Results for “Performance Analysis” — Page 16

DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Preliminary Safety Analysis for a 400-kW Molten Salt Nuclear Battery

The Molten Salt Nuclear battery (MsNB) is a microreactor based on technology developed and operated at Oak Ridge National Laboratory in the 1960s [1]. But the MsNB has been simplified by removing pumps and valves for improved reliability and safety. The MsNB can safely provide up to 10 MWe of power for up to 10 years and potentially it can be transported by air, truck, and rail thanks due to to its small footprint. A computer model has been developed to represent a prototypical 400-kW MsNB using the ANL system code SAM [2]. The model has been used to produce a reactor thermal-hydraulic performance map and perform analysis of unprotected loss of heat sink accident scenario.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Comparison between PARFUME and Bison Using the AGR-2 Irradiation Experiment

This report documents comparisons between Fuel Model (PARFUME) model predictions versus Bison for selected compacts from the second irradiation test of the Advanced Gas Reactor (AGR) program that occurred from June 2010 to October 2013 in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). PARFUME is a fuel performance analysis and modeling code, used for evaluating gas-reactor tristructural isotropic (TRISO) coated particle fuel for prismatic, pebble bed, plate, and cylindrical type fuel geometries. PARFUME is an integrated mechanistic computer code that evaluates the thermal, mechanical, and physico-chemical behavior of TRISO coated-fuel particles and the probability for fuel failure given the particle-to-particle statistical variations in physical dimensions and material properties that arise during the fuel fabrication process. Bison is a nuclear fuel performance application built using the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element library (Permann 2020). Bison is capable of modeling multiple fuel forms in a wide variety of dimensions and geometries. It solves coupled nonlinear partial differential equations, including heat conduction, mechanics, fission product species transport etc., in a fully implicit manner. Comparisons between PARFUME and Bison were performed using four compacts from the AGR-2 experiment. Selected outputs were chosen based on their impact on the probability of the SiC layer failing. In general there was good agreement between PARFUME and Bison with the exception of predicting the gap formed between the buffer and IPyC. Further comparisons between PARFUME and Bison are planned to further develop Bison’s capabilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Cooled Gas Turbine and Combined Cycle Analysis for NH 3 -CH 4 Fuel Mixes (Up to 100% NH 3 )

In this study, a cooled gas turbine (GT) analysis was conducted for varying levels of ammonia (NH 3 ) blends with methane. The ultimate goal is to have a gas turbine design that can be used for all the fuel blends (including 100% NH 3 ) without any changes to the system. The technological developments in the cooling system, gas turbine design, and materials that will be required for NH 3 combustion were identified and analyzed in this study to develop an advanced gas turbine design for NH 3 fuels. The study includes a combined cycle performance analysis with the NH 3 fuel blends using the advanced gas turbine design developed in this study. A techno-economic analysis was conducted for analyzing the impact of the NH 3 fuels on the levelized cost of electricity and cost sensitivities to fuel price and capacity factor.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Comparative genetics of Enterococcus faecalis intestinal tissue isolates before and after surgery in a rat model of colon anastomosis

We have recently demonstrated that collagenolytic Enterococcus faecalis plays a key and causative role in the pathogenesis of anastomotic leak, an uncommon but potentially lethal complication characterized by disruption of the intestinal wound following segmental removal of the colon (resection) and its reconnection (anastomosis). Here we hypothesized that comparative genetic analysis of E. faecalis isolates present at the anastomotic wound site before and after surgery would shed insight into the mechanisms by which collagenolytic strains are selected for and predominate at sites of anastomotic disruption. Whole genome optical mapping of four pairs of isolates from rat colonic tissue obtained following surgical resection (herein named "pre-op" isolates) and then 6 days later from the anastomotic site (herein named "post-op" isolates) demonstrated that the isolates with higher collagenolytic activity formed a distinct cluster. In order to perform analysis at a deeper level, a single pair of E. faecalis isolates (16A pre-op and 16A post-op) was selected for whole genome sequencing and assembled using a hybrid assembly algorithm. Comparative genomics demonstrated absence of multiple gene clusters, notably a pathogenicity island in the post-op isolate. No differences were found in the fsr-gelE-sprE genes (EF1817-1822) responsible for regulation and production of collagenolytic activity. Analysis of unique genes among the 16A pre-op and post-op isolates revealed the predominance of transporter systems-related genes in the pre-op isolate and phage-related and hydrolytic enzyme-encoding genes in the post-op isolate. Despite genetic differences observed between pre-op and post-op isolates, the precise genetic determinants responsible for their differential expression of collagenolytic activity remains unknown.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Cooled Gas Turbine and Combined Cycle Analysis for H 2 -CH 4 Fuel Mixes (Up to 100% H 2 )

In this study, a cooled gas turbine analysis was conducted for varying levels of hydrogen (H 2 ) blends with the natural gas. The ultimate goal is to have a gas turbine design that can be used for all the fuel blends (including 100% H 2 ) without any changes to the system. The technological developments in the cooling system, gas turbine design and materials that will be required for H 2 combustion were identified and analyzed in this study to develop an advanced gas turbine design for H 2 fuels. The study includes a combined cycle performance analysis with the H 2 fuel blends using the advanced gas turbine design developed in this study. A techno-economic analysis was conducted for analyzing the impact of the H 2 fuels on the levelized cost of electricity and cost sensitivities to fuel price and capacity factor.

