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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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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform

Abstract Many spectral responses in materials science, physics, and chemistry experiments can be characterized as resulting from the superposition of a number of more basic individual spectra. In this context, unmixing is defined as the problem of determining the individual spectra, given measurements of multiple spectra that are spatially resolved across samples, as well as the determination of the corresponding abundance maps indicating the local weighting of each individual spectrum. Matrix factorization is a popular linear unmixing technique that considers that the mixture model between the individual spectra and the spatial maps is linear. Here, we present a tutorial paper targeted at domain scientists to introduce linear unmixing techniques, to facilitate greater understanding of spectroscopic imaging data. We detail a matrix factorization framework that can incorporate different domain information through various parameters of the matrix factorization method. We demonstrate many domain-specific examples to explain the expressivity of the matrix factorization framework and show how the appropriate use of domain-specific constraints such as non-negativity and sum-to-one abundance result in physically meaningful spectral decompositions that are more readily interpretable. Our aim is not only to explain the off-the-shelf available tools, but to add additional constraints when ready-made algorithms are unavailable for the task. All examples use the scalable open source implementation from https://github.com/ramkikannan/nmflibrary that can run from small laptops to supercomputers, creating a user-wide platform for rapid dissemination and adoption across scientific disciplines.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

EGS Collab Earth Modeling: Integrated 3D Model of the Testbed

The EGS Collab project is conducting a series of stimulation and interwell flow tests in an intermediate scale (~10-20 m) testbed located on the 4850 level in the Sanford Underground Research Facility (SURF) in Lead, South Dakota. The testbed consists of eight ~200 ft (~60 m) HQ-diameter (9.6 cm) boreholes that are drilled into the Poorman Formation from the western rib of the West Access Drift. Of the eight boreholes, one borehole is used as an injection/stimulation well, another sub-parallel borehole located about 10 m away from the injection well is used as a production well, and rest of the other boreholes are used as monitoring wells. For 3D visualization of the testbed as well as to provide spatially accurate and consistent parameter data for geomechanical and geophysical process modeling tasks, we are using Leapfrog Software (Seequent Limited). For analysis of fractures and creation of conditioned stochastic discrete fracture network (CS-DFN) model of the testbed, we are using FracMan (Golder Associates Inc.). As of mid-May 2019, the EGS Collab Leapfrog database contains the general layout of the drifts/shafts at the 4850 and 4100 levels; generalized geologic framework model of a domain that spans both the 4850 and 4100 levels; the CS-DFN of the testbed; and layouts of the EGS Collab testbed boreholes, kISMET testbed boreholes, and other existing boreholes at the 4850 level. This information has been obtained from the SURF Vulcan database, detailed laser scan and point surveys of borehole collar locations, directional surveys of borehole trajectories, and mapping of features in the drifts, boreholes, and cores. Similarly, other types of data that have been incorporated into the Leapfrog database include static features of the testbed such as natural discontinuities (e.g., fractures, weeps), location of monitoring instruments, and stimulation notches. Baseline geophysical characterization results (e.g., seismic, ERT, temperature, etc.) are also imported into the Leapfrog database. The location and size of packers, various point-source signals detected during stimulation and flow tests (e.g., temperature anomalies, micro-earthquake events, etc.) are also compiled in the database. All feature locations are reported using the local Homestake Mine coordinate system. In this paper, we present some of the geostructural characteristics as well as the status of the testbed as represented by Leapfrog/FracMan database/visualizations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

