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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Differences in dose-volumetric data between the analytical anisotropic algorithm and the x-ray voxel Monte Carlo algorithm in stereotactic body radiation therapy for lung cancer

The objective of this study was to evaluate the differences in dose-volumetric data obtained using the analytical anisotropic algorithm (AAA) vs the x-ray voxel Monte Carlo (XVMC) algorithm for stereotactic body radiation therapy (SBRT) for lung cancer. Dose-volumetric data from 20 patients treated with SBRT for solitary lung cancer generated using the iPlan XVMC for the Novalis system consisting of a 6-MV linear accelerator and micro-multileaf collimators were recalculated with the AAA in Eclipse using the same monitor units and identical beam setup. The mean isocenter dose was 100.2% and 98.7% of the prescribed dose according to XVMC and AAA, respectively. Mean values of the maximal dose (D{sub max}), the minimal dose (D{sub min}), and dose received by 95% volume (D{sub 95}) for the planning target volume (PTV) with XVMC were 104.3%, 75.1%, and 86.2%, respectively. When recalculated with the AAA, those values were 100.8%, 77.1%, and 85.4%, respectively. Mean dose parameter values considered for the normal lung, namely the mean lung dose, V{sub 5}, and V{sub 20}, were 3.7 Gy, 19.4%, and 5.0% for XVMC and 3.6 Gy, 18.3%, and 4.7% for the AAA, respectively. All of these dose-volumetric differences between the 2 algorithms were within 5% of the prescribed dose. The effect of PTV size and tumor location, respectively, on the differences in dose parameters for the PTV between the AAA and XVMC was evaluated. A significant effect of the PTV on the difference in D{sub 95} between the AAA and XVMC was observed (p = 0.03). Differences in the marginal doses, namely D{sub min} and D{sub 95}, were statistically significant between peripherally and centrally located tumors (p = 0.04 and p = 0.02, respectively). Tumor location and volume might have an effect on the differences in dose-volumetric parameters. The differences between AAA and XVMC were considered to be within an acceptable range (<5 percentage points)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Development of a Technology Roadmap for Online Monitoring of Nuclear Power Plants

The nuclear power industry has recently recognized the potential cost saving that can be achieved from migrating the current manual and labor dependent surveillance and preventive maintenance activities to data-driven online monitoring methods. Since then, several efforts have been launched with various degrees of momentum to tackle specific surveillance or preventive maintenance activities. The light water reactor sustainability program (LWRS) program has anticipated this need and launched an effort that aimed to develop a technology roadmap for the nuclear power industry. Because the roadmap has to be focused towards the user or application’s value, and because nuclear power plants have different process efficiencies and deficiencies, the technology roadmap has to be plant independent. Achieving this objective required optimizing the extent of details described by a technology roadmap. The approach followed is for the roadmap to describe the sequence of activities needed to automate a process or equipment activity, but not describe the sequence of processes or equipment to target. For example, the steps required to migrate the inspection of a pump are plant independent. However, defining that the migration of feed water pumps inspections to online monitoring should be performed before inspections of feed water valves is dependent on multiple plant factors such as the plant requirements, labor work capacity, and process or equipment conditions. The technology roadmap was broken down into six elements, four of which have a sequential path of advancement, while the other two are supporting elements to the sequential elements. The sequential part of the roadmap analyzes data collection, analytics, visualization, and management. For these elements of the technology roadmap, the migration of an activity is defined in three states. The base state is defined as the most primitive, manual, and labor dependent process used by some nuclear power plants. The modern state is defined as the state that can be achieved if the activities are augmented or replaced by current technologies that are commercially available or are soon to be available. The state of the art is defined as using technologies of the future, i.e. concept and technologies that are being researched. The data collection part of the technology roadmap defines three states of every process performed in a nuclear power plant. However, the data analytics part is described based on equipment, because automating an activity performed on one equipment required considering multiple data sources. The visualization and data management elements are process or equipment independent. Visualization describes the means to use human factor science and technologies advancement in presenting the data collected and the data analytics results. The data management targets storage, communication, and computational power need, in addition to data integration and sharing. Since storage, communication, and computational power technologies and path for deployment are known to any information technology organization, this effort targeted the cost of data management. This has a direct impact on the feasibility of the automation effort. The supporting elements analyzes the value of automating the activity and the change challenges to tackle. For the support elements, the roadmap targeted describing activities that need to be performed as part of the roadmap development. The value element of the roadmap describes how to analyze the cost saving and the associated risk. The change management identifies multiple factors to consider such as regulation, cybersecurity, resource availability, supporting infrastructure availability, feasibility (power, fitness, environment tolerance, facilities), and cultural change.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

