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DOE OSTI2019

Dismantling of the Spent Resins Hot Compaction Unit at Tihange NPP, Belgium - 19139

Design, construction and commissioning of the Spent Ion Exchange Resins Hot Compaction Unit at Tihange Nuclear Power Plant took place from 2005 till 2011. In this conditioning process, batches of radioactive resin beads were first dried to less than 4% moisture by a conical vacuum dryer. Resins were then poured in compactable 200-liter drums and super-compacted. Resulting pellets were emplaced in standard 400-liter drums, the packages qualified for final disposal in Belgium, and eventually grouted. The Belgian Waste Management Agency (ONDRAF/NIRAS) was consulted before initiating the project and issued a statement of no objection. The technology turned out to be used for the first time in the particular case of bead shaped resins (unlike powdered resins) and with a grouting requirement. Finally, commissioning tests ended in a safe and reproducible waste form, compliant with prevailing Waste Acceptance Criteria. Volume Reduction Factors of about 2 were recorded after drying and compaction. New tests were carried out to support the Qualification File of the process, which was submitted to ONDRAF/NIRAS. After performing many trials using inactive resins, the Operator dried two batches of radioactive resins and super-compacted four drums: this was a planned qualification step, aiming at the validation of process parameters. In the meantime, ONDRAF/NIRAS made parallel progress in the design of the future Near Surface Repository, where conditioned resins will be disposed of, and found some incompatibility between encapsulated dried resin beads and water intrusion hazards inside the repository. Based on the risk that dried resin beads will swell if they absorb water, causing mechanical stresses likely to compromise the integrity of the disposal site, ONDRAF/NIRAS informed Tihange NPP that the Hot Compaction Unit would not be qualified as spent resins conditioning process. Further to this irrevocable decision, the Operator undertook three actions: - The first one was the qualification and purchase of waste containers to provide additional temporary storage capacity on site for its operational spent resins; - The second one is the selection of a new spent resins conditioning process and the performance of the related qualification tests to demonstrate it meets the updated Waste Acceptance Criteria; - The last one is the dismantling of the Hot Compaction Unit to clear the space and enable the installation of new equipment. This latter task was carried out in 2018, involving decontamination and free release measurements of all the materials, a very limited fraction of it having to be further processed as radioactive waste. Nevertheless, Tihange NPP wishes to keep the super-compactor and its hydraulic unit for a possible resale or reuse for the treatment of mixed waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Composition and Realization of Source-to-Sink High-Performance Flows: File Systems, Storage, Hosts, LAN and WAN

A number of Department of Energy (DOE) science applications, involving exascale computing systems and large experimental facilities, are expected to generate large volumes of data, in the range of petabytes to exabytes, which will be transported over wide-area networks for the purpose of storage, visualization, and analysis. To support such capabilities, significant progress has been made in various components including the deployment of 100 Gbps networks with future 1 Tbps bandwidth, increases in end-host capabilities with multiple cores and buses, capacity improvements in large disk arrays, and deployment of parallel file systems such as Lustre and GPFS. High-performance source-to-sink data flows must be composed of these component systems, which requires significant optimizations of the storage-to-host data and execution paths to match the edge and long-haul network connections. In particular, end systems are currently supported by 10-40 Gbps Network Interface Cards (NIC) and 8-32 Gbps storage Host Channel Adapters (HCAs), which carry the individual flows that collectively must reach network speeds of 100 Gbps and higher. Indeed, such data flows must be synthesized using multicore, multibus hosts connected to high-performance storage systems on one side and to the network on the other side. Current experimental results show that the constituent flows must be optimally composed and preserved from storage systems, across the hosts and the networks with minimal interference. Furthermore, such a capability must be made available transparently to the science users without placing undue demands on them to account for the details of underlying systems and networks. And, this task is expected to become even more complex in the future due to the increasing sophistication of hosts, storage systems, and networks that constitute the high-performance flows. The objectives of this proposal are to (1) develop and test the component technologies and their synthesis methods to achieve source-to-sink high-performance flows, and (2) develop tools that provide these capabilities through simple interfaces to users and applications. In terms of the former, we propose to develop (1) optimization methods that align and transition multiple storage flows to multiple network flows on multicore, multibus hosts; and (2) edge and long-haul network path realization and maintenance using advanced provisioning methods including OSCARS and OpenFlow. We also propose synthesis methods that combine these individual technologies to compose high-performance flows using a collection of constituent storage-network flows, and realize them across the storage and local network connections as well as long-haul connections. We propose to develop automated user tools that profile the hosts, storage systems, and network connections; compose the source-to-sink complex flows; and set up and maintain the needed network connections. These solutions will be tested using (1) 100 Gbps connection(s) between Oak Ridge National Laboratory (ORNL) and Argonne National Laboratory (ANL) with storage systems supported by Lustre and GPFS file systems with an asymmetric connection to University of Memphis (UM); (2) ORNL testbed with multicore and multibus hosts, switches with OpenFlow capabilities, and network emulators; and (3) 100 Gbps connections from ESnet and their Openflow testbed, and other experimental connections. This proposal brings together the expertise and facilities of the two national laboratories, ORNL and ANL, and UM. It also represents a collaboration between DOE and the Department of Defense (DOD) projects at ORNL by sharing technical expertise and personnel costs, and leveraging the existing DOD Extreme Scale Systems Center (ESSC) facilities at ORNL.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server

