity; specific heat; trace substance collection/release dependability, sensitivity and repeatability; thickness; reusability; durability; stability for thermal cleaning; and the like. In one form the sampler has a glass fiber core with a thickness less than 0.3 millimeter that is coated with a polymer including one or more of: polymeric organofluorine, polyimide, polyamide, PolyBenzlmidazole (PBI), PolyDiMethylSiloxane (PDMS), sulfonated tetrafluoroethylene (PFSA) and Poly(2,6-diphenyl-p-phenylene Oxide) (PPPO). Multiple polymer coatings with the same or different polymer types may be included, core/substrate surface functionalization utilized, and/or the core/substrate may be at partially filled with thermally conductive particles.

Addleman, Raymond S.↗
DOE OSTI2020

A Look At Dissolved Organic Carbon In Streams and Its Effect On DGT Hg Collection

Savannah River Site (SRS) and Oak Ridge National Laboratory (ORNL) were both major material production sites for the U.S.'s nuclear program during the 20. century. Both sites discharged mercury (Hg) to streams. Oak Ridge streams have accumulated significant levels of Hg from early 'historical' discharges. Diffusive Gradient in Thin Films (DGT) samplers may be a labor and material saving method to monitor the Hg contamination of streams at both sites. Humic and Fulvic acids are released during the degradation of plant and animal residue. They are part of the Dissolved Organic Matter that colors the black water streams of the southern U.S.. Humic acid and Fulvic acids have a tendency to sequester heavy metals, like Hg, and may interfere with the reactions needed to draw the Hg through the thin films of the DGT samplers. Project Objective: Use a UV-Vis spectrophotometer to estimate the levels of Dissolved Organic Carbon (DOC) in the streams selected to test the effect of DOC on DGT samplers. Conclusion: UV-Vis is a convenient way to estimate the amount of DOC in streams and may be used to provide more context in DGT Hg sampling. efforts. Future Work: Analyze data from SRS and ORNL DGT samples; Compare the results of the DGT samplers with more traditional speciating methods from ORNL; Use a TOC Spectrophotometer to gain a more accurate picture of stream DOC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Sequential Kalman tuning of the t -preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems

Ensemble Kalman Inversion (EKI) has been proposed as an efficient method for the approximate solution of Bayesian inverse problems with expensive forward models. However, when applied to the Bayesian inverse problem EKI is only exact in the regime of Gaussian target measures and linear forward models. Here, in this work we propose embedding EKI and Flow Annealed Kalman Inversion, its normalizing flow (NF) preconditioned variant, within a Bayesian annealing scheme as part of an adaptive implementation of the t-preconditioned Crank-Nicolson (tpCN) sampler. The tpCN sampler differs from standard pCN in that its proposal is reversible with respect to the multivariate t-distribution. The more flexible tail behaviour allows for better adaptation to sampling from non-Gaussian targets. Within our Sequential Kalman Tuning (SKT) adaptation scheme, EKI is used to initialize and precondition the tpCN sampler for each annealed target. The subsequent tpCN iterations ensure particles are correctly distributed according to each annealed target, avoiding the accumulation of errors that would otherwise impact EKI. We demonstrate the performance of SKT for tpCN on three challenging numerical benchmarks, showing significant improvements in the rate of convergence compared to adaptation within standard SMC with importance weighted resampling at each temperature level, and compared to similar adaptive implementations of standard pCN. The SKT scheme applied to tpCN offers an efficient, practical solution for solving the Bayesian inverse problem when gradients of the forward model are not available. Code implementing the SKT schemes for tpCN is available at https://github.com/RichardGrumitt/KalmanMC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling

We introduce a novel semi-supervised learning approach, named Teacher-Student Bayesian Optimization (TSBO ), integrating the teacher-student paradigm into BO to minimize expensive labeled data queries for the first time. TSBO incorporates a teacher model, an unlabeled data sampler, and a student model. The student is trained on unlabeled data locations generated by the sampler, with pseudo labels predicted by the teacher. The interplay between these three components implements a unique selective regularization to the teacher in the form of student feedback. This scheme enables the teacher to predict high-quality pseudo labels, enhancing the generalization of the GP surrogate model in the search space. To fully exploit TSBO , we propose two optimized unlabeled data samplers to construct effective student feedback that well aligns with the objective of Bayesian optimization. Furthermore, we quantify and leverage the uncertainty of the teacher-student model for the provision of reliable feedback to the teacher in the presence of risky pseudo-label predictions. TSBO demonstrates significantly improved sample-efficiency in several global optimization tasks under tight labeled data budgets. The implementation is available at https://github.com/reminiscenty/TSBO-Official.