High-Performance data flows using analytical models and measurements

The combination of analytical models and measurements provide practical configurations and parameters to achieve high data transport rates: (a) buffer sizes and number of parallel streams for improved memory and file transfer rates, (b) Hamilton and Scalable TCP congestion control modules for memory transfers in place of default CUBIC, and (c) direct IO mode for Lustre file systems for wide-area transfers. Conventional parameter selection using full sweeps is impractical in many cases since it takes months. By exploiting the unimodality of throughput profiles, we developed the d-w method that significantly reduces the number of measurements needed for parameter identification. This heuristic method was effective in practice in reducing the measurements by about 90% for Lustre and XFS file transfers.

Rao, Nageswara S↗
DOE OSTI2017

The Bird project: Using Big Data tools to support Search Analytics

The Bird project explored the use of big data analytics tool to improve the findability of information within the Sandia internal network. We were able to perform query classification utilizing the supervised learning algorithms in the Apache Spark library. By relying on the distributed processing capabilities provided by the Apache Hadoop framework, we successfully processed the large query log files needed to train the models in this effort. The capabilities developed in this project are being used to enhance the effectiveness of the enterprise search engine.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Data Architecture and Analytics Requirements for Artificial Intelligence and Machine Learning Applications to Achieve Condition-Based Maintenance

This report identified some of the important requirements that needs to be taken into consideration as part of the data evolution for the CBM application of a CWS in a NPP. In the data evolution process, the information is converted into insight leading into actions using advancements in AI/ML technologies. A notion of RESET AI: design, development, deployment, and operation principals are introduced to lifecycle of AI technologies. Towards the end of the report, we discussed how this CBM can be realized in a SDE. As path forward, this report lays the foundation for developing a more detailed industry guidance supporting data evolution for other plant applications like operations and plant support. These would be developed as part of ongoing research in the fiscal year 2023.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Rethinking Visual Analytics for Streaming Data Applications

In the age of data science, the use of interactive information visualization techniques has become increasingly ubiquitous. From online scientific journals to the New York Times graphics desk, the utility of interactive visualization for both storytelling and analysis has become ever more apparent. As these techniques have become more readily accessible, the appeal of combining interactive visualization with computational analysis continues to grow. Arising out of a need for scalable, human-driven analysis, primary objective of visual analytics systems is to capitalize on the complementary strengths of human and machine analysis, using interactive visualization as a medium for communication between the two. These systems leverage developments from the fields of information visualization, computer graphics, machine learning, and human-computer interaction to support insight generation in areas where purely computational analyses fall short. Over the past decade, visual analytics systems have generated remarkable advances in many historically challenging analytical contexts. These include areas such as modeling political systems [Crouser et al. 2012], detecting financial fraud [Chang et al. 2008], and cybersecurity [Harrison et al. 2012]. In each of these contexts, domain expertise and human intuition is a necessary component of the analysis. This intuition is essential to building trust in the analytical products, as well as supporting the translation of evidence into actionable insight. In addition, each of these examples also highlights the need for scalable analysis. In each case, it is infeasible for a human analyst to manually assess the raw information unaided, and the communication overhead to divide the task between a large number of analysts makes simple parallelism intractable. Regardless of the domain, visual analytics tools strive to optimize the allocation of human analytical resources, and to streamline the sensemaking process on data that is massive, complex, incomplete, and uncertain in scenarios requiring human judgment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

An integrated online radioassay data storage and analytics tool for nEXO

Large-scale low-background detectors are increasingly used in rare-event searches as experimental collaborations push for enhanced sensitivity. However, building such detectors, in practice, creates an abundance of radioassay data especially during the conceptual phase of an experiment when hundreds of materials are screened for radiopurity. A tool is needed to manage and make use of the radioassay screening data to quantitatively assess detector design options. We have developed a Materials Database Application for the nEXO experiment to serve this purpose. Furthermore, this paper describes this database application, explains how it functions, and discusses how it streamlines the design of the experiment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

