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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Evaluating the Accuracy of Various Irradiance Models in Detecting Soiling of Irradiance Sensors

We evaluate the feasibility of using various clear-sky models or purchased satellite data for estimating the soiling of a reference cell irradiance sensor. We find results to be more accurate for models that consider local meteorological conditions. We conclude that given the data sets considered, and depending on the requirements of the data analyst, choosing to use purchased satellite irradiance data from Solargis to estimate the soiling of a reference cell sensor tends to yield more accurate results, although there are instances where a clear-sky model performs better. The SOLIS clear-sky model in PVLIB with variable P wat provided useful soiling results, implying that the general method of using a clear-sky model with local meteorological data may provide a low-cost tool for detecting soiling of irradiance sensors.

14 SOLAR ENERGY↗
DOE OSTI2022

Supply Chain Risk Management: Data Structuring

Supply chain risk management (SCRM) is an area of research that addresses both logistics concepts to maximize efficiency, reliability, and revenue as well as risk features, such as potential weak points, break points, and vulnerabilities within the supply chain. SCRM is used to find risks introduced at each node in a supply chain and how these risks can impact a company’s products, individuals, customers, and reputation. SCRM is a relatively new field, so standardized processes including data structuring are not fully documented. This paper explains the importance of a standard data structuring methodology and how it can enhance current SCRM efforts. Data ingest, structuring, and analysis are predominantly managed by humans. Automating some of the less complex steps can positively impact SCRM by allowing human analysts to focus on more strategic analyses. Types of data to be collected and structured are collected via publicly available information related to hardware, software, and corporate entities. After the data has been collected, the information is formatted in a specific manner, conforming to a schema, to allow for more effective and efficient ingest for further analysis. This paper outlines data structures used by Pacific Northwest National Laboratory for SCRM research and analysis purposes. These structures have been used for hundreds of analyses and have been successful in developing a common baseline. Data structuring is one of the first steps in data standardization, which will further mature and enhance the SCRM research area.

supply chain risk management, data structuring, re↗
DOE OSTI2022

Exaflops Biomedical Knowledge Graph Analytics

We are motivated by newly proposed methods for mining large-scale corpora of scholarly publications (e.g., full biomedical literature), which consists of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover relationships among concepts. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as an all-pairs shortest paths (APSP) and validate connective paths against curated biomedical knowledge graphs (e.g., Spoke). In this context, we present Coast (Exascale Communication-Optimized All-Pairs Shortest Path) and demonstrate 1.004 EF/s on 9,200 Frontier nodes (73,600 GCDs). We develop hyperbolic performance models (HYPERMOD), which guide optimizations and parametric tuning. The proposed Coast algorithm achieved the memory constant parallel efficiency of 99% in the single-precision tropical semiring. Looking forward, Coast will enable the integration of scholarly corpora like PubMed into the Spoke biomedical knowledge graph.

Kannan, Ramakrishnan {ramki}↗
DOE OSTI2020

Scalable Knowledge Graph Analytics at 136 Petaflop/s

We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover how concepts relate to one another. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as one of computing all-pairs shortest paths (APSP), which becomes a significant bottleneck. In this context, we present a new high-performance algorithm and implementation of the Floyd-Warshall algorithm for distributed-memory parallel computers accelerated by GPUs, which we call DSNAPSHOT (Distributed Accelerated Semiring All-Pairs Shortest Path). For our largest experiments, we ran DSNAPSHOT on a connected input graph with millions of vertices using 4, 096nodes (24,576GPUs) of the Oak Ridge National Laboratory's Summit supercomputer system. We find DSNAPSHOT achieves a sustained performance of 136×1015 floating-point operations per second (136petaflop/s) at a parallel efficiency of 90% under weak scaling and, in absolute speed, 70% of the best possible performance given our computation (in the single-precision tropical semiring or “min-plus” algebra). Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligence-driven natural language processing workflows at scale.

