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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

IMG/M: integrated genome and metagenome comparative data analysis system

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support for examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review(ER) companion system (IMG/M ER: https://img.jgi.doe.gov/ mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes

The Integrated Microbial Genomes & Microbiomes system v.5.0 (IMG/M: https://img.jgi.doe.gov/m/) contains annotated datasets categorized into: archaea, bacteria, eukarya, plasmids, viruses, genome fragments, metagenomes, cell enrichments, single particle sorts, and metatranscriptomes. Source datasets include those generated by the DOE's Joint Genome Institute (JGI), submitted by external scientists, or collected from public sequence data archives such as NCBI. All submissions are typically processed through the IMG annotation pipeline and then loaded into the IMG data warehouse. IMG's web user interface provides a variety of analytical and visualization tools for comparative analysis of isolate genomes and metagenomes in IMG. IMG/M allows open access to all public genomes in the IMG data warehouse, while its expert review (ER) system (IMG/MER: https://img.jgi.doe.gov/mer/) enabled registered users to access their private genomes and to store their private datasets in workspace for sharing and for further analysis. IMG/M data content has increased by 60% since the last report published in the 2017 NAR Database Issue. IMG/M v.5.0 has a new and more powerful genome search feature, new statistical tools, and supports metagenome binning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Topological Guided Detection of Extreme Wind Phenomena: Implications for Wind Energy

Extreme wind phenomena play a crucial role in the efficient operation of wind farms for renewable energy generation. However, existing detection methods are computationally expensive, limited to specific coordinate. In real-world scenarios, understanding the occurrence of these phenomena over a large area is essential. Therefore, there is a significant demand for a fast and accurate approach to forecast such events. In this paper, we propose a novel method for detecting wind phenomena using topological analysis, leveraging the gradient of wind speed or critical points in a topological framework. By extracting topological features from the wind speed profile within a defined region, we employ topological distance to identify extreme wind phenomena. Our results demonstrate the effectiveness of utilizing topological features derived from regional wind speed profiles. We validate our approach using high-resolution simulations with the Weather Research and Forecasting model (WRF) over a month in the US East Coast.

MATHEMATICS AND COMPUTING,WIND ENERGY↗
DOE OSTI2018

Challenges in Visual Analysis of Ensembles

Modeling physical phenomena through computational simulation increasingly relies on generating a collection of related runs, known as an ensemble. In this paper, we explore the challenges we face in developing analysis and visualization systems for large and complex ensemble data sets, which we seek to understand without having to view the results of every simulation run. Implementing approaches and ideas developed in response to this goal, we demonstrate the analysis of a 15K run material fracturing study using Slycat, our ensemble analysis system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Blending Machine Learning and Interaction Design in Audio Explorer

The results of machine learning models can often be difficult to interpret, especially for domain experts. Audio Explorer, the winning entry of the 2018 VAST Challenge, is an interactive data exploration tool that effectively communicates machine learning results, using coordinated geospatial, temporal, and auditory visualizations to promote information discovery.

Visual analytics, VAST Challenge↗
DOE OSTI2024

A New Default Colormap for ParaView

ParaView is one of the most prominent software tools for scientific visualization used by scientists around the world. Color is a primary conduit to visually map data to its representation and, thus, enable investigation and interpretation of the data. Colormap selection has a significant impact on the data revealed; its design and selection is a critical aspect of scientific data visualization. A common choice for a user is the program’s default colormap, so careful consideration of this default is consequential. Although the current default colormap in ParaView, a succession of hues from cool blue to warm red, has served the community well, research shows that more nuanced colormap configurations increase discriminability while maintaining other critical metrics. These findings inspire us to revisit and update the default colors in ParaView. Here, in this study, we present a new ParaView default colormap, the criteria and methods of development, and example visualizations and analytic metrics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A Structure-Based Distance Metric for High-Dimensional Space Exploration with Multi-Dimensional Scaling

Although the Euclidean distance does well in measuring data distances within high-dimensional clusters, it does poorly when it comes to gauging inter-cluster distances. This significantly impacts the quality of global, low-dimensional space embedding procedures such as the popular multi-dimensional scaling (MDS) where one can often observe non-intuitive layouts. We were inspired by the perceptual processes evoked in the method of parallel coordinates which enables users to visually aggregate the data by the patterns the polylines exhibit across the dimension axes. We call the path of such a polyline its structure and suggest a metric that captures this structure directly in high-dimensional space. This allows us to better gauge the distances of spatially distant data constellations and so achieve data aggregations in MDS plots that are more cognizant of existing high-dimensional structure similarities. Our MDS plots also exhibit similar visual relationships as the method of parallel coordinates which is often used alongside to visualize the high-dimensional data in raw form. We then cast our metric into a bi-scale framework which distinguishes far-distances from near-distances. The coarser scale uses the structural similarity metric to separate data aggregates obtained by prior classification or clustering, while the finer scale employs the appropriate Euclidean distance.

