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Validation of Multisystem Countermeasures Protocol for Spaceflight during Antarctica Winter-over at Palmer Station (Palmer Countermeasures)

Stressors associated with spaceflight induce persistent immune compromise in astronauts which increase subclinical latent virus reactivation. In select crews, adverse clinical events have been documented. Antarctica winter-over (AWO) mission most closely reproduces these mission stressors: prolonged deployment, extreme environment, circadian misalignment, isolation, station lifestyle, and personal risk. The US maintains three primary stations in Antarctica: South Pole Station, McMurdo, and Palmer. Previous studies suggest that stations located near the interior of Antarctica (South Pole, McMurdo) have confounding effects on the immune system due to persistent hypobaric hypoxia. We hypothesized that winter-over at a coastal station (Palmer) would be more akin to spaceflight due to its normoxic but still extreme environment. Therefore, AWO at Palmer Station was selected, and validated in a pilot study, as the platform for testing and validating the effectiveness of an immune-restorative countermeasure protocol designed for deep space missions. Specifics include diet modifications, nutritional supplementation (vitamin D, probiotic, etc.), prescribed aerobic and resistive exercise, and a protocol of stress relieving virtual reality exercises. A multitude of biological sample types, including blood, saliva, and hair will be collected in tandem with the countermeasures in order to examine the combined effectiveness of the countermeasures. Samples and logs from subjects will be transported from Palmer Station to Johnson Space Center for further processing and distribution to co-investigators at the end of each winter-over. Extracted samples will be analyzed by appropriate testing platforms (Multiplex, qPCR, ELISA, etc.) to monitor alterations in leukocyte distribution, T cell and NK function, cytokine profiles, reactivation of latent herpesviruses, and nutritional factors. The data collected will be compared to a control year in which no countermeasures were deployed to evaluate the overall effectiveness of the analog and to validate the candidate immune countermeasure strategy. AWO 2023 concluded with the 4th in-mission timepoint conducted in September 2023. Samples for 16 subjects, including blood, saliva, hair, surveys, and PCR data, were all successfully returned from Antarctica to NASA/JSC mid-November 2023. Samples have since been distributed to co-investigators for further processing and analysis. With the completion of the first countermeasure year, preliminary data on the effectiveness of the deep-space protocol is being evaluated, however, no conclusions can be drawn yet until the completion of the second AWO countermeasure year, AWO 2024. AWO 2024 commenced in late-March 2024, with 13 subjects consenting and performing their baseline data collections (BDCs). Unique to the 2024 deployment, NSF lifted certain COVID restrictions and rallied the crewmembers in Punta Arenas, Chile. All NSF activities were transferred to this location and NASA was allowed, for the first time, to perform consent briefings, baseline samplings and training in person. This augment greatly increased the likelihood of success for the overwinter activities.

