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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Review of Computational Models for the Flow of Milled Biomass Part I: Discrete-Particle Models

Biomass is a renewable and sustainable energy resource. Current design of biomass handling and feeding equipment leverage both experiments and numerical modeling. This paper reviews the state-of-the-art discrete element methods (DEM) for the flow of milled biomass (Part I), accompanied by a comprehensive review on continuum-based computational models (Part II). The present review on DEM is primarily focused on the features and suitability of various particle shape models for different types of milled biomass because particle shape is the predominant attribute controlling the flow behavior of complex-shaped granular material. The general strengths and weaknesses in the applicability of those models for the milled biomass modeling are summarized. In particular, comments are provided to balance the numerical model capabilities and the computational cost for the development of DEM models. To our best knowledge, this is the first-of-its-kind review on DEM specifically for biomass. Our study indicates that the current DEM models require further development, calibration, and validation based on a deep understanding of biomass particle contact mechanics and experimental data support before they can be reliably used for predictive simulations in handling and feeding systems.

42 ENGINEERING↗
DOE OSTI2020

ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists

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

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Lifetime extension drop-test of real-world corroded 5 Quart Hagan nuclear material storage container

A 5Qt Hagan container with a 20-year history of nuclear material storage was challenged with three successive drop tests at a height of 3.7 meters. The total mass of the test package was 12.1 kg. The 1st and 2nd drop tests (center of gravity over the container bottom corner but 180 degrees apart on the container bottom face) passed the pre- and post- impact helium leak criterion at less than 1.00E-6 atm-cc/sec (ambient cubic centimeters per second). The 3rd and final test (center of gravity over top corner) failed with a post-impact gross leak of 1.1E-1 atmcc/sec. The RRFMC (Respirable Release Fraction Measurement Chamber) is a drop tower test system that is critical for the sustainability of the SAVY-4000™ series and Hagan-type (NFT Inc. Golden CO) nuclear material storage containers. These are the primary in-use nuclear material storage container types at the Los Alamos National Laboratory TA-55 facility. Results are presented to expand the technical knowledge basis for container lifetime, regarding actual exposure to corrosive gas species on the container inner surfaces. The primary source of general corrosion throughout the container is gaseous hydrogen chloride (HCl). This gas is generated by the degradation of the polyvinylchloride (PVC) bag-out bag. Additionally, in most cases, the nuclear material itself also releases HCl gas (due to residual chemical components associated with the material formation). The RRFMC drop tower gives the end-user the ability record and analyze high-speed video and photography and if needed aerosol mass release measurements. In this report the principal issue is the physical deformation of the 5Qt Hagan container. There were no mass release experiments of test aerosol mass in the present study.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Degradability of Biodegradable Soil Moisture Sensor Components and Their Effect on Maize (Zea mays L.) Growth

Inexpensive and no-maintenance biodegradable soil moisture sensors could improve existing knowledge on spatial and temporal variability of available soil water at field-scale. Such sensors can unlock the full potential of variable-rate irrigation (VRI) systems to optimize water applications in irrigated cropping systems. The objectives of this study were to assess (i) the degradation of soil moisture sensor component materials and (ii) the effects of material degradation on maize (Zea Mays L.) growth and development. This study was conducted in a greenhouse at Colorado State University, Colorado, USA, by planting maize seeds in pots filled with three growing media (field soil, silica sand, and Promix commercial potting media). The degradation rate of five candidate sensor materials (three blends of beeswax and soy wax, balsa wood, and PHBV (poly(3-hydroxybutyrate-co-3-hydroxyvalerate))) was assessed by harvesting sensor materials at four maize growth stages (30, 60, 90, and 120 days after transplanting). All materials under consideration showed stability in terms of mass and dimension except PHBV. PHBV was degraded entirely within 30 days in soil and Promix, and within 60 days in sand. Balsa wood did now show any significant reduction in mass and dimensions in all growth media. Similarly, there was no significant mass loss across wax blends (p = 0.05) at any growth stage, with a few exceptions. Among the wax blends, 3:1 (beeswax:soy wax) was the most stable blend in terms of mass and dimension with no surface cracks, making it a suitable encapsulant for soil sensor. All materials under consideration did not have any significant effect on maize growth (dry biomass, green biomass, and height) as compared to control plants. These results indicated that 3:1 beeswax:soy wax blend, PHBV, and balsa wood could be suitable candidates for various components of biodegradable soil moisture sensors.

