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DOE OSTI2015

Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks

Abstract Empirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy fluxes to regional and global scales. The predictive capacity of such models largely depends on the data used for parameterization and identification of input‐output relationships, while prediction for conditions outside the training domain is generally uncertain. In this work, artificial neural networks (ANNs) were used for the prediction of gross primary production (GPP) and latent heat flux (LE) on local and European scales with the aim to assess the portion of uncertainties in extrapolation due to sample selection. ANNs were found to be a useful tool for GPP and LE prediction, in particular for extrapolation in time (mean absolute error MAE for GPP between 0.53 and 1.56 gC m −2 d −1 ). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7–1.41 gC m −2 d −1 ), while extrapolation in areas with different seasonal cycles and controlling factors (e.g., the tropical regions) showed noticeably higher errors (GPP MAE 0.8–2.09 gC m −2 d −1 ). The distribution and the number of sites used for ANN training had a remarkable effect on prediction uncertainty in both, regional GPP and LE budgets and their interannual variability. Results obtained show that for ANN upscaling for continents with relatively small networks of sites, the error due to the sampling can be large and needs to be considered and quantified. The analysis of the spatial variability of the uncertainty helped to identify the meteorological drivers driving the uncertainty.

Papale, Dario↗

DOE OSTI2021

Orientation-Dependent Distortion of Lamellae in a Block Copolymer Electrolyte under DC Polarization

Lithium-salt-doped block copolymers have the potential to serve as solid electrolytes in rechargeable batteries with lithium metal anodes. In this work, we use small-angle X-ray scattering (SAXS) to study the structure of polystyrene-block-poly(ethylene oxide) (PS-b-PEO) doped with bis-(trifluoromethylsulfonyl)amine lithium salt (LiTFSI) during direct current (dc) polarization experiments in lithiu-lithium symmetric cells. The block copolymer studied is nearly symmetric in composition, has a total molecular weight of 39 kg mol -1 , and exhibits a lamellar morphology at all studied salt concentrations. When ionic current is passed through the electrolyte, a salt concentration gradient forms that induces a spatial gradient in the domain spacing, d. The dependence of d on distance from the positive electrode, x, was determined experimentally by scanning the incident X-ray beam from one lithium electrode to the other. By studying the two-dimensional (2D) SAXS patterns as a function of azimuthal scattering angle, we find that lamellae with PS/PEO interfaces oriented perpendicular to the flow of ionic current (LAM ⟂ ) swell and contract to a greater degree than those with interfaces oriented parallel to the current direction (LAM || ). While domains with the LAM ⟂ do not provide direct conducting pathways between the electrodes, our analysis suggests that they play an important role in establishing the salt concentration gradient necessary for sustaining a large ionic current through greater expansion and contraction.

25 ENERGY STORAGE↗

DOE OSTI2021

The large-scale environment of thermonuclear and core-collapse supernovae

ABSTRACT The new generation of wide-field time-domain surveys has made it feasible to study the clustering of supernova (SN) host galaxies in the large-scale structure (LSS) for the first time. We investigate the LSS environment of SN populations, using 106 dark matter density realisations with a resolution of ∼3.8 Mpc, constrained by the 2M+ + galaxy survey. We limit our analysis to redshift z < 0.036, using samples of 498 thermonuclear and 782 core-collapse SNe from the Zwicky Transient Facility’s Bright Transient Survey and Census of the Local Universe catalogues. We detect clustering of SNe with high significance; the observed clustering of the two SNe populations is consistent with each other. Further, the clustering of SN hosts is consistent with that of the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey DR12 spectroscopic galaxy sample in the same redshift range. Using a tidal shear classifier, we classify the LSS into voids, sheets, filaments, and knots. We find that both SNe and SDSS galaxies are predominantly found in sheets and filaments. SNe are significantly under-represented in voids and over-represented in knots compared to the volume fraction in these structures. This work opens the potential for using forthcoming wide-field deep SN surveys as a complementary LSS probe.

