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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

The Future of Robotics/Remote Handling Solutions for the Nuclear Industry - 17347

The nuclear industry is seeing a resurgence of development and utilization of advanced integrated systems; fused sensor arrays; autonomous, remote and hybrid operations; and augmented reality display and control system interfaces. A new age of 'robotics' and remote handling technologies. For many years the use of robotics in nuclear applications has been done sparingly due to technical limitations, perceived and real reliability issues, perceived and real risk concerns, and cost. Although robotics have been used successfully on a variety of projects globally, the underuse of this technology, particularly within the nuclear industry both public and private, is overwhelming. As the number of complex, high hazard projects in the nuclear industry increases, the use of robotics for maintenance, inspection, repair, decommissioning, and remediation activities will become increasingly vital in the future. The risks associated with direct human exposure are high and thus eliminating the risk, or managing it to acceptable levels by using robotics/remote handling as well as other technologies is critical to success as more extreme environment operations and tasks are undertaken. Recent and ongoing advances in technology now allows us to safely and effectively remove the direct / in-situ human element from these type of activities. All the while delivering the same or better outcomes when considering the alternatives when limited or no direct / in-situ human intervention would be allowed. The biggest obstacles the nuclear industry faces center on the cost, reliability, maintainability, effectiveness and appropriate uses of robotics/remote handling solutions in a nuclear (radiation and/or contamination) environment. In order to mitigate these concerns, a sound approach to the use of robotics needs to be addressed. This approach must include: 1. A better understanding of what technology is available and how it can be utilized across multiple sectors, e.g. Commercial Off-the-Shelf (COTS) technology modified / adapted for the nuclear sector; 2. How to implement technology in an integrated pragmatic approach that is cost effective by combining technological know-how / subject matter expertise (SME) with the equivalent subject matter expertise in regulatory, operational and maintenance requirements and limitations, such that a more complete set of functional and operational requirements can be jointly developed in the process. 3. The integration of requirements (project, site, regulatory, etc.), risk management (human factors) and technology, i.e. a marriage between political, programmatic, and technological factors. As the development of robotic/remote handling technology progresses and the successful implementation on projects is demonstrated, the better off the industry will be. This paper provides an overview of the fundamental issues facing the nuclear industry with regard to effective and successful technology selection, maturation, deployment, and integrated risk management, and offers suggestions on how we may overcome these persistent issues now and in the future. Robotic systems and solution experts that are focused on the integration of proven technologies - and that are an integrated part of the project team from its onset - are a necessity for successful planning during nuclear operations. The approach to nuclear operations should always be, 'what is the simplest, most effective way we can approach a particular project need?'. Understanding and implementing the right technologies is where the true value will be recognized. The integration of systems, which includes more than just robotics, is key when developing schemes for these types of complex operations. Often times during planning, the remote handling needs of the job are set aside to be evaluated at a later time due to risk concerns and lack of knowledge. The issue is, experts in the area of robotics or remote handling are rarely involved in the upfront planning. As a result, unnecessary constraints, risks, and obstacles are imposed on project teams to deliver. If done intelligently, this should be introduced at the forefront of planning. Using this type of sound approach leads to effective task organization for the end user. By developing an overall philosophy that leverages proven technologies with implementation expertise; i.e. solutions versus products, is a recipe for success. This is the area that is under-appreciated in the industry and one that needs to be recognized on a much broader scale. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Defining the Minimal Set of Microbial Genes Required for Valorization of Lignin Biomass (Final Report)