Yin, Yuxuan↗
DOE OSTI2020

Mercury Speciation via Diffusive Gradients Thin-Films Technology

Objectives: Develop and test various diffusive gradient in thin films (DGT) samplers for mercury - SRNL is developing reactive DGTS (rDGTs). Test chemistry to differentiate methylmercury from total and/or inorganic mercury in environmental samples. Fabricate and test rDGT samplers for deployment. Deploy the rDGTs in variable settings. Mercury is a persistent-bioaccumulative-toxic environmental pollutant. Mercury exists in different species such as elemental, inorganic, and methyl. Mercury speciation determine mercury behavior and toxicity. For example, methylmercury strongly accumulates in biota such as fish resulting in potential human health impacts. DGTs are innovative samplers to measure water concentration by diffusion and capture. Types of Hg DGTs used: Total Hg, Inorganic Hg, and Methyl Hg. Deployment Sites: Upper Three Runs, Lower Three Runs, Steele Creek, East Fork Poplar Creek (SRNL Sites), Horizons Creek, NOA Creek, Bruners Creek (ORNL Sites). DGT concentration estimates closely matched alternative measurements for soluble mercury at each Oak Ridge site - DGTs did not respond to particulate mercury. DGTs provide a representative measure for biota uptake since they are left out longer which allows for an average exposure concentration. DMA-80 provided an efficient and quick analysis. Variability in site location made a difference in Hg species. DGT preparations and sampling wasn't as quick and simple as microcolumn technology. Copper reagent degraded agarose gel - more work and cleaner data are needed to assess speciation in rDGTs. Future Work: Use cross-linked polyacrylamide for the collection layer for a stronger plastic. Make a copper diffusion layer without agarose. Test copper separation in lab using realistic stream conditions such as high organic carbon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Molten Salt Sampling Techniques and Analytical Approaches

Recent global interest in pyroprocessing and molten salt reactors has brought salt sampling methods and techniques back to the forefront of nuclear safeguards concerns. Issues with uranium supplies have also encouraged various countries to pursue advanced nuclear fuel cycles. Tracking nuclear material in molten salt has proven to be a challenge and updating molten salt sampling will greatly help in this endeavor. Molten salt is problematic to sample due to salt stratification, lack of homogeneity, solids, and difficulty with hot cell adaptations. Various salt sampling techniques have been used since before the 1960s including surface, spoon/spatula, and bar solidification. Since then, new types of sampling techniques have been developed to improve sampling results. These include rod/dip, pipet, suction, filtered sampling along with devices such as the Valve Core Sampler and the Multi-Level Sampler. These different approaches are being analyzed and improved upon along with developing requirements for an improved salt sampling device. Work continues to develop salt samplers that are more robust, easier to segment, collect at a specific depth, can work with filters, and can collect fines. Sampling parameters are also being narrowed in terms of stirring, settling time, filtration, depth, etc. In the future, we hope to address deficiencies for process control and nuclear material accountancy control by determining the best way to collect samples that minimizes contaminants and is representative. A compilation of salt sampling approaches, analyses techniques, and an evaluation of findings will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Extension of SCALE/Sampler’s sensitivity analysis

Nuclear data are a major source of uncertainties in reactor physics calculations. The propagation of nuclear data uncertainties to important system responses is instrumental when determining appropriate safety margins in reactor safety analyses. It is also important to understand the major contributors to the observed uncertainties to make recommendations for further measurements and evaluations and aid in the understanding of the studied system. The SCALE code system allows for nuclear data uncertainty analysis based on the random sampling approach as implemented in SCALE’s Sampler sequence. Sampler was recently extended by a sensitivity analysis in terms of the calculation of two correlation-based sensitivity indices. This analysis allows for the identification of the top contributing nuclear reactions to any analyzed output uncertainty. This paper presents the sensitivity indices, along with their interpretation and limitations. It demonstrates the application in an eigenvalue and decay heat analysis for a boiling water reactor fuel assembly.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