Clustering↗
DOE OSTI2020

Geometry-Driven Detection, Tracking and Visual Analysis of Viscous and Gravitational Fingers

Viscous and gravitational flow instabilities cause a displacement front to break up into finger-like fluids. The detection and evolutionary analysis of these fingering instabilities are critical in multiple scientific disciplines such as fluid mechanics and hydrogeology. However, previous detection methods of the viscous and gravitational fingers are based on density thresholding, which provides limited geometric information of the fingers. The geometric structures of fingers and their evolution are important yet little studied in the literature. In this work, we explore the geometric detection and evolution of the fingers in detail to elucidate the dynamics of the instability. Additionally, we propose a ridge voxel detection method to guide the extraction of finger cores from three-dimensional (3D) scalar fields. After skeletonizing finger cores into skeletons, we design a spanning tree based approach to capture how fingers branch spatially from the finger skeletons. Finally, we devise a novel geometric-glyph augmented tracking graph to study how the fingers and their branches grow, merge, and split over time. Feedback from earth scientists demonstrates the usefulness of our approach to performing spatio-temporal geometric analyses of fingers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Scalable Comparative Visualization of Ensembles of Call Graphs

Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and application parameters, and are interested in detecting and understanding bottlenecks in different executions. They often collect hierarchical performance profiles represented as call graphs, which combine performance metrics with their execution contexts. The crucial task of exploring multiple call graphs together is tedious and challenging because of the many structural differences in the execution contexts and significant variability in the collected performance metrics (e.g., execution runtime). In this paper, we present Ensemble CallFlow to support the exploration of ensembles of call graphs using new types of visualizations, analysis, graph operations, and features. We introduce ensemble-Sankey , a new visual design that combines the strengths of resource-flow (Sankey) and box-plot visualization techniques. Whereas the resource-flow visualization can easily and intuitively describe the graphical nature of the call graph, the box plots overlaid on the nodes of Sankey convey the performance variability within the ensemble. Our interactive visual interface provides linked views to help explore ensembles of call graphs, e.g., by facilitating the analysis of structural differences, and identifying similar or distinct call graphs. Finally, we demonstrate the effectiveness and usefulness of our design through case studies on large-scale parallel codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Concept Lens: Visual Comparison and Evaluation of Generative Model Manipulations

Generative models are becoming a transformative technology for the creation and editing of images. However, it remains challenging to harness these models for precise image manipulation. These challenges often manifest as inconsistency in the editing process, where both the type and amount of semantic change, depend on the image being manipulated. Moreover, there exist many methods for computing image manipulations, whose development is hindered by the matter of inconsistency. This paper aims to address these challenges by improving how we evaluate, compare, and explore the space of manipulations offered by a generative model. We present Concept Lens, a visual interface that is designed to aid users in understanding semantic concepts carried in image manipulations, and how these manipulations vary over generated images. Given the large space of possible images produced by a generative model, Concept Lens is designed to support the exploration of both generated images, and their manipulations, at multiple levels of detail. To this end, the layout of Concept Lens is informed by two hierarchies: a hierarchical organization of (1) original images, grouped by their similarities, and (2) image manipulations, where manipulations that induce similar changes are grouped together. This layout allows one to discover the types of images that consistently respond to a group of manipulations, and vice versa, manipulations that consistently respond to a group of codes. We show the benefits of this design across multiple use cases, specifically, studying the quality of manipulations for a single method, and offering a means of comparing different methods.

clustering↗
DOE OSTI2014

Modeling in Big Data Environments

Human-Centered Big Data Research (HCBDR) is an area of work that focuses on the methodologies and research areas focused on understanding how humans interact with “big data”. In the context of this paper, we refer to “big data” in a holistic sense, including most (if not all) the dimensions defining the term, such as complexity, variety, velocity, veracity, etc. Simply put, big data requires us as researchers of to question and reconsider existing approaches, with the opportunity to illuminate new kinds of insights that were traditionally out of reach to humans. The purpose of this article is to summarize the discussions and ideas about the role of models in HCBDR at a recent workshop. Models, within the context of this paper, include both computational and conceptual mental models. As such, the discussions summarized in this article seek to understand the connection between these two categories of models.

big data↗
DOE OSTI2018

Sonar

Sonar contains software for managing a HPC performance-monitoring software stack. This includes streaming data into a distributed Kafka message queue, storing/querying from a distributed Cassandra database, and front-end tools for secure access, visualization, and analytics of facility-wide performance data.

Gimenez, AlfredoA.↗
DOE OSTI2020

Merope: Unsupervised Learning for Network Operations (Merope) v1

The ability to analyze network flow characteristics is critical to understand network patterns that can help us understand how our networks are behaving. Merope is a collection of many unsupervised learning approaches and visualizers to allow networks to be more visual and analytical.