SNL Lesson Learned and Guidance for Data Repositories and Analytic Frameworks

This document represents the results of deliverable D05.02 (Identify relevant efforts at SNL and other institutions) under the activity area Relevant Efforts Review. The goal of the Relevant Efforts Review activity is to identify relevant data integration efforts at SNL and possibly other institutions and compile lessons learned that are relevant to the development of a framework for data integration efforts in support of analysts and decision makers. The intent of this activity is to provide, by examples, context of how the requirements-gathering process has already been implemented in other instances and to guide the development of such a process for OCIA's needs. Information for this report was gathered through SNL staff interviews and the team members' knowledge and project experiences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A Visual Analytics Approach to Structured Data Analysis to Enhance Nonproliferation and Arms Control Verification Activities

Analysis activities for Nonproliferation and Arms Control verification require the use of many types of data. Tabular structured data, such as Excel spreadsheets and relational databases, have traditionally been used for data mining activities, where specific queries are issued against data to look for matching results. The application of visual analytics tools to structured data enables further exploration of datasets to promote discovery of previously unknown results. This paper discusses the application of a specific visual analytics tool to datasets related to the field of Arms Control and Nonproliferation to promote the use of visual analytics more broadly in this domain. Visual analytics focuses on analytical reasoning facilitated by interactive visual interfaces (Wong and Thomas 2004). It promotes exploratory analysis of data, and complements data mining technologies where known patterns can be mined for. Also with a human in the loop, they can bring in domain knowledge and subject matter expertise. Visual analytics has not widely been applied to this domain. In this paper, we will focus on one type of data: structured data, and show the results of applying a specific visual analytics tool to answer questions in the Arms Control and Nonproliferation domain. We chose to use the T.Rex tool, a visual analytics tool developed at PNNL, which uses a variety of visual exploration patterns to discover relationships in structured datasets, including a facet view, graph view, matrix view, and timeline view. The facet view enables discovery of relationships between categorical information, such as countries and locations. The graph tool visualizes node-link relationship patterns, such as the flow of materials being shipped between parties. The matrix visualization shows highly correlated categories of information. The timeline view shows temporal patterns in data. In this paper, we will use T.Rex with two different datasets to demonstrate how interactive exploration of the data can aid an analyst with arms control and nonproliferation verification activities. Using a dataset from PIERS (PIERS 2014), we will show how container shipment imports and exports can aid an analyst in understanding the shipping patterns between two countries. We will also use T.Rex to examine a collection of research publications from the IAEA International Nuclear Information System (IAEA 2014) to discover collaborations of concern. We hope this paper will encourage the use of visual analytics structured data analytics in the field of nonproliferation and arms control verification. Our paper outlines some of the challenges that exist before broad adoption of these kinds of tools can occur and offers next steps to overcome these challenges.

arms control↗
DOE OSTI2020

On the Feasibility of Using Reduced-Precision Tensor Core Operations for Graph Analytics

Today’s data-driven analytics and machine learning workload have been largely driven by the General-PurposeGraphics Processing Units (GPGPUs). To accelerate dense matrix multiplications on the GPUs, Tensor Core Units (TCUs) have been introduced in recent years. In this paper, we study linear-algebra-based and vertex-centric algorithms for various graph kernels on the GPUs with an objective of applying this new hardware feature to graph applications. We identify the potential stages in these graph kernels that can be executed on the Tensor Core Units. In particular, we leverage the reformulation of the reduction and scan operations in terms of matrix multiplication [1]on the TCUs. We demonstrate that executing these operations on the TCUs, available inside different graph kernels, can assist in establishing an end-to-end pipeline on the GPGPUs without depending on hand-tuned external libraries and still can deliver comparable performance for various graph analytics.