Abstract With machine learning applications now spanning a variety of computational tasks, multi-user shared computing facilities are devoting a rapidly increasing proportion of their resources to such algorithms. Graph neural networks (GNNs), for example, have provided astounding improvements in extracting complex signatures from data and are now widely used in a variety of applications, such as particle jet classification in high energy physics (HEP). However, GNNs also come with an enormous computational penalty that requires the use of GPUs to maintain reasonable throughput. At shared computing facilities, such as those used by physicists at Fermi National Accelerator Laboratory (Fermilab), methodical resource allocation and high throughput at the many-user scale are key to ensuring that resources are being used as efficiently as possible. These facilities, however, primarily provide CPU-only nodes, which proves detrimental to time-to-insight and computational throughput for workflows that include machine learning inference. In this work, we describe how a shared computing facility can use the NVIDIA Triton Inference Server to optimize its resource allocation and computing structure, recovering high throughput while scaling out to multiple users by massively parallelizing their machine learning inference. To demonstrate the effectiveness of this system in a realistic multi-user environment, we use the Fermilab Elastic Analysis Facility augmented with the Triton Inference Server to provide scalable and high-throughput access to a HEP-specific GNN and report on the outcome.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Power Take-off System for Marine Renewable Devices

The Power Take-off (PTO) System for Marine Renewable Devices (Project) envisioned the design, manufacture and testing of a generator that would ultimately be suitable for fully flooded seawater operation. This would require a generator design and bearing system capable of operating in the presence of seawater without corrosion. The original generator concept design focused on a switched reluctance electromagnetic design for the generator with the understanding that the switched reluctance design would be more robust and tolerant of a seawater medium. A trade study was conducted to prove that hypothesis, but the results of the study indicated that there was no benefit to adopting a switched reluctance design over a more conventional synchronous permanent magnet design. In addition, the novelty of the switched reluctance design added more technical risk to the Project than was warranted. ORPC’s cost of energy analyses pointed to reliability as being the most critical factor for achieving Project systems performance advancement (SPA) metrics. Consequently, the choice of a permanent magnet (PM) machine design over a potentially less reliable switched reluctance design was necessary to achieve the Project results. As a result, ORPC determined that a PM design was a more appropriate design to achieve the Project goals and ORPC selected a highly qualified PM generator firm for the design, manufacture and factory testing of the generator. The factory acceptance test (FAT) of the generator took place on March 20-22, 2017 and all tests were successfully passed. Developing a design for a driveline as part of the PTO system required a full understanding of the loads and operating conditions expected. Detailed analyses for developing turbine loads and subsequent definition of bearing specifications were performed using various analytical methods. Two candidate bearing designs were proposed as part of the Project. After initial design work had been completed, ORPC designed a representative full-scale driveline to validate the detailed design of the advanced driveline components. Full-scale components were manufactured, instrumented and tested, simulating actual system parameters. This full-scale bearing test was conducted at the University of Maine Advanced Structures and Composites Laboratory. The purpose of this test was to characterize the frictional losses associated with bearings and test the suitability and functionality of some commercially available shaft couplings and expansion bushings in cross-flow turbine drivelines. In parallel with component testing and dynamometer testing of the PTO system, ORPC performed design work to demonstrate how the advanced PTO system will be integrated with all ORPC turbine generator units (TGUs). The work would focus primarily on the TidGen® TGU and assess how the advanced PTO would be integrated into TidGen® 001 or other units, then field-deployed and tested in an open water environment under actual operational conditions. In addition to the benefits provided to the MHK industry in general, and ORPC in particular, the Project provided benefits to the general public, including the following: • Increased general awareness of the potential for generation of electricity from ocean energy resources • Demonstrated continuing advancements in the commercialization of MHK technologies, making them more likely to contribute to the electricity generation supply in America in the coming years • Continued awareness of the local economic benefits from the MHK industry through job creation and local spending • Confirmation of a path to improvements in availability, power-to-weight ratio (PWR) and reduction in levelized cost of energy (LCOE). All Project tasks were completed, and the Project objectives were accomplished. All project milestones and deliverables were met. The designs, methodologies, practices, testing, data, analysis and lessons learned from the Project are a step forward in the development of the U.S. MHK industry and provide a sound basis for ultimate commercialization of ORPC’s power systems. Based on the what was learned and demonstrated during the Project, ORPC has determined that the ORPC power systems have the potential of accelerating the commercialization of tidal and ocean current MHK power systems.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