A lock-free priority queue design based on multi-dimensional linked lists

The throughput of concurrent priority queues is pivotal to multiprocessor applications such as discrete event simulation, best-first search and task scheduling. Existing lock-free priority queues are mostly based on skiplists, which probabilistically create shortcuts in an ordered list for fast insertion of elements. The use of skiplists eliminates the need of global rebalancing in balanced search trees and ensures logarithmic sequential search time on average, but the worst-case performance is linear with respect to the input size. In this paper, we propose a quiescently consistent lock-free priority queue based on a multi-dimensional list that guarantees worst-case search time of O(logN) for key universe of size N. The novel multi-dimensional list (MDList) is composed of nodes that contain multiple links to child nodes arranged by their dimensionality. The insertion operation works by first injectively mapping the scalar key to a high-dimensional vector, then uniquely locating the target position by using the vector as coordinates. Nodes in MDList are ordered by their coordinate prefixes and the ordering property of the data structure is readily maintained during insertion without rebalancing nor randomization. Furthermore, in our experimental evaluation using a micro-benchmark, our priority queue achieves an average of 50% speedup over the state of the art approaches under high concurrency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CIRCLEZ : Reliable photometric redshifts for active galactic nuclei computed solely using photometry from Legacy Survey Imaging for DESI

Photometric redshifts for galaxies hosting an accreting supermassive black hole in their center, known as active galactic nuclei (AGNs), are notoriously challenging. At present, they are most optimally computed via spectral energy distribution (SED) fittings, assuming that deep photometry for many wavelengths is available. However, for AGNs detected from all-sky surveys, the photometry is limited and provided by a range of instruments and studies. This makes the task of homogenizing the data challenging, presenting a dramatic drawback for the millions of AGNs that wide surveys such as SRG/eROSITA are poised to detect. This work aims to compute reliable photometric redshifts for X-ray-detected AGNs using only one dataset that covers a large area: the tenth data release of the Imaging Legacy Survey (LS10) for DESI. LS10 provides deep grizW1-W4 forced photometry within various apertures over the footprint of the eROSITA-DE survey, which avoids issues related to the cross-calibration of surveys. We present the results from CIRCLEZ, a machine-learning algorithm based on a fully connected neural network. CIRCLEZ is built on a training sample of 14 000 X-ray-detected AGNs and utilizes multi-aperture photometry, mapping the light distribution of the sources. The accuracy (σNMAD) and the fraction of outliers (η) reached in a test sample of 2913 AGNs are equal to 0.067 and 11.6%, respectively. The results are comparable to (or even better than) what was previously obtained for the same field, but with much less effort in this instance. We further tested the stability of the results by computing the photometric redshifts for the sources detected in CSC2 and Chandra-COSMOS Legacy, reaching a comparable accuracy as in eFEDS when limiting the magnitude of the counterparts to the depth of LS10. The method can be applied to fainter samples of AGNs using deeper optical data from future surveys (for example, LSST, Euclid), granting LS10-like information on the light distribution beyond the morphological type. Along with this paper, we have released an updated version of the photometric redshifts (including errors and probability distribution functions) for eROSITA/eFEDS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

SECARB-USA: Needs Assessment Framework for Storage Complexes (Task 2.1.b)

A team of SECARB storage experts examined 63 formations at 39 sites that were selected to represent the range of diversity of newly assessed, as well as well-advanced, storage prospects in the SECARB region. We inventoried the data needs triggered by the requirements to obtain a Class VI UIC storage permit, the data needs that results from the requirement to create geocellular fluid flow models to support that permit, and by pragmatic and best practice inputs such as public acceptance, regulatory readiness, and pore space leasing. We anchored both the needs inventory and the processes for and cost of meeting the needs with data from 9 sites in in the SECARB area that have advanced far in characterization. Results show that the total cost of characterization prior to obtaining a permit is convergent, because the permit and modeling requirements drive projects to obtain the same types of data for all cases. The high cost data that control cost are 1) drilling, coring, core-testing, logging, sampling and testing a characterization well and 2) collection of a 3-D seismic survey to map reservoir and confining system properties over the area of the plume or the area of elevated pressure. In 5 of our case study sites we determined that one or both of these costs could be avoided because the needs are met by available data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Automatic log analysis with NLP for the CMS workflow handling

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Layer, Lukas↗
DOE OSTI2015

Digital Autoradiography as a novel complementary technique for the investigation of radioactive contamination in nuclear facilities under dismantlement

