Results for “Behavior based safety” — Page 7

DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗

NASA NTRSDate not supplied

Understanding and Verifying Neural Networks

Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors

Deep Neural Networks↗

NASA NTRS2017

Prognostics and Health Monitoring: Application to Electric Vehicles

As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Prognostics↗

NASA NTRSDate not supplied

Statistical learning framework for safety and failure analysis of a DNN-based autonomous aircraft system

Deep Neural Networks (DNNs) and Machine Learning technology is increasingly used for safety-critical applications in the Aerospace domain. To ensure safe operations, the DNN and the system must undergo rigorous verification and validation, including advanced statistical analyses. Performance and safety of the DNN and system behavior must not only be analyzed for the nominal case, but under numerous off-nominal and failure cases. In this paper we will describe how our statistical learning framework SYSAI can efficiently perform such analyses using the tool’s unique combination of advanced learning modeling and statistical analysis techniques. SYSAI can effectively explore the high-dimensional state and failure space of the system under test; geometrical shape detection of safety regions and boundaries support explainability of the results to the designer. In this paper, we report experiments and results obtained with a vision-based DNN control system (ACT) that is capable of autonomously steering an aircraft down a runway.

Yuning He↗

DOE OSTI2022

A Robust Numerical Treatment of Solid-Phase Diffusion in Pseudo Two-Dimensional Lithium-Ion Battery Models

Solid-phase diffusion in active materials of lithium-ion batteries significantly affects charging and safety-related behavior of lithium-ion batteries. Therefore, it is essential to develop an efficient and robust numerical algorithm for solving solid-phase diffusion equations in physics-based battery models. In this work, we discuss the origins of numerical instabilities that can occur when solving the solid-phase diffusion equations using iterative methods. Then, in order to resolve such issues, we propose a simple numerical treatment to the surface flux term of discretized solid-phase diffusion equations. To demonstrate its numerical robustness, the proposed method is implemented into a pseudo two-dimensional (P2D) physics-based battery model and simulations are conducted at wide ranges of operating conditions. Even with extremely poor initial guesses for the Li+ concentrations of the active materials, computations using the proposed method do not diverge and the their computational speeds are comparable to those with conventional initial guesses. Comprehensive tests of the proposed method are also performed with a dynamic current profile based on US06 driving profile and a multi-stage charging profile with very high initial C-rate (12C).

battery modeling↗

NASA NTRS1979

Performability evaluation of the SIFT computer

Performability modeling and evaluation techniques are applied to the SIFT computer as it might operate in the computational evironment of an air transport mission. User-visible performance of the total system (SIFT plus its environment) is modeled as a random variable taking values in a set of levels of accomplishment. These levels are defined in terms of four attributes of total system behavior: safety, no change in mission profile, no operational penalties, and no economic process whose states describe the internal structure of SIFT as well as relavant conditions of the environment. Base model state trajectories are related to accomplishment levels via a capability function which is formulated in terms of a 3-level model hierarchy. Performability evaluation algorithms are then applied to determine the performability of the total system for various choices of computer and environment parameter values. Numerical results of those evaluations are presented and, in conclusion, some implications of this effort are discussed.

Meyer, J. F.↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Batteries↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Battery Prognostics↗

DOE OSTI2019

Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility

The sequence of instantaneous driving decisions and its variations, known as driving volatility, prior to involvement in safety critical events can be a leading indicator of safety. Here, this study focuses on the component of “driving volatility matrix” related to specific normal and safety-critical events, named “event-based volatility.” The research issue is characterizing volatility in instantaneous driving decisions in the longitudinal and lateral directions, and how it varies across drivers involved in normal driving, crash, and/or near-crash events. To explore the issue, a rigorous quasi-experimental study design is adopted to help compare driving behaviors in normal vs unsafe outcomes. Using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 9593 driving events featuring 2.2 million temporal samples of real-world driving are analyzed. This study features a plethora of kinematic sensors, video, and radar spatiotemporal data about vehicle movement and therefore offers the opportunity to initiate such exploration. By using information related to longitudinal and lateral accelerations and vehicular jerk, 24 different aggregate and segmented measures of driving volatility are proposed that captures variations in extreme instantaneous driving decisions. In doing so, careful attention is given to the issue of intentional vs. unintentional volatility. The volatility indices, as leading indicators of near-crash and crash events, are then linked with safety critical events, crash propensity, and other event specific explanatory variables. Owing to the presence of unobserved heterogeneity and omitted variable bias, fixed- and random-parameter discrete choice models are developed that relate crash propensity to unintentional driving volatility and other factors. Statistically significant evidence is found that driver volatilities in near-crash and crash events are significantly greater than volatility in normal driving events. After controlling for traffic, roadway, and unobserved factors, the results suggest that greater intentional volatility increases the likelihood of both crash and near-crash events. A one-unit increase in intentional volatility is associated with positive vehicular jerk in longitudinal direction increases the chance of crash and near-crash outcome by 15.79 and 12.52 percentage points, respectively. Importantly, intentional volatility in positive vehicular jerk in lateral direction has more negative consequences than intentional volatility in positive vehicular jerk in longitudinal direction. Compared to acceleration/deceleration, vehicular jerk can better characterize the volatility in microscopic instantaneous driving decisions prior to involvement in safety critical events. Finally, the magnitudes of correlations exhibit significant heterogeneity, and that accounting for the heterogeneous effects in the modeling framework can provide more reliable and accurate results. The study demonstrates the value of quasi-experimental study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2016

