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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Comprehensive Assessment of the Viability of Small Modular Reactors in Africa: A Nuclear Security and Nonproliferation Perspective

In response to escalating global energy demands driven by industrialization and the pressing need for decarbonization, this paper explores the potential of Small Modular Reactors (SMRs) as a sustainable energy solution in Africa. Focusing on nuclear security and non-proliferation concerns, the study assesses Africa's energy landscape, emphasizing the need for diverse and reliable power sources. While highlighting the scalability and cost-effectiveness of SMRs, the analysis acknowledges potential challenges associated with their introduction, particularly concerning nuclear security and non-proliferation. Given the recommendation to use High Assay Low Enriched Uranium (HALEU) in some SMRs, it is important to critically consider the security implications of transporting nuclear materials, the proximity of the public to the plant, and the time taken to respond to planned assaults. The possibility of using HALEU makes the nuclear material more prone to adversaries such as sabotage, theft, and terrorism. Utilizing PESTLE analysis, this research seeks to outline the detailed political, economic, social, technological, environmental, and legal readiness of Africa to embrace the first-of-a-kind technology (SMR) while fulfilling its mandate to the Non-Proliferation Treaty (NPT). Examining regulatory frameworks, international cooperation, and safety protocols, the study underscores the importance of regional collaboration to prevent the misuse of nuclear technology for military and malicious intent. Drawing insights from successful case studies, the paper concludes by synthesizing key findings and proposing recommendations for policymakers and stakeholders. These recommendations encompass regulatory enhancement, capacity building, technology transfer, and diplomatic efforts to strengthen nuclear security, non-proliferation, and safeguards in Africa.

Prah, Christina↗
DOE OSTI2023

Second Report of the Nuclear Data Subcommittee of the Nuclear Science Advisory Committee

The central importance of the nuclear data curated by the US Nuclear Data Program (USNDP) for clean energy generation, national security, nonproliferation, medical applications, and space exploration as well as basic science was described in a prior report issued by the DOE/NSF Nuclear Science Advisory Committee subcommittee on Nuclear Data (NSAC-ND) in September 2022. In this report, we present a set of fourteen (14) recommendations that will enhance and advance DOE-NP's stewardship of nuclear data. The first three recommendations focus on the existing core USNDP capabilities, namely: 1) Support the nuclear structure evaluation workforce to improve the currency, consistency, and accessibility of the Evaluated Nuclear Structure Data File (ENSDF); 2) Enhance nuclear reaction evaluation within the USNDP in support of the Evaluated Nuclear Data File (ENDF) through expansion of the workforce and integration of high-performance computing, automation, and machine learning and; 3) Continue atomic mass evaluation in support AME and NUBASE databases. This is followed by eight (8) recommendations representing new cross-cutting initiatives involving both measurement and evaluation to address outstanding nuclear data needs. These new initiatives require a highly trained, diverse workforce that includes personnel with expertise from both inside and outside the nuclear physics community from which evaluators have traditionally been recruited. As such, many of these initiatives are accomplished via a Topical Nuclear Data Collaborations (TNDC). A TNDC is made up of domestic and international stakeholders, subject matter and nuclear data experts, and nuclear data evaluators and features a workforce development plan to ensure that nuclear data evaluators maintain currency in the relevant applications and are seen as equity partners in the endeavor. These include: 1) Establish a coordinated effort to improve evaluation and modeling in nuclear astrophysics for stellar dynamics, multi-messenger astronomy and nucleosynthesis; 2) Initiate a TNDC to develop and maintain nuclear structure evaluation beyond discrete states, including nuclear level densities, photon strength functions and photonuclear data for improved reaction modeling, and exploring nuclear structure at finite temperature; 3) Create a TNDC to perform correlated fission data evaluation, including cross sections, fragment yields, v(A), v(E n ) for nuclear energy, national security, nonproliferation and basic science; 4) From a panel of subject matter experts to establish and annually update a roster of key decay data to nurture its accelerated dissemination including both measurement and evaluation for targeted high-value nuclides for national security, nonproliferation and medical applications; 5) Comprehensive, consistent neutron-induced structure and reaction data for nuclear energy, national security, nonproliferation and planetary nuclear spectroscopy; 6) Charged-particle stopping powers for detector design, space effects and ion beam therapy; 7) High-energy reactions for space exploration and medical nuclide production, and; 8) The creation of an infrastructure for open data and data preservation for use by the entire nuclear physics community. All told, these initiatives require approximately $6.5M increase in NP support of the USNDP in fiscal year 2023 dollars and would require at least 3-5 years to carry out due to the length of time needed to recruit and train new nuclear data researchers. This relatively modest investment would help ensure that the fruits of the nuclear data research carried out by DOE-NP and its collaborators would be brought to bear to address some of the most important needs of our nation and the world. To ensure effective execution of this plan, we present an overview of recruitment, training, and retention goals for the USNDP, the centerpiece of which is a mutually agreed upon code of conduct. Finally, we identify the facility and instrumentation needed to perform the recommended experimental activities. This includes a short review of target fabrication capabilities, reactors, neutron beam, light- and heavy-stable ion, gamma-ray, high-energy and radioactive ion beam facilities. Lastly, a more complete appendix of experimental facilities previously compiled is included with new input provided for 6 facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
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