The detection (location and quantification) of nuclear facilities to be dismantled possible contamination with low-range particles emitters ({sup 3}H, other low-energy β emitters, a emitters) remains a tedious and expensive task. Indeed, usual remote counters show a too low sensitivity to these non-penetrating radiations, while conventional wipe tests are irrelevant for fixed radioactivity evaluation. The only method to accurately measure activity levels consists in sampling and running advanced laboratory analyses (spectroscopy, liquid scintillation counting, pyrolysis...). Such measurements generally induce sample preparation, waste production (destructive analyses, solvents), nuclear material transportation, long durations, and significant labor mobilization. Therefore, the search for the limitation of their number and cost easily conflicts with the necessity to perform a dense screening for sampling (to maximize the representativeness of the samples), in installations of thousands of square meters (floors, wells, ceilings), plus furniture, pipes, and other wastes. To overcome this contradiction, Digital Autoradiography (D. A.) was re-routed from bio molecular research to radiological mapping of nuclear installations under dismantling and to waste and sample analysis. After in-situ exposure to the possibly-contaminated areas to investigate, commercial reusable radiosensitive phosphor screens (of a few 100 cm{sup 2}) were scanned in the proper laboratory device and sharp quantitative images of the radioactivity could be obtained. The implementation of geostatistical tools in the data processing software enabled the exhaustive characterization of concrete floors at a rate of 2 weeks / 100 m{sup 2}, at lowest costs. Various samples such as drilled cores, or tank and wood pieces, were also successfully evaluated with this method, for decisive results. Thanks to the accurate location of potential contamination spots, this approach ensures relevant and representative sampling for further laboratory analyses and should be inserted in the range of common tools used in dismantling. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

The Effect of the Prior and the Experimental Design on the Inference of the Precision Matrix in Gaussian Chain Graph Models

Here, we investigate whether (and how) experimental design could aid in the estimation of the precision matrix in a Gaussian chain graph model, especially the interplay between the design, the effect of the experiment and prior knowledge about the effect. Estimation of the precision matrix is a fundamental task to infer biological graphical structures like microbial networks. We compare the marginal posterior precision of the precision matrix under four priors: flat, conjugate Normal-Wishart, Normal-MGIG and a general independent. Under the flat and conjugate priors, the Laplace-approximated posterior precision is not a function of the design matrix rendering useless any efforts to find an optimal experimental design to infer the precision matrix. In contrast, the Normal-MGIG and general independent priors do allow for the search of optimal experimental designs, yet there is a sharp upper bound on the information that can be extracted from a given experiment. We confirm our theoretical findings via a simulation study comparing (i) the KL divergence between prior and posterior and (ii) the Stein’s loss difference of MAPs between random and no experiment. Our findings provide practical advice for domain scientists conducting experiments to better infer the precision matrix as a representation of a biological network.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hybrid Oscillator-Qubit Quantum Processors: Instruction Set Architectures, Abstract Machine Models, and Applications

This tutorial offers a pedagogical guide to hybrid quantum processors that integrate discrete-variable (DV) qubits and continuous-variable (CV) oscillators. Aimed at computer scientists, engineers, and physicists, it provides an overview of the experimental, algorithmic, and architectural aspects of this novel and rapidly developing hardware model. Experimental realizations of this model include superconducting, trapped-ion, and neutral-atom platforms. By combining DV and CV components, hybrid oscillator-qubit processors enable a powerful new paradigm that offers complementary strengths for quantum control, error correction, computation, and simulation. Working toward the goal of a full-stack system connecting applications to CV-DV hardware, we define and formulate abstract machine models and instruction set architectures. These essential abstractions enable codesign of hardware and software, and resource estimation for exploring the potential of current and future hardware for computational and simulation tasks. Using these abstractions, we present both new and existing examples that illustrate the benefits of hybrid CV-DV processors relative to traditional DV-only hardware in computation as well as quantum simulation of physical models. Examples include algorithms for transferring states between DV and CV systems, performing the quantum Fourier transform, and simulation of lattice gauge theories. Relative to qubit-only hardware, the bosonic degrees of freedom natively available in hybrid architectures can substantially reduce the circuit complexity of simulations for physical models containing bosons. A key technique is the extension of quantum signal processing ideas to CV-DV systems. This work is intended to serve as a timely and comprehensive guide to this relatively unexplored yet promising approach to quantum computation and to provide a road map to guide future development.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Low Temperature Geothermal Play Fairway Analysis For The Appalachian Basin: Phase 1 Revised Report November 18, 2016

