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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Initial tests of large format sensors for the ATLAS ITk strip tracker

For the construction of the Inner Tracker (ITk) as part of the phase-II upgrade programme of the ATLAS detector for the High-Luminosity (HL) LHC, batches of Long Strip (LS) and Short Strip (SS) n + -in-p type micro-strip sensors have been produced by Hamamatsu Photonics and Infineon. The full size sensors measure approximately 98 × 98 mm 2 and are designed and engineered for tolerance against the 9.7 × 10 14 1 MeV n eq /cm 2 fluence expected at the HL-LHC, including a safety factor of 1.5. Each sensor has 2 or 4 columns of 1280 individual channels arranged at 75.5 μ m horizontal pitch. To ensure the sensors comply with their specifications, a Quality Control (QC) procedure has been implemented, comprising measurements on every individual sensor as well as on a sample basis. Every sensor is subjected to an initial visual inspection, after which the full surface of the sensor is captured with very high resolution by an automated camera setup. Non-contact metrology is performed to obtain the sensor surface profile. Electrical measurements establishing the reverse bias leakage current and depletion voltage are then conducted automatically. Sample sensors from every batch are subjected to 40 h of leakage stability checks in controlled atmosphere, and tests on every channel measuring leakage current, coupling capacitance and bias resistance are done. The recorded results are uploaded to a production database following data quality checks. In this paper, QC test validation data and the compiled results for the first batches of production grade sensors are presented. The QC protocol was validated, and the first production sensors were confirmed to be within specification. The results are compared to those from the previous generation of prototype sensors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Diverging polygon-based modeling (DPBM) of concentrated solar flux distributions

This paper presents an efficient and robust methodology for modeling concentrated solar flux distributions. Compared to ray tracing methods, which provide high accuracy but can be computationally intensive, this approach makes a number of simplifying assumptions in order to reduce complexity by modeling incident and reflected flux as a series of simple geometric diverging polygons, then applying shading and blocking effects. A reduction in processing time (as compared to ray tracing) allows for evaluating and visualizing numerous combinations of engineering and operational variables (easily exceeding 106 unique iterations) to ascertain instantaneous, transient, and annual system performance. The method is demonstrated on a linear Fresnel reflector array and a number of variable iteration examples presented. While some precision is sacrificed for computational speed, flux distributions were compared to ray tracing (SolTrace) and average concentration ratio generally found to agree within ~3%. Furthermore, this method presents a quick and very flexible coarse adjust method for concentrated solar power (CSP) field design, and can be used to both rapidly gain an understanding of system performance as well as to narrow variable constraint windows for follow-on high accuracy system optimization.

14 SOLAR ENERGY↗
DOE OSTI2024

Role of Exciton Diffusion in the Efficiency of Mn Dopant Emission in Two-Dimensional Perovskites

Two-dimensional (2D) metal-halide perovskites have promising characteristics for optoelectronic applications. By incorporating Mn 2+ ions into the perovskite structure, improved photoluminescence quantum yield can be achieved. This has been attributed to the formation of defect states that act as efficient recombination centers. Here, we make use of transient photoluminescence microscopy to characterize important material parameters of Mn 2+ -doped 2D perovskites with different doping levels. From these measurements, we visualize the importance of exciton transport as an intermediate step in the excitation of Mn 2+ . We model the spatiotemporal dynamics of the excited states to extract the diffusion constant and the transfer rate of the excitations to the Mn dopant sites. Interestingly, from these models, we find that the average distance an exciton needs to travel before transferring to a Mn site is significantly larger than expected from the Mn concentration obtained from elemental analysis. These insights are critical from a device design perspective.

2D perovskites↗
DOE OSTI2020

How Do Visual Explanations Foster End Users' Appropriate Trust in Machine Learning?