Validation of advanced in-pin metallic fuel models of SAS4A

The SAS4A safety analysis code has been extended to include mechanistic and physics-based models of U-Pu-Zr and U-Zr metallic fuel pins. The predictions of the code for various phenomena such as fission gas behavior, clad wastage formation, thermal expansion, creep, and gas swelling induced axial fuel relocation, in-pin molten fuel ejection, and clad failure models have been significantly improved. The paper presents the validation effort of the new code using the results of eight sets of TREAT MSeries overpower experiments, prior to clad failure. Fuel axial expansion reactivity and clad failure behavior was benchmarked. The results show that the code predictions are satisfactory. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2011

High Temperature Reactor (HTR) Deep Burn Core and Fuel Analysis: Design Selection for the Prismatic Block Reactor With Results from FY-2011 Activities

The Deep Burn (DB) Project is a U.S. Department of Energy sponsored feasibility study of Transuranic Management using high burnup fuel in the high temperature helium cooled reactor (HTR). The DB Project consists of seven tasks: project management, core and fuel analysis, spent fuel management, fuel cycle integration, TRU fuel modeling, TRU fuel qualification, and HTR fuel recycle. In the Phase II of the Project, we conducted nuclear analysis of TRU destruction/utilization in the HTR prismatic block design (Task 2.1), deep burn fuel/TRISO microanalysis (Task 2.3), and synergy with fast reactors (Task 4.2). The Task 2.1 covers the core physics design, thermo-hydraulic CFD analysis, and the thermofluid and safety analysis (low pressure conduction cooling, LPCC) of the HTR prismatic block design. The Task 2.3 covers the analysis of the structural behavior of TRISO fuel containing TRU at very high burnup level, i.e. exceeding 50% of FIMA. The Task 4.2 includes the self-cleaning HTR based on recycle of HTR-generated TRU in the same HTR. Chapter IV contains the design and analysis results of the 600MWth DB-HTR core physics with the cycle length, the average discharged burnup, heavy metal and plutonium consumptions, radial and axial power distributions, temperature reactivity coefficients. Also, it contains the analysis results of the 450MWth DB-HTR core physics and the analysis of the decay heat of a TRU loaded DB-HTR core. The evaluation of the hot spot fuel temperature of the fuel block in the DB-HTR (Deep-Burn High Temperature Reactor) core under full operating power conditions are described in Chapter V. The investigated designs are the 600MWth and 460MWth DB-HTRs. In Chapter VI, the thermo-fluid and safety of the 600MWth DB-HTRs has been analyzed to investigate a thermal-fluid design performance at the steady state and a passive safety performance during an LPCC event. Chapter VII describes the analysis results of the TRISO fuel microanalysis of the 600MWth and 450MWth DB-HTRs. The TRISO fuel microanalysis covers the gas pressure buildup in a coated fuel particle including helium production, the thermo-mechanical behavior of a CFP, the failure probabilities of CFPs, the temperature distribution in a CPF, and the fission product (FP) transport in a CFP and a graphite. In Chapter VIII, it contains the core design and analysis of sodium cooled fast reactor (SFR) with deep burn HTR reactor. It considers a synergistic combination of the DB-MHR and an SFR burner for a safe and efficient transmutation of the TRUs from LWRs. Chapter IX describes the design and analysis results of the self-cleaning (or self-recycling) HTR core. The analysis is considered zero and 5-year cooling time of the spent LWR fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2010