Dahal, Subash (ORCID:0000000205489103)↗
DOE OSTI2024

MetaPoL: Immersive VR based Indoor Patterns of Life (PoL) and Anomalies Data Generation for Insider Threat Modeling in Nuclear Security

Insider threats are perhaps the most serious challenges that nuclear and radiological security systems face. Insiders pose such a great threat due to their access, authority, and knowledge, granting them opportunities to bypass dedicated nuclear and radiological security elements. For example, in one of the latest major insider threat incidents to nuclear security, the Doel-4 nuclear powerplant in Belgium suffered a shutdown, the threat of nuclear materials diversion, and long-term loss of tens of millions of dollars. Seven years of investigation concluded that it was an inside job and attempted sabotage. In this regard, there is an immediate need for R&D and technology integration in the domain of modeling indoor Patterns-of-Life (PoL) and anomaly detection. This can be achieved by using datasets of facility users’ mobility and activity, which can support the design of algorithms for insider threat modeling and detection. However, due to classification, privacy, sensitivity, and safety protocols, such datasets from real physical nuclear reactor facilities are not only hard to share, but also not always feasible to deploy and collect. Aiming to find an alternate solution, our proposed demonstration work - MetaPoL, is the first-ever (for the application space) immersive VR (virtual reality) environment of a real-world secure facility and allows users to move-and-stay through the designed indoor physical layout and also encounter NPCs (non-player characters) that emulate other facility users. In the MetaPoL an interactive user performs realistic spatio-temporal movement, dwelling and activities using a Meta Quest Pro VR headset, and that generates high-frequency (in time) high-resolution (in space) indoor spatial-temporal datasets that are valuable for PoL modeling and anomaly detection research specifically for insider threat modeling and detection mission. Such generated realistic, rich in context, and mission specific datasets can boost AI/Machine Learning based research for modeling and detecting insider threats in nuclear security and nonproliferation.

Gunaratne, Chathika↗
DOE OSTI2020

Extension of OpenMC for Fixed Source Transmutation Calculations

This report documents work performed under a Strategic Partnership Project between Argonne National Laboratory (ANL) and the United Kingdom Atomic Energy Authority (UKAEA). The overall goal of this project is to extend OpenMC [1], a community-developed Monte Carlo particle transport code, to be able to perform fixed-source transmutation calculations. In fission and fusion reactors, the high flux of energetic neutrons causes materials within the reactor to “transmute,” or undergo a nuclear reaction that results in the addition/removal of neutrons and protons from the nucleus of an atom. If subjected to these reactions for long enough, the overall composition and physical properties of the material itself begin to change as a result of transmutation. Such a feature is vital for predicting the decrease in tritium production rate within a breeder blanket during the lifetime of a fusion reactor. OpenMC is capable of simulating neutron transport in fission/fusion systems, thereby allowing it to estimate the flux that causes transmutation. It is also capable of solving the transmutation equations, which determine how the composition of a material changes over time due to neutron irradiation and radioactive decay. However, solving the transmutation equations was previously only possible when the source of neutrons came from a fission system. In a fusion system, the source of neutrons is typically determined by a separate code and then given as an input to the particle transport simulation. This is known as a fixed source calculation. Through this project, we have extended OpenMC to solve the transmutation equations for a fixed source calculation. Evaluating the change in material compositions due to transmutation and its effect on physical properties is of key importance to a range of engineering analyses for fission and fusion systems. For example, in a fusion reactor, estimating the dose rate at different physical locations resulting from irradiated materials in the reactor allows designers to ensure that workers are not exposed to doses beyond applicable regulations. In order to properly dispose of irradiated materials, designers also need to estimate the radiotoxicity, which again relies on knowledge of the material composition at some future time. The specific tasks for this project that were agreed to between ANL and UKAEA were as follows: 1. Make changes and additions in the openmc.deplete and related modules in OpenMC to support transmutation calculations following a fixed source transport simulation. 2. Make necessary changes to OpenMC to model transmutation due to an arbitrary set of reactions needed for fusion applications. Use this new capability to generate a depletion chain file based on the TENDL nuclear data library. 3. Improve the openmc.deplete module in OpenMC to keep track of gases produced as a by-product of nuclear reactions during transmutation calculations. 4. Validate the new capabilities by carrying out fixed-source transmutation calculations on a suitable benchmark problem using OpenMC and a comparable Monte Carlo neutron transport code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Considerations for Hydride Moderator Readiness in Microreactors

