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NASA NTRS2022

An Earth System Digital Twin for Flood Prediction and Analysis

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observation data, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. The NASA’s Advanced Information Systems Technology (AIST)’s Integrated Digital Earth Analysis System (IDEAS) project is to establish an extensible architectural solution to develop digital twins of our physical environment for Earth Science. IDEAS delivers a formal system architecture with mechanisms for the outputs of one model to feed into others; for driving models with observation data; and for harmonizing observation data and model outputs for analysis. To validate and demonstrate the IDEAS architecture, this project collaborates with the Space Climate Observatory (SCO)’s FloodDAM project and the Centre National d’Etudes Spatiales (CNES) to focus on floods detection, prediction and their impacts.

Kettig, Peter↗

DOE OSTI2018

In Situ Analysis and Visualization of Fusion Simulations: Lessons Learned

The trends in high performance computing, where far more data can be computed that can ever be stored, have made in situ techniques an important area of research and development. Simulation campaigns, where domain scientists work with computer scientists to run a simulation and perform in situ analysis and visualization are important, and complex undertakings. In this paper we report our experiences performing in situ analysis and visualization on two campaigns. The two campaigns were related, but had important differences in terms of the codes that were used, the types of analysis and visualization required, and the visualization tools used. Further, we report the lessons learned from each campaign.

Kim, Mark B.↗

DOE OSTI2020

GIS Visualization of Transportation Energy Consumption

Transportation is witnessing unprecedented transformation, where emerging technologies are disrupting the way we travel. Be it sharing economy, or micro-mobility, the landscape of urban transportation is undergoing a much-needed paradigm shift. In order to capture and model these shifts, researchers need to be agile in their studies of the current and future transportation landscape. The need for agility in turn calls for sophisticated, intuitive, and reliable means to visualize and share transportation data. Travel is inherently spatial, so methods of visualizing transportation information should also be rooted in spatial analysis. Geographic Information Systems (GIS) are the premier technological framework to analyze and display spatial data. GIS tools have been used in the past to depict flow of vehicles, and display of network conditions (speed, congestion, etc.). However, in an ever-changing technological landscape, spanning advancements in vehicle as well as information systems, depicting vehicle flows falls short of providing a comprehensive picture of the impact these advancements have on travel related energy consumption. To address this issue, this research effort presents a web-based mapping application to visualize how travel related energy flows across a city. The application is being developed by National Renewable Energy Laboratory researchers to integrate various transportation energy consumption models within a GIS schema. This has the goal of enabling rapid analysis of energy impacts of the dramatically evolving transportation environment. The application is being developed using a Python framework for ArcGIS Online. Using road network data from the City of Columbus, Ohio and traffic data from CATT Laboratory's Regional Integrated Transportation Information System, transportation energy consumption will be modeled at a macro level. The application aims to provide highly accurate data while still maintaining the flexibility needed to adapt the model when new technologies arise. The methodology presented through this effort is expected to provide insights into how light-duty vehicles use energy on a large scale based on the road network they use. The application will also provide data exports to enable sharing and collaboration. " need for agility in turn calls for sophisticated, intuitive, and reliable means to visualize and share transportation data. Travel is inherently spatial, so methods of visualizing transportation information should also be rooted in spatial analysis. Geographic Information Systems (GIS) are the premier technological framework to analyze and display spatial data. GIS tools have been used in the past to depict flow of vehicles, and display of network conditions (speed, congestion, etc.). However, in an ever-changing technological landscape, spanning advancements in vehicle as well as information systems, depicting vehicle flows falls short of providing a comprehensive picture of the impact these advancements have on travel related energy consumption. To address this issue, this research effort presents a web-based mapping application to visualize how travel related energy flows across a city. The application is being developed by National Renewable Energy Laboratory researchers to integrate various transportation energy consumption models within a GIS schema. This has the goal of enabling rapid analysis of energy impacts of the dramatically evolving transportation environment. The application is being developed using a Python framework for ArcGIS Online. Using road network data from the City of Columbus, Ohio and traffic data from CATT Laboratory's Regional Integrated Transportation Information System, transportation energy consumption will be modeled at a macro level. The application aims to provide highly accurate data while still maintaining the flexibility needed to adapt the model when new technologies arise. The methodology presented through this effort is expected to provide insights into how light-duty vehicles use energy on a large scale based on the road network they use. The application will also provide data exports to enable sharing and collaboration.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1980