High Temperature Reactor (HTR) Deep Burn Core and Fuel Analysis: Design Selection for the Prismatic Block Reactor

The Deep Burn (DB) Project is a U.S. Department of Energy sponsored feasibility study of Transuranic Management using high burnup fuel in the high temperature helium cooled reactor (HTR). The DB Project consists of seven tasks: project management, core and fuel analysis, spent fuel management, fuel cycle integration, TRU fuel modeling, TRU fuel qualification, and HTR fuel recycle. In the Phase II of the Project, we conducted nuclear analysis of TRU destruction/utilization in the HTR prismatic block design (Task 2.1), deep burn fuel/TRISO microanalysis (Task 2.3), and synergy with fast reactors (Task 4.2). The Task 2.1 covers the core physics design, thermo-hydraulic CFD analysis, and the thermofluid and safety analysis (low pressure conduction cooling, LPCC) of the HTR prismatic block design. The Task 2.3 covers the analysis of the structural behavior of TRISO fuel containing TRU at very high burnup level, i.e. exceeding 50% of FIMA. The Task 4.2 includes the self-cleaning HTR based on recycle of HTR-generated TRU in the same HTR. Chapter IV contains the design and analysis results of the 600MWth DB-HTR core physics with the cycle length, the average discharged burnup, heavy metal and plutonium consumptions, radial and axial power distributions, temperature reactivity coefficients. Also, it contains the analysis results of the 450MWth DB-HTR core physics and the analysis of the decay heat of a TRU loaded DB-HTR core. The evaluation of the hot spot fuel temperature of the fuel block in the DB-HTR (Deep-Burn High Temperature Reactor) core under full operating power conditions are described in Chapter V. The investigated designs are the 600MWth and 460MWth DB-HTRs. In Chapter VI, the thermo-fluid and safety of the 600MWth DB-HTRs has been analyzed to investigate a thermal-fluid design performance at the steady state and a passive safety performance during an LPCC event. Chapter VII describes the analysis results of the TRISO fuel microanalysis of the 600MWth and 450MWth DB-HTRs. The TRISO fuel microanalysis covers the gas pressure buildup in a coated fuel particle including helium production, the thermo-mechanical behavior of a CFP, the failure probabilities of CFPs, the temperature distribution in a CPF, and the fission product (FP) transport in a CFP and a graphite. In Chapter VIII, it contains the core design and analysis of sodium cooled fast reactor (SFR) with deep burn HTR reactor. It considers a synergistic combination of the DB-MHR and an SFR burner for a safe and efficient transmutation of the TRUs from LWRs. Chapter IX describes the design and analysis results of the self-cleaning (or self-recycling) HTR core. The analysis is considered zero and 5-year cooling time of the spent LWR fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Reduced Order Model Implementation in the Risk-Informed Safety Margin Characterization Toolkit

The RISMC project aims to develop new advanced simulation-based tools to perform Probabilistic Risk Analysis (PRA) for the existing fleet of U.S. nuclear power plants (NPPs). These tools numerically model not only the thermo-hydraulic behavior of the reactor primary and secondary systems but also external events temporal evolution and components/system ageing. Thus, this is not only a multi-physics problem but also a multi-scale problem (both spatial, µm-mm-m, and temporal, ms-s-minutes-years). As part of the RISMC PRA approach, a large amount of computationally expensive simulation runs are required. An important aspect is that even though computational power is regularly growing, the overall computational cost of a RISMC analysis may be not viable for certain cases. A solution that is being evaluated is the use of reduce order modeling techniques. During the FY2015, we investigated and applied reduced order modeling techniques to decrease the RICM analysis computational cost by decreasing the number of simulations runs to perform and employ surrogate models instead of the actual simulation codes. This report focuses on the use of reduced order modeling techniques that can be applied to any RISMC analysis to generate, analyze and visualize data. In particular, we focus on surrogate models that approximate the simulation results but in a much faster time (µs instead of hours/days). We apply reduced order and surrogate modeling techniques to several RISMC types of analyses using RAVEN and RELAP-7 and show the advantages that can be gained.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