The emergence of microreactor technology has helped to drive supporting nuclear materials qualification and acceptance processes. One essential component in these small reactors is a solid moderator, which typically consists of metal hydride and cladding. While the behavior and performance of metal-hydride moderators go back to early advanced reactor development for nuclear-powered aviation and space propulsion, there remains a knowledge gap in the understanding of hydrogen transport–related phenomena and irradiation performance for hydride moderators. This impacts the acceptance/qualification of hydride moderators for microreactors. The goal of this technical note is to lay out a potential path forward for advanced moderator qualification and acceptance for designers and developers of microreactors. The proposed approach has benefited from a model microreactor core with the design parameters of a hydride moderator. Based on the model core and design parameters, a simple chart was developed for the major challenges of hydride moderators where potential incidents, causes, effects, and resolutions are described. The relation between the offered resolutions and the maturity of the metal-hydride moderator technology was emphasized using technological readiness. Technological readiness levels (TRLs) were clustered to three sets: physical phenomena related, reactor irradiations, and system demonstration. Some essential needs to fill the knowledge gaps are discussed for physical phenomena–related TRLs. For reactor irradiations, the importance of identifying goals and priorities is stressed to reach certain TRLs. For system demonstration, it is noted that metal-hydride moderator qualification must comply with the overall microreactor design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximations

Knowledge of the chemical bonding of HfO and HfB ground and low-lying electronic states provides essential insights into a range of catalysts and materials that contain Hf–O or Hf–B moieties. Here, we carry out high-level multi-reference configuration interaction theory and coupled cluster quantum chemical calculations on these systems. We compute full potential energy curves, excitation energies, ionization energies, electronic configurations, and spectroscopic parameters with large quadruple-ζ and quintuple-ζ quality correlation consistent basis sets. We also investigate equilibrium chemical bonding patterns and effects of correlating core electrons on property predictions. Differences in the ground state electron configuration of HfB(X 4 Σ - ) and HfO(X 1 Σ + ) lead to a significantly stronger bond in HfO than HfB, as judged by both dissociation energies and equilibrium bond distances. We extend our analysis to the chemical bonding patterns of the isovalent HfX (X = O, S, Se, Te, and Po) series and observe similar trends. We also note a linear trend between the decreasing value of the dissociation energy (D e ) from HfO to HfPo and the singlet–triplet energy gap (ΔE S–T ) of the molecule. Finally, we compare these benchmark results to those obtained using density functional theory (DFT) with 23 exchange–correlation functionals spanning multiple rungs of “Jacob’s ladder.” When comparing DFT errors to coupled cluster reference values on dissociation energies, excitation energies, and ionization energies of HfB and HfO, we observe semi-local generalized gradient approximations to significantly outperform more complex and high-cost functionals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Development of In-Situ Corrosion Kinetics and Salt Property Measurements (Final Technical Report)

The goal of this research is to fill the knowledge gaps of salt properties and gain a fundamental understanding of corrosion mechanisms, thereby to guiding material selections of salts and containment materials. The proposed research is focused on building unique cross-cutting research capabilities that can perform research and analysis relevant to the following three research topics important for Generation 3 Concentrating Solar Power Systems: (1) Material characterization including investigations of fluid thermophysical properties and stability, (2) Durability testing of containment materials, and (3) Corrosion behavior characterization relative to levels of known contaminants (e.g., water and oxygen) in Heat Transfer Fluid (HTF).