Geothermal energy is an attractive sustainable energy source. Yet project developers need confirmation of the resource base to warrant their time and financial resources. The Geothermal Play Fairway Analysis of the Appalachian Basin evaluated risk metrics that communicate the favorability of potential low-temperature geothermal energy resources in reservoirs more than 1000 m below the surface. This analysis is focused on the direct use of the heat, rather than on electricity production. Four risk factors of concern for direct-use geothermal plays in the Appalachian Basin portions of New York, Pennsylvania, and West Virginia are examined individually, and then in combination: 1) thermal resource quality, 2) natural reservoir quality, 3) induced seismicity, and 4) utilization opportunities. Uncertainty in the risk estimation is quantified. Based on these metrics, geothermal plays in the Appalachian Basin were identified as potentially viable for a variety of direct-use-heat applications. The methodologies developed in this project may be applied in other sedimentary basins as a foundation for low temperature (50-150 °C), direct use geothermal resource, risk, and uncertainty assessment. Three methods with which to combine the four risk factors were used. Among these, the averaging of the individual risk factors indicates the most favorable counties within the study area are the West Virginia counties of Monongalia, Harrison, Lewis (dubbed the Morgantown–Clarksburg play fairway), Putnam, and Kanawha (Charleston play fairway), the New York counties of Chemung and Steuben plus adjacent Bradford county in Pennsylvania (Corning–Ithaca play fairway), and the Pennsylvania counties of Mercer, Crawford, Erie, and Warren, and adjacent Chautauqua county in New York (together, the Meadville–Jamestown play fairway). These higher priority regions are surrounded by broader medium priority zones. Also worthy of additional exploration is a broad region near Pittsburgh Pennsylvania, for which the available geological data are insufficient to fully analyze the geological risks but yet the population is high. First, to assess the spatial variation in the depth to which one would need to drill to obtain geothermal temperatures that are useful to a future project, the project used bottom-hole temperature data from Appalachian Basin oil and gas exploration. These bottom hole temperature data are abundant but of low quality. Second, the project examined the potential for sufficient water flow rates through rocks to harvest heat from a geothermal well field, considering only natural reservoirs. This analysis provides a very incomplete picture of spatial variability of natural reservoirs because the oil and gas reservoir data lack key properties and are spatially biased toward those locations with profitable amounts of hydrocarbons in the rock pore spaces. Third, in light of the fact that earthquake activity has been induced in several states by subsurface work related to the oil and gas industry, this project examined the potential for similar activity in the Appalachian Basin. Acknowledging that data for such a task are insufficient, we utilized what was available: records of seismic activity, regional estimates of the orientations of stress in the rocks, and locations and orientations of zones of lateral change in rock properties at depths down to several kilometers below Earth’s surface. With these data, we created a first approximation of spatially variable risks for induced earthquakes. Because no data existed with which to test the reliability of these methods, the results have a high degree of uncertainty. Fourth, we examined the spatial variability of the above-the-ground factors that contribute to the economical viability of projects to tap low-temperature geothermal resources for direct-use. We worked principally with population density as a regionally known variable that would impact the cost of district heating. The resulting maps omit the costs of producing the hot water from the ground, because the below-ground costs are directly coupled to the thermal resource risk factor and natural reservoir risk factor – later analyses of those costs will be needed. The result of the district heating analysis is highly skewed: few census locations yielded a low estimated surface cost. The team also identified more than 165 prospects for high value direct-use geothermal energy opportunities such as industrial sites, university campuses, and federal facilities, among others. At the closure of this regional analysis, the most significant technical uncertainties are 1) reservoir distribution and capacities; 2) validity of thermal resource maps, and 3) the holistic estimation of Levelized Cost of Heat for favorable geological situations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product