Kannan, Ramakrishnan {ramki}↗
DOE OSTI2016

Adaptive Visual Sort and Summary of Micrographic Images of Nanoparticles for Forensic Analysis

Forensic analysis of nanoparticles is often conducted through the collection and identifi- cation of electron microscopy images to determine the origin of suspected nuclear material. Each image is carefully studied by experts for classification of materials based on texture, shape, and size. Manually inspecting large image datasets takes enormous amounts of time. However, automatic classification of large image datasets is a challenging problem due to the complexity involved in choosing image features, the lack of training data available for effective machine learning methods, and the availability of user interfaces to parse through images. Therefore, a significant need exists for automated and semi-automated methods to help analysts perform accurate image classification in large image datasets. We present INStINCt, our Intelligent Signature Canvas, as a framework for quickly organizing image data in a web based canvas framework. Images are partitioned using small sets of example images, chosen by users, and presented in an optimal layout based on features derived from convolutional neural networks.

active canvas↗
DOE OSTI2020

PredCom: A Predictive Approach to Collecting Approximated Communication Traces

Communication traces collected from MPI applications are an important source of information for performance optimization as they can help analysts determine communication patterns and identify inefficiencies. However, their collection, especially at scale, is time consuming, since it usually requires running the complete target application on a large number of nodes. In this work, we present PredCom, a tool-chain to generate a predictive communication proxy based on information gathered from a few small scale runs, which allows us to extract approximate communication traces with an accuracy high enough for most analysis goals. For this, we combine LLVM passes on the original source code (to capture static program structure) with parameter prediction (to capture dynamic and scaling behavior). This approach drastically reduces the time needed for collecting the communication traces, even for traces on large numbers of MPI processes. Here, we demonstrate that PredCom generates communication traces of various applications up to 1612x faster with an accuracy loss of 0.11 on average compared to the original large-scale traces, and we show that the generated traces can be used to optimize process placement.

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↗
DOE OSTI2019

Visualizing Hierarchical Performance Profiles of Parallel Codes Using CallFlow

Calling context trees (CCTs) couple performance metrics with call paths, helping understand the execution and performance of parallel programs. To identify performance bottlenecks, programmers and performance analysts visually explore CCTs to form and validate hypotheses regarding degraded performance. However, due to the complexity of parallel programs, existing visual representations do not scale to applications running on a large number of processors. In this work, we present CallFlow, an interactive visual analysis tool that provides a high-level overview of CCTs together with semantic refinement operations to progressively explore CCTs. Using a flow-based metaphor, we visualize a CCT by treating execution time as a resource spent during the call chain, and demonstrate the effectiveness of our design with case studies on large-scale, production simulation codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Reimagining Disassembly Interfaces With Visualization: Combining Instruction Tracing and Control Flow With DisViz

In applications where efficiency is critical, developers may examine their compiled binaries, seeking to understand how the compiler transformed their source code and what performance implications that transformation may have. This analysis is challenging due to the vast number of disassembled binary instructions and the many-to-many mappings between them and the source code. These problems are exacerbated as source code size increases, giving the compiler more freedom to map and disperse binary instructions across the disassembly space. Interfaces for disassembly typically display instructions as an unstructured listing or sacrifice the order of execution. Here, we design a new visual interface for disassembly code that combines execution order with control flow structure, enabling analysts to both trace through code and identify familiar aspects of the computation. Central to our approach is a novel layout of instructions grouped into basic blocks that displays a looping structure in an intuitive way. We add to this disassembly representation a unique block-based mini-map that leverages our layout and shows context across thousands of disassembly instructions. Finally, we embed our disassembly visualization in a web-based tool, DisViz, which adds dynamic linking with source code across the entire application. DizViz was developed in collaboration with program analysis experts following design study methodology and was validated through evaluation sessions with ten participants from four institutions. Participants successfully completed the evaluation tasks, hypothesized about compiler optimizations, and noted the utility of our new disassembly view. Our evaluation suggests that our new integrated view helps application developers in understanding and navigating disassembly code.

Computer science↗
DOE OSTI2016

CyberPetri at CDX 2016: Real-time Network Situation Awareness

CyberPetri is a novel visualization technique that provides a flexible map of the network based on available characteristics, such as IP address, operating system, or service. Previous work introduced CyberPetri as a visualization feature in Ocelot, a network defense tool that helped security analysts understand and respond to an active defense scenario. In this paper we present a case study in which we use the CyberPetri visualization technique to support real-time situation awareness during the 2016 Cyber Defense Exercise.