Astronomy & Astrophysics↗

DOE OSTI2021

UKy-CAER Heat Integrated Transformative CO 2 Capture Process in Pulverized Coal Power Plants

The goal of this Phase II Topical Report is to summarize the work for public domain conducted on project DE-FE0031583. Building upon the Design Basis completed during Phase I and in accordance with the Statement of Project Objectives (SOPO), the University of Kentucky (UK) Center for Applied Energy Research (CAER) (Recipient) along with team members has completed the Front End Engineering Design (FEED) for its advanced, versatile post-combustion CO 2 capture system (CCS) at the 10 MWe large pilot scale to be installed at the Wyoming (WY) Integrated Test Center (ITC), using a heat integrated process with split-rich fed, two-stage stripping and any advanced solvent to enhance the CO 2 absorber performance and lower the cost of CO 2 capture. The proposed project (Phases I-III) involves the design, fabrication, installation, testing and evaluation of a large pilot scale facility that will demonstrate the UK CAER transformative CCS integrated with an operating power plant. The work performed in Phase I, detailed in the Phase I Topical Report that was submitted in March 2019, included formation of a project team, securing the host site, and completing pre-FEED design basis, a cost estimation, a preliminary techno-economic analysis (TEA) and an environmental information volume (EIV). The work performed in Phase II, detailed in this Phase II Topical Report, includes completion of FEEDs, completion of a process risk analysis (PRA), obtaining necessary permits, updating the EIV and TEA, updating the cost and schedule estimate for the Phase III scope of work (SOW) and securing the necessary cost share. Updated information is provided on the current status of the UK CAER CCS technology and the project is ready to begin immediately detailed design, construction and operation of the proposed pilot.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRS1991

Control system failure monitoring using generalized parity relations

Many applications require that a control system must be tolerant to the failure of its components. This is especially true for large space-based systems that must work unattended and with long periods between maintenance. Fault tolerance can be obtained by detecting the failure of the control system component, determining which component has failed, and reconfiguring the system so that the failed component is isolated from the controller. Component failure detection experiments that were conducted on an experimental space structure, the NASA Langley Mini-Mast are presented. Two methodologies for failure detection and isolation (FDI) exist that do not require the specification of failure modes and are applicable to both actuators and sensors. These methods are known as the Failure Detection Filter and the method of Generalized Parity Relations. The latter method was applied to three different sensor types on the Mini-Mast. Failures were simulated in input-output data that were recorded during operation of the Mini-Mast. Both single and double sensor parity relations were tested and the effect of several design parameters on the performance of these relations is discussed. The detection of actuator failures is also treated. It is shown that in all the cases it is possible to identify the parity relations directly from input-output data. Frequency domain analysis is used to explain the behavior of the parity relations.

Vanschalkwyk, Christiaan Mauritz↗

DOE OSTI2018

RitR is an archetype for a novel family of redox sensors in the streptococci that has evolved from two-component response regulators and is required for pneumococcal colonization

To survive diverse host environments, the human pathogen Streptococcus pneumoniae must prevent its self-produced, extremely high levels of peroxide from reacting with intracellular iron. However, the regulatory mechanism(s) by which the pneumococcus accomplishes this balance remains largely enigmatic, as this pathogen and other related streptococci lack all known redox-sensing transcription factors. Here we describe a two-component-derived response regulator, RitR, as the archetype for a novel family of redox sensors in a subset of streptococcal species. We show that RitR works to both repress iron transport and enable nasopharyngeal colonization through a mechanism that exploits a single cysteine (Cys128) redox switch located within its linker domain. Biochemical experiments and phylogenetics reveal that RitR has diverged from the canonical two-component virulence regulator CovR to instead dimerize and bind DNA only upon Cys128 oxidation in air-rich environments. Atomic structures show that Cys128 oxidation initiates a “helical unravelling” of the RitR linker region, suggesting a mechanism by which the DNA-binding domain is then released to interact with its cognate regulatory DNA. Expanded computational studies indicate this mechanism could be shared by many microbial species outside the streptococcus genus.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Forward and backward stimulated Raman scattering in multi-speckled beams: Density dependence and effects on cross-beam energy transfer