A domain wall-magnetic tunnel junction artificial synapse with notched geometry for accurate and efficient training of deep neural networks

Inspired by the parallelism and efficiency of the brain, several candidates for artificial synapse devices have been developed for neuromorphic computing, yet a nonlinear and asymmetric synaptic response curve precludes their use for backpropagation, the foundation of modern supervised learning. Spintronic devices—which benefit from high endurance, low power consumption, low latency, and CMOS compatibility—are a promising technology for memory, and domain-wall magnetic tunnel junction (DW-MTJ) devices have been shown to implement synaptic functions such as long-term potentiation and spike-timing dependent plasticity. In this work, we propose a notched DW-MTJ synapse as a candidate for supervised learning. Using micromagnetic simulations at room temperature, we show that notched synapses ensure the non-volatility of the synaptic weight and allow for highly linear, symmetric, and reproducible weight updates using either spin transfer torque (STT) or spin–orbit torque (SOT) mechanisms of DW propagation. We use lookup tables constructed from micromagnetics simulations to model the training of neural networks built with DW-MTJ synapses on both the MNIST and Fashion-MNIST image classification tasks. Accounting for thermal noise and realistic process variations, the DW-MTJ devices achieve classification accuracy close to ideal floating-point updates using both STT and SOT devices at room temperature and at 400 K. Our work establishes the basis for a magnetic artificial synapse that can eventually lead to hardware neural networks with fully spintronic matrix operations implementing machine learning.

42 ENGINEERING↗

DOE OSTI2016

Studies of HED Plasmas with Self-Generated Magnetic Field

High-amplitude sub-Larmor-scale electromagnetic turbulence is ubiquitous in high-energy density environments, such as laboratory plasmas produced by high-intensity lasers, e.g., NIF, Omega-EP, Trident, and others, and in astrophysical and space plasmas, e.g., at high-Mach-number collisionless shocks in weakly magnetized plasmas upstream regions of quasi-parallel shocks, sites of magnetic reconnection and others. Studies of plasmas and turbulence in these environments are important for fusion energy sciences and the inertial confinement concept, in particular, as well as to numerous astrophysical systems such as gamma-ray bursts, supernovae blast waves, jets of quasars and active galactic nuclei, shocks in the interplanetary medium, solar flares and many more. Such turbulence can be of various origin and thus have rather different properties, from being purely magnetic (Weibel) turbulence to various types of electromagnetic turbulence (for example, whistler wave turbulence or turbulence produced by filamentation or Weibel-type streaming instability), to purely electrostatic Langmuir turbulence. In this project we use analytical and numerical tools to study the transport, radiative, and magneto-optical properties of plasmas with sub-Larmor-scale turbulence. We discovered the connection of transport/diffusion properties to certain spectral benchmark features of (jitter) radiation produced by the plasma and radiation propagation through it. All regimes, from the relativistic to non-relativistic, were thoroughly investigated and predictions were made for laboratory plasmas and astrophysical plasmas. Thus, all the tasks outlined in the proposal were fully and successfully accomplished.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Protein folding from heterogeneous unfolded state revealed by time-resolved X-ray solution scattering

