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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Physics-Based Optical Neuromorphic Classification

Typical approaches to classify scenes from light convert the light field to electrons to perform the computation in the digital electronic domain. This conversion and downstream computational analysis require significant power and time. Diffractive neural networks have recently emerged as unique systems to classify optical fields at lower energy and high speeds. Previous work has shown that a single layer of diffractive metamaterial can achieve high performance on classification tasks. In analogy with electronic neural networks, it is anticipated that multilayer diffractive systems would provide better performance, but the fundamental reasons for the potential improvement have not been established. In this work, we present extensive computational simulations of two - layer diffractive neural networks and show that they can achieve high performance with fewer diffractive features than single layer systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Risk Informed Timelines (RIT) v.1.0

Risk Informed Timelines (RIT) is a more modern and holistic method for understanding how access delay timelines are developed and utilized in physical security system evaluations. The software utilizes Bayesian methods to combine subject matter expert judgement and small performance test datasets in a consistent and defensible way. It allows a more holistic view of delay performance that provides distributions of task times and task success probabilities to account for tasks that, if failed, would result in failure of the attack. The RIT methodology is meant to enable security risk managers to begin the transition into more comprehensive delay timeline analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-8294 O

Noel, ToddG.↗
DOE OSTI2024

Nanoscopic Imaging of Self-Propelled Ultrasmall Catalytic Nanomotors

Ultrasmall nanomotors (<100 nm) are highly desirable nanomachines for their size-specific advantages over their larger counterparts in applications spanning nanomedicine, directed assembly, active sensing, and environmental remediation. While there are extensive studies on motors larger than 100 nm, the design and understanding of ultrasmall nanomotors have been scant due to the lack of high-resolution imaging of their propelled motions with orientation and shape details resolved. Here, we report the imaging of the propelled motions of catalytically powered ultrasmall nanomotors─hundreds of them─at the nanometer resolution using liquid-phase transmission electron microscopy. These nanomotors are Pt nanoparticles of asymmetric shapes (“tadpoles” and “boomerangs”), which are colloidally synthesized and observed to be fueled by the catalyzed decomposition of NaBH4 in solution. Statistical analysis of the orientation and position trajectories of fueled and unfueled motors, coupled with finite element simulation, reveals that the shape asymmetry alone is sufficient to induce local chemical concentration gradient and self-diffusiophoresis to act against random Brownian motion. Our work elucidates the colloidal design and fundamental forces involved in the motions of ultrasmall nanomotors, which hold promise as active nanomachines to perform tasks in confined environments such as drug delivery and chemical sensing.

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

Novel Module Architecture for Lower CapEx and Improved Recyclability for c-Si PV Modules

Photovoltaic (PV) energy production is currently increasing at a rate at which recycling is becoming necessary. A novel module architecture has been demonstrated that has potential for high value recycling for c-Si PV. This architecture eliminates the vacuum lamination process and cross-linked en capsulants. Functioning prototypes of c-Si have been fabricated for stress testing in collaboration with NREL. These modules are being tested against traditionally manufactured modules. Based on preliminary results, this module architecture is a potentially viable solution for improving the manufacturing cost and recyclability of PV modules while retaining module performance.

computer architecture↗
DOE OSTI2015

Combined Heat and Power Systems Technology Development and Demonstration 370 kW High Efficiency Microturbine