Cody L Gutierrez↗

DOE OSTI2014

Application of Gaussian Process Modeling to Analysis of Functional Unreliability

This paper applies Gaussian Process (GP) modeling to analysis of the functional unreliability of a “passive system.” GPs have been used widely in many ways [1]. The present application uses a GP for emulation of a system simulation code. Such an emulator can be applied in several distinct ways, discussed below. All applications illustrated in this paper have precedents in the literature; the present paper is an application of GP technology to a problem that was originally analyzed [2] using neural networks (NN), and later [3, 4] by a method called “Alternating Conditional Expectations” (ACE). This exercise enables a multifaceted comparison of both the processes and the results. Given knowledge of the range of possible values of key system variables, one could, in principle, quantify functional unreliability by sampling from their joint probability distribution, and performing a system simulation for each sample to determine whether the function succeeded for that particular setting of the variables. Using previously available system simulation codes, such an approach is generally impractical for a plant-scale problem. It has long been recognized, however, that a well-trained code emulator or surrogate could be used in a sampling process to quantify certain performance metrics, even for plant-scale problems. “Response surfaces” were used for this many years ago. But response surfaces are at their best for smoothly varying functions; in regions of parameter space where key system performance metrics may behave in complex ways, or even exhibit discontinuities, response surfaces are not the best available tool. This consideration was one of several that drove the work in [2]. In the present paper, (1) the original quantification of functional unreliability using NN [2], and later ACE [3], is reprised using GP; (2) additional information provided by the GP about uncertainty in the limit surface, generally unavailable in other representations, is discussed; (3) a simple forensic exercise is performed, analogous to the inverse problem of code calibration, but with an accident management spin: given an observation about containment pressure, what can we say about the system variables? References 1. For an introduction to GPs, see (for example) Gaussian Processes for Machine Learning, C. E. Rasmussen and C. K. I. Williams (MIT, 2006). 2. Reliability Quantification of Advanced Reactor Passive Safety Systems, J. J. Vandenkieboom, PhD Thesis (University of Michigan, 1996). 3. Z. Cui, J. C. Lee, J. J. Vandenkieboom, and R. W. Youngblood, “Unreliability Quantification of a Containment Cooling System through ACE and ANN Algorithms,” Trans. Am. Nucl. Soc. 85, 178 (2001). 4. Risk and Safety Analysis of Nuclear Systems, J. C. Lee and N. J. McCormick (Wiley, 2011). See especially §11.2.4.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

DEVELOPMENT AND TESTING OF FAULT-DIAGNOSIS ALGORITHMS FOR REACTOR PLANT SYSTEMS

Argonne National Laboratory is further developing fault diagnosis algorithms for use by the operator of a nuclear plant to aid in improved monitoring of overall plant condition and performance. The objective is better management of plant upsets through more timely, informed decisions on control actions with the ultimate goal of improved plant safety, production, and cost management. Integration of these algorithms with visual aids for operators is taking place through a collaboration under the concept of an operator advisory system. This is a software entity whose purpose is to manage and distill the enormous amount of information an operator must process to understand the plant state, particularly in off-normal situations, and how the state trajectory will unfold in time. The fault diagnosis algorithms were exhaustively tested using computer simulations of twenty different faults introduced into the chemical and volume control system (CVCS) of a pressurized water reactor (PWR). The algorithms are unique in that each new application to a facility requires providing only the piping and instrumentation diagram (PID) and no other plant-specific information; a subject-matter expert is not needed to install and maintain each instance of an application. The testing approach followed accepted procedures for verifying and validating software. It was shown that the code satisfies its functional requirement which is to accept sensor information, identify process variable trends based on this sensor information, and then to return an accurate diagnosis based on chains of rules related to these trends. The validation and verification exercise made use of GPASS, a one-dimensional systems code, for simulating CVCS operation. Plant components were failed and the code generated the resulting plant response. Parametric studies with respect to the severity of the fault, the richness of the plant sensor set, and the accuracy of sensors were performed as part of the validation exercise. The background and overview of the software will be presented to give an overview of the approach. Following, the verification and validation effort using the GPASS code for simulation of plant transients including a sensitivity study on important parameters will be presented