One of the most challenging tasks in biological science is to understand how a protein folds. In theoretical studies, the hypothesis adopting a funnel-like free-energy landscape has been recognized as a prominent scheme for explaining protein folding in views of both internal energy and conformational heterogeneity of a protein. Despite numerous experimental efforts, however, comprehensively studying protein folding with respect to its global conformational changes in conjunction with the heterogeneity has been elusive. Here we investigate the redox-coupled folding dynamics of equine heart cytochrome c (cyt-c) induced by external electron injection by using time-resolved X-ray solution scattering. A systematic kinetic analysis unveils a kinetic model for its folding with a stretched exponential behavior during the transition toward the folded state. With the aid of the ensemble optimization method combined with molecular dynamics simulations, we found that during the folding the heterogeneously populated ensemble of the unfolded state is converted to a narrowly populated ensemble of folded conformations. These observations obtained from the kinetic and the structural analyses of X-ray scattering data reveal that the folding dynamics of cyt-c accompanies many parallel pathways associated with the heterogeneously populated ensemble of unfolded conformations, resulting in the stretched exponential kinetics at room temperature. This finding provides direct evidence with a view to microscopic protein conformations that the cyt-c folding initiates from a highly heterogeneous unfolded state, passes through still diverse intermediate structures, and reaches structural homogeneity by arriving at the folded state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

The SprayList: a scalable relaxed priority queue

High-performance concurrent priority queues are essential for applications such as task scheduling and discrete event simulation. Unfortunately, even the best performing implementations do not scale past a number of threads in the single digits. This is because of the sequential bottleneck in accessing the elements at the head of the queue in order to perform a DeleteMin operation. In this paper, we present the SprayList, a scalable priority queue with relaxed ordering semantics. Starting from a non-blocking SkipList, the main innovation behind our design is that the DeleteMin operations avoid a sequential bottleneck by ``spraying'' themselves onto the head of the SkipList list in a coordinated fashion. The spraying is implemented using a carefully designed random walk, so that DeleteMin returns an element among the first O(p log^3 p) in the list, with high probability, where p is the number of threads. We prove that the running time of a DeleteMin operation is O(log^3 p), with high probability, independent of the size of the list. Our experiments show that the relaxed semantics allow the data structure to scale for high thread counts, comparable to a classic unordered SkipList. As a result, we observe that, for reasonably parallel workloads, the scalability benefits of relaxation considerably outweigh the additional work due to out-of-order execution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Distributed-memory lattice H-matrix factorization

We parallelize the LU factorization of a hierarchical low-rank matrix (H-matrix) on a distributed-memory computer. This is much more difficult than the H-matrix-vector multiplication due to the dataflow of the factorization, and it is much harder than the parallelization of a dense matrix factorization due to the irregular hierarchical block structure of the matrix. Block low-rank (BLR) format gets rid of the hierarchy and simplifies the parallelization, often increasing concurrency. However, this comes at a price of losing the near-linear complexity of the H-matrix factorization. In this work, we propose to factorize the matrix using a “lattice H-matrix” format that generalizes the BLR format by storing each of the blocks (both diagonals and off-diagonals) in the H-matrix format. These blocks stored in the H-matrix format are referred to as lattices. Thus, this lattice format aims to combine the parallel scalability of BLR factorization with the near-linear complexity of H-matrix factorization. We first compare factorization performances using the H-matrix, BLR, and lattice H-matrix formats under various conditions on a shared-memory computer. Our performance results show that the lattice format has storage and computational complexities similar to those of the H-matrix format, and hence a much lower cost of factorization than BLR. In conclusion, we then compare the BLR and lattice H-matrix factorization on distributed-memory computers. Our performance results demonstrate that compared with BLR, the lattice format with the lower cost of factorization may lead to faster factorization on the distributed-memory computer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Integrated modeling applications for tokamak experiments with OMFIT