Cybersecurity↗
DOE OSTI2017

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis

State-of-the-art visual analytics models and frameworks mostly assume a static snapshot of the data, while in many cases it is a stream with constant updates and changes. Exploration of streaming data poses unique challenges as machine-level computations and abstractions need to be synchronized with the visual representation of the data and the temporally evolving human insights. In the visual analytics literature, we lack a thorough characterization of streaming data and analysis of the challenges associated with task abstraction, visualization design, and adaptation of the role of human-in-the-loop for exploration of data streams. We aim to fill this gap by conducting a survey of the state-of-the-art in visual analytics of streaming data for systematically describing the contributions and shortcomings of current techniques and analyzing the research gaps that need to be addressed in the future. Our contributions are: i) problem characterization for identifying challenges that are unique to streaming data analysis tasks, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space for dynamic data and the role of the human-in-the-loop, and iii) reflections on the design-trade-offs for streaming visual analytics techniques and their practical applicability in real-world application scenarios.

streaming data↗
DOE OSTI2012

Life Cycle Greenhouse Gas Emissions of Coal-Fired Electricity Generation: Systematic Review and Harmonization

This systematic review and harmonization of life cycle assessments (LCAs) of utility-scale coal-fired electricity generation systems focuses on reducing variability and clarifying central tendencies in estimates of life cycle greenhouse gas (GHG) emissions. Screening 270 references for quality LCA methods, transparency, and completeness yielded 53 that reported 164 estimates of life cycle GHG emissions. These estimates for subcritical pulverized, integrated gasification combined cycle, fluidized bed, and supercritical pulverized coal combustion technologies vary from 675 to 1,689 grams CO{sub 2}-equivalent per kilowatt-hour (g CO{sub 2}-eq/kWh) (interquartile range [IQR]= 890-1,130 g CO{sub 2}-eq/kWh; median = 1,001) leading to confusion over reasonable estimates of life cycle GHG emissions from coal-fired electricity generation. By adjusting published estimates to common gross system boundaries and consistent values for key operational input parameters (most importantly, combustion carbon dioxide emission factor [CEF]), the meta-analytical process called harmonization clarifies the existing literature in ways useful for decision makers and analysts by significantly reducing the variability of estimates ({approx}53% in IQR magnitude) while maintaining a nearly constant central tendency ({approx}2.2% in median). Life cycle GHG emissions of a specific power plant depend on many factors and can differ from the generic estimates generated by the harmonization approach, but the tightness of distribution of harmonized estimates across several key coal combustion technologies implies, for some purposes, first-order estimates of life cycle GHG emissions could be based on knowledge of the technology type, coal mine emissions, thermal efficiency, and CEF alone without requiring full LCAs. Areas where new research is necessary to ensure accuracy are also discussed.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2012

Background and Reflections on the Life Cycle Assessment Harmonization Project

Despite the ever-growing body of life cycle assessment (LCA) literature on electricity generation technologies, inconsistent methods and assumptions hamper comparison across studies and pooling of published results. Synthesis of the body of previous research is necessary to generate robust results to assess and compare environmental performance of different energy technologies for the benefit of policy makers, managers, investors, and citizens. With funding from the U.S. Department of Energy, the National Renewable Energy Laboratory initiated the LCA Harmonization Project in an effort to rigorously leverage the numerous individual studies to develop collective insights. The goals of this project were to: (1) understand the range of published results of LCAs of electricity generation technologies, (2) reduce the variability in published results that stem from inconsistent methods and assumptions, and (3) clarify the central tendency of published estimates to make the collective results of LCAs available to decision makers in the near term. The LCA Harmonization Project's initial focus was evaluating life cycle greenhouse gas (GHG) emissions from electricity generation technologies. Six articles from this first phase of the project are presented in a special supplemental issue of the Journal of Industrial Ecology on Meta-Analysis of LCA: coal (Whitaker et al. 2012), concentrating solar power (Burkhardt et al. 2012), crystalline silicon photovoltaics (PVs) (Hsu et al. 2012), thin-film PVs (Kim et al. 2012), nuclear (Warner and Heath 2012), and wind (Dolan and Heath 2012). Harmonization is a meta-analytical approach that addresses inconsistency in methods and assumptions of previously published life cycle impact estimates. It has been applied in a rigorous manner to estimates of life cycle GHG emissions from many categories of electricity generation technologies in articles that appear in this special supplemental supplemental issue, reducing the variability and clarifying the central tendency of those estimates in ways useful for decision makers and analysts. Each article took a slightly different approach, demonstrating the flexibility of the harmonization approach. Each article also discusses limitations of the current research, and the state of knowledge and of harmonization, pointing toward a path of extending and improving the meta-analysis of LCAs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Tiered Approach to Resilience Assessment