Particle-in-cell simulations of cross-beam energy transfer (CBET) between multi-speckled beams reveal a potentially prominent energy loss from forward stimulated Raman scattering (FSRS) in the amplified seed beam [Yin et al., Phys. Plasmas 26, 082708 (2019)]. In this work, an intensity scan is used to examine this energy loss in the CBET-amplified seed beam at intensities of a few times 1014 W/cm2. Electron tracers demonstrate both the trapping in the electron plasma waves that is sustained over multiple speckles and the subsequent acceleration to velocities well beyond the phase velocity. Motivated by expanding our understanding of this energy loss mechanism, we isolate the FSRS density dependence by using a 2×1014 W/cm2 pulse to scan densities between 0.06 ncr and 0.12 ncr; a spectral analysis characterizes the energy losses from scattering with the changing densities. At saturation, both greater beam attenuation is measured at the laser frequency and larger scattering FSRS angles are observed as density increases, along with a rapid transition to primarily backscatter SRS between 0.10 ncr and 0.12 ncr, earlier than predicted by linear theory. Density gradients are shown to effectively mitigate oblique FSRS and beam energy losses in the two lower density cases, whereas 0° forward scattering is primarily suppressed in the higher density cases. Finally returning to the two-beam CBET system of interest, the interplay and mitigation of the secondary instabilities at larger domain and beam spatial scales are discussed, here also examining backward stimulated Brillouin scattering.

Stark, D. J. (ORCID:0000000203642733)↗

DOE OSTI2017

Stability and Control of Burning Tokamak Plasmas with Resistive Walls: Final Technical Report

This project is focused on theoretical and computational development for quantitative prediction of the stability and control of the equilibrium state evolution in toroidal burning plasmas, including its interaction with the surrounding resistive wall. The stability of long pulse burning plasmas is highly sensitive to the physics of resonant layers in the plasma, sources of momentum and flow, kinetic effects of energetic particles, and boundary conditions at the wall, including feedback control and error fields. In ITER in particular, the low toroidal flow equilibrium state, sustained primarily by energetic alpha particles from fusion reactions, will require the consideration of all of these key elements to predict quantitatively the stability and evolution. The principal investigators on this project have performed theoretical and computational analyses, guided by analytic modeling, to address this physics in realistic configurations. The overall goal has been to understand the key physics mechanisms that describe stable toroidal burning plasmas under active feedback control. Several relevant achievements have occurred during this project, leading to publications and invited conference presentations. In theoretical efforts, with the physics of the resonant layers, resistive wall, and toroidal momentum transport included, this study has extended from cylindrical resistive plasma - resistive wall models with feedback control to toroidal geometry with strong shaping to study mode coupling effects on the stability. These results have given insight into combined tearing and resistive wall mode behavior in simulations and experiment, while enabling a rapid exploration of plasma parameter space, to identify possible domains of interest for large plasma codes to investigate in more detail. Resonant field amplification and quasilinear torques in the presence of error fields and velocity shear have also been investigated. Here it was found, surprisingly, that the Maxwell torque on resonant layers in the plasma which exhibit finite real frequencies ωr in their response is significantly different from the conventional results based on tearing layers with pure real growth (or damping) rates. This observation suggests the possibility that the torque on the tearing layers can lock the plasma rotation to this finite phase velocity, which may lead to locking in which velocity shear is maintained. More broadly, the sources of all torques driving flows in magnetic confinement experiments is not fully understood, and this theoretical work may shed light on puzzling experimental results. It was also found that real frequencies occur over a wide range of plasma response regimes, and are indeed the norm and not the exception, often leading to profound effects on the locking torque. Also, the influence of trapped energetic ions orbiting over the resistive plasma mode structure, a critical effect in burning plasmas, was investigated through analytic modeling and analysis of simulations and experiment. This effort has shown that energetic ions can drive the development of disruptive instabilities, but also damp and stabilize the instabilities, depending on the details of the shear in the equilibrium magnetic field. This finding could be critical to maintaining stable operations in burning plasmas. In the most recent work, a series of simulations have been conducted to study the effect of differential flow and energetic ion effects on entry to the onset of a disruptive instability in the most realistic conditions possible, with preexisting nonlinearly saturated benign instabilities. Throughout this work, the linear and quasilinear theory of resonant layers with differential flow between them, their interaction with resistive wall and error fields, and energetic ions effects, have been used to understand realistic simulations of mode onset and the experimental discharges they represent. These studies will continue to answer remaining questions about the relation between theoretical results obtained in this project and observations of the onset and evolution of disruptive instabilities in experiment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2003