The C370 Program was awarded in October 2010 with the ambitious goal of designing and testing the most electrically efficient recuperated microturbine engine at a rated power of less than 500 kW. The aggressive targets for electrical efficiency, emission regulatory compliance, and the estimated price point make the system state-of-the-art for microturbine engine systems. These goals will be met by designing a two stage microturbine engine identified as the low pressure spool and high pressure spool that are based on derivative hardware of Capstone’s current commercially available engines. The development and testing of the engine occurred in two phases. Phase I focused on developing a higher power and more efficient engine, that would become the low pressure spool which is based on Capstone’s C200 (200kW) engine architecture. Phase II integrated the low pressure spool created in Phase I with the high pressure spool, which is based on Capstone’s C65 (65 kW) commercially available engine. Integration of the engines, based on preliminary research, would allow the dual spool engine to provide electrical power in excess of 370 kW, with electrical efficiency approaching 42%. If both of these targets were met coupled with the overall CHP target of 85% total combined heating and electrical efficiency California Air Resources Board (CARB) level emissions, and a price target of $600 per kW, the system would represent a step change in the currently available commercial generation technology. Phase I of the C370 program required the development of the C370 low pressure spool. The goal was to increase the C200 engine power by a minimum of 25% — 250 kW — and efficiency from 32% to 37%. These increases in the C200 engine output were imperative to meet the power requirements of the engine when both spools were integrated. An additional benefit of designing and testing the C370 low pressure spool was the possibility of developing a stand-alone product for possible commercialization. The low pressure spool design activity focused on an aeropath derivative of the current C200 engine. The aeropath derivative included changes to the compressor section —compressor and inducer — and to the turbine nozzle. The increased power also necessitated a larger, more powerful generator and generator controller to support the increased power requirements. These two major design changes were completed by utilizing both advanced 3D modeling and computational fluid dynamics modelling. After design, modeling, and analysis, the decision was made to acquire and integrate the components for testing. The second task of Phase I was to integrate and test the components of the low pressure spool to validate power and efficiency. Acquisition of the components for the low pressure spool was completed utilizing Capstone’s current supplier base. Utilization of Capstone’s supply base for integration of the test article would allow — if the decision was made —expedited commercialization of the product. After integration of the engine components, the engine was tested and evaluated for performance and emissions. Test data analysis confirmed that the engine met all power and efficiency requirements and did so while maintaining CARB level emissions. The emissions were met without the use of any post processing or catalyst. After testing was completed, the DOE authorized — via a milestone review — proceeding to Phase II: the development of the integrated C370 engine. The C370 high pressure spool design activity required significant changes to the C65 engine architecture. The engine required a high power density generator, completely redesigned compressor stage, turbine section, recuperator, controls architecture, and intercooler stage asThe two most critical design challenges were the turbine section (the nozzle and turbine) and the controls architecture. The design and analysis of all of the components was completed and integrated into a system model. The system model — after numerous iterations — indicated that, once integrated, the engine will meet or exceed all system requirements. Unfortunately, the turbine section’s life requirements remain a technical challenge and will require continued refinement of the bi-metallic turbine wheel design and manufacturing approach to meet the life requirement at theses high temperatures. The current controls architecture requires substantial effort to develop a system capable of handling the high-speed, near real-time controls requirement, but it was determined not to be a technical roadblock for the project. The C370 Program has been a significant effort with state-of-the-art technical targets. The targets have pushed Capstone’s designers to the limits of current technology. The program has been fortunate to see many successes: the successful testing of the low pressure spool (C250), the development of new material processes, and the implementation of new design practices. The technology and practices learned during the program will be utilized in Capstone’s current product lines and future products. The C370 Program has been a resounding success on many fronts for the DOE and for Capstone.

none↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Extending the Roofline Model for Asynchronous Many-Task Runtimes

New architectural trends and extreme scale parallelism challenge the efficient mapping of applications to large scale and emerging systems. Fine-grained Asynchronous Manytask Runtimes (AMR) offer a unique flexibility to hide the varying latencies of contentious operations, and facilitate the effective exploitation of abundant resources. By decomposing work into smaller asynchronous chunks, the impact of unpredictable operations can be mitigated through a tighter overlapping of tasks. Unfortunately, the cost of this decomposition comes at a potentially steep price in the form of runtime overhead which can at times rival the cost of the computation itself. This leads to the question, how large should a task be? A common practice for application developers is to experimentally determine the granularity of a task after a code has been parallelized. Instead, we propose a new methodology based on an extended Roofline model to provide practical upper bounds on the throughput performance of an application. First, we extend the Roofline model to support not only latency hiding analysis, but also a multidimensional amortized analysis. By combining this new methodology with a serial application and an AMR implementation, we can predict the worst case runtime overhead attribution of individual runtime features prior to the development of parallel code. Thus, this runtimecentric methodology can provide a vehicle for application/runtime codesign by providing a comprehensive bottleneck analysis based on existing runtime features.