We developed one modeling framework for integrated tasks (OMFIT) is a comprehensive integrated modeling framework which will enable physics codes to interact in complicated workflows, and support scientists at all stages of the modeling cycle. The OMFIT development follows a unique bottom-up approach, where the framework design and capabilities organically evolve to support progressive integration of the components that are required to accomplish physics goals of increasing complexity. OMFIT provides a workflow for easily generating full kinetic equilibrium reconstructions that are constrained by magnetic and motional Stark effect measurements, and kinetic profile information that includes fast-ion pressure modeled by a transport code. It was found that magnetic measurements can be used to quantify the amount of anomalous fast-ion diffusion that is present in DIII-D discharges, and provide an estimate that is consistent with what would be needed for transport simulations to match the measured neutron rates. OMFIT was used to streamline edge-stability analyses, and evaluate the effect of resonant magnetic perturbation (RMP) on the pedestal stability, which have been found to be consistent with the experimental observations. Moreover, the development of a five-dimensional numerical fluid model for estimating the effects of the interaction between magnetohydrodynamic (MHD) and microturbulence, and its systematic verification against analytic models was also supported by the framework. OMFIT was used for optimizing an innovative high-harmonic fast wave system proposed for DIII-D. For a parallel refractive index n(parallel to) > 3, the conditions for strong electron-Landau damping were found to be independent of launched n(parallel to) and poloidal angle. OMFIT has been the platform of choice for developing a neural-network based approach to efficiently perform a non-linear multivariate regression of local transport fluxes as a function of local dimensionless parameters. Transport predictions for thousands of DIII-D discharges showed excellent agreement with the power balance calculations across the whole plasma radius and over a broad range of operating regimes. Concerning predictive transport simulations, the framework made possible the design and automation of a workflow that enables self-consistent predictions of kinetic profiles and the plasma equilibrium. It is found that the feedback between the transport fluxes and plasma equilibrium can significantly affect the kinetic profiles predictions. Finally, such a rich set of results provide tangible evidence of how bottom-up approaches can potentially provide a fast track to integrated modeling solutions that are functional, cost-effective, and in sync with the research effort of the community.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Modular performance prediction for scientific workflows using Machine Learning

Scientific workflows provide an opportunity for declarative computational experiment design in an intuitive and efficient way. A distributed workflow is typically executed on a variety of resources, and it uses a variety of computational algorithms or tools to achieve the desired outcomes. Such a variety imposes additional complexity in scheduling these workflows on large scale computers. As computation becomes more distributed, insights into expected workload that a workflow presents become critical for effective resource allocation. In this paper, we present a modular framework that leverages Machine Learning for creating precise performance predictions of a workflow. The central idea is to partition a workflow in such a way that makes the task of forecasting each atomic unit manageable and gives us a way to combine the individual predictions efficiently. We recognize a combination of an executable and a specific physical resource as a single module. This gives us a handle to characterize workload and machine power as a single unit of prediction. Overall, our modular technique of creating atomic modules and deployment of longest-path approach to estimate workflow performance, allows the framework to adapt to highly complex nested directed acyclic workflows and scale to new scenarios, since it does not make assumptions of underlying workflow structure. We present performance estimation results of independent workflow modules executed on the XSEDE SDSC Comet cluster using various Machine Learning algorithms. The results provide insights into the behavior and effectiveness of different algorithms in the context of scientific workflow performance prediction.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

TU-AB-207A-03: Image Quality, Dose, and Clinical Applications

Practicing medical physicists are often time charged with the tasks of evaluating and troubleshooting complex image quality issues related to CT scanners. This course will equip them with a solid and practical understanding of common CT imaging chain and its major components with emphasis on acquisition physics and hardware, reconstruction, artifacts, image quality, dose, and advanced clinical applications. The core objective is to explain the effects of these major system components on the image quality. This course will not focus on the rapid-changing advanced technologies given the two-hour time limit, but the fundamental principles discussed in this course may facilitate better understanding of those more complicated technologies. The course will begin with an overview of CT acquisition physics and geometry. X-ray tube and CT detector are important acquisition hardware critical to the overall image quality. Each of these two subsystems consists of several major components. An in-depth description of the function and failure modes of these components will be provided. Examples of artifacts related to these failure modes will be presented: off-focal radiation, tube arcing, heel effect, oil bubble, offset drift effect, cross-talk effect, and bad pixels. The fundamentals of CT image reconstruction will first be discussed on an intuitive level. Approaches that do not require rigorous derivation of mathematical formulations will be presented. This is followed by a detailed derivation of the Fourier slice theorem: the foundation of the FBP algorithm. FBP for parallel-beam, fan-beam, and cone-beam geometries will be discussed. To address the issue of radiation dose related to x-ray CT, recent advances in iterative reconstruction, their advantages, and clinical applications will also be described. Because of the nature of fundamental physics and mathematics, limitations in data acquisition, and non-ideal conditions of major system components, image artifact often arise in the reconstructed images. Because of the limited scope of this course, only major imaging artifacts, their appearance, and possible mitigation and corrections will be discussed. Assessment of the performance of a CT scanner is a complicated subject. Procedures to measure common image quality metrics such as high contrast spatial resolution, low contrast detectability, and slice profile will be described. The reason why these metrics used for FBP may not be sufficient for statistical iterative reconstruction will be explained. Optimizing radiation dose requires comprehension of CT dose metrics. This course will briefly describe various dose metrics, and interaction with acquisition parameters and patient habitus. CT is among the most frequently used imaging tools due to its superior image quality, easy to operate, and a broad range of applications. This course will present several interesting CT applications such as a mobile CT unit on an ambulance for stroke patients, low dose lung cancer screening, and single heartbeat cardiac CT. Learning Objectives: Understand the function and impact of major components of X-ray tube on the image quality. Understand the function and impact of major components of CT detector on the image quality. Be familiar with the basic procedure of CT image reconstruction. Understand the effect of image reconstruction on CT image quality and artifacts. Understand the root causes of common CT image artifacts. Be familiar with image quality metrics especially high and low contrast resolution, noise power spectrum, slice sensitivity profile, etc. Understand why basic image quality metrics used for FBP may not be sufficient to characterize the performance of advanced iterative reconstruction. Be familiar with various CT dose metrics and their interaction with acquisition parameters. New development in advanced CT clinical applications. JH: Employee of GE Healthcare. FD: No disclosure.; J. Hsieh, Jiang Hsieh is an employee of GE Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