The “Brookhaven Brassie” and the Response to the Three Mile Island Accident

In the mid-1970s, American civil defense authorities became increasingly concerned with the potential threat of fission reactor accidents. Research from the Defense Civil Preparedness Agency began to be aimed not just at the ever-present threat of nuclear warfare, but also peacetime emergencies as part of a "dual use" philosophy. The Brookhaven National Laboratory received funding to create a prototype radioiodine air sampling system, with multiple publications detailing the creation of the air sampler itself and a special CD V-700 survey meter that accompanied it. In late March 1979, the system found its first operational employment at the reactor accident at Three Mile Island, Pennsylvania. Despite successful use and further study, the BNL Air Sampler was not widely fielded by the federal government. However, lessons learned from its employment and development may be applicable to contemporary radiological emergency response.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Generalized Bayesian Framework for Evaluation of Integral Benchmark Experiments

A recently published generalized Bayesian optimization framework has provided a way to retract any or all of the three common assumptions underlying the conventional Generalized Linear Least Squares (GLLS) optimization method based on the concepts introduced in reference two. These assumptions are: 1. Perfection: The model used for data evaluation and the prior probability distribution function (PDF) of generalized* data are perfect; 2. Normality: The prior and posterior PDF are normal; and 3. Linearity: The model is linear. In this work we outline how the framework in 1 could be directly adopted for improved evaluation of nuclear criticality integral benchmark experiments (IBEs) by: 1. Removing the first assumption alone by utilizing the concept of imperfections introduced in 1 to enable evaluation in the presence of discrepancies between the data and model or of missing covariance information by a GLLS method that will be seen as a generalization of the conventional GLLS method employed by the TSURFER code, and by 2. Removing the remaining two assumptions by implement- ing a Markov Chain Monte Carlo method for computation of the posterior PDF in the SAMPLER code, where TSURFER and SAMPLER are the uncertainty quantification (UQ) codes for IBEs in the SCALE code system based on the GLLS, and the stochastic method, respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Field-Scale Lysimeter Studies of Glass and Cementitious Waste Forms at the Hanford Site - 20392

The Hanford site Integrated Disposal Facility (IDF) will receive waste forms from vitrification activities at the Hanford Waste Treatment and Immobilization Plant (WTP). The waste form inventory to be disposed of at the IDF will consist of vitrified low-activity waste (LAW) in glass forms and solidified (or encapsulated) secondary wastes in cementitious forms. The IDF is a near-surface burial facility located near Hanford's Waste Treatment and Immobilization Plant in the 200 East Area of the site's Central Plateau. An extensive set of laboratory experimental data has been collected to support IDF performance assessment calculations of the eventual degradation of waste forms and mobility of contaminants. This paper describes the start of a field experiment to generate waste-form performance data on a larger scale (tens of centimeters), over a longer duration (five years or more), and under field conditions representative of the IDF. Results from this study are expected to improve model descriptions of contaminant mobility, reduce uncertainties about the representativeness of laboratory results in the IDF performance assessment, improve stakeholder confidence in the safe disposal of treated waste at the IDF, and facilitate the adoption of informed facility designs with the potential to reduce operational costs. A lysimeter test facility near Hanford's 200 West Area has been repurposed to carry out long-term assessments of waste forms buried in sediments excavated from the IDF. The waste forms are buried in large drainage lysimeters, open caissons that are two meters in diameter and three meters deep. Waste forms are buried in up to six locations in each lysimeter. Samplers collect pore water and drainage water beneath the waste forms and air from the pore space adjacent to waste forms. Sensors measure temperature, soil water content, soil water tension, and drainage flux. One lysimeter containing cementitious waste forms and one containing glass waste forms have been completed, along with a control lysimeter (without waste forms). The cementitious waste forms are Cast Stone grout formulations using a liquid secondary waste simulant and a generic Hanford high-salt simulant, as well as a hydrated lime-based grout formulation using a liquid secondary waste simulant. Technetium-99 and iodine-127 were added as tracers to monitor contaminant mobility. Glass waste forms were fabricated in two formulations (LAWA44 and ORLEC28), with rhenium and molybdenum tracers to monitor waste form degradation. The predicted performance of the waste forms in the lysimeters was modeled prior to installation to determine waste form size, tracer concentration/activity, and locations of sensors and samplers. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Possible impacts of molten salt reactors on the International Monitoring System