03 NATURAL GAS↗

DOE OSTI2021

Propagation Pattern for Moment Representation of the Lattice Boltzmann Method

A propagation pattern for the moment representation of the regularized lattice Boltzmann method (LBM) in three dimensions is presented. Using effectively lossless compression, the simulation state is stored as a set of moments of the lattice Boltzmann distribution function, instead of the distribution function itself. An efficient cache-aware propagation pattern for this moment representation has the effect of substantially reducing both the storage and memory bandwidth required for LBM simulations. This article extends recent work with the moment representation by expanding the performance analysis on central processing unit (CPU) architectures, considering how boundary conditions are implemented, and demonstrating the effectiveness of the moment representation on a graphics processing unit (GPU) architecture.

42 ENGINEERING↗

DOE OSTI2023

Large-scale cascading of first-order FBG array in a highly multimode coreless fiber using femtosecond laser for distributed thermal sensing

This research focuses on the performance analysis and characterization of a fiber Bragg gratings (FBGs) array, consisting of 10 first-order FBGs inscribed by a femtosecond (FS) laser in a highly multimode coreless fiber. The study evaluates the FBG array's ability to function as a distributed thermal sensing (DTS) platform, with the coreless fiber chosen as the sensing element due to its immunity to dopant migration at high temperatures. The design of a large cascaded first-order FBG array effectively eliminates unwanted harmonic peaks across a wide spectrum range. In contrast, higher-order FBGs introduce limitations due to the overlapping of Bragg peaks with harmonics. The FBG array's performance is evaluated by measuring the reflection spectrum of each grating at different temperatures, showing a high temperature sensitivity of 15.05 pm/°C at a Bragg wavelength of 1606.3 nm, with a linear response in the temperature range of 24 - 1100 °C. The FBG array was designed for a spatial resolution of 5 mm. A mode scrambler in the sensing network is employed, which suppresses multimodal interference, characterizes FBG peak visibility, and stabilizes the interference spectrum. The stability of the FBG array is also assessed over 24 hrs at 1100 °C, and it is observed to be stable during thermal treatment. Heat treatment at 1100°C improves the signal to noise ratio of the FBG array, demonstrating the robustness and suitability of the proposed FBG array on highly multimode coreless fiber as a potential sensing platform for DTS applications in harsh environmental conditions, overcoming the issues of dopant migration presented by dopes silica optical fibers at high temperatures.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Polymer, Additives, and Processing Effects on N95 Filter Performance

The current severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) pandemic has highlighted the need for personal protective equipment, specifically filtering facepiece respirators like N95 masks. While it is common knowledge that polypropylene (PP) is the industry standard material for filtration media, trial and error is often required to identify suitable commercial precursors for filtration media production. This work aims to identify differences between several commercial grades of PP and demonstrate the development of N95 filtration media with the intent that the industry partners can pivot and help address N95 shortages. Three commercial grades of high melt flow index PP were melt blown at Oak Ridge National Laboratory and broadly characterized by several methods including differential scanning calorimetry (DSC), X-ray diffraction (XRD), and neutron scattering. Despite the apparent similarities (high melt flow and isotacticity) between PP feedstocks, the application of corona charging and charge enhancing additives improve each material to widely varying degrees. From the analysis performed here, the most differentiating factor appears to be related to crystallization of the polymer and the resulting electret formation. Materials with higher crystallization onset temperatures, slower crystallization rates, and larger number of crystallites form a stronger electret and are more effective at filtration.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Operational Focused Data Analytics for Optimizing Radiation Portal Monitor-Based Nuclear Smuggling Detection Systems at Global Ports of Entry

The National Nuclear Security Administration’s Office of Nuclear Smuggling Detection and Deterrence has deployed a fleet of radiation portal monitors (RPMs) across the world at global ports of entry including seaports, airports, and land border crossings. These RPMs are integrated into radiation detection systems (RDS) that also include fixed cameras, optical character recognition (OCR) systems, primary scanning systems (e.g., X-ray or gamma-ray), and secondary scanning systems (e.g., spectroscopic radiation portal monitors, portable radiation detection systems). The data from these sensing technologies is collected at the Central Alarm Station (CAS) where servers and computers reside to control and operate the system. Operators utilize the data collected by the CAS and declared cargo information to make decisions on how to respond to an alarm.This work explores the use of CAS-located data, looking at both the sensor data streams and operator inputs, to perform analysis which supports customs and border protection agencies to improve training capability and operational effectiveness. We focus on analyzing site level effectiveness and behavior by rolling up CAS-located data collected from individual occurrences. To-date, more than 15 sites (e.g., seaports, airports, border crossings) have been analyzed in this manner with the goal of understanding system operations to verify effectiveness and recommend potential improvements. This work first aims to provide background information on relevant CAS-located data sources and our current operational system analytics process including example results. After summarizing our current analytic techniques, we discuss how the future data analytics systems can provide key benefits to improving operational performance while minimizing the burden these detection systems place on operators.

