DOE's Safety Analysis Report for Packages (SARP) Review Course: Development and Evolution
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Combined Effort to De-Inventory the New Brunswick Laboratory of Plutonium Certified Reference Materials - 20504
To de-inventory plutonium Certified Reference Materials standards (CRMs) from the New Brunswick National Laboratory (NBL), the Argonne National Laboratory (ANL) and Savannah River National Laboratory (SRNL) formed a unique team. This teaming arrangement leveraged the different capabilities and expertise of both laboratories, helping to ensure the effort would be performed both safely and cost-effectively. With the NBL then physically housed on the ANL site, ANL served as the host-site, providing the necessary facilities and support services (e.g., supplying everything from hoods, loading equipment, and scales to Health Physics supports). From a packaging perspective, ANL's responsibilities focused mainly on characterizing and preparing the contents for shipment, followed by performing the Shipper of Record activities. With SRNL being the Package Design Authority of the 9978, the Type B packaging used for this effort, SRNL's scope included developing a Safety Analysis Report in Packaging (SARP) Letter Amendment to add the CRMs as authorized contents to the 9978. Recognizing competency, ANL prodigiously tasked SRNL to perform the actual loading of the CRMs into the 9978s, while physically on the ANL site. This paper details five different aspects of this effort. They are: 1. Characterization/validation of the CRM contents. 2. Development of the 9978 SARP Letter Amendment for the content change. 3. Development of the nitrogen inerting method to address SARP identified potential flammability concerns. 4. Renewal of the SRNL 9978 loading procedure. 5. Performance of the actual work execution activities. In 2017, the first nine packages were successfully loaded and shipped from NBL, while in 2019, the last ten were successfully loaded and planned for shipment. Without ANL leveraging SRNL's strengths, the de-inventorying of NBL of its plutonium CRMs would have likely taken many years longer. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Safety Analysis Report For Packaging, Y-12 National Security Complex, Model ES-3100 Package with Bulk HEU Contents
This safety analysis report for packaging (SARP) presents the results of the safety analysis prepared in support of the Consolidated Nuclear Security, LLC (CNS) request for licensing of the Model ES-3100 package with bulk highly enriched uranium (HEU) contents and issuance of a Type B(U) Fissile Material Certificate of Compliance. This SARP, published in the format specified in the U.S. Nuclear Regulatory Commission (NRC) Regulatory Guide 7.9 and using information provided in UCID-21218 and NRC Regulatory Guide 7.10, demonstrates that the Y-12 National Security Complex (Y-12) ES-3100 package with bulk HEU contents meets the established NRC regulations for packaging, preparation for shipment, and transportation of radioactive materials given in Title 10, Part 71, of the Code of Federal Regulations (CFR) [10 CFR 71] as well as U.S. Department of Transportation (DOT) regulations for packaging and shipment of hazardous materials given in Title 49 CFR.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Safety Analysis Report for Packagings – Model 9979 Type AF Shipping Package
This Safety Analysis Report for Packaging (SARP) documents the performance of the Department of Energy (DOE) Model USA/9979/AF-96 shipping package and compliance with the regulatory safety requirements of Title 10 and Title 49 of the Code of Federal Regulations. The SARP is prepared in accordance with U.S. Department of Energy (DOE) Order 460.1D. The Model 9979 Type A Fissile shipping package replaces the Department of Transportation (DOT) UN1A2 Specification Packaging. Formerly, the UN1A2 was authorized under Title 49 of the Code of Federal Regulations, Section 173.417 (2004) for transport of Type A quantities of fissile material over public highways. In accordance with the Federal Register, final rule making issued on January 26, 2004, the Nuclear Regulatory Commission, in a joint effort with the Department of Transportation, initiated a planned phase out of multiple Specification Packagings from 49 CFR 173, the UN1A2 being one. The rulemaking mandated, by law, removal of these specification packagings from service no later than September 30, 2008. The 9979 is a Performance Packaging and replaces the UN1A2 Specification Packaging. The Packaging Technology and Transportation Engineering (PT&TE) organization of the Savannah River National Laboratory (SRNL) is the Design Authority and Design Agency for the 9979.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Monolayer Superconductivity and Tunable Topological Electronic Structure at the Fe(Te,Se)/Bi 2 Te 3 Interface