Fault Diagnosis Algorithm↗

DOE OSTI2016

A Field Evaluation of a Prototype Global Identifier for UF6 Cylinders

The U.S. Department of Energy s National Nuclear Security Administration (DOE/NNSA), members of the U.S. national laboratories, UF6 industry stakeholders, and international inspectorates have been working on developing a global identifier (ID) for UF6 cylinders. This industry-driven project has identified efficiency gains for facility operations, state and/or regional regulation, and inspections by the International Atomic Energy Agency. The global ID features standardized alphanumeric characters, a large font, a barcode (for automated reading), and it is affixed to the cylinder. Four years of active engagement with all of the stakeholders has resulted in on the development of user requirements, implementation guidelines, and a preferred design for the ID. Although this project was conceived and has been largely managed from the DOE/NNSA side to address international non-proliferation concerns, it remains an industry-driven initiative. When designing the field evaluation, the NNSA team worked closely with a World Nuclear Transport Institute (WNTI) Working Group on UF6 Cylinder Identification to determine features that would provide the most benefit. The WNTI Working Group consists predominately of industry members associated with cylinder fabrication, UF6 conversion, enrichment, fuel fabrication, and cylinder transport. Despite its industry-laden focus, the DOE/NNSA team realized that a vote of confidence from the IAEA could serve as a catalyst for the overall project, and its eventual implementation. In April of 2016, a field evaluation was conducted to demonstrate how the key features of the identifier would work in an operational setting. A selected team travelled to Vienna to evaluate the benefits of a global identifier in performing a PIV in a cylinder storage area containing ~50-100 cylinders. The mock tag-checking exercise was conducted three separate times, with varying scenarios with three different teams. The first group performed the exercise according to current inspection methods. The second group focused on the addition of a new global identifier to the cylinders, and the third group used the new global identifier with an automated reader. This paper will discuss the preparation and execution of the field evaluation, its results, and the next steps for the Global Cylinder Identifier Project.

White-Horton, Jessica L↗

DOE OSTI2016

Implementing Machine Learning in the PCWG Tool

The Power Curve Working Group (www.pcwg.org) is an ad-hoc industry-led group to investigate the performance of wind turbines in real-world conditions. As part of ongoing experience-sharing exercises, machine learning has been proposed as a possible way to predict turbine performance. This presentation provides some background information about machine learning and how it might be implemented in the PCWG exercises.

17 WIND ENERGY↗

DOE OSTI2017

Coarse Particulate Organic Matter: Storage, Transport, and Retention

Coarse particulate organic matter, or CPOM, is a basal energy and nutrient resource in many stream ecosystems and is provided by inputs from the riparian zone, incoming tributaries, and to a lesser extent from in-stream production. The ability of a stream to retain CPOM or slow its transport is critical to its consumption and assimilation by stream biota. In this chapter, we describe basic exercises to measure (1) the amount of CPOM in the streambed and (2) the retention of CPOM from standardized particle releases. We further describe advanced exercises that (1) experimentally enhance the retentiveness of a stream reach and (2) measure organic carbon transport and turnover (i.e., spiraling) in the channel.

Tiegs, Scott↗

DOE OSTI2017

Coarse Particulate Organic Matter: Storage, Transport, and Retention

Tiegs, Scott↗

DOE OSTI2018

OECD-NEA Expert Group on Multi-Physics Experimental Data, Benchmarks and Validation

High-fidelity, multi-physics modeling and simulation (M&S) tools are being developed and utilized for a variety of applications in nuclear science and technology and show great promise in their abilities to reproduce observed phenomena for many applications. Even with the increasing fidelity and sophistication of coupled multi-physics M&S tools, the underpinning models and data still need to be validated against experiments that may require a more complex array of validation data because of the great breadth of the time, energy and spatial domains of the physical phenomena that are being simulated. The Expert Group on Multi-Physics Experimental Data, Benchmarks and Validation (MPEBV) of the Nuclear Energy Agency (NEA) of the Organization for Economic Cooperation and Development (OECD) was formed to address the challenges with the validation of such tools. The work of the MPEBV expert group is shared among three task forces to fulfill its mandate and specific exercises are being developed to demonstrate validation principles for common industrial challenges. This paper describes the overall mission of the group, the specific objectives of the task forces, the linkages among the task forces, and the development of a validation exercise that focuses on a specific reactor challenge problem.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

The GIF Proliferation Resistance and Physical Protection Methodology Applied to Gen IV System Designs: Some Reflections