Suetterlein, Joshua D.↗
DOE OSTI2023

Driving Next-Generation Workflows from the Data Plane

We observe the emergence of a new generation of scientific workflows that process data produced at a sustained rate by scientific instruments and large scale numerical simulations. This data is consumed by multiple analysis, visualization, or Machine Learning components not only to enable inference and justify the scientific program, but also to monitor and steer the evolution of these experiments. In such workflows, moving intermediate data efficiently is key to performance, more than efficiently scheduling computational tasks. However, most traditional workflow management systems focus on optimizing task scheduling and then deal with data management, assuming a “move little, compute for long” model, which makes them unfit to the efficient management of this new generation of workflows. Therefore, we advocate for a new way to manage scientific workflows. We propose to consider an efficiently and independently managed data plane that can store and stream data. Workflows compute components, in the application plane can then interact with the data plane, abstracted from complexities of data management. Then, the role of a workflow management system would become that of a control plane that allows users to connect services together to execute the workflow and manages connections between the application and data planes. In this position paper, we characterize several next-generation workflow motifs and describe how their interaction with the data plane is a challenge to traditional workflow management systems. Then, we express a set of requirements that a workflow management system should meet to efficiently manage next-generation workflows at different scales. Based on these requirements, we expose our vision of driving next-generation workflows from the data plane and list remaining open challenges.

Suter, Fred↗
DOE OSTI2020

ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists

Recently, advances in machine learning and artificial intelligence have been playing more and more critical roles in a wide range of areas. For the last several years, industries have shown that how learning from data, identifying patterns and making decisions with minimal human intervention can be extremely useful to their business (e.g., image classification, recommending a product to a customer, finding friends in a social network, predicting customers actions, etc.). These success stories have been motivating scientists who study physics, chemistry, materials, medicine, and many other subjects, to explore a new pathway of utilizing machine learning techniques like regression and classification for their scientific activities. However, most existing machine learning tools, systems, and methodologies have been developed for programming experts but not for scientists (or any users) who have no or little knowledge of programming. ASCENDS is a toolkit that is developed to assist scientists (or any persons) who want to use their data for machine learning tasks, more specifically, correlation analysis, regression, and classification. ASCENDS does not require programming skills. Instead, it provides a set of simple but powerful CLI (Command Line Interface) and GUI (Graphic User Interface) tools for non-data scientists to be able to intuitively perform advanced data analysis and machine learning techniques. ASCENDS has been implemented by wrapping around opensource software including Keras, TensorFlow, and scikit-learn.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Low-pressure

This final technical progress report describes work conducted by Membrane Technology and Research, Inc. (MTR) for the Department of Energy (DOE NETL) on development of low-pressure membrane contactors for carbon dioxide (CO 2 ) capture from power plant flue gas (award number DE-FE0007553). The work was conducted from October 1, 2011 through September 30, 2014. The overall goal of this three-year project was to build and operate a prototype 500 m 2 low-pressure sweep membrane module specifically designed to separate CO 2 from coal-fired power plant flue gas. MTR was assisted in this project by a research group at the University of Toledo, which contributed to the computational fluid dynamics (CFD) analysis of module design and process simulation. This report details the work conducted to develop a new type of membrane contactor specifically designed for the high-gas-flow, low-pressure, countercurrent sweep operation required for affordable membrane-based CO 2 capture at coal power plants. Work for this project included module development and testing, design and assembly of a large membrane module test unit at MTR, CFD comparative analysis of cross-flow, countercurrent, and novel partial-countercurrent sweep membrane module designs, CFD analysis of membrane spacers, design and fabrication of a 500 m 2 membrane module skid for field tests, a detailed performance and cost analysis of the MTR CO 2 capture process with low-pressure sweep modules, and a process design analysis of a membrane-hybrid separation process for CO 2 removal from coal-fired flue gas. Key results for each major task are discussed in the report.

42 ENGINEERING↗
DOE OSTI2017

Application of a Novel DCPD Adjustment Method for the J-R Curve Characterization: A study based on ORNL and ASTM Interlaboratory Results