Interactions between the biosphere and the atmosphere can be well characterized by fluxes between the two. In particular, carbon and energy fluxes play a major role in understanding biogeochemical processes on an ecosystem level or global scale. However, the fluxes can only be measured at individual sites, e.g., by eddy covariance towers, and an upscaling of these local observations is required to analyze global patterns. Previous work focused on upscaling monthly, 8-day, or daily average values, and global maps for each flux have been provided accordingly. In this paper, we raise the upscaling of carbon and energy fluxes between land and atmosphere to the next level by increasing the temporal resolution to subdaily timescales. We provide continuous half-hourly fluxes for the period from 2001 to 2014 at 0.5° spatial resolution, which allows for analyzing diurnal cycles globally. The data set contains four fluxes: gross primary production (GPP), net ecosystem exchange (NEE), latent heat (LE), and sensible heat (H). We propose two prediction approaches for the diurnal cycles based on large-scale regression models and compare them in extensive cross-validation experiments using different sets of predictor variables. We analyze the results for a set of FLUXNET tower sites showing the suitability of our approaches for this upscaling task. Finally, we have selected one approach to calculate the global half-hourly data products based on predictor variables from remote sensing and meteorology at daily resolution as well as half-hourly potential radiation. In addition, we provide a derived product that only contains monthly average diurnal cycles, which is a lightweight version in terms of data storage that still allows studying the important characteristics of diurnal patterns globally. We recommend to primarily use these monthly average diurnal cycles, because they are less affected by the impacts of day-to-day variation, observation noise, and short-term fluctuations on subdaily timescales compared to the full half-hourly flux products. The global half-hourly data products are available at https://doi.org/10.17871/BACI.224 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Determination of the Complete Spin Density Distribution in 13 C-Labeled Protein-Bound Radical Intermediates Using Advanced 2D Electron Paramagnetic Resonance Spectroscopy and Density Functional Theory

Determining the complete electron spin density distribution for protein-bound radicals, even with advanced pulsed electron paramagnetic resonance (EPR) methods, is a formidable task. Here we present a strategy to overcome this problem combining multifrequency HYSCORE and ENDOR measurements on site-specifically 13 C-labeled samples with DFT calculations on model systems. As a demonstration of this approach, pulsed EPR experiments are performed on the primary Q A and secondary Q B ubisemiquinones of the photosynthetic reaction center from Rhodobacter sphaeroides 13C-labeled at the ring and tail positions. Despite the large number of nuclei interacting with the unpaired electron in these samples, two-dimensional X- and Q-band HYSCORE and orientation selective Q-band ENDOR resolve and allow for a characterization of the eight expected 13 C resonances from significantly different hyperfine tensors for both semiquinones. From these results we construct, for the first time, the most complete experimentally determined maps of the s- and p π -orbital spin density distributions for any protein organic cofactor radical to date. This work lays a foundation for understanding the relationship between the electronic structure of semiquinones and their functional properties, and introduces new techniques for mapping out the spin density distribution that are readily applicable to other systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Review of Technical Photovoltaic Key Performance Indicators and the Importance of Data Quality Routines

Technical key performance indicators (KPIs) are important metrics used to assess and quantitatively summarize various aspects of photovoltaic (PV) systems, including long-term performance, economic viability, and carbon footprint. Herein, a group of experts of the International Energy Agency's Photovoltaic Power Systems Programme Task 13 collect and describ the most important technical KPIs used in the industry. Thereby, a set of best practices for reliably handling PV system data is presented and the impact of data quality and climatic variability on KPI calculation is investigated. Further, the effective use of technical KPIs allows triggering data-driven and informed decisions to optimize PV systems and providing a comprehensive overview of how PV systems operate across different conditions and climates. With the worldwide growth of the PV industry, more companies operate/own PV systems in different regions, where the climatic and seasonal profiles differ. This requires context-aware evaluation of KPIs, or the judicious application of multiple KPIs, to ensure that each asset is evaluated correctly. Beyond that, there is untapped potential in the utilization of KPIs through geospatial mapping and extrapolation of fleet KPIs. This study demonstrates that the uncertainty in KPI estimation is not well understood and depends on data quality, climatic variability, and system configuration.