The Generation Four International Forum (GIF) formed a proliferation resistance and physical protection (PR&PP) Working Group (PRPPWG) to develop a methodology to evaluate the six GIF reactor technologies. The PRPPWG developed the methodology through a series of development and demonstration case studies, using a hypothetical “Example Sodium Fast Reactor” (ESFR). The ESFR assessment enabled testing of the full methodology on a complete nuclear energy system and many insights were gained from the process. The PRPPWG, in collaboration with representatives of the GIF System Steering Committees/provisional Steering Committees (SSCs/pSSCs) for each of the six GIF reactor technologies, analyzed the PR&PP aspects of the six reactor technologies. The outcome of this activity is documented in a joint PRPPWG and SSCs/pSSCs report made publicly available in 2011, highlighting the analysis performed and its main outcomes. Currently, the PRPPWG and the six GIF SSCs/pSSCs are in the process of repeating the exercise, based on the current status of the six GIF system design concepts, taking into account the designs’ evolution since 2011. In line with Safeguards by Design concept, whilst during the first exercise the level of detail of the design options enabled only a high-level evaluation of the systems’ proliferation resistance, the early design stage allowed designers to introduce modifications for improving the systems’ performance. With the designs’ current level of detail, the opportunity for proposing modifications diminishes, whilst at the same time increasing the opportunity to inform the safeguards R&D community on potential R&D needs to meet safeguardability issues. The paper will first summarize the main concepts behind the PR&PP evaluation methodology. It will then summarize the main outcomes and lessons learned, as in the current white papers on the PR aspects of the six GIF design concepts, and will finally describe the ongoing activity to update them.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2019

The GIF Proliferation Resistance and Physical Proection Methodology Applied to GEN IV System Designs: An Update

The Generation IV International Forum (GIF) formed a proliferation resistance and physical protection (PR&PP) Working Group (PRPPWG) to develop a methodology to evaluate the six GIF reactor technologies. The PRPPWG developed the methodology through a series of development and demonstration case studies and many insights were gained from the process. The PRPPWG, in collaboration with representatives of the GIF System Steering Committees/provisional Steering Committees (SSCs/pSSCs) for each of the six GIF reactor technologies, analyzed and evaluated the PR&PP aspects of the six reactor technologies. The outcome of this activity is documented in a joint PRPPWG and SSCs/pSSCs report made publicly available in 2011, highlighting the analysis performed and its main outcomes. Currently, the PRPPWG and the six GIF SSCs/pSSCs are in the process of repeating the exercise, based on the status of the six GIF system design concepts, considering the designs’ evolution since 2011. In line with the PR&PP by Design concept, whilst during the first exercise the level of detail of the design options enabled only a high-level evaluation of the systems’ proliferation resistance, the early design stage allowed designers to introduce modifications for improving the systems’ performance. With the designs’ current level of development, the opportunity for proposing modifications diminishes, whilst at the same time increasing the opportunity to inform the safeguards R&D community on potential R&D needs to meet safeguardability issues. The paper will first summarize the main concepts behind the PR&PP evaluation methodology. It will then present the main outcomes and lessons learned as in the current white papers on the PR aspects of the six GIF design concepts and will finally describe the ongoing activity to update them within a PR&PP-by-Design context.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2019

Comparison of Mesoscale Model Setups for Offshore Wind Resource Assessment: A New Jersey Case Study