DECOVALEX-2023: Task F1 Final Report

DECOVALEX-2023 Task F is a comparison of models and methods for post-closure performance assessment (PA) of a deep geologic repository for radioactive waste. The general aims of Task F are to build confidence in the models, methods, and software used for PA and to stimulate additional research and development in PA methodologies. The task objectives are to motivate development of PA modelling skills and capabilities, to examine the influence of model choices on calculated repository performance, and to compare the uncertainties introduced by model choices to other sources of uncertainty. Task F involves no actual experiment or site. It is a PA modelling exercise that requires the conceptual development of hypothetical repository designs and geologic settings. Because three of the teams were interested in salt and the rest of the teams were interested in crystalline rock, Task F was split into two branches: Task F1 for crystalline rock and Task F2 for salt. This report is for Task F1, crystalline rock. Teams from seven countries (Canada, Czech Republic, Germany, Korea, Sweden, Taiwan, and United States) participated in Task F1. The teams worked together to define the features, events, and processes of the reference case repository and established a set of performance measures. In addition, they defined a set of benchmark problems designed to test and compare modelling capabilities for fracture flow and transport at different scales. The repository design and benchmark problems are documented in a Task Specification that evolved over time as the group honed the specifications. The benchmark problems verified that each team can aptly model flow and transport in fractured media in 1-, 2-, and 3-dimensions. Two general approaches were used for the 3-dimensional benchmarks: discrete fracture network (DFN) and equivalent continuous porous medium (ECPM). DFN modelling involves explicit meshing of each fracture while ECPM modelling aims to capture the effective porosity and directional permeability of each cell in a space-filling mesh as affected by intersecting fractures. In some models, a combination of the two is used, i.e., DFN for large known fractures and ECPM for the rest of the domain. Transport is solved by using either the advection-dispersion equation or particle tracking. Although some variation is observed among model breakthrough curves in the benchmark problems, there is strong agreement in breakthrough behaviour up to at least the 75 th percentile for all benchmarks. At the 90 th percentile, breakthrough results show larger differences, suggesting several models retain substantially higher fractions of tracer in regions of slower moving water. In addition to the flow and transport benchmarks, several teams completed the source term benchmark, verifying capabilities for modelling radionuclide decay and ingrowth, waste package breach, instant release fractions, fuel matrix degradation rates, and radionuclide solubility limitations. The reference case is conceptualized as a generic spent fuel repository at a depth of 450 m in fractured crystalline rock. The repository has 50 parallel backfilled drifts, each with 50 deposition holes 6 m apart. Each deposition hole contains a 4-PWR waste package and bentonite buffer. The rock domain is 5 km in length, 2 km in width, and 1 km in depth. It has 6 deterministic fractured deformation zones and a multitude of stochastic fractures. Teams generally used the ECPM approach for the entire rock or a hybrid approach in which the deterministic fracture zones are modelled with a DFN and the rest of the rock is modelled by ECPM. Of the reference case problems specified, only the results of the initial reference case problem are compared in this report. The initial problem focuses on transport from the deposition holes to the surface, i.e., it neglects waste package performance. Tracers are released at all waste package locations at time zero and tracked for their releases to the near field and ground surface. The water fluxes calculated at the ground surface entry and exit regions of the domain are similar for all models except for two that have considerably lower fluxes. For tracer transport, large differences are observed among models in the magnitude of tracer transported. Much of the difference appears to be due to how the repository is implemented and hence the different degrees of repository simplification. Models that exclude the drifts, buffer, and backfill from the domain tend to show greater release of tracers and radionuclides from the repository. The initial study presented here indicates that major differences in modelling important processes within the repository (e.g., diffusion through buffer and backfill) can produce broadly different release and transport results, especially when those processes are excluded. Even for the models that included all specified features, events, and processes, the results show significant differences and demonstrate the importance of examining multiple modelling approaches in performance assessment. The differences in results observed in this study are expected to motivate teams to either increase complexity in future versions of the reference case models or to improve methods to account for the effects of simplified features and processes. Either way, future improvements in these models are expected to produce results that more closely agree.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2014