14 SOLAR ENERGY↗
DOE OSTI2018

H-Canyon Ventilation System Exhaust Tunnel Inspection at the Savannah River Site - 18052

The Department of Energy (DOE) H-Canyon Facility at the Savannah River Site (SRS) is operated by Savannah River Nuclear Solutions (SRNS). The Facility Structural Integrity Program (SIP) performs periodic visual inspections to confirm the component is capable of performing its safety function. This Program provides the process to document the conditions of safety related structural systems and components, their damage mechanisms, and recommend repairs if required before there is impact on the safety envelope. The H-Canyon Exhaust Tunnel is part of the H-Canyon Exhaust Ventilation System and is periodically inspected under the Structural Integrity Program. The Tunnel performs a Safety Class passive design feature function that is available 100% of the time. The Tunnel directs Canyon process air from the Canyon to the Sand Filter System. The radiological and chemical airborne activity coupled with the physical configuration of the tunnel precludes a manned entry into the tunnel. Tunnel inspections have been performed using cameras on a stick, referred to as pole cameras prior to CY2003. Since CY2003, five camera equipped crawlers have been used with varying levels of inspection success. With each successive crawler inspection, expectations have increased to perform tasks beyond visual examinations. The 2017 crawler performed the visual inspection but also included improvements on crawler performance, such as testing the use of wireless capability and remediation of crawler travel route, to the addition of stationary cameras and lighting at key locations. The latest 2017 crawler successfully traveled the entire length of the tunnel between the Canyon and Sand Filter System for the second time since development, thus providing key visual data for the integrity program [2]. In addition, several access ports were installed in 2017 following the removal of concrete core samples that allow for new visual inspection of the Canyon Crossover tunnel, thus providing information for an area that was generally inaccessible for monitoring. Planning for the 2019 crawler inspection is underway with the expectations to improve upon inspection cameras and also introduce mapping technologies such as LIDAR. The challenge to obtain meaningful data from the remote inspections of a 60+ year operating Canyon Exhaust tunnel continues to be a complex endeavor requiring close coordination of multiple organizations, adherence to Conduct of Operations, Radiological Safety, Worker Safety, etc. principles as well as alignment with Facility Safety Basis requirements. This paper discusses the continuing improvements in inspection techniques and the challenges associated these inspections. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Automated Assembly Progress Monitoring in Modular Construction Factories Using Computer Vision-Based Instance Segmentation

Modular construction has recently gained interest as a transformative construction method. In this method, a large portion of the construction is performed inside factories, where processes are fast-paced and interdependent; therefore, any deviation from the schedule can delay the production. Such deviations are frequent in modular factories due to the labor-intensive nature of the tasks. This propagation of delays can be mitigated by continuously monitoring each process; however, current manual monitoring methods are laborious, and recently proposed contact sensor-based methods are intrusive to the work. In addition, recent computer vision-based monitoring methods inside factories are limited to detection algorithms that fail to provide the pixel-level accuracy required for assembly progress monitoring in highly occluded factory scenes, and they require a large number of manual annotations. Therefore, this paper proposes a method to monitor the installation of subassemblies in modular construction factories using mask R-CNN instance segmentation and improves the data efficiency of the model using a copy-paste augmentation method. This method was validated on the CCTV videos captured from a modular construction factory in the US, resulting in a 9% mAP improvement in segmentation.

computer vision↗
DOE OSTI2025

Optimizing Alabama’s CO 2 Storage in Shelby County (Project OASIS): Task 6.0 Deliverable – CarbonSAFE Phase III Readiness