Kuhn, Michael↗

DOE OSTI2025

Requirements on the gain calibration for LiteBIRD polarisation data with blind component separation

The detection of primordial B modes of the cosmic microwave background (CMB) could provide information about the early stages of the Universe's evolution. The faintness of this signal requires exquisite calibration accuracy and control of instrumental systematic effects which otherwise could bias the measurements. In this work, we study the impact of an imperfect relative polarisation gain calibration on the recovered value of the tensor-to-scalar ratio r for the LiteBIRD experiment, through the application of the blind Needlet Internal Linear Combination (NILC) foreground-cleaning method. We derive requirements on the relative calibration accuracy of the overall polarisation gain (Δg ν ) for each LiteBIRD frequency channel. Our results show that minimum variance techniques, as NILC, are less sensitive to systematic gain calibration uncertainties compared to a parametric approach, if the latter is not equipped with a proper modelling of these instrumental effects. In this study, the most stringent requirements are found in the channels where the CMB signal is relatively brighter, with the tightest constraints at 166 GHz (Δg ν ≈ 0.16%). This differs from the outcome of an analogous analysis performed with a parametric method, where the tightest requirements are obtained for the foreground-dominated channels. Gain calibration uncertainties, corresponding to the derived requirements, are then simultaneously propagated into all frequency channels. By doing so, we find that the overall impact on estimated r is lower than the total gain systematic budget for LiteBIRD approximately by a factor 5, due to the correlations of the impacts of gain calibration uncertainties in different frequency channels. In order to decouple the systematic effect from the specific choice of the model, we derive the requirements assuming constant spectral parameters for the foreground emission. To assess the robustness of the obtained results against more realistic scenarios, we repeat the analysis assuming sky models of intermediate and high complexity. In these further cases, we adopt an optimised NILC pipeline, called the Multi-Clustering NILC (MC-NILC). We find that the impact of gain calibration uncertainties on r is lower than the LiteBIRD gain systematics budget for the intermediate-complexity sky model. For the high-complexity case, instead, it would be necessary to tighten the requirements by a factor 1.8.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Rare-earth element extraction from geothermal brine using magnetic core-shell nanoparticles-techno-economic analysis

Not Available

Liu, Jian↗

DOE OSTI2023

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Characterization of front contact degradation in monocrystalline and multicrystalline silicon photovoltaic modules following damp heat exposure

Reliability and durability tests play a key role in the photovoltaic (PV) industry by minimizing potential failure risks for both existing and new cell and module technologies. In this work, a detailed study of contact degradation in monocrystalline and multicrystalline PV modules is performed. The modules are subjected to a sequence of damp heat (DH) exposures followed by electrical characterization after each step. Electroluminescence (EL) imaging shows different darkening patterns for monocrystalline modules compared to multicrystalline modules; the former shows darkening near the busbars and the latter shows it across virtually the entire cell surface. The primary loss mechanism is confirmed to be resistive after comparing the current-voltage (I–V) characteristics at each DH exposure step. Representative samples have been cored out from both the degraded modules and controls for materials characterization to gain further insights into the degradation mechanism. Top-down and cross-sectional scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and top-down high-resolution X-ray photoelectron spectroscopy (XPS) analysis performed on the cored samples confirm the degradation is due to metallization corrosion. Our study suggests that the difference in the darkening pattern can most likely be attributed to the different silver paste composition used for contacting each cell technology, particularly the composition of the glass frit.

36 MATERIALS SCIENCE↗

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↗

DOE OSTI2025

Experimental and Theoretical Thermokinetic Analysis of the Na 3 VO 4 –CO 2 Reaction System under Different Physicochemical Conditions

Sodium vanadate (Na 3 VO 4 ) was synthesized, structural and microstructurally characterized as well as tested as possible carbon dioxide (CO 2 ) captor through thermodynamic calculations in addition to thermogravimetric dynamic and isothermal analyses. Ceramic structural characterization evidenced the formation of the Na 3 VO 4 crystal phase, while microstructural features evidenced dense agglomerates with a poor specific surface area, although synthesis temperature (600 °C) was not as high as that for other sodium ceramics. Na 3 VO 4 was investigated for the CO 2 capture process through dynamic and isothermal experiments, in the presence or absence of oxygen. All these experiments showed that Na 3 VO 4 possesses interesting CO 2 capture properties in a specific temperature range (520 and 600 °C). Isothermal products characterization allowed to elucidate the reaction pathway, implying the Na 4 V 2 O 7 formation, as possible reactive intermediate. In fact, reaction path evolution was supported with theoretical thermodynamic data. Moreover, the kinetic analysis, performed using the Avrami-Erofeev mathematical model and subsequent thermodynamic data obtention, probed the positive influence of the temperature, in both CO 2 superficial and bulk chemical sorption processes, while oxygen addition did not enhance the superficial process. Additionally, as it would be expected, kinetics were enhanced as a function of the CO 2 concentration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