We investigated the effects of different visual explanations on users' trust in machine learning classification. We proposed three forms of visual explanations of a classification based on identifying relevant training instances. We conducted a user study to evaluate these visual explanations as well as a no explanation condition. We measured users' trust of a classifier, quantified the effects of these three forms of explanations, and assessed the changes in users' trust. We found that participants trust a classifier appropriately when an explanation is available. The combination of human, classification algorithm and understandable explanation makes better decisions than the classifier and human alone.This work advances the state-of-the-art closer to building trust-able machine learning models and informs the design and appropriate use of automated systems.

Yang, Fumeng↗
DOE OSTI2021

Comparing perceptions of a dimmable LED lighting system between a real space and a virtual reality display

Over the last several decades, designers have used digital screens to view images of real and simulated spaces and make critical design decisions. Screen technology has improved during this time, as technologies like OLED have replaced legacy displays (CRT, plasma, and LCD). These new screens provide a higher pixel resolution, luminous output and contrast ratio. Immersive head-mounted displays now allow designers to view immersive images, and recent developments in real-time rendering have encouraged the uptake of virtual reality (VR) head-mounted displays in mainstream practice and design education. This paper presents an experiment on lighting perception using a series of LED lighting conditions in a real space and a virtual representation of those conditions captured using a 360° high-dynamic-range camera and presented on an HTC Vive Pro HMD. Fifty-three participants were asked to rate each lighting condition by viewing it in a real space (n = 30) or via immersive HDR photographs displayed in a VR HMD (n = 23). The results show that ratings of visual comfort, pleasantness, evenness, contrast and glare are similar between the HTC Vive Pro HMD and our real space when evaluating well-lit scenes, but significant differences emerge in dim and highly contrasted scenes for a number of rating scales.

Rockcastle, S.↗
DOE OSTI2023

GeoThermalCloud: A Machine Learning Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources

In this 25 minute presentation, we showcase our open source “GeoThermalCloud” tool for identifying hidden geothermal resources using a publicly available dataset for southwestern New Mexico. The presenters include Bulbul Ahmmed and Luke Frash. All of the visuals use source material from LA-UR approved publications and this work falls under the Earth Sciences DUSA. The code shown in this video is already released with LANL approval in open source format on GitHub and DockerHub. The audio in this video includes only material on the topics of geothermal energy and machine learning applied to geothermal energy. The primary machine learning method used is LANL’s Non-negative Matrix Factorization “NMFk” method. Modeling work also mentions LANL’s Geothermal Design Tool “GeoDT” which is another approved open source code that has been released by LANL. This work was performed for DOE Geothermal Technologies Office (DE-EE-3.1.8.1). The host for the released video is intended to be YouTube or a suitable perpetual data repository such as GDR.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Radiation Testing for High-Resolution Radiation-Hardened Camera System (Final CRADA Report)

The nuclear energy industry needs higher-image quality and higher rad-hard vision systems for refueling and inspection operations that are required every 12-18 months for all operating commercial reactors. During refueling operations, the serial numbers of fuel assemblies need to be visually verified in a challenging high radiation environment with dose rates on the order of 1kGy/hr. at the top of the core (approximately 10 days after shutdown). Vega Wave Systems has developed and built a radiation hard vision system that: 1. has high radiation hardness, 2. demonstrated best in the industry image quality, 3. has small size and weight, and 4. does not suffer from radiation-induced noise. ANL-1060 (02/13/2023) The proprietary Enduray vision system from Vega Wave Systems, Inc. is designed to withstand more than 400x the radiation level of the Vidicon-based systems, currently the highest radiation tolerant system in use today. The Van De Graaff (VDG) electron accelerator at Argonne National Laboratory is ideally suited for testing of Enduray vision system. VDG provides a broad band (up to 3 MeV) x-ray source that reasonably approximates the energy and spectrum of the gamma radiation expected at the top of a nuclear core 10 days after shutdown (~1kGy/hr.), is easily controlled for variable tests, and will not activate the parts under radiation for ease of timely and close inspection and characterization of the system in case of operational issues.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