Regulatory agencies have long adopted a three-tier structure for risk assessment and risk management. We build on this structure to propose a tiered approach for resilience assessment that can be integrated in the existing regulatory processes. Comprehensive approaches to assessing resilience at appropriate and operational scales, reconciling analytical complexity as needed with stakeholder needs and resources available, and ultimately creating actionable recommendations to enhance resilience is still lacking. Our proposed structure consists of tiers by which analysts can scale a resilience assessment and associated management actions relative to the scope and urgency of the risk and the capacity of resource managers to improve system resilience. We use an application of water allocation decisions in the Platte River Basin, Nebraska, USA to illustrate this approach.

Sustainability, natural resources management, disa↗
DOE OSTI2012

An Improved Coefficient Calculator for the California Energy Commission 6 Parameter Photovoltaic Module Model

This paper describes an improved algorithm for calculating the six parameters required by the California Energy Commission (CEC) photovoltaic (PV) Calculator module model. Rebate applications in California require results from the CEC PV model, and thus depend on an up-to-date database of module characteristics. Currently, adding new modules to the database requires calculating operational coefficients using a general purpose equation solver - a cumbersome process for the 300+ modules added on average every month. The combination of empirical regressions and heuristic methods presented herein achieve automated convergence for 99.87% of the 5487 modules in the CEC database and greatly enhance the accuracy and efficiency by which new modules can be characterized and approved for use. The added robustness also permits general purpose use of the CEC/6 parameter module model by modelers and system analysts when standard module specifications are known, even if the module does not exist in a preprocessed database.

14 SOLAR ENERGY↗
DOE OSTI2018

Using a Dynamic Substructuring Approach to Model the Effects of Acoustic Damping in Coupled Acoustic-Structure Systems

Acoustic-structure coupling can substantially alter the frequency response of air-filled structures. Coupling effects typically manifest as two resonance peaks at frequencies above and below the resonant frequency of the uncoupled structural system. In this study, a dynamic substrucuring approach is applied to a simple acoustic-structure system to expose how the system response depends on the damping in the acoustic subsystem. Parametric studies show that as acoustic damping is increased, the frequencies and amplitudes of the coupled resonances in the structural response undergo a sequence of changes. For low levels of acoustic damping, the two coupled resonances have amplitudes approximating the corresponding in vacuo resonance. As acoustic damping is increased, resonant amplitudes decrease dramatically while the frequency separation between the resonances tends to increase slightly. When acoustic damping is increased even further, the separation of the resonant frequencies decreases below their initial separation. Finally, at some critical value of acoustic damping, one of the resonances abruptly disappears, leaving just a single resonance. Counterintuitively, increasing acoustic damping beyond this point tends to increase the amplitude of the remaining resonance peak. Finally, these results have implications for analysts and experimentalists attempting to understand, mitigate, or otherwise compensate for the confounding effects of acoustic-structure coupling in fluid-filled test structures.

42 ENGINEERING↗
DOE OSTI2021

Embedded Error Bayesian Calibration of Thermal Decomposition of Organic Materials

Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to tradiational materials they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chemical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis. This process requires the calibration of many model parameters to closely match experimental data. Previous efforts in this field have largely been limited to finding a single best-fit set of parameters even though the experimental data may be very noisy. Furthermore the chemical kinetics models are often simplified representations of the true de- composition process. The simplification induces model-form errors that the fitting process cannot capture. In this work we propose a methodology for calibrating decomposition models to thermogravimetric analysis data that accounts for uncertainty in the model-form and experimental data simultaneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. In conclusion, the distributions may be used in forward propagation of uncertainty in models that leverage this material.

36 MATERIALS SCIENCE↗