Material fracture toughness in the fully ductile region can be described by a J-integral vs. crack growth resistance curve (J-R curve). As a conventional J-R curve measurement method, the elastic unloading compliance (EUC) method becomes impractical for elevated temperature testing due to relaxation of the material and friction induced back-up shape of the J-R curve. One alternative solution of J-R curve testing applies the Direct Current Potential Drop (DCPD) technique for measuring crack extension. However, besides crack growth, potential drop can also be influenced by plastic deformation, crack tip blunting, etc., and uncertainties exist in the current DCPD methodology especially in differentiating potential drop due to stable crack growth and due to material deformation. Thus, using DCPD for J-R curve determination remains a challenging task. In this study, a new adjustment procedure for applying DCPD to derive the J-R curve has been developed for conventional fracture toughness specimens, including compact tension, three-point bend, and disk-shaped compact specimens. Data analysis has been performed on Oak Ridge National Laboratory (ORNL) and American Society for Testing and Materials (ASTM) interlaboratory results covering different specimen thicknesses, test temperatures, and materials, to evaluate the applicability of the new DCPD adjustment procedure for J-R curve characterization. After applying the newly-developed procedure, direct comparison between the DCPD method and the normalization method on the same specimens indicated close agreement for the overall J-R curves, as well as the provisional values of fracture toughness near the onset of ductile crack extension, Jq, and of tearing modulus.

DCPD↗
DOE OSTI2020

An Approximation Algorithm for a Task Allocation, Sequencing and Scheduling Problem Involving a Human-Robot Team

Here we present an approximation algorithm for a Task Allocation, Sequencing and Scheduling Problem (TASSP) involving a team of human operators and robots. The robots have to travel to a given set of targets and collaboratively work on the tasks at the targets with the human operators. The problem aims to find a sequence of targets for each robot to visit and schedule the tasks at the targets with the human operators such that each target is visited exactly once by some robot, the scheduling constraints are satisfied and the maximum mission time of any robot is minimum. This problem is a generalization of the single Traveling Salesman Problem and is NP-Hard. Given k robots and m human operators, an algorithm is developed for solving the TASSP with an approximation ratio equal to 5/2- 1/k when m ≥ k and equal to 7/2 -1/k otherwise. Computational results are also presented to corroborate the performance of the proposed algorithm.

42 ENGINEERING↗
DOE OSTI2025

Data Agnostic Feature-Target Analysis & Ranking Machine Learning Pipeline (DAFTAR-ML) v0.1.0

DAFTAR-ML is a specialized machine-learning pipeline that identifies relevant features based on their relationship to a target variable. Many ML pipelines focus solely on prediction, and feature ranking is often absent or lacks robust statistical methods. DAFTAR-ML performs its tasks with this outcome in mind. Model training is robust, using nested cross-validation and hyperparameter tuning. Instead of relying on native feature-importance scores, it employs SHAP (SHapley Additive exPlanations) to quantify feature importance. The pipeline also produces comprehensive results, including publication-quality visualizations.

Melie, Tina [Lawrence Berkeley National Laboratory↗
DOE OSTI2022

Enabling machine learning-ready HPC ensembles with Merlin

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. Here, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for $\mathscr{O}$(N) task execution and $\mathscr{O}$(N ln N) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Analysis of the Impact of Radiation on Cameras and Robotic Perception - 19242