The OASIS CarbonSAFE Phase II Project OASIS (Optimizing Alabama’s CO 2 Storage in Shelby County) is a geologic and reservoir characterization study designed to evaluate deep saline formations for potential long-term carbon dioxide (CO 2 ) storage in central Alabama near the National Carbon Capture Center (NCCC) and Alabama Power’s Plant Gaston. The project centers on understanding the potential of the Cambro-Ordovician Knox Group and underlying strata such as the Conasauga and Rome Formations for geologic storage of CO 2 . These formations were investigated as part of SECARB-USA (DE-FE0031830) and Project OASIS through the drilling of two stratigraphic test wells to obtain electronic well logs, core, and sidewall core plugs. These data provide direct measurements of porosity, permeability, and lithologic variability critical for reservoir characterization. Complementing the well program, a limited 2D seismic survey was conducted to help select the site for Westover #2 as part of SECARB-USA (DE-FE0031830), and a more regional Seismic Exchange (SEI) seismic survey was licensed and interpreted to define structural and stratigraphic frameworks in a new Static Earth Model (SEM), map reservoir continuity, and to identify potential sealing intervals. Integrated with geologic and reservoir modeling, these datasets form the basis for evaluating storage capacity, injectivity, and containment. While this document endeavors to provide readers with a high-level overview of Project OASIS activities and its suitability for subsequent CarbonSAFE Phases, such as a Phase III effort. Other project deliverables and milestones will provide more details on individual subjects.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2016

SeeSnake: Radiological Characterisation of Complex Nuclear Spaces - 16162

Decommissioning of retired nuclear plant can be a difficult task for many reasons. A number of locations around the world have active cells awaiting decommissioning without a clear understanding of the internal structure and possible locations of radiological sources. Furthermore, many nuclear plant drawings and models do not reflect the 'as built' dimensions, and precise data required for safe and cost effective decommissioning is not readily available. This presents decommissioning teams with significant challenges when attempting to plan and execute their decommissioning programs. Due to the complex nature and constrained environments typical of legacy nuclear plant, it is often not possible to deploy traditional remote handling/inspection technologies. Even where possible, identifying the correct sensor, or suite of sensors, to characterize the environment is problematic. To address this common issue Create Technologies Ltd (Createc) and OC Robotics collaborated on SeeSnake, a project aimed at leveraging radiometric source mapping with 3D scanning and confined space remote handling. Createc's imaging and sensing systems have strong capabilities in the nuclear industry, led by their N-Visage products, as do OC Robotics' snake-arm robots as a remote deployment platform for confined and hazardous spaces. The snake-arm robot enables access through small penetrations with the ability to navigate internal structures and other obstacles without support from the environment; whilst multiple re-positioning of the N-Visage sensor, integrated with the snake-arm system, enables a thorough 3D map to be created. The SeeSnake project demonstrates the integrated systems ability to capture valuable radiation, visual and positional data enabling the characterization of a complex active environment. To mature the concept into an integrated, functional technology the SeeSnake partners overcame a number of challenges before demonstrating to key stakeholders in the UK nuclear industry. This paper describes these challenges and the solutions implemented, along with the results from early system trials and stakeholder demonstrations. It also addresses the next steps for the SeeSnake technology and identifies a number of applications which will may benefit from its exploitation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Data Visualization Saliency v. 1.0

Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off human subject studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. The visual saliency model for data visualizations described here addresses this need for evaluation tools. Visual saliency models assess the visual features (e.g. color, contrast, edges) of an image to predict which areas of that image will draw a viewer’s attention. Providing predictions of where viewers will look in a visualization based on the saliency of the various regions and elements in that image could help designers to assess whether or not their design will draw the viewer’s attention as intended. The Data Visualization Saliency model, a saliency model for data visualizations, consists of two parts: a text saliency map and a modified version of the Itti, Koch and Niebur model [1]. The final saliency map is a linear combination of the two. The text saliency map is created from an algorithm that computes the likelihood of belonging to a text region for each pixel of an input visualization image. The modified Itti model transforms the original input image to a representation in CIE LAB color space, which provides an accurate representation of perceptual color opponency. For a given input data visualization image, the Data Visualization Saliency model computes and returns the visualization saliency map highlighting areas of the data visualization that will draw a viewer’s attention. It also returns the lower level saliency maps for text and the modified Itti model.

Haass, Michael↗