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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Criticality Safety: Early Accidents at Los Alamos [Slides]

The results of an experiment can never be considered a failure. Analyses of the results often suggest modifications to theories and procedures to improve the understanding of the underlying principles. In a similar way, observation and analyses of accidents suggest modifications to procedures and behavior to minimize repetition. The lecture covers early accidents at Los Alamos National Laboratory.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Warhead verification concepts and technologies [Slides]

Weapons verification concepts and technologies are discussed related to Arms Control instrumentation, which is a three-part problem: measurement; certification, and authentication.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

FY20 ASC IC L2 Milestone 7180: Performance Portability of SIERRA Mechanics Applications to ATS-1 and ATS-2. Executive Summary

The overall goal of this work was to accelerate simulations supporting the nuclear deterrence (ND) mission through improved performance of key algorithms in the ASC IC Sierra multi-physics application suite. This work focused on porting and optimizing algorithms for the graphics processing units (GPU) on the second ASC advanced technology system (ATS-2), while maintaining or improving performance on commodity technology systems (CTS) and ATS-1. Furthermore, these algorithmic developments used the ASC developed Kokkos performance portability abstraction library to maintain high performance across platforms using identical code, and enable sustainable reduced-cost migration and performance optimization to emerging hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Establishing a sustainable regulatory framework for the security of radioactive sources through harmonization with a safety regulatory framework

In order to establish and maintain sustainable nuclear security regulatory infrastructures for radioactive sources, it is important for States to develop nuclear security regulations with regulatory requirements and relevant criteria for security, which are consistent and well integrated with those for radiation safety. In establishing national regulations, experts worldwide follow the international recommendations on safety and security of radioactive sources published by the International Atomic Energy Agency (IAEA). Within the IAEA publications on safety and security of radioactive sources, some international recommendations are identical or very similar for both safety and security, for example, the requirement for the establishment of a national registry of radioactive sources. However, some other international recommendations are unique to the security area, such as the recommendation to examine the trustworthiness of employees, or to the safety area, such as the need to establish public exposure controls. Additionally, many international recommendations fall somewhere in between, such as the need for effective authorization of facilities and activities, a regulatory inspection and enforcement regime and the graded approach to establish and apply regulatory requirements. This paper examines how the IAEA international recommendations for establishing regulatory frameworks for safety and security relate to one another.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Impact of MURR LEU Conversion on Beryllium Reflector Lifetime

The U.S. National Nuclear Security Administration (NNSA) Office of Material Management and Minimization (M 3 ) has developed and is pursuing an integrated approach to address the persistent threat posed by unintentional proliferation of nuclear materials. The NNSA M 3 approach reduces the risk of highly enriched uranium (HEU) and plutonium falling into the hands of non-state actors by minimizing the use of and, when possible, eliminating weapons-usable nuclear material around the world. In this geopolitical context, most research and test reactors, both domestic and international, have completed or started a program of conversion from the use of HEU to low-enriched uranium (LEU) as fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Conceptual Design for a Mobile Application to Support Infield Inventory Activities

This paper introduces an inventory assistant being developed at Oak Ridge National Laboratory (ORNL) that we believe will empower users to perform inventory activities at nuclear facilities more accurately, reliably, and quickly. Inventory activities at nuclear facilities are often conducted using pen and paper, which can be time-consuming, tedious, and susceptible to reading or transcription errors. The proposed inventory assistant would replace the paper-based process used by International Atomic Energy Agency (IAEA) inspectors, nuclear facility operators, or verification monitors to complete an inventory of nuclear and non-nuclear items. In general, the inventory assistant would ingest an inventory list, distribute assigned items from the inventory list to one or more mobile devices, enable inventory teams to record their observations in the field, and then enable an inventory lead to integrate and reconcile the observations to produce a final report. The assistant consists of two software components—one for the inventory teams to record observations in the field (In-Field Observations App [IFOA]) and one for the inventory lead to reconcile the inventory list with observations (Distribution, Integration, and Reconciliation Application [DIRA]). This paper introduces the overall workflow of the inventory assistant and describes the IFOA user experience in more detail. To demonstrate the concept, the authors present a use case of IAEA inspectors conducting item counting and tag checking activities of UF6 cylinders at a gas centrifuge enrichment plant with a large number of UF6 cylinders (e.g., thousands). These activities can currently require 30–40 person-days of inspection to complete. Based on experiences during an exercised performed at the IAEA by the ORNL team in 2016, we believe an inventory assistant could allow the IAEA to complete item counting and tag checking using the global identifier or the operator’s barcode in 8–10 person-days of inspection. We would expect other users (e.g., facility operators or verification monitors) to also benefit from significant time savings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