Theory of Collective Intelligence

In this chapter an analysis of the behavior of an arbitrary (perhaps massive) collective of computational processes in terms of an associated "world" utility function is presented We concentrate on the situation where each process in the collective can be viewed as though it were striving to maximize its own private utility function. For such situations the central design issue is how to initialize/update the collective's structure, and in particular the private utility functions, so as to induce the overall collective to behave in a way that has large values of the world utility. Traditional "team game" approaches to this problem simply set each private utility function equal to the world utility function. The "Collective Intelligence" (COIN) framework is a semi-formal set of heuristics that recently have been used to construct private utility. functions that in many experiments have resulted in world utility values up to orders of magnitude superior to that ensuing from use of the team game utility. In this paper we introduce a formal mathematics for analyzing and designing collectives. We also use this mathematics to suggest new private utilities that should outperform the COIN heuristics in certain kinds of domains. In accompanying work we use that mathematics to explain previous experimental results concerning the superiority of COIN heuristics. In that accompanying work we also use the mathematics to make numerical predictions, some of which we then test. In this way these two papers establish the study of collectives as a proper science, involving theory, explanation of old experiments, prediction concerning new experiments, and engineering insights.

Nash equilibrium↗

DOE OSTI2021

Environmental and cost benefits of multi‐purpose buffers in an agricultural watershed for biomass production

Abstract The cost‐benefit of riparian buffers to landowners has not been fully understood despite its ecosystem benefits. This study investigates the environmental and economic value of multi‐purpose buffers for biomass production under alternative land‐management scenarios at the watershed scale. The work incorporates the Soil and Water Assessment Tool with a field‐scale tool, the Agriculture Conservation Practice Framework, and cost‐benefit analysis. Our work took place in the agriculturally dominant Raccoon River watershed (RRW) in Iowa. The cost analysis includes implementing and harvesting the switchgrass buffers, the value of nitrogen and phosphorus fertilizer, and the forgone income of cropland to determine the net return under 12 alternative feedstock scenarios. We estimated that 14 603 Mg of nitrogen and 2563 Mg of phosphorus runoffs can be intercepted by riparian buffers in the RRW, representing a nutrient loss reduction of 11% of nitrogen and 26% of phosphorus. These nutrients are valued at $1.31 million (M) for phosphorus and $1.24 M for nitrogen as fertilizers. The trapped nutrients account for 12% of the riparian buffers’ total cost. We found that net economic returns increase with biomass market price, increase with maintenance fertilizer application, and decrease with the cropland areas that are converted to buffers. Under current land management, a biomass market price at $60 per dry ton of biomass would be economically viable at the sub‐basin level when maintenance fertilizers are applied. At $80 per dry ton, all 108 sub‐basins in the RRW would be profitable up to $517 a year. The results highlight the trade‐offs between water quality and profitability. Published 2021. This article is a U.S. Government work and is inthe public domain in the USA. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗

NASA NTRS2018

GeneLab Analysis Working Group Kick-Off Meeting

Goals to achieve for GeneLab AWG - GL vision - Review of GeneLab AWG charter Timeline and milestones for 2018 Logistics - Monthly Meeting - Workshop - Internship - ASGSR Introduction of team leads and goals of each group Introduction of all members Q/A Three-tier Client Strategy to Democratize Data Physiological changes, pathway enrichment, differential expression, normalization, processing metadata, reproducibility, Data federation/integration with heterogeneous bioinformatics external databases The GLDS currently serves over 100 omics investigations to the biomedical community via open access. In order to expand the scope of metadata record searches via the GLDS, we designed a metadata warehouse that collects and updates metadata records from external systems housing similar data. To demonstrate the capabilities of federated search and retrieval of these data, we imported metadata records from three open-access data systems into the GLDS metadata warehouse: NCBI's Gene Expression Omnibus (GEO), EBI's PRoteomics IDEntifications (PRIDE) repository, and the Metagenomics Analysis server (MG-RAST). Each of these systems defines metadata for omics data sets differently. One solution to bridge such differences is to employ a common object model (COM) to which each systems' representation of metadata can be mapped. Warehoused metadata records are then transformed at ETL to this single, common representation. Queries generated via the GLDS are then executed against the warehouse, and matching records are shown in the COM representation (Fig. 1). While this approach is relatively straightforward to implement, the volume of the data in the omics domain presents challenges in dealing with latency and currency of records. Furthermore, the lack of a coordinated has been federated data search for and retrieval of these kinds of data across other open-access systems, so that users are able to conduct biological meta-investigations using data from a variety of sources. Such meta-investigations are key to corroborating findings from many kinds of assays and translating them into systems biology knowledge and, eventually, therapeutics.

GeneLab↗

DOE OSTI2023

Structural and functional validation of a highly specific Smurf2 inhibitor

Smurf1 and Smurf2 are two closely related member of the HECT (homologous to E6AP carboxy terminus) E3 ubiquitin ligase family and play important roles in the regulation of various cellular processes. Both were initially identified to regulate transforming growth factor‐β and bone morphogenetic protein signaling pathways through regulating Smad protein stability and are now implicated in various pathological processes. Generally, E3 ligases, of which over 800 exist in humans, are ideal targets for inhibition as they determine substrate specificity; however, there are few inhibitors with the ability to precisely target a particular E3 ligase of interest. In this work, we explored a panel of ubiquitin variants (UbVs) that were previously identified to bind Smurf1 or Smurf2. In vitro binding and ubiquitination assays identified a highly specific Smurf2 inhibitor, UbV S2.4, which was able to inhibit ligase activity with high potency in the low nanomolar range. Orthologous cellular assays further demonstrated high specificity of UbV S2.4 toward Smurf2 and no cross‐reactivity toward Smurf1. Structural analysis of UbV S2.4 in complex with Smurf2 revealed its mechanism of inhibition was through targeting the E2 binding site. In summary, we investigated several protein‐based inhibitors of Smurf1 and Smurf2 and identified a highly specific Smurf2 inhibitor that disrupts the E2–E3 protein interaction interface.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Dominant balance-based adaptive mesh refinement for incompressible fluid flows

This work introduces a novel adaptive mesh refinement (AMR) method that utilizes dominant balance analysis (DBA) for efficient and accurate grid adaptation in computational fluid dynamics (CFD) simulations. The proposed method leverages a Gaussian mixture model (GMM) to classify grid cells into active and passive regions based on the dominant physical interactions within the equation space. By modeling truncation error probabilistically from discretized terms, the method identifies regions of high interaction where numerical accuracy is most sensitive to resolution. Unlike traditional AMR strategies, this approach does not rely on heuristic-based sensors or user-defined thresholds, providing a fully automated and problem-independent framework for AMR. Applied to the incompressible Navier-Stokes equations for steady and unsteady flow past a cylinder, the DBA-based AMR method achieves comparable accuracy to high-resolution grids while reducing computational costs by up to 70 %. The validation highlights the method’s effectiveness in capturing complex flow features while minimizing grid cells, directing computational resources toward regions with the most critical dynamics. This modular and scalable strategy is adaptable to a wide range of applications, presenting a promising tool for efficient high-fidelity simulations in CFD and other multiphysics domains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective

Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Baskaran, Arun↗

DOE OSTI2019

Passively Sampled Environmental Films Show Geographic Variability and Host a Variety of Microorganisms