TH-E-9A-01: Medical Physics 1.0 to 2.0, Session 4: Computed Tomography, Ultrasound and Nuclear Medicine

Medical Physics 2.0 is a bold vision for an existential transition of clinical imaging physics in face of the new realities of value-based and evidencebased medicine, comparative effectiveness, and meaningful use. It speaks to how clinical imaging physics can expand beyond traditional insular models of inspection and acceptance testing, oriented toward compliance, towards team-based models of operational engagement, prospective definition and assurance of effective use, and retrospective evaluation of clinical performance. Organized into four sessions of the AAPM, this particular session focuses on three specific modalities as outlined below. CT 2.0: CT has been undergoing a dramatic transition in the last few decades. While the changes in the technology merits discussions of their own, an important question is how clinical medical physicists are expected to effectively engage with the new realities of CT technology and practice. Consistent with the upcoming paradigm of Medical Physics 2.0, this CT presentation aims to provide definitions and demonstration of the components of the new clinical medical physics practice pertaining CT. The topics covered include physics metrics and analytics that aim to provide higher order clinicallyrelevant quantification of system performance as pertains to new (and not so new) technologies. That will include the new radiation and dose metrics (SSDE, organ dose, risk indices), image quality metrology (MTF/NPS/d’), task-based phantoms, and the effect of patient size. That will follow with a discussion of the testing implication of new CT hardware (detectors, tubes), acquisition methods (innovative helical geometries, AEC, wide beam CT, dual energy, inverse geometry, application specialties), and image processing and analysis (iterative reconstructions, quantitative CT, advanced renditions). The presentation will conclude with a discussion of clinical and operational aspects of Medical Physics 2.0 including training and communication, use optimization (dose and technique factors), automated analysis and data management (automated QC methods, protocol tracking, dose monitoring, issue tracking), and meaningful QC considerations. US 2.0: Ultrasound imaging is evolving at a rapid pace, adding new imaging functions and modes that continue to enhance its clinical utility and benefits to patients. The ultrasound talk will look ahead 10–15 years and consider how medical physicists can bring maximal value to the clinical ultrasound practices of the future. The roles of physics in accreditation and regulatory compliance, image quality and exam optimization, clinical innovation, and education of staff and trainees will all be considered. A detailed examination of expected technology evolution and impact on image quality metrics will be presented. Clinical implementation of comprehensive physics services will also be discussed. Nuclear Medicine 2.0: Although the basic science of nuclear imaging has remained relatively unchanged since its inception, advances in instrumentation continue to advance the field into new territories. With a great number of these advances occurring over the past decade, the role and testing strategies of clinical nuclear medicine physicists must evolve in parallel. The Nuclear Medicine 2.0 presentation is designed to highlight some of the recent advances from a clinical medical physicist perspective and provide ideas and motivation for designing better evaluation strategies. Topics include improvement of traditional physics metrics and analytics, testing implications of hybrid imaging and advanced detector technologies, and strategies for effective implementation into the clinic. Learning Objectives: Become familiar with new physics metrics and analytics in nuclear medicine, CT, and ultrasound. To become familiar with the major new developments of clinical physics support. To understand the physics testing implications of new technologies, hardware, software, and applications. Identify approaches for implementing comprehensive medical physics services in future imaging practices.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Development and applications of retro-reflective surfaces for ultrasound in LBE