CENC PSN VOID FY20 Report

Maritime trade accounts for approximately 80 percent of international commerce. The high volume of vessels traversing domestic and international ports makes port areas prime targets for terrorism as well as illegal trafficking of drugs and arms (conventional or nuclear). Port security is therefore a worldwide concern affecting global economies, freedom of movement, and national security. However, extensive port monitoring is inherently complex and time consuming — making it truly viable only via an automated framework that can detect potential illicit activity and alert authorities in a timely manner. The development of image processing algorithms for this purpose requires access to large, labeled datasets that cover the breadth of targets of interest as well as the environments that they are observed within. Curated and labeled datasets of this nature are of enormous value to Sandia's Defense Nuclear Nonproliferation and National Security Program portfolios, as well as to Sandia's machine learning/automatic target recognition (ML/ATR) algorithm development and R&D communities. The goal of this project is to create a commercial satellite imagery dataset of labeled maritime vessels in port areas to support the development of ML/ATR algorithms for port security nonproliferation purposes. This dataset — Port Security Nonproliferation Vessel Overhead Imagery Dataset (PSN VOID) — has the potential to support a variety of other ancillary missions, such as maritime domain awareness, domestic and international security, drug interdiction, and weapons trafficking.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Filter Test System for Nuclear Container Filters

The purpose of this procedure is to measure the collection efficiency of the filters that are integrated into the lids of containers for nuclear material at Los Alamos National Laboratory (LANL). As an application of this procedure, a filter test report certificate can be created to document the measurement process. This procedure is intended to describe the TA-55, PF-4 (room 6A) operation of a Filter Test System (FTS) for Hagan and SAVY storage containers.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Machine Learning in Safeguards at Pebble Bed Reactors

The goal of this project is to investigate and demonstrate the applicability of machine learning (ML) in safeguards at pebble bed reactors (PBRs). The detailed scope of work includes working with DOE-Nuclear Energy and other domain experts to examine current safeguards approaches at PBRs, defining ML tasks that can potentially strengthen the safeguards at PBRs, selecting ML task(s) for proof of concept based on safeguards needs and availability of testbeds and datasets, and developing ML algorithm(s) to demonstrate the feasibility of ML in PBR safeguards.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

SEE LANL: Student Educational Experience at LANL [PowerPoint]

SEE LANL is designed to provide university students a hands-on nuclear safeguards training course based on the courses given to all IAEA inspectors. It aims to encourage networking and collaborations between university students/professors and laboratory staff, introduce students to the variety of safeguards-related technical work at LANL and provides early-career technical staff an opportunity to teach. Further details on the program are provided.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

2020 LLNL Nuclear Science and Security Summer Internship Program

The Lawrence Livermore National Laboratory (LLNL) Nuclear Science and Security Summer Internship Program (NS 3 IP) is designed to give graduate students an opportunity to come to LLNL for 8–10 weeks of hands-on research. Students conduct research under the supervision of a staff scientist, attend a weekly lecture series, interact with other students, and present their work in poster format at the end of the program. Students also have the opportunity to meet staff scientists one-on-one, participate in LLNL facility tours (e.g., the National Ignition Facility and Center for Accelerator Mass Spectrometry), and gain a better understanding of the various science programs at LLNL. Due to the travel and access restrictions imposed by the COVID-19 pandemic, the 2020 NS 3 IP was organized as an “all-virtual” internship program. With LLNL’s extensive institutional support, students accessed the laboratory’s cyberinfrastructure through a secure virtual desktop environment and all seminars, mentor interactions, summer presentations, and laboratory tours were performed remotely. While this virtual internship format did not allow for hands-on laboratory research projects, both the interns and their mentors constructed creative research projects that maximized student exposure to nuclear science research that is relevant to DTRA and LLNL interests in nuclear security.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Los Alamos National Laboratory Nuclear Explosives Safety Office - An Overview

Nuclear Explosive Safety Study Groups (NESSG) are convened to evaluate NEOs to determine if positive measures are adequate to meet the Standards as specified in DOE O 452.2E - Nuclear Explosive Safety, DOE O 452.4E – Security and Use Control of Nuclear Explosives and Nuclear Weapons, and NA SD 452.2.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Capability Based Deterrence

Outline: 1) What is Capability Based Deterrence and its implementation in policy; 2) Assessment of Capability Based Deterrence compared to other options; 3) Characteristics of an effective Capability Based Deterrent

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