AN OVERVIEW OF REDUCED ORDER MODELING TECHNIQUES FOR SAFETY APPLICATIONS

The RISMC project is developing new advanced simulation-based tools to perform Computational Risk Analysis (CRA) for the existing fleet of U.S. nuclear power plants (NPPs). These tools numerically model not only the thermal-hydraulic behavior of the reactors primary and secondary systems, but also external event temporal evolution and component/system ageing. Thus, this is not only a multi-physics problem being addressed, but also a multi-scale problem (both spatial, µm-mm-m, and temporal, seconds-hours-years). As part of the RISMC CRA approach, a large amount of computationally-expensive simulation runs may be required. An important aspect is that even though computational power is growing, the overall computational cost of a RISMC analysis using brute-force methods may be not viable for certain cases. A solution that is being evaluated to assist the computational issue is the use of reduced order modeling techniques. During the FY2015, we investigated and applied reduced order modeling techniques to decrease the RISMC analysis computational cost by decreasing the number of simulation runs; for this analysis improvement we used surrogate models instead of the actual simulation codes. This article focuses on the use of reduced order modeling techniques that can be applied to RISMC analyses in order to generate, analyze, and visualize data. In particular, we focus on surrogate models that approximate the simulation results but in a much faster time (microseconds instead of hours/days).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Optimal CO 2 storage management considering safety constraints in multi-stakeholder multi-site GCS projects: A Markov game perspective

Geological carbon storage (GCS) projects could involve a diverse array of stakeholders or players from public, private, and regulatory sectors, each with different objectives and responsibilities. Given the complexity, scale, and long-term nature of GCS operations, determining whether individual stakeholders can independently optimize their interests — or whether collaborative coalition agreements are needed — remains a central question for effective GCS project planning and management. To access large, high-quality storage resources, future GCS deployment may increasingly occur in geologically connected sites, where shared geological features such as pressure space and reservoir pore capacity can lead to competitive behavior among stakeholders. In this work, we propose a paradigm based on Markov games to quantitatively investigate how different coalition structures affect the goals of stakeholders. We frame this multi-stakeholder multi-site problem as a multi-agent reinforcement learning problem with safety constraints. Our approach enables agents to learn optimal strategies while complying with safety regulations. We present an example where multiple operators are injecting CO 2 into their respective project areas in a geologically connected basin. To address the high computational cost of repeated simulations of high fidelity models, a previously developed surrogate model based on the Embed-to-Control (E2C) framework is employed. Our results demonstrate the effectiveness of the proposed framework in addressing optimal management of CO 2 storage when multiple stakeholders with different objectives and goals are involved.

58 GEOSCIENCES↗

NASA NTRS2013

Development of Large-Scale Spacecraft Fire Safety Experiments

The status is presented of a spacecraft fire safety research project that is under development to reduce the uncertainty and risk in the design of spacecraft fire safety systems by testing at nearly full scale in low-gravity. Future crewed missions are expected to be more complex and longer in duration than previous exploration missions outside of low-earth orbit. This will increase the challenge of ensuring a fire-safe environment for the crew throughout the mission. Based on our fundamental uncertainty of the behavior of fires in low-gravity, the need for realistic scale testing at reduced gravity has been demonstrated. To address this gap in knowledge, a project has been established under the NASA Advanced Exploration Systems Program under the Human Exploration and Operations Mission directorate with the goal of substantially advancing our understanding of the spacecraft fire safety risk. Associated with the project is an international topical team of fire experts from other space agencies who conduct research that is integrated into the overall experiment design. The experiments are under development to be conducted in an Orbital Science Corporation Cygnus vehicle after it has undocked from the ISS. Although the experiment will need to meet rigorous safety requirements to ensure the carrier vehicle does not sustain damage, the absence of a crew removes the need for strict containment of combustion products. The tests will be fully automated with the data downlinked at the conclusion of the test before the Cygnus vehicle reenters the atmosphere. A computer modeling effort will complement the experimental effort. The international topical team is collaborating with the NASA team in the definition of the experiment requirements and performing supporting analysis, experimentation and technology development. The status of the overall experiment and the associated international technology development efforts are summarized.