The future of the NPT: REVCON and beyond

The Treaty on the Nonproliferation of Nuclear Weapons (NPT), with its quinquennial review conferences (RevCons), has become the foundation of the international nonproliferation regime over the course of its 50-year history. With more states parties than any other arms control agreement (currently 191), the Treaty provides a legally binding framework for all states parties, constraining proliferation of nuclear weapons and committing the five recognized states who possess nuclear weapons to work toward disarmament. The NPT has enjoyed wide success in constraining the spread of nuclear weapons. Only one state party has left the agreement and gone on to acquire these weapons, and the overall track record of compliance with IAEA safeguards has been robust. However, for all its effectiveness in the area of nonproliferation, its achievements on the disarmament front have been relatively unimpressive. While the NWS – most notably the United States and the Soviet Union/Russia – have decreased the overall size of their nuclear arsenals significantly since 1970, these arsenals have remained an important feature of NWS security policies. The limited progress towards disarmament has created friction between the NNWS and NWS and has been the source of mounting tension within the NPT regime. The 2020 RevCon, the 50th anniversary of the NPT and 25th anniversary of its indefinite extension, faces a range of contentious challenges, old and new, which call into question the Treaty’s future viability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Evaluation of Light-Element Reactions in the Resolved Resonance Region

Light-element reactions at low energies in the resolved resonance region are important for a range of applications in basic and applied sciences including nuclear reactors, nonproliferation, cultural heritage, forensics and environmental control, rare event investigations and nuclear astrophysics. In this paper, we report on an effort to evaluate charged-particle cross sections in the resolved resonance region and produce evaluated nuclear data files for further processing and inclusion in evaluated data libraries. We discuss the open issues in R-matrix calculations as we extend to higher energies, such as dealing with the rapidly growing number of open channels and merging with the regime of smooth cross sections described by the statistical model, and present attempts to address these issues in neutron-induced reactions relevant to nuclear reactor applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Thermoluminescence and optically stimulated luminescence response of Al 2 O 3 coatings deposited by mist-chemical vapor deposition

Aluminum oxide (Al 2 O 3 ) is a widely used ceramic material which can be applied as a protective coating on metallic structures to improve corrosion resistance and mechanical properties. Al 2 O 3 also has great potential as a functional thin film device in optoelectronics. When it is doped with carbon (Al 2 O 3 :C), this material system can serve as a reliable radiation dosimeter. The combination of optoelectronic, dosimetric, and protective properties demonstrated by Al 2 O 3 uniquely positions it for dual use applications in nuclear forensics, nonproliferation and safeguards, emergency response, retrospective dosimetry, and nondestructive inspection. Here, in this study, an innovative ultrasonic mist-chemical vapor deposition (Mist-CVD) system was used to deposit thin (nm level) Al 2 O 3 coatings on 304 stainless steel substrates. The single aliquot regeneration (SAR) and additive dose reconstruction techniques were then used to investigate the thermoluminescence (TL) and optically stimulated luminescence (OSL) response of the coated substrates to increasing doses of β irradiation. This study demonstrates that nm-thick Al 2 O 3 coatings hold promise as novel nano-dosimeters which could be leveraged for a series of applications such as nonproliferation and safeguards, retrospective dosimetry, and nondestructive inspection.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