14 SOLAR ENERGY↗
DOE OSTI2020

Production of National Nuclear Material Archive Subsamples for High Precision Chemical Analysis

With its historical mission as the focal point for production of uranium components for the NNSA nuclear weapons program, the National Nuclear Materials Archive (NNMA) makes use of Y-12's wide range of uranium materials processing knowledge and onsite materials to identify, collect, and preserve sample materials for nuclear forensics purposes. In 2019, Y-12 took on the responsibility for identifying, subsampling, and shipping a total of 24 NNMA samples to the Lawrence Livermore National Laboratory (LLNL) for further forensics analysis. These were highly enriched uranium metal pieces representative of different weapon systems components produced by Y-12 from approximately 1963 to 1993. The purpose was to provide the NNMA program with higher precision analyses of these materials than what is currently available from historical Y-12 production stream data.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Statistical Approaches for Pebble Bed Reactor Operations and Safeguards

The design of pebble bed reactors (PBRs) and their method of operation align more closely with statistical approaches used in manufacturing and process control than traditional safeguards statistical approaches. The reason is PBRs will employ a nondestructive assay (NDA) measurement (burnup measurement system [BUMS]) that is part of the fuel handling system supporting discharge decisions in addition to the reactor code which monitor performance. The integration of these two approaches provides the opportunity to monitor reactor performance statistically for both operations and safeguards in ways not achievable using other reactor designs. For light water reactors (LWRs), knowledge about reactor code performance in predicting irradiated special nuclear material (SNM) content historically was only achieved from special measurement campaigns or from fuel reprocessing. Conversely, through statistical comparison of the BUMS with the reactor code-predicted values, PBRs can achieve this in real time. The resulting SNM distribution is an indicator of reactor performance because factors such as transit time and path of the pebble fuel through the reactor determine the plutonium production and uranium depletion. By analyzing the predicted and measured values, opportunities exist to adjust operating parameters, fuel design, and other characteristics to optimize performance and fuel utilization. From a safeguards perspective, this approach also provides the information necessary to validate declared values and evaluate whether the reactor is being operated as expected. This paper outlines statistical approaches for PBRs that can be used to support both operations and safeguards.

Ball, Cory↗
DOE OSTI2022

Strengthening the Security of Operational Technology: Understanding Contemporary Bill of Materials

The evolution of cyber-physical infrastructure has made its security more challenging. The last few years have witnessed a convergence of hardware and software segments in various domains, including operational technology (OT) which is responsible for carrying out critical tasks such as monitoring and controlling power grids, nuclear plants, transportation, and emergency services. Both hardware and software encapsulate numerous open source and proprietary subcomponents, making it crucial for end-users to understand the composition of the products they are using. For example, wind turbines incorporate thousands of lines of code (software) used for the turbine's design, planning, operation, and analytics in addition to the numerous hardware subcomponents that construct it. Due to the highly complex nature of software and hardware, knowledge of the components and subcomponents is required to mitigate cyber vulnerabilities and defend against cyberattacks. There has also been a transformation from a traditional linear supply chain into a global, dynamic, diverse, and interconnected system. The digitization of the supply chain makes it easier to find and exploit vulnerabilities. Critical infrastructures (e.g., power grids, oil, natural gas, water, and wastewater) rely on OT to function, and if the OT is compromised, equipment damage and potential interruption of services could result. A significant security measure to protect OT systems from disruption is to develop a supply chain bill of materials (BoM) corresponding to the software and hardware used in OT, along with attestations amongst vendors and asset owners. A supply chain BoM is a proactive way to understand the inherent vulnerabilities in the system and mitigate them in advance of being exploited. BoMs bolster the trust placed in the digital infrastructure and enhance software supply chain security by sustaining the management of component obsolescence and compliance, along with the seclusion of unsafe segments of a specific product. Adopting BoM tools is becoming increasingly important across various government sectors, as evidenced by the recent U.S. executive order on cybersecurity (NIST 2021). This paper aims to classify BoMs based on structure, functionality, component type, and architecture. The work also discusses case studies to further highlight the benefits of BoMs. In addition, it identifies missing pieces in existing BoM implementations so that future research may identify bounds on where it could expect to make improvements and directly enable researchers to identify promising areas for exploration. Further, the authors provide valuable recommendations to tool developers, researchers, and standardizing organizations (policymakers), additionally benefitting critical infrastructure owners and government executives. This aids in paving a path for future work, thereby, providing suggestions to determine a tool for consumers that best suit their needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Probing the electrolyte/electrode interface with vibrational sum frequency generation spectroscopy: A review