Project Goals: Lignin is the second most abundant biopolymer on earth and represents a critically underutilized biomass resource for hydrocarbon feedstocks. Despite substantial effort, there is still no efficient process to convert lignin to useable carbon-based platform chemicals and materials. The goal of this project is identify a minimal set of microbial enzymes necessary for lignin breakdown and sufficient for the synthesis of valuable chemical intermediates from lignin isolated as a byproduct of lignocellulosic ethanol production. These genes will be then used to engineer functional whole cell biocatalysts for tunable lignin metabolism. To date, although a number of enzymes have been associated with lignin degradation, most have been tested in isolation (as individual enzymes) and on drastically different substrates -- often dyes that are not related to lignin. In contrast, lignin utilization in nature likely occurs by microbial consortia with multiple enzymes acting synergistically. We propose to examine two separate stages of lignin breakdown carried out by the microbes that do it best: (1) early breakdown of native polymeric lignin into soluble fragments by a set of sequenced wood-rotting fungal species, and (2) downstream metabolism of these soluble lignin fragments to useful chemical intermediates by a panel of sequenced soil saprophytes. Our approach involves testing sets of genes that will be assayed combinatorially in the context of a heterologous expression host. The resulting engineered strains will be systematically assayed using soluble lignin fragments, synthetic defined polymeric lignin, and finally lignin directly sourced from lignocellulosic processing streams. In addition to resulting in a functional whole cell biocatalyst for lignin utilization, we anticipate that this approach will allow us to address key unanswered questions about lignin metabolism in nature, including: (1) Why does the Trametes versicolor genome contain 25 different class II peroxidases? (2) What is the role of laccases in lignin metabolism? Why do some aggressive lignin degraders have many laccases (e.g. >7 in T. versicolor) while others have none (e.g. P. chrysosporium)? (3) How is peroxide provided in a controlled manner to drive peroxidase activity without causing the enzyme inhibition that is so often observed in vitro? (4) What strategies do microbial lignin degraders use to avoid the problem of repolymerization during active lignin degradation? and (5) Can microbial lignin metabolism be diverted for high level production of defined aromatics? A final critical question is whether combining key minimal sets of enzymes from a wide range of organisms will result in engineered strains capable of highly efficient, streamlined pathways for lignin utilization that can be tuned for a specific carbon output. This effort will leverage DOE investments in microbial genome sequencing, and secure a critical channel for lignin biomass utilization that will also help to render lignocellulosic a viable feedstock for the production of renewable liquid biofuels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Error Analysis of CM Data Products Sources of Uncertainty

This goal of this project is to address the current inability to assess the overall error and uncertainty of data products developed and distributed by DOE’s Consequence Management (CM) Program. This is a widely recognized shortfall, the resolution of which would provide a great deal of value and defensibility to the analysis results, data products, and the decision making process that follows this work. A global approach to this problem is necessary because multiple sources of error and uncertainty contribute to the ultimate production of CM data products. Therefore, this project will require collaboration with subject matter experts across a wide range of FRMAC skill sets in order to quantify the types of uncertainty that each area of the CM process might contain and to understand how variations in these uncertainty sources contribute to the aggregated uncertainty present in CM data products. The ultimate goal of this project is to quantify the confidence level of CM products to ensure that appropriate public and worker protections decisions are supported by defensible analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Search for neutral-current induced single photon production at the ND280 near detector in T2K

Neutrino neutral-current (NC) induced single photon production is a sub-leading order process for accelerator-based neutrino beam experiments including T2K. It is, however, an important process to understand because it is a background for electron (anti)neutrino appearance oscillation experiments. Here, we performed the first search of this process below 1 GeV using the fine-grained detector at the T2K ND280 off-axis near detector. By reconstructing single photon kinematics from electron–positron pairs, we achieved 95% pure gamma ray sample from protons-on-targets neutrino mode data. We do not find positive evidence of NC induced single photon production in this sample. We set the model-dependent upper limit on the cross-section for this process, at 0.114 x 10 –38 cm 2 (90% C.L.) per nucleon, using the J-PARC off-axis neutrino beam with an average energy of E v ~ 0.6 GeV. This is the first limit on this process below 1 GeV which is important for current and future oscillation experiments looking for electron neutrino appearance oscillation signals.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

WE-F-201-02: Review TG-186 and WGMBDCA Guidelines, Commission Process, and Dosimetry Benchmarks

With the recent introduction of heterogeneity correction algorithms for brachytherapy, the AAPM community is still unclear on how to commission and implement these into clinical practice. The recently-published AAPM TG-186 report discusses important issues for clinical implementation of these algorithms. A charge of the AAPM-ESTRO-ABG Working Group on MBDCA in Brachytherapy (WGMBDCA) is the development of a set of well-defined test case plans, available as references in the software commissioning process to be performed by clinical end-users. In this practical medical physics course, specific examples on how to perform the commissioning process are presented, as well as descriptions of the clinical impact from recent literature reporting comparisons of TG-43 and heterogeneity-based dosimetry. Learning Objectives: Identify key clinical applications needing advanced dose calculation in brachytherapy. Review TG-186 and WGMBDCA guidelines, commission process, and dosimetry benchmarks. Evaluate clinical cases using commercially available systems and compare to TG-43 dosimetry.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Zero Energy University Campuses: A 2018 Progress Update on Reaching Campus Energy Goals: Preprint

