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DOE OSTI2018

Novel capabilities for examining and summarizing hierarchical and geographic resiliency profiles

The geospatial research and development team in the National and Homeland Security Division at Idaho National Laboratory was tasked with providing tools to derive insight from the substantial amount of data currently available - and continuously being produced - associated with the critical infrastructure of the US. This effort is in support of the Department of Homeland Security, whose mission includes the protection of this infrastructure and the enhancement of its resilience to hazards, both natural and human. We present geovisual-analytics-based approaches for analysis of vulnerabilities and resilience of critical infrastructure, designed so that decision makers, analysts, and infrastructure owners and managers can manage risk, prepare for hazards, and direct resources before and after an incident that might result in an interruption in service. Our designs are based on iterative discussions with DHS leadership and analysts, who in turn will use these tools to explore and communicate data in partnership with utility providers, law enforcement, and emergency response and recovery organizations, among others. In most cases these partners desire summaries of large amounts of data, but increasingly, our users seek the additional capability of focusing on, for example, a specific infrastructure sector, a particular geographic region, or time period, or of examining data in a variety of generalization or aggregation levels. These needs align well with tenets of in-formation-visualization design; in this paper, selected applications among those that we have designed are described and positioned within geovisualization, geovisual analytical, and information visualization frameworks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Toward a Deep Learning System for Making Sense of Unlabeled Multimodal Data

Analysts and decision makers are constantly charged with making sense of large amounts of disparate data. With the proliferation of smartphones and the ever-growing array of new sensor technologies, the old adage of swimming in sensors but drowning in data remains especially relevant. As collections of images, video, audio, and text continue to grow, the need for computer-assisted analysis likewise increases. The “data deluge” presents both daunting challenges as well as remarkable opportunities. In this article, we describe Deep Learning approaches for learning representations of data from vast amounts of unlabeled or weakly labeled training sets. These feature representations enable the computer-assisted retrieval and tagging that can help our analysts find patterns in the vast ocean of collected data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2011

Developing Narratives for Next Generation Scenarios for Climate Change Research and Assessment

The implications of anthropogenic climate change for the environment and society depend not only on the response of the Earth system to changes in atmospheric composition and land cover, but also on human responses. These responses are often classified into “adaptation”—changes in activities, infrastructure, or systems tailored to new climate conditions—and “mitigation”—actions to reduce net greenhouse gas emissions. Increasingly, analysts and researchers are examining adaptation and mitigation together, as both will involve changes in technology, economies, lifestyles, and policy that will interact in important ways geographically and sectorally. All of these processes—across the socioeconomic, environmental, and climate domains—are subject to extensive uncertainties. Scenarios are used by researchers and other analysts to evaluate how human choices about mitigation and adaption to future climate change will fare under uncertain future socioeconomic and climate conditions. Scenarios used in climate research and analysis cover a wide range of topics including human activities and systems, emissions of greenhouse gases and other pollutants, land use change, future climate conditions, environmental factors such as sea level rise and air/water quality, and attributes of society that influence vulnerability and resilience to climate change. This paper provides a brief overview of a new “parallel process” for developing and applying scenarios for climate change research and assessment. This parallel process was developed through a series of meetings and research papers from 2006-2010 and is described in the report of an expert meeting of the Intergovernmental Panel on Climate