Over the past decades, Lithium-ion batteries have seen extensive improvements, and as a result are now the primary choice in many applications for their power, energy, and durability. In recent years, battery cost has reduced by orders of magnitude through adoption of new materials and processes. Despite these advances, interfaces in these battery systems are yet to be fully understood. This is seen as a major limitation to further increase cycle life, calendar life, abuse tolerance, and performances. A major obstacle is a lack of comprehensive understanding of the complex dynamic chemical processes occurring at the electrolyte/electrode interface. In this context, vibrational sum frequency generation (vSFG) spectroscopy possesses the unique capability of probing a molecularly thin interfacial layer to obtain molecular-level information through nonlinear optical interaction. Probing the molecular level processes at the interfaces using such a versatile technique would be a game changer in the advancement of current battery research knowledge. This review article summarizes recent vSFG studies on the electrolyte/electrode interface of various electrode materials and nonaqueous electrolytes for LIBs and discusses future research perspectives. Finally, overall, this focused review highlights the advantages and versatility of vSFG that can be used to further advance present-day battery research.

25 ENERGY STORAGE↗
DOE OSTI2023

A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems

Abstract Rare-earth elements (REEs) supply raw materials that constitute many of our modern critical infrastructure, defense, technology, and electrification needs. Despite REE accumulations occurring in conventional bedrock and ion-adsorption deposits sourced from weathering of igneous rocks, unconventional host materials such as coal and related sedimentary strata have been identified as promising sources of REEs to meet growing demand. To maximize the potential of unconventional resources such as REE-coal systems, new approaches are needed overcome challenges from mineral systems with no known deposits and areas with sparse geochemical data. This article presents a systematic knowledge-data resource assessment method for predicting and identifying REE resource potential and occurrence in these unconventional systems. The method utilizes a geologic and geospatial knowledge-data approach informed and guided by REE accumulation mechanisms to systematically assess and identify areas of higher enrichment. An assessment of the Powder River Basin is presented as a test case to demonstrate the method workflow and results. The key output is a potential enrichment score map reported with varying confidence levels based on the amount of supporting evidence. Results from the test case indicate several locations with promising potential for different types of coal-REE deposits, demonstrating the viability of the method for exploration and assessment of unconventional REE resources. The method is flexible by design and, with sufficient applicable knowledge and data, can be adapted for assessing critical mineral systems in other sedimentary systems as well.