Many universities have aggressive energy-reduction and climate action plans. To reach these goals, leading universities have developed a suite of common energy-efficiency and renewable generation strategies. In this paper, we present a common set of approaches across the following areas: zero energy campus definitions and planning, best practices for including energy targets in new construction procurement contracts and design guidelines, implementing pilot new construction zero energy projects, aggressively implementing best-in-class laboratory energy-efficiency strategies, planning for and optimizing on-site and off-site renewable energy projects, and high-performance district energy systems. Best practices are compiled from various experiences, including programs funded by the U.S. Department of Energy, Energy Efficiency and Renewable Energy program. The summary of these best practices is to reduce campus energy use and then meet the remaining campus energy needs with renewable energy. The fivestep process to developing and implementing campus energy-reduction plans is described: 1) determine baseline energy consumption, 2) analyze technology options, 3) develop a plan and set priorities, 4) implement the plan, and 5) evaluate progress. The process follows a logical hierarchy of actions to evaluate options by energy sector and set specific targets. It encompasses all energy systems on campus, recognizing that campus-wide measures have increased potential for reducing energy use. Common strategies are explained along with examples and results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Zero Energy University Campuses: A 2018 Progress Update on Reaching Campus Energy Goals

Many universities have aggressive energy-reduction and climate action plans. To reach these goals, leading universities have developed a suite of common energy-efficiency and renewable generation strategies. In this paper, we present a common set of approaches across the following areas: zero energy campus definitions and planning, best practices for including energy targets in new construction procurement contracts and design guidelines, implementing pilot new construction zero energy projects, aggressively implementing best-in-class laboratory energy-efficiency strategies, planning for and optimizing on-site and off-site renewable energy projects, and high-performance district energy systems. Best practices are compiled from various experiences, including programs funded by the U.S. Department of Energy, Energy Efficiency and Renewable Energy program. The summary of these best practices is to reduce campus energy use and then meet the remaining campus energy needs with renewable energy. The fivestep process to developing and implementing campus energy-reduction plans is described: 1) determine baseline energy consumption, 2) analyze technology options, 3) develop a plan and set priorities, 4) implement the plan, and 5) evaluate progress. The process follows a logical hierarchy of actions to evaluate options by energy sector and set specific targets. It encompasses all energy systems on campus, recognizing that campus-wide measures have increased potential for reducing energy use. Common strategies are explained along with examples and results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

A processing diagram for high-density Ti-6Al-4V by selective laser melting

Density optimization is the first critical step in building additively manufactured parts with highquality and good mechanical properties. In earlier work, we developed an approach that combines simulations and experiments to identify processing parameters for high-density parts. Here we present the results for Ti-6Al-4V using the laser powder-bed-fusion technique. We show how simple, but approximate, simulations are used to design parameters for a limited set of single track experiments. The resulting melt-pool characteristics are then used to identify processing parameters for high-density pillars. From these experimental data, a processing diagram based on the normalized energy density concept is constructed, illustrating an optimized processing window for high- or low-density samples. Excellent mechanical properties are obtained for Ti-6Al-4V samples built from the optimized window. We discuss the limitations and implications of the processing diagram on the microstructure and mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Biopolymer Modified Cementitious Systems with Radically Superior Strength and Durability

This project was a pioneering study of the effect of nanochitin additives on setting time, rheological, and mechanical properties of cementitious materials. Chitin is the second most abundant biopolymer in nature after cellulose and is readily available from byproducts of the growing seafood industry. In this study, chitin powder from shrimp shells was processed into chitin nanocrystals (ChNCs) and chitin nanofibers (ChNFs). Ch-Cs delayed initial and final set times with their high ionic surface charges likely by electrostatic repulsion. The viscosity of fresh cement paste increased with Ch-NFs, while Ch-NCs did not significantly affect rheological properties. The distinct effects of the two nanochitin forms are attributed to the mobility of the different morphologies of the rod-like ChNCs vs. the entangled and restrained network of the ChNFs. Nanochitin forms showed encouraging results by significantly increasing flexural strength by ~40% at the optimum concentration of 0.05 wt% and significant increases in fracture energy. These bio-additives were found promising and future studies should continue to evaluate their use in concrete.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mega-Gauss Plasma Jet Creation Using a Ring of Laser Beams