As the offshore wind energy industry begins to emerge on the U.S. northeast coast, there is an urgent need for an accurate characterization of the offshore wind resource. The offshore regime features various resource characteristics not seen onshore, including frequent coastal low-level jets, non-steady surface conditions (e.g. waves), complex air-sea and land-sea interactions, and coastal mesoscale circulations (e.g. sea breeze). An accurate characterization of these phenomena in mesoscale models is critical to support accurate energy production estimates and forecasting for future offshore wind farms. However, a robust validation of mesoscale model performance in the offshore U.S. has been limited due to sparse data and difficulty obtaining offshore measurements. Consequently, it is unclear how mesoscale models perform offshore and which suite of parameterizations and input data are best suited for accurate resource characterization. To begin addressing this knowledge gap, the National Renewable Energy Laboratory, in collaboration with the Rutgers Center for Ocean Observing Leadership, conducted a mesoscale model validation campaign in the offshore environment. The Weather Research and Forecasting (WRF) model was assessed and the New Jersey offshore coastal area served as the validation domain. An ensemble of WRF simulations conducted over a one-year period sampled from a range of model setups, including different reanalysis and forecast data inputs, sea surface temperature data inputs, planetary boundary layer parameterizations, and WRF versions. The performance of each ensemble member was validated against a network of observations including offshore floating lidars, coastal met towers, offshore platform measurements, and offshore buoys. Model performance was assessed on annual, seasonal, and diurnal scales with particular emphasis on the vertical wind speed profile and temperature stratification. Preliminary results from this validation work will be presented here. The key WRF inputs and parameters driving changes in the modeled wind resource will be discussed and preliminary recommendations on WRF setups for offshore wind resource modeling will be proposed. The application of this validation exercise to other proposed offshore wind energy areas in the U.S. will also be discussed. mesoscale circulations (e.g. sea breeze). An accurate characterization of these phenomena in mesoscale models is critical to support accurate energy production estimates and forecasting for future offshore wind farms. However, a robust validation of mesoscale model performance in the offshore U.S. has been limited due to sparse data and difficulty obtaining offshore measurements. Consequently, it is unclear how mesoscale models perform offshore and which suite of parameterizations and input data are best suited for accurate resource characterization. To begin addressing this knowledge gap, the National Renewable Energy Laboratory, in collaboration with the Rutgers Center for Ocean Observing Leadership, conducted a mesoscale model validation campaign in the offshore environment. The Weather Research and Forecasting (WRF) model was assessed and the New Jersey offshore coastal area served as the validation domain. An ensemble of WRF simulations conducted over a one-year period sampled from a range of model setups, including different reanalysis and forecast data inputs, sea surface temperature data inputs, planetary boundary layer parameterizations, and WRF versions. The performance of each ensemble member was validated against a network of observations including offshore floating lidars, coastal met towers, offshore platform measurements, and offshore buoys. Model performance was assessed on annual, seasonal, and diurnal scales with particular emphasis on the vertical wind speed profile and temperature stratification. Preliminary results from this validation work will be presented here. The key WRF inputs and parameters driving changes in the modeled wind resource will be discussed and preliminary recommendations on WRF setups for offshore wind resource modeling will be proposed. The application of this validation exercise to other proposed offshore wind energy areas in the U.S. will also be discussed.

17 WIND ENERGY↗

DOE OSTI2020

Issue Summary of INL Phase IV Transient Results for IAEA CRP on HTGR UAM Benchmark

This report details the Parallel and Highly Innovative Simulation for Idaho National Laboratory (INL) Code System (PHISICS)/Reactor Excursions and Leak Analysis Program (RELAP5)-3D results obtained for the transient core exercises defined for Phase IV of the International Atomic Energy Agency (IAEA) Coordinated Research Project (CRP) on high-temperature gas cooled reactor (HTGR) uncertainty analysis in modeling (UAM). The Phase III models and results are linked to the earlier Standardized Computer Analyses for Licensing Evaluation (SCALE)/Sampler/New ESC-based Weighting Transport (NEWT) data generated for the lattice physics (lattice) stage Phase I of the CRP. The focus of this report is the Uncertainty/Sensitivity Assessment (U/SA) of the prismatic modular high-temperature gas cooled reactor (MHTGR)-350 design, and specifically for Exercises IV-1 and IV-2 of the benchmark: the Control Rod Withdrawal (CRW) and Pressurised Loss of Cooling (PLOFC) events. The statistical U/SA methodology is implemented and demonstrated using the RAVEN code, based on perturbed cross-section libraries obtained from the SCALE/Sampler sequence. Uncertainties in nuclear data (cross-sections and the average number of neutrons produced per fission, 235U[¯v ]) lead to standard deviations (uncertainties of one s) of approximately 0.5% in the core eigenvalues of the MHTGR-350 and core models. For the coupled neutronics/thermal fluid model, local power density uncertainties up to 3.6% were observed in the colder regions of the core, while the local maximum fuel temperature uncertainties reached 1.5% for the models that included thermal fluid uncertainties. The addition of thermal fluid uncertainties dominated the impacts of nuclear data uncertainties in all cases. The main contributors to uncertainties in the power density and fuel temperatures during the transients were uncertainties in the reactor operating conditions (total power, inlet mass flow rate and inlet gas temperature). Variations in the bypass flows did not have significant impact on any of the output variables. For the nuclear data uncertainties it was found that the 235U(¯v ) / 235U(¯v ) covariance produced the largest sensitivities in terms of its impact on the eigenvalue and peak reactor power. It was also observed that the impact of any nuclear data uncertainties on the maximum fuel temperature was much less significant that the impact on eigenvalue and power. Another important finding was that although the use of eight or more energy groups is recommended for best-estimate HTGR simulation, two-group models produced acceptable uncertainty and sensitivity results for most FOMs. Since the statistical U/SA methodology is computationally expensive, and most transient solver requirements will scale directly with the number of energy groups, two energy groups could be used by HTGR developers during the early stages of design when larger uncertainty margins can be tolerated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Uniformly Ordered Binary Decision Algorithm for Benchmark Experiment Correlations in Whisper Validation