Moss, Richard H.↗

DOE OSTI2020

Automatic Seismic Phase Picking Using Deep Learning for the EGS Collab project

Microseismic monitoring plays an important role in many energy-related and environmental industries.The microseismic event catalog and seismic structure of the subsurface are two of the primary outputsof the microseismic monitoring system. Though rough locations of microseismic events can be estimated automatically, obtaining high-resolution microseismic event locations requires a significant amount of human laborespecially on seismic phase picking. Unlike traditional automatic pickers that are usually less precise than human analysts, a fewrecently proposed algorithms based on deepneural networks(DNN)were able to match or surpass human performance for earthquake signals. Due to differences in the spatial scale of the study area, sensor sampling rate, and geometry of the monitoring system, it is not clear whether these deepneural networkmodels can be used to speed up microseismic data processing. In this paper, we adapted the DNN based technique for automatic phase picking of microseismic signals. We usedmicroseismic data recorded at the experiment 1 site of the enhancedgeothermal system (EGS) Collab project anddesigneda workflowthat we call transfer-learning aided double-difference tomography (TADT),thatcombines transfer learning and seismic tomography. We re-train an existing DNN with our data to obtain a newmodel using around 3500 seismogramsand associated manual phase picks. Thistransfer learnedmodel is able to reach human performance but muchfaster than human analysts. The transfer-learning-derived phase pickswereused to improve microseismic event locations and imagethesubsurface. The results are similar to or slightly better than those obtained with manual phase picks.

Chai, Chengping↗

DOE OSTI2020

Deep Learning for Automated Identification of Eels in Sonar Data

Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.

13 HYDRO ENERGY↗

DOE OSTI2021

Uniformly Ordered Binary Decision Algorithm for Benchmark Experiment Correlations in Whisper Validation

When performing a validation exercise for determining the upper subcritical limit of a nuclear criticality safety application, an analyst should select and perform a statistical analysis on a population of benchmark experiments that are neutronically similar to the application. The size of this population should be sufficiently large such that the statistical analysis has a high degree of confidence that the bias plus bias uncertainty (calculational margin) has been accurately quantified. A complication arises because many benchmark experiments share common components, leading to correlations in their measured effective multiplication factors. Correlations between benchmark experiments within the population reduces its predictive power. This motivates the need for methods that consider benchmark experiment correlations and ensure adequate statistical significance of results. The Whisper code is a statistical analysis pack- age that incorporates nuclear data sensitivity coefficients from MCNP to assess benchmark experiment similarity and then performs an extreme-value analysis to estimate the bias plus bias uncertainty. The original methodology in Whisper does not consider the effect of benchmark experiment correlations when making this estimation, and this summary proposes the uniformly ordered binary decision algorithm to address this shortcoming. The original methodology in Whisper computes similarity coefficients ck for an application compared to all benchmark experiments in its library and develops weighting factors for a selected population proportional to the ck values. The methodology can be interpreted as statistically emulating a validation exercise for a particular application where the weighting factors may be viewed as the likelihood that an analyst would include a particular benchmark experiment within the population. The effective sample size of the population is the expected or mean number of benchmark experiments in the population. The uniformly ordered binary decision algorithm identifies clusters of correlated benchmark experiments within the population and then computes adjusted weighting factors based on the magnitude of the correlation coefficients within the cluster to compute a reduced effective sample size accounting for the lower information content because of correlations. Benchmark experiments within the cluster are ordered randomly with equal probability and probabilistic decisions are made as to whether a benchmark. experiment within the cluster should treated as redundant with a previous one; if two redundant benchmark experiments are included, then the conservative worst case bias plus bias uncertainty is used and the pair is counted as a single benchmark experiment in the population. Results are provided for HEU solutions in a research version of the Whisper software using benchmark experiment correlations provided by DICE, the Database for the International Criticality Safety Benchmark Evaluation Project (ICSBEP). These show that there can be a significant increase in the bias plus bias uncertainty because the effective sample size is reduced, and therefore the algorithm, needing to meet sample size requirements, expands the benchmark experiment population by accepting less similar benchmark experiments that would have otherwise not been included.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2022