Using 20 OMEGA laser beams at the Laboratory for Laser Energetics, University of Rochester, to irradiate a flat plastic target in a hollow ring configuration, we created supersonic cylindrical stable plasma jets with self-generated megagauss magnetic fields extending out to >4 mm. These well-collimated magnetized jets possess a number of distinct and novel properties that will allow us to study the dynamics, physical processes, and scaling properties of astrophysical jets with a dynamic range exceeding those of previous laboratory settings. The dimensionless parameters of these laboratory jets fall in the same regime as those of young stellar object jets. These jets will also provide new versatile laser-based platforms to study magnetized shocks, shear flows, and other plasma processes under controllable conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

SU-E-J-26: Analysis of Image Quality in CBCT QA Using a Treatment Planning System

Purpose: The objective of this study is to propose an alternative QA technique that analyzes imaging quality(IQ) in CBCT-QA processing. Methods: A catphan phantom was used to take CT imaging data set that were imported into a treatment planning system - Eclipse. The image quality was analyzed in terms of in-slice geometry resolution, Hounsfield numbers(HU) accuracy, mean-slice thickness, edge-to-center uniformity, low contrast resolution, and high contrast spatial resolution in Eclipse workstation. The CBCT-QA was also analyzed by OBI-workstation and a commercial software. Comparison was made to evaluation feasibility in a TPS environment. Results: The analysis of IQ was conducted in Eclipse v10.0 TPS. In-slice geometric resolution was measured between 2-rods in section CTP404 and repeated for all 4 rods with the difference between expected and measured values less than +/−0.1 cm. For HU, the difference between expected and measured values in HU was found much less than +/−40. Mean slice thickness measured by a distance on the wire proportional to scanner increment multiplying by a factor of 0.42. After repeating measurements to 4 wires, the average difference between expected and measured values was less +/−0.124 mm in slice thickness. HU uniformity was measured in section CTP486 with the tolerance less than +/−40 HU. Low contrast resolution in section CTP515 and high contrast resolution in section CTP528 were found to be 7 disks in diameter of 4 mm and 6 lp/cm, respectively. Eclipse TPS results indicated a good agreement to those obtained in OBI workstation and ImagePro software for major parameters. Conclusion: An analysis of IQ was proposed as an alternative CBCT QA processing. Based upon measured data assessment, proposed method was accurate and consistent to IQ evaluation and TG142 guideline. The approach was to utilize TPS resource, which can be valuable to re-planning, verification, and delivery in adaptive therapy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Data-Driven Selection of Tessellation Models Describing Polycrystalline Microstructures

Tessellation models have proven to be useful for the geometric description of grain microstructures in polycrystalline materials. With the use of a suitable tessellation model, the complex morphology of grains can be represented by a small number of parameters assigned to each grain, which not only entails a significant reduction in complexity, but also facilitates the investigation of certain geometric features of the microstructure. However, for a given set of microstructural data, the choice of a particular geometric model is traditionally based on researcher intuition. The model should provide a sufficiently good approximation to the data, while keeping the number of parameters small. In this paper, we discuss general aspects of the process of model selection and suggest several criteria for selecting an appropriate candidate from a certain set of tessellation models. The choice of candidate represents a trade-off between accuracy and complexity of the model. Here, the selected model is used solely to approximate given data samples, but it also provides guidance for developing stochastic tessellation models and generating virtual microstructures. Model fitting is carried out by simulated annealing, applied in a consistent manner to twelve different tessellation models.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure

Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy 2 Ti 2 O 7 , using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Characterization Report for the MP-1 Experiment Fabrication Campaign