58 GEOSCIENCES↗
DOE OSTI2024

Developing an intrinsically secure information barrier for arms control verification through machine learning

Near-term solutions are needed to allow for flexible engagement in future nuclear arms control discussions. This project developed a method for implementing an information barrier (IB) on commercial systems, shortening the research and development lifecycle for warhead verification technologies while offering improved and inherently flexible capabilities. The crux of the verification challenge remains the difficulty in developing an authenticatable IB which prevents sensitive host country information from inadvertent transmission to an inspector. Many concepts for IB’s rely on dedicated “trusted” processor modules developed with dedicated custom radiation detection systems and associated algorithms. Without a priori knowledge of the treaty item, the parameter space for measurements can be nearly infinite and robustness against spoofing without the ability to view sensitive data is key. This project has produced an unclassified framework capable of ingesting data from common gamma detectors and identifying the presence of weapons grade nuclear material at over 90% accuracy.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Examining Autonomous Inspection of Geologic Repositories

Geological repositories for nuclear waste, including spent nuclear fuel, present a significant challenge for traditional International Atomic Energy Agency (IAEA) safeguards tools due to their inaccessibility and demanding operational conditions. The IAEA has been working closely with Member State organizations currently involved in repository construction and planning including Euratom, the Finnish and Swedish regulatory authorities, and relevant facility operators. However, the verification challenge remains unsolved, and there persists an out-standing need for tools and approaches that will help the IAEA verify that no nuclear material is diverted from a repository environment. The challenge is also not static as activities must encompass verification of the design prior to and during the construction/operation phase, and post backfill. Throughout these various phases, it is imperative that the IAEA maintains a continuity of knowledge (CoK) of all material, including information on material inventory and flow. This paper highlights these challenges and outlines how they might be addressed by using remote or autonomous vehicles. Specifically, it discusses the current state of the art in robotic autonomy for known or partially known environment mapping and patrolling, as well as shared autonomy, where humans collaborate with closed loop autonomation to complete tasks. The feasibility of using rovers for these verification tasks is explored, along with the challenges associated with system implementation. Hardware and software suggestions are provided based on the adoption of similar technologies in other comparable areas and ability to close technical gaps. Finally, human-robotic interactions are considered based on the challenges of the environment of the repository and effective deployment and continued operation of the robot system

autonomous monitoring↗
DOE OSTI2020

Development of a Convolutional Neural Network Classifier for Data Starved Spectra - 20199

The Institute for Clean Energy Technology (ICET) at Mississippi State University is exploring the utility of machine learning in augmenting its mobile radiation surveying platforms, which are currently being developed as means to survey depleted uranium contaminated areas in support of remediation and decommissioning efforts. Mobile survey platforms provide a means to efficiently scan large areas of interest while reducing human exposure to radiation and other hazards. The survey platforms can also be used for scanning for any gamma emitting isotope in addition to depleted uranium. The spectral data that the platforms collect may be data starved with relatively low counts and poorly defined spectral features depending on the speed of the platforms and scintillation detector selection. Such data-starved spectra are difficult to use for isotope identification, requiring advanced knowledge of the possible radionuclides that could be present and environmental factors that could attenuate signals or introduce background noise. These factors in combination with the volume of survey data increases the time it takes to perform analysis of survey data when the source type is unknown. There are a number of algorithms in the field of machine learning that can be used to classify data that would be challenging and time-consuming for a human to identify. Supervised machine learning algorithms train models based on extensive amounts of human-labeled training data. Once sufficiently trained, these models can be used to quickly make high-fidelity predictions on new data. Convolutional neural networks are machine learning algorithms that excel in learning representations of 'shapes'. They do this by taking numerical input data and convolving them with spatial feature detectors referred to as filters. These filters are incrementally adjusted to reduce the prediction error on the data during the backpropagation step of training. Discussed in this paper is the development of a convolutional neural network classifier (CNNC) that can utilize spectral survey data for source discrimination and isotope identification. Bench-top laboratory experiments data using LaBr{sub 3}(Ce) scintillation detectors were used to train and evaluate the performance of the developed CNNC. The CNNC is capable of discriminating a variety of gamma emitting source types, differentiating different forms of uranium (depleted vs. natural), and estimating the amount of uranium for a known geometry. The discussed CNNC may be useful in scenarios where survey systems are deployed in situations where hazardous radioactive material maybe present, but the type is unknown. When used in remediation applications the CNNC can be used to screen-out false positives, helping reduce remediation costs. (authors)

07 ISOTOPE AND RADIATION SOURCES↗