When performing a validation exercise for determining the upper subcritical limit of a nuclear criticality safety application, an analyst should select and perform a statistical analysis on a population of benchmark experiments that are neutronically similar to the application. The size of this population should be sufficiently large such that the statistical analysis has a high degree of confidence that the bias plus bias uncertainty (calculational margin) has been accurately quantified. A complication arises because many benchmark experiments share common components, leading to correlations in their measured effective multiplication factors. Correlations between benchmark experiments within the population reduces its predictive power. This motivates the need for methods that consider benchmark experiment correlations and ensure adequate statistical significance of results. The Whisper code is a statistical analysis pack- age that incorporates nuclear data sensitivity coefficients from MCNP to assess benchmark experiment similarity and then performs an extreme-value analysis to estimate the bias plus bias uncertainty. The original methodology in Whisper does not consider the effect of benchmark experiment correlations when making this estimation, and this summary proposes the uniformly ordered binary decision algorithm to address this shortcoming. The original methodology in Whisper computes similarity coefficients ck for an application compared to all benchmark experiments in its library and develops weighting factors for a selected population proportional to the ck values. The methodology can be interpreted as statistically emulating a validation exercise for a particular application where the weighting factors may be viewed as the likelihood that an analyst would include a particular benchmark experiment within the population. The effective sample size of the population is the expected or mean number of benchmark experiments in the population. The uniformly ordered binary decision algorithm identifies clusters of correlated benchmark experiments within the population and then computes adjusted weighting factors based on the magnitude of the correlation coefficients within the cluster to compute a reduced effective sample size accounting for the lower information content because of correlations. Benchmark experiments within the cluster are ordered randomly with equal probability and probabilistic decisions are made as to whether a benchmark. experiment within the cluster should treated as redundant with a previous one; if two redundant benchmark experiments are included, then the conservative worst case bias plus bias uncertainty is used and the pair is counted as a single benchmark experiment in the population. Results are provided for HEU solutions in a research version of the Whisper software using benchmark experiment correlations provided by DICE, the Database for the International Criticality Safety Benchmark Evaluation Project (ICSBEP). These show that there can be a significant increase in the bias plus bias uncertainty because the effective sample size is reduced, and therefore the algorithm, needing to meet sample size requirements, expands the benchmark experiment population by accepting less similar benchmark experiments that would have otherwise not been included.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Report of the 2021 U.S. Community Study on the Future of Particle Physics (Snowmass 2021) Summary Chapter

The 2021-22 High-Energy Physics Community Planning Exercise (a.k.a. ``Snowmass 2021'') was organized by the Division of Particles and Fields of the American Physical Society. Snowmass 2021 was a scientific study that provided an opportunity for the entire U.S. particle physics community, along with its international partners, to identify the most important scientific questions in High Energy Physics for the following decade, with an eye to the decade after that, and the experiments, facilities, infrastructure, and R&D needed to pursue them. This Snowmass summary report synthesizes the lessons learned and the main conclusions of the Community Planning Exercise as a whole and presents a community-informed synopsis of U.S. particle physics at the beginning of 2023. This document, along with the Snowmass reports from the various subfields, will provide input to the 2023 Particle Physics Project Prioritization Panel (P5) subpanel of the U.S. High-Energy Physics Advisory Panel (HEPAP), and will help to guide and inform the activity of the U.S. particle physics community during the next decade and beyond.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2013