Robotic systems are increasingly used for inspection, maintenance, and disaster assistance for nuclear power plants. These robots utilize increasingly high levels of autonomy to operate in environments that are unsafe for humans to operate the robot in or require more precise control over the robot than a human can offer. In these cases, the robot must include perception and computation components to autonomously sense and navigate through the environment to fulfill its mission. This difficult task is complicated by radiation interfering with sensors' operation. The impact of this interference is largely unexplored, as existing works on radiation interference on sensors focus on estimating the incident radiation, rather than how it impacts a robotic system using the sensor. In this work, we seek to quantify and describe the impact of radiation on cameras, and how the radiation affects robotic performance. In this paper we study cameras that are suitable for small, lightweight unmanned aerial vehicles (UAVs) that are increasingly deployed to fly in confined areas in emergencies, where the surrounding environment is unsafe for workers to operate. These small UAVs lack the payload capacity to carry significant radiation shielding, so any onboard sensors will be affected by radiation. In the case of cameras, this radiation appears as noise in the camera imagery. This noise will interfere with the UAV's operation, by obscuring obstacles and interfering with localization and optical flow algorithms that detect detailed features in the environment. To date, there is a lack of information on how the radiation noise in the camera image impacts the overall robot system performance Existing work focuses on estimating the amount of incident radiation by measuring the corruption in the images. However, these works suffer several shortcomings for robotic systems. First, a robot must be able to successfully navigate its environment during a successful mission, and merely quantifying the amount of radiation noise in an image does not adequately explain how it may interfere with higher level navigation and perception tasks. Second, radiation estimation procedures rely on focusing the camera on a known and static background. This is unsuitable for robotic exploration tasks, where the robot frequently moves around an unknown environment that may be dynamically changing. Our work contains two contributions that address these shortcomings. First, we conduct a detailed analysis of the noise radiation causes in cameras by exposing cameras that are commonly used on UAVs [e.g., small form factor complementary metal-oxide-semiconductor (CMOS) cameras] to varying levels of radiation that may be present in emergency scenarios. From this exposure, we quantify not only how much noise is present, but quantify its appearance. By quantifying the appearance and amount of noise in a camera image, researchers can design mitigation strategies for the corruption, so that their robotic systems can operate in these challenging environments. Second, we demonstrate how to leverage this experimental data in robotic simulations. Using data from the first set of experiments allows us to test and verify the robots in simulation. Using simulators allows us to verify the robots will perform as expected during critical times without needing expensive test equipment or damaging valuable robot prototypes. Other researchers using robots for exploration of irradiated environments will benefit from our research by incorporating similar noise models in their research. 1 Southwest Research Institute{sup R} is a registered trademark of Southwest Research Institute in the United States and/or other countries. This will increase the reliability of robotic systems prior to deployment in emergency situations, which will in turn increase their effectiveness and speed the development of robots that would otherwise need expensive and time-consuming test procedures to validate. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Statistical characterization of experimental magnetized liner inertial fusion stagnation images using deep-learning-based fuel–background segmentation

Significant variety is observed in spherical crystal x-ray imager (SCXI) data for the stagnated fuel–liner system created in Magnetized Liner Inertial Fusion (MagLIF) experiments conducted at the Sandia National Laboratories Z-facility. As a result, image analysis tasks involving, e.g., region-of-interest selection (i.e. segmentation), background subtraction and image registration have generally required tedious manual treatment leading to increased risk of irreproducibility, lack of uncertainty quantification and smaller-scale studies using only a fraction of available data. We present a convolutional neural network (CNN)-based pipeline to automate much of the image processing workflow. This tool enabled batch preprocessing of an ensemble of N scans = 139 SCXI images across N exp = 67 different experiments for subsequent study. The pipeline begins by segmenting images into the stagnated fuel and background using a CNN trained on synthetic images generated from a geometric model of a physical three-dimensional plasma. The resulting segmentation allows for a rules-based registration. Our approach flexibly handles rarely occurring artifacts through minimal user input and avoids the need for extensive hand labelling and augmentation of our experimental dataset that would be needed to train an end-to-end pipeline. Here we also fit background pixels using low-degree polynomials, and perform a statistical assessment of the background and noise properties over the entire image database. Our results provide a guide for choices made in statistical inference models using stagnation image data and can be applied in the generation of synthetic datasets with realistic choices of noise statistics and background models used for machine learning tasks in MagLIF data analysis. We anticipate that the method may be readily extended to automate other MagLIF stagnation imaging applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Regulatory Technology Development Plan - Sodium Fast Reactor. Mechanistic Source Term - Trial Calculation. Work Plan

The overall objective of the SFR Regulatory Technology Development Plan (RTDP) effort is to identify and address potential impediments to the SFR regulatory licensing process. In FY14, an analysis by Argonne identified the development of an SFR-specific MST methodology as an existing licensing gap with high regulatory importance and a potentially long lead-time to closure. This work was followed by an initial examination of the current state-of-knowledge regarding SFR source term development (ANLART-3), which reported several potential gaps. Among these were the potential inadequacies of current computational tools to properly model and assess the transport and retention of radionuclides during a metal fuel pool-type SFR core damage incident. The objective of the current work is to determine the adequacy of existing computational tools, and the associated knowledge database, for the calculation of an SFR MST. To accomplish this task, a trial MST calculation will be performed using available computational tools to establish their limitations with regard to relevant radionuclide release/retention/transport phenomena. The application of existing modeling tools will provide a definitive test to assess their suitability for an SFR MST calculation, while also identifying potential gaps in the current knowledge base and providing insight into open issues regarding regulatory criteria/requirements. The findings of this analysis will assist in determining future research and development needs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