Graph algorithms, GPU computing↗
DOE OSTI2022

For your consideration: Distance Analysis for PDV Data

An analytical study that discusses photonic Doppler velocimetry data analysis methods. The author intends to share this study with experts in this field at National Laboratory partners, e.g., LLNL, LANL, and Sandia. There is no plan to submit this work to a professional (public) conference or a journal publisher.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Analytic continuation of noisy data using Adams Bashforth residual neural network

We propose a data-driven learning framework for the analytic continuation problem in numerical quantum many-body physics. Designing an accurate and efficient framework for the analytic continuation of imaginary time using computational data is a grand challenge that has hindered meaningful links with experimental data. The standard Maximum Entropy (MaxEnt)-based method is limited by the quality of the computational data and the availability of prior information. Also, the MaxEnt is not able to solve the inversion problem under high level of noise in the data. Here we introduce a novel learning model for the analytic continuation problem using a Adams-Bashforth residual neural network (AB-ResNet). Additionally, the advantage of this deep learning network is that it is model independent and, therefore, does not require prior information concerning the quantity of interest given by the spectral function. More importantly, the ResNet-based model achieves higher accuracy than MaxEnt for data with higher level of noise. Finally, numerical examples show that the developed AB-ResNet is able to recover the spectral function with accuracy comparable to MaxEnt where the noise level is relatively small.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Analytic Approximations for the Extrapolation of Lattice Data

We present analytic approximations of chiral SU(3) amplitudes for the extrapolation of lattice data to the physical masses and the determination of Next-to-Next-to-Leading-Order low-energy constants. Lattice data for the ratio F{sub K}/F{sub {pi}} is used to test the method.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Regional Feedstock Partnership 2019 Workshop Report

The U.S. Department of Energy (DOE) and the Sun Grant Initiative began the Regional Feedstock Partnership (RFP) in 2007 primarily to address information gaps associated with sustainably and reliably producing a billion-tons of biomass annually by the year 2030 for the U.S. bioenergy industry. The culmination of these efforts were reported in the Sun Grant/DOE Regional Feedstock Partnership Final Technical Report. The same biomass resources used to support these yield and sustainability efforts included analytical characterization data representing biomass quality. Considerable effort has been put into collection, harmonization, and archival of the analytical data, metadata, and physical samples associated with the thousands of samples collected from the RFP field trials. This data provides a unique opportunity to understand the impacts of agronomic and forest production practices, as well as environmental factors, on biomass material attributes relevant to bioenergy production. To more efficiently develop these relationships, this workshop was held to bring together the experts who conducted the RFP field trials and the biomass attribute data analysts. The objectives of the workshop included the following: • Enable face-to-face interactions with field trial experts, allowing for insights into the species and field trials themselves that are not obvious looking at data alone • Fill in metadata and analytical gaps from the RFP for future analyses • Share results from analysis of RFP biomass properties for each species along with associated field experiment observations/results • Discuss the potential to develop biomass quality maps • Discuss preparation of peer-reviewed publications for each species • Discuss outlines for a comprehensive summary report on the evaluation of RFP biomass material attributes for quality determination, focusing on the results of peer-reviewed publications and analysis of compiled RFP biomass attribute datasets

09 BIOMASS FUELS↗
DOE OSTI2018

Visual Analytics of Heterogeneous Data Using Hypergraph Learning

For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. Thesemodels represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., in public health datasets, more than two patient groups admitted by the same hospital in 2014). In this article, we propose a visual analytics approach forthe classification on heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a unified hypergraph structure. We use the hypergraph learning algorithm for predicting missing labels in the datasets.To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. In addition, we also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. Here, we demonstrate the capability of our approach via two real-world cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

CMed: Crowd Analytics for Medical Imaging Data

In this work, we present a visual analytics framework, CMed, for exploring medical image data annotations acquired from crowdsourcing. CMed can be used to visualize, classify, and filter crowdsourced clinical data based on a number of different metrics such as detection rate, logged events, and clustering of the annotations. CMed provides several interactive linked visualization components to analyze the crowd annotation results for a particular video and the associated workers. Additionally, all results of an individual worker can be inspected using multiple linked views in our CMed framework. We allow a crowdsourcing application analyst to observe patterns and gather insights into the crowdsourced medical data, helping him/her design future crowdsourcing applications for optimal output from the workers. We demonstrate the efficacy of our framework with two medical crowdsourcing studies: polyp detection in virtual colonoscopy videos and lung nodule detection in CT thin-slab maximum intensity projection videos. We also provide experts' feedback to show the effectiveness of our framework. Lastly, we share the lessons we learned from our framework with suggestions for integrating our framework into a clinical workflow.

97 MATHEMATICS AND COMPUTING↗