Environmental films are ubiquitous coatings that form on virtually all surfaces exposed to the troposphere. This work analyzes large molecular weight species in native environmental films acquired at three collection sites in the vicinity of Iowa City, Iowa, USA that include an urban residence, a wooded area, and a metropolitan building rooftop. The films are developed for up to twelve months on polished silicon wafer substrates housed in flat-plate passive samplers. Film composition was assessed by Fourier transform ion cyclotron resonance secondary ion mass spectrometry (FTICR-SIMS), and spatially resolved chemical morphology was analyzed via timeof- flight secondary ion mass spectrometry (ToF-SIMS) imaging. Mass analysis spanning up to m/z 2,000 included detection of a variety of organic and inorganic compounds. We characterize MS data with PCA to identify variability between the collection sites and the respective films generated therein, where we detected a variety of organic and biotically generated species consistent with fungal and bacterial microorganisms. Here, the film collection site as well as deposition time contribute to spectral variability, suggesting that geographic location as well as seasonal diversity may affect composition. SIMS analyses were complemented with microscopy and SYBR-Safe staining to highlight biological microorganisms within the films, which include evidence of fungi, protozoa, and bacteria. SIMS imaging also reveals domains concentrated with fragments indicative of proteinaceous macromolecules and microbial biomass, characteristic of biological presence within the film.

42 ENGINEERING↗

DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1994

An HP Adaptive Discontinuous Galerkin Method for Hyperbolic Conservation Laws

This dissertation addresses various issues for model classes of hyperbolic conservation laws. The basic approach developed in this work employs a new family of adaptive, hp-version, finite element methods based on a special discontinuous Galerkin formulation for hyperbolic problems. The discontinuous Galerkin formulation admits high-order local approximations on domains of quite general geometry, while providing a natural framework for finite element approximations and for theoretical developments. The use of hp-versions of the finite element method makes possible exponentially convergent schemes with very high accuracies in certain cases; the use of adaptive hp-schemes allows h-refinement in regions of low regularity and p-enrichment to deliver high accuracy, while keeping problem sizes manageable and dramatically smaller than many conventional approaches. The use of discontinuous Galerkin methods is uncommon in applications, but the methods rest on a reasonable mathematical basis for low-order cases and has local approximation features that can be exploited to produce very efficient schemes, especially in a parallel, multiprocessor environment. The place of this work is to first and primarily focus on a model class of linear hyperbolic conservation laws for which concrete mathematical results, methodologies, error estimates, convergence criteria, and parallel adaptive strategies can be developed, and to then briefly explore some extensions to more general cases. Next, we provide preliminaries to the study and a review of some aspects of the theory of hyperbolic conservation laws. We also provide a review of relevant literature on this subject and on the numerical analysis of these types of problems.

Bey, Kim S.↗

DOE OSTI2024

Mixed Delay/Nondelay Embeddings Based Neuromorphic Computing with Patterned Nanomagnet Arrays

Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previous works have demonstrated methods to physically probe these magnetization dynamics of PNAs to realize neuromorphic reservoir systems that exhibit chaotic dynamical behavior and high-dimensional nonlinearity. These PNA reservoir systems from prior works leverage echo state properties and linear/nonlinear short-term memory of component reservoir nodes to map and preserve the dynamical information of the input time-series data into nondelay spatial embeddings. Such mappings enable these PNA reservoir systems to imitate and predict/forecast the input time series data. However, these prior PNA reservoir systems are based solely on the nondelay spatial embeddings obtained at component reservoir nodes. As a result, they require a massive number of component reservoir nodes, or a very large spatial embedding (i.e., high-dimensional spatial embedding) per reservoir node, or both, to achieve acceptable imitation and prediction accuracy. These requirements reduce the practical feasibility of such PNA reservoir systems. To address this shortcoming, we present a mixed delay/nondelay embeddings-based PNA reservoir system. Our system uses a single PNA reservoir node with the ability to obtain a mixture of delay/nondelay embeddings of the dynamical information of the time-series data applied at the input of a single PNA reservoir node. Our analysis shows that when these mixed delay/nondelay embeddings are used to train a perceptron at the output layer, our reservoir system outperforms existing PNA-based reservoir systems for the imitation of NARMA 2, NARMA 5, NARMA 7, and NARMA 10 time series data, and for the short-term and long-term prediction of the Mackey Glass time series data.

Ti, Changpeng↗