Molten salt reactors (MSRs) are gaining support as many countries look for ways to increase power generation and replace aging nuclear energy production facilities. MSRs have inherently safe designs, are scalable in size, can burn transuranic wastes from traditional solid fuel nuclear reactors, can store excess heat in thermal reservoirs for water desalination, and can be used to produce medical isotopes as part of the real-time liquid-fuel recycling process. The ability to remove 135 Xe in real time from the fuel improves the power production in an MSR because 135 Xe is the most significant neutron-absorbing isotope generated by nuclear fission. Xenon-135, and other radioactive gases, are removed by sparging the fuel with an inert gas while the liquid fuel is recirculated from the reactor inner core through the heat exchangers. Without effective abatement technologies, large amounts of radioactive gas could be released during the sparging process. This work examines the potential impact of radioxenon releases on samplers used by the International Monitoring System (IMS) to detect nuclear explosions. Here, atmospheric transport simulations from seven hypothetical MSRs on different continents were used to evaluate the holdup time needed before release of radioxenon so IMS samplers would register few detections. Abatement technologies that retain radioxenon isotopes for at least 120 d before their release will be needed to mitigate the impacts from a molten salt breeder reactor used to replace a nuclear power plant. A holdup time of about 150 d is needed to reduce emissions to the average level of current nuclear power plants.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Nuclear explosion monitoring network design considerations

Design of an efficient monitoring network requires information on the type and size of releases to be detected, the accuracy and reliability of the measuring equipment, and the desired network performance. This work provides a scientific basis for optimizing or minimizing networks of 133 Xe samplers to achieve a desired performance level for different levels of release. The approach of this work varies the density of sampling locations to find optimal location subsets, and to explore the properties of variations of those subsets – how crucial is a specific subset; are substitutions problematic? The choice of possible station locations is arbitrary but constrained to some extent by the location of islands, land masses, difficult topography (mountains, etc.) and the places where infrastructure exists to run and support a sampler. Performance is evaluated using hypothetical releases and atmospheric transport models that cover an entire year.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Bayesian uncertainty quantification of tristructural isotropic particle fuel silver release: Decomposing model inadequacy plus experimental noise and parametric uncertainties

Tristructural isotropic (TRISO) particle fuel is one of the most promising fuel concepts enabling high temperature and high burnup reactor operation. One dominant source of radioactivity released from the TRISO particles is silver (Ag), which is subject to a high release fraction and long decay life compared to other fission products. Previous modeling efforts using the fuel performance code BISON indicated nonnegligible uncertainties in modeling the diffusion process of fission products in TRISO compared to the Advanced Gas Reactor experiments. The overall uncertainties observed when modeling the fission product diffusion can result from uncertainties in model parameters, noisy experimental measurements, and deficiencies in the developed models. The three types of underlying uncertainties have not yet been properly quantified in open literature. Here, this paper presents the Bayesian uncertainty quantification (UQ) using massively parallelizable Markov chain Monte Carlo samplers. The uncertainties due to model parameters, model inadequacy, and experimental measurement noise are quantified, with the σ term used to represent the sum of the model inadequacy and measurement noise uncertainties. It is worth noting that this is the first time the σ term is inferred for nuclear fuel experiments, as compared to using prescribed values for uncertainty quantification in previous work. The parallelizable Markov chain Monte Carlo samplers efficiently infer the model parameters and the σ term, giving insight into physical parameters like diffusion coefficients and the combined model discrepancy and measurement noise. A subsequent forward uncertainty quantification (UQ) is also performed based on the calibration results to generate more accurate predictions of the Ag release. The model inadequacy plus experimental noise is the most dominant source of uncertainty compared to the parametric uncertainty. All the UQ analyses presented in this work are based on the second series of the irradiation experiments in the Advanced Gas Reactor program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