New developments and prospects on COSI, the simulation software for fuel cycle analysis

COSI, software developed by the Nuclear Energy Direction of the CEA, is a code simulating a pool of nuclear power plants with its associated fuel cycle facilities. This code has been designed to study various short, medium and long term options for the introduction of various types of nuclear reactors and for the use of associated nuclear materials. In the frame of the French Act for waste management, scenario studies are carried out with COSI, to compare different options of evolution of the French reactor fleet and options of partitioning and transmutation of plutonium and minor actinides. Those studies aim in particular at evaluating the sustainability of Sodium cooled Fast Reactors (SFR) deployment and the possibility to transmute minor actinides. The COSI6 version is a completely renewed software released in 2006. COSI6 is now coupled with the last version of CESAR (CESAR5.3 based on JEFF3.1.1 nuclear data) allowing the calculations on irradiated fuel with 200 fission products and 100 heavy nuclides. A new release is planned in 2013, including in particular the coupling with a recommended database of reactors. An exercise of validation of COSI6, carried out on the French PWR historic nuclear fleet, has been performed. During this exercise quantities like cumulative natural uranium consumption, or cumulative depleted uranium, or UOX/MOX spent fuel storage, or stocks of reprocessed uranium, or plutonium content in fresh MOX fuel, or the annual production of high level waste, have been computed by COSI6 and compared to industrial data. The results have allowed us to validate the essential phases of the fuel cycle computation, and reinforces the credibility of the results provided by the code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Study of influence of plastic scintillators thicknesses to detect Beta particles and Gamma radiation by means of spectral analysis of {sup 90}Sr, {sup 90}Y and {sup 137}Cs sources

The Nuclear and Energy Research Institute - IPEN, offers post-graduate programs, namely: Nuclear Technology - Applications (TNA), Nuclear Technology - Materials (TNM), Nuclear Technology - Reactors (TNR). The Institute programs mission is to form expert technicians, physicists and engineers with a strong knowledge in their discipline to work in the nuclear area. The course: 'Theoretical Fundamentals and Practices of the Instrumentation used in Nuclear Data Acquisition' covers the use of laboratory nuclear instrumentation and the accomplishment of experiments to obtain nuclear parameters. One of these experimental exercises is object of this work: 'Study of influence of plastic scintillators to detect Beta particles and Gamma radiation by means of spectral analysis of {sup 90}Sr, {sup 90}Y and {sup 137}Cs sources'. The use of scintillators plastic for the detection has the advantage of low cost, high mechanical strength, is not hygroscopic and can be manufactured in large volumes. This work aims to present the analysis of relative efficiency of detection of plastic scintillators of various thicknesses for beta particles and gamma radiation by the spectrum of {sup 137}Cs and {sup 90}Sr. Due to lack of resolution of the detectors plastic scintillators we worked with relative efficiency. The evaluation was done by reading deposited energy, using the software MAESTRO, for each detector thickness. For beta particles was observed an ideal thickness around 3 mm and the better photon efficiency was observed with increasing the thickness of the detector. The present experiment does not intend to establish a new technique for this subject: it solely aims student's practical exercises in nuclear properties of elements and detectors being part of the nuclear experimental course. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Inventory of Radioactive Waste in Canada - 19336