The interface between 2D topological Dirac states and an s-wave superconductor is expected to support Majorana-bound states (MBS) that can be used for quantum computing applications. Realizing these novel states of matter and their applications requires control over superconductivity and spin-orbit coupling to achieve spin-momentum-locked topological interface states (TIS) which are simultaneously superconducting. While signatures of MBS have been observed in the magnetic vortex cores of bulk FeTe 0.55 Se 0.45 , inhomogeneity and disorder from doping make these signatures unclear and inconsistent between vortices. In this work, superconductivity is reported in monolayer (ML) FeTe 1–y Se y (Fe(Te,Se)) grown on Bi 2 Te 3 by molecular beam epitaxy (MBE). Spin and angle-resolved photoemission spectroscopy (SARPES) directly resolve the interfacial spin and electronic structure of Fe(Te,Se)/Bi 2 Te 3 heterostructures. For y = 0.25, the Fe(Te,Se) electronic structure is found to overlap with the Bi 2 Te 3 TIS and the desired spin-momentum locking is not observed. In contrast, for y = 0.1, reduced inhomogeneity measured by scanning tunneling microscopy (STM) and a smaller Fe(Te,Se) Fermi surface with clear spin-momentum locking in the topological states are found. Hence, it is demonstrated that the Fe(Te,Se)/Bi 2 Te 3 system is a highly tunable platform for realizing MBS where reduced doping can improve characteristics important for Majorana interrogation and potential applications.
36 MATERIALS SCIENCE↗
Computed Tomography-Based Delta-Radiomics Analysis for Discriminating Radiation Pneumonitis in Patients With Esophageal Cancer After Radiation Therapy
Our purpose was to construct a computed tomography (CT)–based delta-radiomics nomogram and corresponding risk classification system for individualized and accurate estimation of severe acute radiation pneumonitis (SARP) in patients with esophageal cancer (EC) after radiation therapy.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
SMC 2021 : Analyzing Resource Utilization and User Behavior on Titan Supercomputer
Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.
42 ENGINEERING↗
Fiberboard Properties and Degradation Rates for Storage of 9975 Shipping Package in KAC (2020 Status Report)
Savannah River Site (SRS) stores plutonium materials within model 9975 shipping packages in the K-Area Complex (KAC). The 9975 shipping package consists of a 35 gallon stainless steel drum, Celotex fiberboard insulation, lead shield, and primary and secondary containment vessels. The 9975 shipping package design, performance, and analysis for safe transport of radioactive material are described in the Safety Analysis Report for Packaging (SARP). Celotex fiberboard provides three safety functions: thermal insulation to limit internal temperature during a fire, criticality control, and resistance to package crushing. The fiberboard material must retain its dimensions and density within certain ranges to provide the required impact resistance, criticality control, and fire resistance. The SRS Surveillance Program monitors material performance to establish a basis for service life and ensures the continued integrity of 9975 packages. Fiberboard samples, taken from multiple fiberboard assemblies fabricated from cane and softwood fiberboard, have been conditioning in elevated temperature environments since 2005. The samples are periodically examined to monitor thermal, mechanical, and physical properties, and assess degradation trends. Fiberboard properties of interest that are evaluated to demonstrate acceptable long-term performance include dimensional stability, density, compressive strength, thermal conductivity, and specific heat capacity. Duplicate samples from multiple package sources have been tested to identify the range of variability in fiberboard properties and degradation rates. Baseline and long-term testing of fiberboard material properties have been reported previously; reference 6 summarized experimental results of cane and softwood fiberboard through May 2019 and presented degradation models for the measured properties. This report presents the cumulative data collected through June 2020 and the corresponding updated aging models.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Package Review Guide for Reviewing Safety Analysis Reports for Packages (Revision 4)
This Package Review Guide (PRG) provides guidance for Department of Energy (DOE) review and approval of packages to transport fissile and Type B quantities of radioactive material. It fulfills, in part, the requirements of DOE Order 460.1D for the Headquarters Certifying Official to establish standards and to provide guidance for the preparation of Safety Analysis Reports for Packages (SARP). This Package Review Guide is intended for use by the United States Department of Energy Headquarters Certifying Official and their staff, DOE Secretarial offices, operations/field offices, and applicants for Department of Energy package approval. This Package Review Guide is generally organized at the chapter-level in a format similar to that recommended in United States Nuclear Regulatory Commission Regulatory Guide 7.9. One notable exception is the addition of Chapter 9 Quality Assurance, which is not included as a separate chapter in Regulatory Guide 7.9. Within each chapter, this Package Review Guide addresses the technical and regulatory bases for the review, the manner in which the review is accomplished, and findings that are generally applicable for a package that meets the approval standards.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Foam Degradation Model during Thermal Hypothetical Accident Conditions