This report covers the results of MP 1 fuel characterization analysis to provide a detailed understanding of the as-fabricated fuel that would be irradiated in the MP 1 (Mini-Plate 1) experiment. The work was performed at Pacific Northwest National Laboratory (PNNL), Idaho National Laboratory (INL), and Los Alamos National Laboratory (LANL). Characterization of the as-fabricated fuel was performed in accordance with Characterization Plan for the Fabrication of U 10Mo for the MP 1 Experiment, hereinafter the MP 1 Characterization Plan (MAQP 2016). Under the MP 1 Characterization Plan, fuel foils at different points in the foil fabrication process (e.g., hot rolled annealed, cold rolled annealed) were studied to understand the effect of various thermomechanical processes on the fuel microstructure. Samples were sent to each of the three organizations so that testing and analysis could be performed independently using similar equipment and a standardized set of measurement and analysis procedures. The MP 1 experiment will provide an opportunity to understand the effects of processing conditions on the final fuel microstructure, to compare independently obtained results, and achieve a three-way validation. PNNL characterized 28 uranium–10 wt% molybdenum (U 10Mo) samples. Six pieces/specimens from each sample/foil were sectioned, in accordance with the MP 1 Characterization Plan. Similarly, INL and LANL also examined 24 and 17 U 10Mo specimens, respectively. These total 69 samples consist of six types of MP 1 characterization foils fabricated by Babcock and Wilcox Technologies (BWXT) from ingots produced by the Y-12 National Security Complex: • 0.047 in.-thick hot-rolled and annealed samples with and without Zr layers • 0.02 in.-thick cold-rolled and annealed samples with and without Zr layers • 0.01 in.-thick cold-rolled and annealed samples with and without Zr layers. Microstructure, chemical composition, carbide morphology, U 10Mo foil thickness, Zr thickness, mechanical properties (microhardness), and density were evaluated in both longitudinal and transverse directions for foils of the three different thicknesses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Characterization Report for the MP-1 Experiment Fabrication Campaign (Rev.1)

This report covers the results of MP 1 fuel characterization analysis to provide a detailed understanding of the as-fabricated fuel that would be irradiated in the MP 1 (Mini-Plate 1) experiment. The work was performed at Pacific Northwest National Laboratory (PNNL), Idaho National Laboratory (INL), and Los Alamos National Laboratory (LANL). Characterization of the as-fabricated fuel was performed in accordance with Characterization Plan for the Fabrication of U 10Mo for the MP 1 Experiment, hereinafter the MP 1 Characterization Plan (MAQP 2016). Under the MP 1 Characterization Plan, fuel foils at different points in the foil fabrication process (e.g., hot rolled annealed, cold rolled annealed) were studied to understand the effect of various thermomechanical processes on the fuel microstructure. Samples were sent to each of the three organizations so that testing and analysis could be performed independently using similar equipment and a standardized set of measurement and analysis procedures. The MP 1 experiment will provide an opportunity to understand the effects of processing conditions on the final fuel microstructure, to compare independently obtained results, and achieve a three-way validation. PNNL characterized 28 uranium–10 wt% molybdenum (U 10Mo) samples. Six pieces/specimens from each sample/foil were sectioned, in accordance with the MP 1 Characterization Plan. Similarly, INL and LANL also examined 24 and 17 U 10Mo specimens, respectively. These total 69 samples consist of six types of MP 1 characterization foils fabricated by Babcock and Wilcox Technologies (BWXT) from ingots produced by the Y-12 National Security Complex: (a.) 0.047 in.-thick hot-rolled and annealed samples with and without Zr layers; (b.) 0.02 in.-thick cold-rolled and annealed samples with and without Zr layers; and (c.) 0.01 in.-thick cold-rolled and annealed samples with and without Zr layers. Microstructure, chemical composition, carbide morphology, U 10Mo foil thickness, Zr thickness, mechanical properties (microhardness), and density were evaluated in both longitudinal and transverse directions for foils of the three different thicknesses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Harnessing expert knowledge: Defining a Bayesian network decision model with limited data-Model structure for the vibration qualification problem

As systems become more complex, systems engineers rely on experts to inform decisions. There are few experts and limited data in many complex new technologies. This challenges systems engineers as they strive to plan activities such as qualification in an environment where technical constraints are coupled with the traditional cost, risk, and schedule constraints. Bayesian network (BN) models provide a framework to aid systems engineers in planning qualification efforts with complex constraints by harnessing expert knowledge and incorporating technical factors. By quantifying causal factors, a BN model can provide data about the risk of implementing a decision supplemented with information on driving factors. This allows a systems engineer to make informed decisions and examine “what-if” scenarios. This paper discusses a novel process developed to define a BN model structure based primarily on expert knowledge supplemented with extremely limited data (25 data sets or less). The model was developed to aid qualification decisions—specifically to predict the suitability of six degrees of freedom (6DOF) vibration testing for qualification. The process defined the model structure with expert knowledge in an unbiased manner. Finally, validation during the process execution and of the model provided evidence the process may be an effective tool in harnessing expert knowledge for a BN model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.

54 ENVIRONMENTAL SCIENCES↗