Kiran, Mariam↗
DOE OSTI2025

ACTIVE

The Automated Control Testbed for Integration, Verification, and Emulation (ACTIVE) framework is a software platform designed to support the optimized operation and management of a wide range of building types. It enables the development, testing, and validation of diverse control strategies, including AI-based, rule-based, and model-based approaches. The platform facilitates a seamless transition from simulation-based evaluation of control strategies to real-world field validation and deployment. ACTIVE supports the full building management lifecycle, encompassing data acquisition and management, system monitoring, optimized control, adaptive learning services, device dispatch and coordination, as well as advanced analytics and visualization. Together, these capabilities provide an integrated environment for improving building performance, operational efficiency, reducing energy cost, and reliability.

Smith, Robert [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2019

State-based network similarity visualization

We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. Here, we present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Complexity and entropy representation for machine component diagnostics

The Complexity-entropy causality plane (CECP) is a parsimonious representation space for time series. It has only two dimensions: normalized permutation entropy and Jensen-Shannon complexity of a time series. This two-dimensional representation allows for detection of slow or rapid drifts in the condition of mechanical components monitored through sensor measurements. The CECP representation can be used for both predictive analytics and visual monitoring of changes in component condition. This method requires minimal pre-processing of raw signals. Furthermore, it is insensitive to noise, stationarity, and trends. These desirable properties make CECP a good candidate for machine condition monitoring and fault diagnostics. In this work we study the effectiveness of CECP on three rotary component condition assessment applications. We use CECP representation of vibration signals to differentiate various machine component health conditions for rotary machine components, namely roller bearing and gears. The results confirm that the CECP representation is able to detect, with high accuracy, changes in underlying dynamics of machine component degradation states. From class separability perspective, the CECP representation is able to generate linearly separable classes for the classification of different fault states. This classification performance improves with increasing signal length. For signal length of 16,384 data points, the fault classification accuracy varies from 90% to 100% for bearing applications, and from 85% to 100% for gear applications. We observed that the optimum parameter for CECP representating depends on the application. For bearing applications we found that embedding dimension D = 4, 5, 6, and embedding delay τ = 1, 2, 3 are suitable for good fault classification. For gear applications we find that embedding dimension D = 4, 5, and embedding delay τ = 1, 5 are suitable for fault classification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

EDX ClaiMM

EDX ClaiMM is a centralized data & analytical platform designed to revolutionize U.S. critical minerals and materials (CMM) activities. By providing a robust digital infrastructure, ClaiMM will accelerate the combination, leveraging, and rapid utilization of vital data, advanced tools, and cutting-edge research advancements in CMM. This adaptive digital research hub connects the CMM community to essential knowledge products and offers access to interoperable datasets, databases, models, software, and tools from the National Energy Technology’s (NETL’s) Energy Data eXchange (EDX) and other authoritative sources, serving both public and private sectors. EDX ClaiMM delivers AI-informed solutions to address fundamental knowledge gaps and fosters the innovation of new techniques for enhanced characterization and recovery of CMMs within the U.S. By leveraging cloud-hosted, scalable digital infrastructure, ClaiMM meets public–private applied energy needs. It equips the CMM community with priority digital resources that harness on-site and cloud compute capabilities, enabling big data storage, advanced processing, analytics, and visualization.

Critical Materials; Critical Minerals; Rare Earth ↗
DOE OSTI2021

CO 2 Storage Site Screening Platform Development and CO 2 Storage Resource Analysis in SECARB Offshore Reservoirs Using SAS Viya

A major goal of the SECARB Offshore Partnership (DE-FE0031557) is to screen deep saline aquifers and hydrocarbon reservoirs in the central Gulf of Mexico for CO 2 sequestration and CO 2 -enhanced oil and gas recovery (EOR/EGR) and estimate the corresponding CO 2 storage resources for select reservoirs. CO 2 storage potential associated with offshore CO 2 -EOR is considerable and likely represents “low hanging fruit” for near-term CO 2 storage given the in-place infrastructure in the region. It is for these reasons that this assessment focuses on oil and gas fields. To this end, three major objectives have been completed and include (1) managing geological data derived from different sources, (2) building a reservoir screening platform for CO 2 storage, and (3) ranking the reservoirs based on the estimated CO 2 storage resources. The SAS ® Viya platform was used for data management and analytics. The Viya platform is a cloud service platform that provides data integration, data management, quick analytics, data visualization, machine learning functions, and application programming interfaces (APIs) for multi-programming languages. Different sources of data containing geologic information, reservoir properties, and EOR/EGR information were collected, cleaned, formatted, and loaded into the SAS ® Viya platform for evaluation. The major geological characteristics of both shelf and deep-water areas of the central Gulf were examined and compared to define the appropriate reservoir screening criteria. Next, a CO 2 storage site screening system was built in the SAS ® Viya platform with the pre-defined criteria. Finally, the CO 2 storage resources of the screened reservoirs were calculated and reported at the BOEM field level to identify fields with the highest estimated CO 2 storage resource. The fields with the largest total estimated CO 2 storage resource are located in the Mississippi Canyon protraction area. Due to proximity to the Mississippi Delta (indicative of less infrastructure) and large estimated CO 2 storage resources, future development activities may wish to focus efforts in the Mississippi Canyon protraction area.

02 PETROLEUM↗