Since 1999, the Government of Canada has been conducting a regular exercise of gathering and compiling Canada's radioactive waste inventory and projection data from waste owners and making it available to the public in the form of a triennial Inventory Summary Report. The goal of this data collection exercise has been to support federal policy development, support the Canadian Nuclear Safety Commission's regulatory activities, provide consolidated radioactive waste information for the nuclear industry, promote transparency, and meet Canada's international commitments and growing demands internationally for radioactive waste data. The detailed national inventory report provides an overview of the production, accumulation and future projections of radioactive waste in Canada. Since the coming into force of the Joint Convention on the Safety of Spent Fuel Management and on the Safety of Radioactive Waste Management in June 2001, the data collection and reporting cycle has been aligned to provide the waste inventory data required for Canada's triennial National Report for the Joint Convention. This paper presents Canada's radioactive waste inventory and projection data as of the end of December 2016, and summarizes key findings from the analysis of the data. For this reporting cycle, the waste data collected was expanded to include more detailed information on the decommissioning schedules and associated waste volume projections for nuclear power plants, and research and prototype reactors, as well as plans for transitioning waste inventories from storage to long-term management and disposal facilities, based on current schedules for establishing these facilities in Canada. With this additional information, the Government of Canada and Canadian stakeholders are better able to understand the waste disposal capacity and schedules required to accommodate all nuclear fuel waste and radioactive waste that will be produced by Canada's existing fleet of nuclear power plants and research reactors. The collection and analysis of projected waste volumes to be emplaced in long-term management facilities is in line with and will support the proposed methodology of presenting waste inventories in terms of management strategy and disposal routes by the Organization for Economic Cooperation and Development Nuclear Energy Agency Expert Group on Waste Inventorying and Reporting Methodology. The paper describes some of the uses that are made of the waste inventory information, as well as Canada's approach to national inventory data collection and its experience and lessons learned. Canada continues to modernize the format of the Inventory Summary Report with the goal of disseminating the technical information into an attractive and public-friendly report. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Commissioning of Robotic Inspection and Automated Analysis System for Assay of Gas Diffusion Piping - 19500

Robotic, in-pipe, nondestructive assay (NDA) is a revolutionary and important means for measuring, analyzing, and reporting radioactive holdup deposits in enrichment piping. This innovation promises vast advantages for efficiency, safety, and cost savings. Payoff, however, lies in utilization much more than innovation. Crossing that threshold from a technical capability to an everyday tool occurs only after rigorous commissioning, adoption, and infusion. This must meet the highest standard, and there can be no compromise. Certification of a new NDA technology must meet the highest standards for quality, safety, and efficacy. Prior to this work, there was no precedent of procedure and method for certification of a robotic methodology for this purpose. The best of robotics had not certified NDA. The best of NDA had not certified robotics. Hence, a great deal of discovery, resourcefulness, and collaboration contributed to this success. This paper chronicles the first-of-kind chartering, co-development, testing, documentation, and site integration that certified this trailblazing in-pipe, robotic NDA. Commissioning had to demonstrate and certify end-to-end NDA automation from robot traversal to computer analysis to report generation of contaminated process piping at the Portsmouth, Ohio gaseous diffusion facility. This was a rigorous campaign of tests, evaluations, and documentation. Highlights included multiple tests in four elevated pipes exercised the gamut of NDA and robotic capabilities. Each of the four test pipes presented a unique loading of U-235. The tests demonstrated operations in both 30- inch and 42-inch diameter pipes with varying lengths and terminated by a variety of valves and fittings. Tests in a fifth pipe at floor level demonstrated deployment and applicability for pipes at this ground height and robot recognition of another fitting type. All operations, calibration, deployment, auto-reporting, and analysis were handled by Portsmouth site personnel. These tests concluded a series of component-level acceptance tests which certified all constituent technologies and operational features. Test metrics included comparison of reported to ground truth results and assessment of data quality from quality assurance and replicate measurement comparison. Beyond the radiometry associated with any NDA method, this unique commissioning path exercised and evaluated all the robotic features that are intrinsic to automation. These include odometry, geometric modeling, autonomy, remote launch and recovery, and end-to-end data flow. Each of these required verification and certification. These evaluations were first-of-kind for the decommissioning community, requiring innovation and resourcefulness in their own right. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