Life Support↗

DOE OSTI2017

Application of the new IAPWS Guideline on the fast and accurate calculation of steam and water properties with the Spline-Based Table Look-Up Method (SBTL) in RELAP-7

The numerical simulation of thermalhydraulic processes in nuclear power plants requires very accurate and extremely fast algorithms for calculating the thermophysical properties of water and steam. In order to provide such algorithms, the International Association for the Properties of Water and Steam (IAPWS) has adopted the new “IAPWS Guideline on the Fast Calculation of Steam and Water Properties with the Spline-Based Table Look-Up Method (SBTL)”. In this article, the SBTL method is applied to property functions of specific volume and specific internal energy (v,e) based on the scientific formulation IAPWS-95 and the latest IAPWS formulations for transport properties. From the newly generated SBTL functions, thermodynamic and transport properties as well as their derivatives and inverse functions are calculable in the fluid range of state for pressures up to 100 MPa and for temperatures up to 1273 K, including the metastable liquid and the metastable vapor regions. The SBTL functions reproduce the underlying formulations with an accuracy of 10–100 ppm and significantly reduced computing times. The SBTL method has been implemented into the nuclear reactor system safety analysis code RELAP-7 [2] to consider the real fluid behavior of water and steam in a novel 7-equation two-phase flow model.

42 ENGINEERING↗

DOE OSTI2012

Ruthenium Behavior at Phase Separation of Borosilicate Glass-12259

The Rokkasho reprocessing plant (RRP) located in Aomori, Japan, vitrifies high level waste (HLW) into a borosilicate glass. The HLW is generated from the reprocessing of spent fuel and contains ruthenium (Ru) and other platinum group metals (PGMs). Based on the recent consequences after a huge earthquake that occurred in Japan, a hypothetical blackout was postulated for the RRP to address additional safety analysis requirements. During a prolonged blackout, the borosilicate glass could phase separate due to cooling of the glass in the melter. The Ru present in the glass matrix could migrate into separate phases and impact the durability of the borosilicate glass. The durability of the glass is important for quality assurance and performance assessment of the vitrified HLW. A fundamental study was performed at an independent university to understand the impact of a prolonged blackout. Simulated HLW glasses were prepared for the RRP, and the Ru behavior in phase separated glasses was studied. The simulated HLW glasses contained nonradioactive elements and PGMs. The glass compositions were then altered to enhance the formation of the phase-separated glasses when subjected to thermal treatment at 700 deg. C for 24 hours. The synthesized simulated glasses contained 1.1 % Ru by weight as ruthenium dioxide (RuO{sub 2}). A portion of the RuO{sub 2} formed needle-shaped crystals in the glass specimens. After the thermal treatment, the glass specimen had separated into two phases. One of the two phases was a B{sub 2}O{sub 3} rich phase, and the other phase was a SiO{sub 2} rich phase. The majority of the chemical species in the B{sub 2}O{sub 3} rich phase was leached away with the Material Characterization Center-3 (MCC-3) protocol standardized by the Pacific Northwest National Laboratory using an aqueous low-concentrated nitric acid solution, but the leaching of the Ru fraction was very limited; less than 1% of the original Ru content. The Ru leaching was much less than those of the other elements, and the needle-shaped crystals of RuO{sub 2} were observed in the B{sub 2}O{sub 3} rich phase in the specimen after the leaching test. Another experiment was performed using another glass specimen which had been prepared with the same frits, but used reagent RuO{sub 2} of granular shape at lower content (0.0073% by weight as RuO{sub 2}). The leached fractions of elements for the latter specimen increased to almost the same fraction (more than 10% of the original Ru content) as observed for boron and sodium, when the phase separated glass was leached using the MMC-3 protocol with non-acidic de-ionized water. Based on the results of this study, it was concluded that needle-shaped RuO{sub 2} crystals are contained in the B{sub 2}O{sub 3}-rich phase after phase separation of the borosilicate glass after a hypothetical blackout. The leaching fraction for the needle-shaped RuO{sub 2} present in the phase separated glass is much lower than those for boron or sodium. Ruthenium behavior has been studied for a hypothetical loss of cooling in the liquid fed ceramic melter for high level waste by taking into account the phase separation of borosilicate glass. The needle-shaped crystal of ruthenium dioxide after bi-nodal-type phase separation of the borosilicate glass at 700 deg. C migrated into the B{sub 2}O{sub 3} rich phase, but remained without dissolution by an acidic aqueous solution. Additionally, granular ruthenium dioxide can be a morphological form of ruthenium after bimodal-type phase separation of the vitrified high level waste with borosilicate glass media. After the phase separation of the borosilicate glass, the crystal shape of the ruthenium dioxide is either needle-shaped or granular, and the leachable fraction of ruthenium is relatively much lower than those of major components (boron and sodium) in the vitrified borosilicate glass. The fraction of leached ruthenium increased to almost the same fraction as observed for boron and sodium when the phase-separated glass was leached with ultrapure water. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