Four decades ago, construction was just beginning on experimental turbines at the National Wind Technology Center (NWTC). Today, the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) facility is the centerpiece of the laboratory's Flatirons Campus, a world-class hub for renewable energy research. The nation's shift to 100% clean electricity by 2035 will require a mix of renewable energy sources and strategies - and together, wind and solar energy could account for 60% to 80% of that clean energy resource. In Fiscal Year (FY) 2022, NREL scientists, engineers, and analysts contributed to these visionary goals through their wind energy research. As wind innovations push into new areas, NREL continues to play a vital role in advancing technology and addressing deployment barriers in pursuit of more efficient, reliable, and predictable wind energy systems. FY 2022 wind research and development explored the potential for dramatic growth in land-based systems, the launch of the nation's first commercial-scale offshore installations, and transmission infrastructure buildout. Land-based wind energy is one of the most cost-effective electricity supply options - but utility-scale deployment will require up to 10 times the current number of turbines. An NREL plan addressed this need to accelerate U.S. wind technology rollout at distributed and utility scales. Another project conducted by NREL and the Pacific Northwest National Laboratory (PNNL) helps position the nation's first major offshore wind corridor for success. The WETO-funded Atlantic Offshore Wind Transmission Study is evaluating options for balancing electricity supply and demand, while supporting resilience of the grid and marine industries. WETO, NREL, and other partners are working to enable the enormous supply chain and workforce changes the U.S. wind energy industry will need to meet net-zero-carbon-emissions targets. As part of a seminal series of DOE-funded supply chain studies, NREL analysts reported on the trade-offs involved in manufacturing large volumes of wind technologies, while addressing cost, workforce, and logistics issues. All of this research is supported by NREL's outstanding research teams, tools, data, and facilities. A WETO-funded international wind energy field campaign, the American WAKE experimeNt (AWAKEN), has brought together experts from NREL, PNNL, and Sandia National Laboratories to create the world's most comprehensive set of high-resolution data on wind energy atmospheric phenomenon. This study could lead to more accurate predictions of losses from turbine-to-turbine wake interactions, eventually helping wind plants capture more energy and operators save millions of dollars. In addition, NREL researchers developed testing, modeling, and analysis tools to improve the security of power grids by identifying wind power plant dynamic stability problems. A new Stochastic Soaring Raptor Simulator (SSRS) protects golden eagles from turbine encounters by predicting flight paths. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2022 (between October 1, 2021, and September 30, 2022).

accomplishments↗

DOE OSTI2022

A multi-modal scanning system to digitize CBRNE emergency response scenes

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

Prins, Nicholas↗

DOE OSTI2017

Preliminary Concept of Operations for the Spent Fuel Management System+ - 17134

The Nuclear Fuels Storage and Transportation Planning Project (NFST) within the US Department of Energy's Office of Nuclear Energy is tasked with identifying, planning, and conducting activities for developing interim storage and transportation capabilities in support of an integrated waste management system (IWMS). The IWMS is expected to transport Spent Nuclear Fuel (SNF) from a reactor and deliver it to a repository in a suitable container, with the possibility of interim storage. The system will be composed of multiple subsystems including possible interim storage facilities (ISFs), one or more repositories, facilities to package and/or repackage SNF, and transportation. The project team is analyzing options for an IWMS to identify viable options. To support analysis, the project team has developed a concept of operations framework for SNF related components of the potential integrated system and the interdependencies between those components. A goal of this work is to aid system analysts in developing consistent models across the project. The concept of operations framework will be updated periodically as new developments emerge. At a high level, SNF is expected to be transported from reactor sites to a repository. Initially, SNF is unloaded from reactors and placed in spent fuel pools (SFPs) for wet storage at utility sites. To maintain space in SFPs, SNF is placed in containers at reactor sites for storage and/or transportation only after the SNF has cooled enough to satisfy loading limits. Once transportation requirements are met, the SNF may be transported to an ISF, where it is stored until a repository is developed, or it is transported directly to a repository if one is available.While the high level operation of the system is straightforward, analysts must evaluate numerous alternatives, such as the number of ISFs (if any), ISF design, the stage at which SNF repackaging occurs (if needed), repackaging technology, types of containers used, repository design, component sizing, and timing of events. These alternatives arise due to technological, economic, or policy considerations. This paper provides an overview of the potential alternatives identified in the concept of operations framework at a conceptual level. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