Provenance-Enabling DOE Visualization Applications

VisTrails, Inc.’s goal main charter has been to develop provenance management solutions for software tools. As the use of computation increases, keeping track of computational processes and the data they consume and derive is essential for many applications. Provenance (also referred to as audit trail, lineage, and pedigree) captures information about the steps used to generate a given data product. Such information provides important documentation that is key to preserving the data, to determining the data’s quality and authorship, and to reproducing as well as validating the results. Given the growing complexity of the computational processes and the volume of data they manipulate, manual approaches to maintaining provenance are both tedious and error-prone. At VisTrails, Inc., we have implemented infrastructure that can be used to provenance-enable existing tools. We have developed a series of plugins for specific applications, including ParaView and VisIt, which enable these applications to capture provenance. While developing these plugins, we identified general patterns across different applications, and these were used to inform the design of a general framework: the VisTrails Provenance Software Development Kit (SDK). The SDK encapsulates a set of core functionality including methods for capture, navigation, analysis and re-use of provenance. It allows application or plugin developers to more effectively provide provenance support: they can use the core functionality and write code only to handle application specific components. The SDK includes features that can be used in any application, for example, a refined interface for browsing the intermediate and final results captured. While the application-specific plugins were written in Python, the SDK was written from scratch in C++. This has led not only to substantial gains in performance but also the ability to integrate the SDK with applications written in other languages, including Java. To ensure that the SDK supported all of the functionality in the application-specific plugins, we have rewritten some of the existing plugins using the SDK. The SDK can be downloaded from www.vistrals.com/sdk.html.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Methods development towards automated, physics-informed, quantitative quality control of TRISO-SiC

Tristructural-isotropic (TRISO) fuel particles have been developed as a high-performance fuel for use in high-temperature gas-cooled reactor (HTGR) systems due to their high efficiency and stability under both normal and off-normal conditions. Broader deployment of this technology in advanced nuclear applications may benefit from quantitative quality assurance and quality control (QA/QC) methods that directly link TRISO properties to downstream performance. A key TRISO property is the SiC layer microstructure, which influences fission product retention during irradiation. However, existing QA/QC for the TRISO-SiC microstructure comprises only a qualitative visual inspection; therefore, there is a clear opportunity for the development of quantitative methods for TRISO QA/QC. Here, to this end, previous work has demonstrated an image processing approach to grain boundary (GB) identification and subsequent extraction of microstructural metrics; however, extensive twinning within the SiC layer complicates such analyses because twin GBs significantly influence microstructural metrics but are not expected to contribute to fission product transport. This study presents the initial development, training, and testing of an ML-based image segmentation algorithm designed to identify and remove twin GBs from standard backscattered electron micrographs, providing an industrially applicable, quantitative, and physically meaningful QA/QC approach for the TRISO-SiC microstructure. Although pixel-wise performance metrics for the twin predictions are low, the change in grain area and the number of GB pixels after twin removal predicted by the ML workflow are within 1% of the true values calculated using crystallographic data. This suggests that the model is well capable of predicting overall twin boundary structures and grain morphology, and continued advancement of this approach could enable automated, scalable, and physics-informed QA/QC for TRISO-SiC microstructures, supporting the reliable qualification of coated particle fuels for next-generation reactor systems.

Computer vision↗
DOE OSTI2021

Polymerized small molecular acceptor based all-polymer solar cells with an efficiency of 16.16% via tuning polymer blend morphology by molecular design

All-polymer solar cells (all-PSCs) based on polymerized small molecular acceptors (PSMAs) have made significant progress recently. Here, we synthesize two A-DA’D-A small molecule acceptor based PSMAs of PS-Se with benzo[c][1,2,5]thiadiazole A’-core and PN-Se with benzotriazole A’-core, for the studies of the effect of molecular structure on the photovoltaic performance of the PSMAs. The two PSMAs possess broad absorption with PN-Se showing more red-shifted absorption than PS-Se and suitable electronic energy levels for the application as polymer acceptors in the all-PSCs with PBDB-T as polymer donor. Cryogenic transmission electron microscopy visualizes the aggregation behavior of the PBDB-T donor and the PSMA in their solutions. In addition, a bicontinuous-interpenetrating network in the PBDB-T:PN-Se blend film with aggregation size of 10~20 nm is clearly observed by the photoinduced force microscopy. The desirable morphology of the PBDB-T:PN-Se active layer leads its all-PSC showing higher power conversion efficiency of 16.16%.