Current practice in Safety Analysis Report for Packaging (SARP) thermal analysis for hypothetical accident conditions (HAC) of packages is to not explicitly model the consequence to the foam impact limiting material, but instead to make conservative estimates of those consequences and begin calculations from that point. This approach is typically used for the foam regression distance, which is the thickness of the degraded foam layer after the 30-minute fire event. Estimates for regression distance can come directly from package burn test results or from correlations based on one-dimensional testing by the foam manufacturer. In either case, this is a subjective process, and although it can be used to produce a conservative result, it is a significant approximation in the analysis. This paper describes testing and refinement of a simplified foam regression model. Comparisons are made against available data from laboratory experiments. Model refinements were tested until satisfactory agreement was achieved. The final modeling approach was then compared with post-test inspection results from HAC burn test data. A new model with an effective thermal conductivity based on foam to char density variation was shown successful in representing laboratory-scale foam thermal degradation experiments and full-scale burn tests of a current package. This model offers a technically defensible and predictive approach for modeling thermal degradation of rigid polyurethane insulation in transport packages.
36 MATERIALS SCIENCE↗
Recertification of the DOE 9978 Type B Package: Lessons L
The history and limited use of the 9978 Type B package history represents a complex case story. Unprecedented delays (from Covid-19) caused the annual maintenance of nine loaded 9978 packages to expire before the actual shipments were initiated. Additionally, with the delays, the content amendment authorizing the Pu and Am standards as authorized content expired, hence additionally requiring a full SARP revision prior to making the shipments
Ketusky, Edward T. [Savannah River National Labora↗
Time Log of the Unloading of an NAC-LWT Cask from a Semi at Oak Ridge National Laboratory in 2016
On Jan 21, 2016, a NAC LWT was unloaded at ORNL. This is a firsthand, moment by moment time record of the movements necessary for unloading (NAC-LWT SARP). This is a legal weight truck cask, conveyed within a shipping container that can be disassembled.
42 ENGINEERING↗
Interpreting Write Performance of Supercomputer I/O Systems with Regression Models
This work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads; (2) build features from write patterns and key parameters of the system architecture and configurations; (3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10× on some samples for both of the target systems.
Xie, Bing↗
I/O Bottleneck Detection and Tuning: Connecting the Dots using Interactive Log Analysis
Using parallel file systems efficiently is a tricky problem due to inter-dependencies among multiple layers of I/O software, including high-level I/O libraries (HDF5, netCDF, etc.), MPI-IO, POSIX, and file systems (GPFS, Lustre, etc.). Profiling tools such as Darshan collect traces to help understand the I/O performance behavior. However, there are significant gaps in analyzing the collected traces and then applying tuning options offered by various layers of I/O software. Seeking to connect the dots between I/O bottleneck detection and tuning, we propose DXT Explorer, an interactive log analysis tool. In this paper, we present a case study using our interactive log analysis tool to identify and apply various I/O optimizations. We report an evaluation of performance improvement achieved for four I/O kernels extracted from science applications.
Bez, Jean Luca↗
Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials
This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.
97 MATHEMATICS AND COMPUTING↗
Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing
The 2021 Smoky Mountains Computational Sciences and Engineering Conference enlists scientists from across Oak Ridge National Laboratory (ORNL) and industry to be data sponsors and help create data analytics and edge computing challenges for eminent datasets in a variety of scientific domains. This work describes the significance of each of the eight datasets and their associated challenge questions. The challenge questions for each dataset were required to cover multiple difficulty levels. An international call for participation was sent to students, asking them to form teams of up to six people and apply novel data analytics and edge computing methods to solve these challenges.
Devineni, Pravallika↗
Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery
Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.
Somnath, Suhas↗
Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics
The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.
Gainaru, Ana↗