System Analysis Support of Consent-Based Siting Efforts - 23400

To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the Integrated Waste Management (IWM) program within the U.S. Department of Energy, Office of Nuclear Energy (DOE-NE) has been sponsoring the development and application of system analysis tools capable of analyzing various system options for the management of spent nuclear fuel (SNF) and high-level radioactive waste. With these tools, integrated waste management system (IWMS) architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analyses and assessments typically use these modeling and simulation tools to investigate things like the implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, and different assumed system operation start dates. Some example system analysis results have been published previously. With the passage of the consolidated appropriations acts for fiscal years 2021 and 2022, Congress provided funding and direction for the DOE to move forward with interim storage to support near-term action in managing the nation’s SNF as an important component of an IWMS. Consent-based siting is a phased, adaptive, and collaborative approach to siting facilities that focuses on the needs and concerns of people and communities. Communities participate in the siting process by working carefully through a series of phases and steps with the DOE (as the implementing organization). Each step and phase helps a community determine whether and how hosting a facility to manage SNF is aligned to the community’s goals. By its nature, a consent-based siting process must be transparent, flexible, adaptive, and responsive to community concerns. Thus, the phases and steps are intended to serve as a guide, not a prescriptive set of instructions. System analysis research analysts interface with DOE’s consent-based siting for a federal interim storage program and can perform preliminary system analyses as requested to support various aspects of the consent-based siting team’s work. This paper provides an overview of IWM program’s system analysis activities that support the ongoing consent-based siting process. The questions that decision-makers, planners, communities, and stakeholders might have during the consent-based siting process are similar to questions that have been explored by system analysts previously. Specifically, this paper discusses work that will inform consent-based siting efforts in the areas of community impacts, cost estimates, various SNF acceptance rates by potential facilities, potential consolidated interim storage facility (CISF) capacities and configurations, and the implications of a CISF on the overall waste management system. It is expected that system analysis will continue to be performed to support the consent-based siting process in the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Radiometric characterization process for locating radioactivity hold-up and measuring non-uniformly distributed sources - 15342

A novel approach to facility radiometric characterization for non-uniformly distributed radiation sources has been developed by ANTECH and is currently being employed at the Argonne National Laboratory. It is being applied to the measurement of a wide range of objects and the interiors and contents of laboratories and buildings. The integrated non destructive assay measurement process involves the use of a scanning spectroscopic gamma ray camera as well as high purity Germanium detectors with different collimators and hence differing detector fields of view to measure and quantify non-uniformly distributed sources of radioactivity in laboratory equipment, glove boxes, storage tanks, pipework and general laboratory objects and process components. The purpose of the radiometric characterization is to quantify radiation sources and radioactive contamination levels in facilities containing nuclear material and to ensure compliance with facility characterization requirements and for decontamination and decommissioning planning and execution. The integrated measurement process also involves automated data capture and processing software and a database with which both measurement instrument operators and subject matter expert analysts can interact. An appropriate measuring instrument (gamma camera, scintillation detector, high purity Germanium detector system, neutron detector or alpha-beta counter) records the measurement data and the measurement site is recorded in a photograph. All of the data relevant to the measurement (or set of measurements for a particular object) are recorded in the database including measured gamma ray spectra, equipment description and relevant calibration, measurement site photograph and measurement descriptive data. Once data analysis protocols are established for specific geometries and measurement situations they can be applied to the measured data in a partially automated manner, reducing analysis and transcription errors. In addition to measurements and data analysis the process also utilises radiometric modelling computer codes including MicroShield and Monte Carlo Neutrons Photons in order to perform model calculations where appropriate. In a typical measurement, the ANTECH RadSearch spectroscopic gamma camera is utilized to find areas of concentrated activity so that these areas can be measured and calculated separately from areas with diffuse contamination. The use of the gamma camera in scanning mode helps to ensure that the assay does not overestimate the detected radiation source, which is a frequent consequence of using standard far field geometry and attenuation analysis codes that assume uniformity of both density and radioactive source distribution. Once the hot spots are identified, high purity Germanium detector measurements are made using various collimators with wide or narrow fields of view as required. These measurements are then processed through a semi-automatic calculation process assuming an appropriate source configuration as identified by RadSearch. These source configurations include point sources, line sources or surface sources. The Generalized Geometry Hold-up algorithms are employed to determine an estimate of the activity of the various sources that are identified in a measurement. The ANTECH integrated measurement process provides an automated calculation procedure where the analyst can select the source type, geometry and appropriate input data, and then the software calculates the activity by radionuclide for all sources identified in the measurement. The measurement and analysis process is controlled under the ANTECH ISO9001: 2008 Quality Assurance System and managed by a site specific Quality Assurance Plan. Both Argonne National Laboratory and Department of Energy technical reviewers have vetted the Integrated Measurement and Analysis Process. The application of the measurement and analysis process is described and measurement examples are provided. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Automated Analysis, Reporting, and Archiving for Robotic Nondestructive Assay of Holdup Deposits - 19508