The Belgian Nuclear Research Centre SCK-CEN is in the process of developing MYRRHA - a fast flux research reactor to replace the aging BR2. MYRRHA is conceptualized as an accelerator driven system cooled by lead bismuth eutectic mixture (LBE). As LBE is opaque to visual light, ultrasonic measurement techniques are employed as the main technology to provide feedback to submerged operations when needed. Conceptually, MYRRHA is a pool type reactor divided in a cold lower plenum and hot upper plenum separated by a diaphragm that forces the main flow through the core. The main flow is cooled by four heat exchangers and driven by two liquid metal pumps. One of the tasks tackled using ultrasound is locating a potentially lost fuel assembly to assist a recovery operation. As all fuel manipulations in MYRRHA are performed in the lower plenum, a potentially lost fuel assembly is located in the lower plenum. Buoyancy will force the lost fuel assembly to float against the diaphragm unless it is still partially inserted in the core. Because of the latter situation, an ultrasonic scan localizing the fuel assembly should be performed from a large distance to avoid a collision with such a partially inserted fuel assembly. Unfortunately, standard machined stainless steel objects, such as a fuel assembly, reflect an ultrasonic pulse in a specular way which induces stringent requirements on the alignment of the ultrasonic sensor with respect to the fuel assembly as we cannot rely on diffuse reflections and/or scattering of the ultrasonic pulse. Moreover, increasing the distance also increases geometric spreading and absorption of the pulse weakening the signal amplitude even faster to noise levels when deviating from perfect alignment. An alternative approach consists in relying on reflections from the known surroundings: a lost fuel assembly will block the line-of-sight to the diaphragm resulting in an anomaly in the reflection - either a shorter than expected time-of-flight of the pulse or a complete absence of a reflection like a shadow. In that way, it suffices to align the sensor with the diaphragm instead of the fuel assembly which is much easier to achieve as the robotics on which the sensor is mounted move parallel with the diaphragm. The alignment requirement in the latter approach can be further relaxed by using a tiling of retro-reflectors on the lower surface of the diaphragm. In that way, alignment becomes less vital and the main source of acoustic energy loss - geometric spread of the beam - is almost completely removed, leaving only absorption losses. In this paper, we present the first results in developing a retro reflectance surface for ultrasound in LBE. We present experimental results for different designs of retro-reflectors in both water and LBE. We discuss both linear and array retro-reflectors of different sizes and investigate the influence of the main relevant ultrasonic parameters such as wavelength and spot size on the strength of the received reflection under different alignment angles. We also demonstrate how retro-reflective surfaces can be exploited when localizing objects using linear and rotating scanning methods. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2017

Build and Execute Environment

At exascale, the challenge becomes to develop applications that run at scale and use exascale platforms reliably, efficiently, and flexibly. Workflows become much more complex because they must seamlessly integrate simulation and data analytics. They must include down-sampling, post-processing, feature extraction, and visualization. Power and data transfer limitations require these analysis tasks to be run in-situ or in-transit. We expect successful workflows will comprise multiple linked simulations along with tens of analysis routines. Users will have limited development time at scale and, therefore, must have rich tools to develop, debug, test, and deploy applications. At this scale, successful workflows will compose linked computations from an assortment of reliable, well-defined computation elements, ones that can come and go as required, based on the needs of the workflow over time. We propose a novel framework that utilizes both virtual machines (VMs) and software containers to create a workflow system that establishes a uniform build and execution environment (BEE) beyond the capabilities of current systems. In this environment, applications will run reliably and repeatably across heterogeneous hardware and software. Containers, both commercial (Docker and Rocket) and open-source (LXC and LXD), define a runtime that isolates all software dependencies from the machine operating system. Workflows may contain multiple containers that run different operating systems, different software, and even different versions of the same software. We will run containers in open-source virtual machines (KVM) and emulators (QEMU) so that workflows run on any machine entirely in user-space. On this platform of containers and virtual machines, we will deliver workflow software that provides services, including repeatable execution, provenance, checkpointing, and future proofing. We will capture provenance about how containers were launched and how they interact to annotate workflows for repeatable and partial re-execution. We will coordinate the physical snapshots of virtual machines with parallel programming constructs, such as barriers, to automate checkpoint and restart. We will also integrate with HPC-specific container runtimes to gain access to accelerators and other specialized hardware to preserve native performance. Containers will link development to continuous integration. When application developers check code in, it will automatically be tested on a suite of different software and hardware architectures.

Guan, Qiang↗

DOE OSTI2017

Build and Execute Environment

Guan, Qiang↗