14 SOLAR ENERGY↗
DOE OSTI2012

Wind Turbine Engineering R&D at Los Alamos National Laboratory [Slides]

There are enough wind resources in the US to provide more than 10 times the electric power we currently use, however wind power only accounts for 3% of our total electricity production. DOE’s goal is to increase wind power capacity to 20% of the total by the year 2030. LANL’s Intelligent Wind Turbine Project is applying leading-edge engineering expertise in modeling and simulation, experimental validation, and advanced sensing technologies to challenges faced in the design and operation of modern wind turbines. One of the main limitations to wind use is cost. Wind power currently costs about 8 cents per kilowatt-hour, which is more than 1.4 times the cost of electricity generated by burning natural gas. DOE desires to reduce wind power costs through technological innovation and reliability improvements. LANL’s engineering expertise, which is well-suited to the wind energy application, is born out of the Laboratory’s core national security mission. Our ability to visualize detailed flowfields around operating wind turbines is founded upon experimental turbulence measurement techniques developed for the nuclear weapons program. The capability to model multiple wind turbines on any terrain is built upon past work in modeling urban dispersion, wildland fires, and hurricane intensification. In addition, research using embedded sensing for the structural assessment of civil structures has led to the ability to detect incipient damage in operating wind turbine blades. This presentation will provide an overview of the innovative wind turbine research ongoing at LANL and its connection to LANL’s core mission.

17 WIND ENERGY↗
DOE OSTI2019

Sealed and Insulated Attic Hygrothermal Performance in New California Homes Using Vapor and Air Permeable Insulation—Field Study and Simulation

This project investigated the thermal and moisture performance of a low-cost approach to sealing and insulating attics using glass fiber insulation. The work included a combination of: (1) field measurements of attic and HVAC system performance in two new, high-performance homes in California’s Central Valley (Fresno), and (2) hygrothermal simulations of attic performance. Each field study attic was continuously monitored at multiple locations for over a year for wood moisture content, air humidity, condensation, temperature, and heat flux, together with on-site weather and solar conditions. The Fresno test home showed periodic condensation and high surface wood moisture content, but no surface mold or degradation upon visual inspection at the end of the test period. The Clovis test home showed less indication of high moisture levels—either from surface condensation or wood moisture content—but did have visible suspected mold growth on the inside of the North sheathing at the end of the field testing. These results show the limitations of current moisture measurement techniques focused on wood moisture content, rather than potential for mold growth. From a thermal/energy perspective the attics were close to indoor conditions thereby realizing the design intent for reducing duct system losses. Simulated site HVAC energy savings for sealed vs. vented attics averaged 18% across California climate regions (8% TDV energy savings). Savings were dominated by heating energy reductions; cooling savings were substantially lower. The moisture issues are investigated in greater detail in the simulations. First, we identified the climate regions and house characteristics that are associated with increased risk of mold growth or wood rot in new CA homes. Climate region was very important in determining risk, as were house features that reduced outside air exchange (e.g., 1-story homes, very tight envelopes, very tight attics, no IAQ fan), along with those that increased flow of moist air from the living space to the attic (e.g., supply ventilation fans, larger ceiling leaks, duct leakage). Second, we investigated several approaches to reduce moisture risks, and the best approach was to use a vapor retarder on the inner face of the fiberglass insulation. Both the field and simulation results indicate that the use of air and vapor permeable insulation can be acceptable from a thermal/energy point of view, but additional measures need to be taken to reduce moisture risks, primarily from mold growth.

42 ENGINEERING↗
DOE OSTI2022

Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination

Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination.