To decommission deactivated gaseous diffusion enrichment facilities, miles of contaminated pipe must be measured. The current method requires thousands of manual measurements, repeated manual data transcription, and months of manual analysis. The Pipe Crawling Activity Measurement System (PCAMS), developed by Carnegie Mellon University and in commissioning for use at the DOE Portsmouth Gaseous Diffusion Enrichment Facility, uses a robot to measure Uranium-235 from inside pipes and automatically log the data. Radiation measurements, as well as imagery, geometric modeling, and precise measurement positioning data are digitally transferred to the PCAMS server. On the server, data can be automatically processed in minutes and summarized for analyst review. Measurement reports are auto-generated with the push of a button. A database specially-configured to hold heterogeneous data such as spectra, images, and robot trajectories serves as archive. This paper outlines the features and design of the PCAMS Post-Processing Software, currently in commissioning for use at the Portsmouth Gaseous Diffusion Enrichment Facility. The analysis process, the analyst interface to the system, and the content of auto-generated reports are each described. Example pipe-interior geometric surface models, illustration of how key report features apply in operational runs, and user feedback are discussed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

An expert-systems approach to automatically determining flaw depth within Candu pressure tubes - 221

Delayed Hydride Cracking (DHC) is a crack growth mechanism that occurs in zirconium alloys, including the pressure tubes of CANDU reactors. DHC is caused by hydrogen in solution in zirconium components being diffused to any flaws present, resulting in an increased concentration of hydrogen within these flaws. An increased hydrogen concentration can lead to brittleness, followed by cracking, in high-stress regions of a pressure tube. Regular in-service ultrasonic inspection of CANDU pressure tubes aims to locate and classify any flaws that pose a potential for DHC initiation. A common approach to inspection is the use of a bespoke tool containing multiple ultrasonic transducers to ensure that each point on the pressure tube is inspected from a minimum of three angles during a scan. All flaws from within the inspected pressure tubes must be characterized prior to restarting the reactor, thus the time-consuming analysis process lies on the critical outage path. This process is manually intensive and often requires a significant amount of expert knowledge. A modular system to automatically process outage data to provide decision support to analysts has been developed. This system saves time on the critical outage path while providing repeatable and explicable measurements. Part of the analysis process requires the depth of all flaws to be measured, which is often the most time consuming stage of the analysis process. This paper describes an approach that utilizes captured analysts knowledge to perform automatic flaw depth estimation. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

Evaluating a Historical Airborne Release Test with Modern Modeling Methods

Safety analysts throughout the U.S. Department of Energy (DOE) complex rely on the data provided in the DOE Handbook, DOE-HDBK-3010, to determine source terms that may be incorporated into the document safety analyses. Most often, analysts simply take the bounding values due to time constraints or simply to bound calculations. This is a safe approach that helps avoid regulatory critique; however, it may not provide results that are meaningful or relevant to the conditions being evaluated. The derivation of the data, such as airborne release fractions (ARFs) and respirable fractions (RFs) in the Handbook often depend on very limited table-top and bench/laboratory experiments, as well as engineering judgment which may not be well substantiated, and may not be representative of the actual situation. One historical dataset included a test where a solid contaminant was sprinkled over a liquid gasoline pool and set on fire (also referred to as Mishima and Schwendiman's test later in this paper). The airborne release was measured over the duration of the burn, but then collected separately for a time after the burning was completed. It can be assumed that the collected contaminant in the latter period corresponds to re-suspended particles, whereas the earlier period collected contaminant includes a combination of directly released as well as early released re-suspended particles. This scenario was recently modeled using SIERRA/Fuego as part of a parametric sensitivity study, however there were some shortcomings of the analysis methods. First, the code at the time lacked a multi-component particle evaporation model, which is necessary to capture the particle size effect of liquid/solid particles that can dynamically evaporate the liquid component. Second, a particle sticking and resuspension model did not exist at the time of the study. Recent model implementation now permits the study be repeated with the improved physical models. In the earlier work, four potential mechanisms were considered to potentially contribute to the release of the particles. These were: 1. Evaporation Induced Entrainment (EIE); 2. Surface Agitation by Wind; 3. Surface Agitation by Boiling; 4. Residue Entrainment (Resuspension). The sensitivity study evaluated mechanism 1 and 3 in the above list. Parameters varied were selected to be a reflection of a practical range of test conditions. The varied parameters included the boiling duration, fuel height, injected mass, particle size, turbulence intensity, and initial particle height. The boiling duration was the most significant parameter. The most significant mechanism was no.3, Surface Agitation by Boiling. This paper reports the implementation of the new multi-component evaporation model and its effect on the findings from the previous effort. The Mishima and Schwendiman test was re-modeled with the new evaporation model, and the results of the re-evaluation are presented. (authors)

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2024

Verification, Validation, and Calibration Through a Causal Lens

While typical validation and verification approaches focus on identifying the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on identifying causal relationships between data elements. Statistical and machine-learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between data sets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify, and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles, it is known as a directed acyclic graph. A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts can identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

VALIDATION, VERIFICATION, AND CALIBRATION THROUGH A CAUSAL LENS

This paper presents an alternative method based on causal inference to perform validation, verification, and calibration of simulation models. While classical validation and verification approaches focus on the identification of the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on the identification of causal relationships between data elements. Statistical and machine learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between datasets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, then the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles it is known as a directed acyclic graph (DAG). A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and from experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts have a means to identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A Primer on Using Analysis to Guide Plastic Circularity

BOTTLE, funded by DOE's Advanced Materials & Manufacturing Technologies Office and Bioenergy Technologies Office (BETO), conducts analysis-guided R&D to change the way we recycle plastics. But what does analysis really mean? In this webinar, BOTTLE Analysis Co-Lead Dr. Taylor Uekert, a researcher with the National Renewable Energy Laboratory (NREL), will introduce key analysis techniques such as techno-economic analysis, life cycle assessment, and environmental justice evaluation. Relevant to both analysts and non-analysts, Dr. Uekert will cover the basics of analysis techniques and discuss how these methods are conducted and interpreted. She will provide examples from the BOTTLE portfolio demonstrating their use in benchmarking and optimizing the costs and environmental impacts of new innovations in plastic redesign and recycling. If you are working in the plastics recycling field - from experimental work to analysis to community-focused projects - you won't want to miss this talk. The webinar will end with a Q&A session.

analysis↗

DOE OSTI2022

Dante

SAND2022-12905 O Dante is a physical security simulation package used in evaluating protective staffing and physical security elements against adversarial threats. Dante simulates force-on-force scenarios in a batch Monte Carlo process. Analysts use the included Scenario Editor to define the scenarios for their site and then execute and analyze the results using the Batch Manager tool. Dante provides insight into how well a security system performs against threats and lets analysts mine the results for key indicators of system performance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hart, Brian↗