Almeida, Sílvia (ORCID:0000000320636826)↗
DOE OSTI2019

Analysis of Radiation Damage in Electronics of Unmanned Aerial and Ground Robotic Platforms - 19470

Gamma ray measurements are important for monitoring of nuclear facilities. Robotic platforms are used to carry radiation detectors and cameras to enable remote sensing and visual surveillance, allowing for the operation in hazardous, hard to reach zones. The prediction of radiation damage in electronic components of robots is vital. The radiation damage analysis was carried out for two robotic platforms exposed to neutron and gamma ray flux: a ground hexapod platform PhantomX (a 'spider robot') and an aerial platform - the DJI S1000+ octocopter. The FLUKA code was used to analyze the displacement per atom values in electronic components of these robots. The packaging of the components and the shielding design was determined to increase the robot's lifetime in areas where radiation dose levels are high. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

MODE: A Web Application for Interactive Visualization and Exploration of Omics Data

Studies generating transcriptomics, proteomics, lipidomics, and metabolomics (colloquially referred to as “omics”) data allow researchers to find biomarkers or molecular targets, or understand complex biological structures and functions by identifying changes in biomolecule abundance and expression between experimental conditions. Omics data is multi-dimensional and oftentimes summarization techniques such as principal component analysis (PCA) are used to identify high-level patterns in data. Though useful, these summaries don’t allow exploration of detailed patterns in omics data that may have biological relevance. The use of interactive HTML displays with plots allows researchers to interact with omics data at a detailed level, but building these displays requires significant coding expertise. To overcome this barrier, the software MODE was built to empower users to build their own interactive HTML displays to support scientific discovery. These displays are easily shareable, do not depend on a specific operating system, and allow users to effortlessly sort and filter plots by categorical or numerical variables. MODE allows users to build and share these displays with several options for plot design and meta selection. In conclusion, the MODE web application and its capabilities are presented and then demonstrated on lipidomics data from a leaf wounding study.

lipidomics↗
DOE OSTI2019

Cross-reactive neutralizing human survivor monoclonal antibody BDBV223 targets the ebolavirus stalk

Three Ebolavirus genus viruses cause lethal disease and lack targeted therapeutics: Ebola virus, Sudan virus and Bundibugyo virus. Monoclonal antibody (mAb) cocktails against the surface glycoprotein (GP) present a potential therapeutic strategy. Here we report two crystal structures of the antibody BDBV223, alone and complexed with its GP2 stalk epitope, an interesting site for therapeutic/vaccine design due to its high sequence conservation among ebolaviruses. BDBV223, identified in a human survivor of Bundibugyo virus disease, neutralizes both Bundibugyo virus and Ebola virus, but not Sudan virus. Importantly, the structure suggests that BDBV223 binding interferes with both the trimeric bundle assembly of GP and the viral membrane by stabilizing a conformation in which the monomers are separated by GP lifting or bending. Targeted mutagenesis of BDBV223 to enhance SUDV GP recognition indicates that additional determinants of antibody binding likely lie outside the visualized interactions, and perhaps involve quaternary assembly or membrane-interacting regions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Schedule Management Optimization (SMO) Domain Model (V.1.2)

Schedule Management Optimization (SMO) is a tool for automatically generating a schedule of project tasks. Project scheduling is traditionally achieved with the use of commercial project management software or case-specific optimization formulations. Commercial software packages are useful tools for managing and visualizing copious amounts of project task data. However, their ability to automatically generate optimized schedules is limited. Furthermore, there are many real-world constraints and decision variables that commercial packages ignore. Case-specific optimization formulations effectively identify schedules that optimize one or more objectives for a specific problem, but they are unable to handle a diverse selection of scheduling problems. SMO enables practitioners to generate optimal project schedules automatically while considering a broad range of real-world problem characteristics. SMO has been designed to handle some of the most difficult scheduling problems – those with resource constraints, multiple objectives, multiple inventories, and diverse ways of performing tasks. This report contains descriptions of the SMO modeling concepts and explains how they map to real-world scheduling considerations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