Appendix E: Research papers. Manual versus digital LANDSAT analysis for modeling river flooding

The comparative value of manual versus digital image analysis for determining flood boundaries is being examined in a study of the use of LANDSAT data for modeling flooding of the Black River, in northern New York. The work is an extension of an earlier study in which Black River flooding was assessed through visually interpreted, multi-date LANDSAT band 7 images. Based on the results to date, it appears that neither color-additive viewing nor digital analysis of LANDSAT data provide improvement in accuracy over visual analysis of band 7 images, for delineating the boundaries of flood-affected areas.

Philipson, W. R.↗

NASA NTRS1981

Manual versus digital Landsat analysis for modeling river flooding

The comparative value of manual versus digital image analysis for determining flood boundaries is being examined in a study of the use of Landsat data for modeling flooding of the Black River, in northern New York. The work is an extension of an earlier study in which Black River flooding was assessed through visually interpreted, multi-date Landsat band 7 images. Based on the results to date, it appears that neither color-additive viewing nor digital analysis of Landsat data provide improvement in accuracy over visual analysis of band 7 images, for delineating the boundaries of flood-affected areas.

Philipson, W. R.↗

NASA NTRS1973

Analysis and application of ERTS-1 data for regional geological mapping

Combined visual and digital techniques of analysing ERTS-1 data for geologic information have been tried on selected areas in Pennsylvania. The major physiolographic and structural provinces show up well. Supervised mapping, following the imaged expression of known geologic features on ERTS band 5 enlargements (1:250,000) of parts of eastern Pennsylvania, delimited the Diabase Sills and the Precambrian rocks of the Reading Prong with remarkable accuracy. From unsupervised mapping, transgressive linear features are apparent in unexpected density, and exhibit strong control over river valley and stream channel directions. They are unaffected by bedrock type, age, or primary structural boundaries, which suggests they are either rejuvenated basement joint directions on different scales, or they are a recently impressed structure possibly associated with a drifting North American plate. With ground mapping and underflight data, 6 scales of linear features have been recognized.

Gold, D. P.↗

DOE OSTI2023

FORCE Output Data Visualization

The Framework for Optimization of ResourCes and Economics (FORCE) is a collection of software tools, models, and datasets acquired and developed under the Integrated Energy Systems (IES) program to enable analysis of technical and economic viability of myriad IES configurations. Idaho National Laboratory is hosting a training session for FORCE users and developers, which is "Integrated Energy Systems Tools FORCE Overview and Training 2023." We will present data output visualization results with FORCE scenarios as examples and show how does the new design improve existing automated visualization for analysis results.

25 ENERGY STORAGE↗

NASA NTRS2018

NASA's GeneLab: An Integrated Omics Data Commons and Workbench

GeneLab (http://genelab.nasa.gov) is a NASA initiative designed to accelerate “open science” biomedical research in support of the human exploration of space and the improvement of life on earth. The GeneLab Data Systems (GLDS) were developed to help investigators corroborate findings from “omics” (genomics, transcriptomics, proteomics, and metabolomics) assays and translate them into systems biology knowledge and, eventually, therapeutics, including countermeasures to support life in space. Phase I of the project (completed) emphasized developing key capabilities for submission, curation, storage, search, and retrieval of omics data from biomedical research in and of space environments. The development focus for Phase II (completed) was federated data search and retrieval of these kinds of data from other open-access repositories. The last phase of the project (in work) entails developing an omics analysis tool set, and a portal to visualize processed omics data, emphasizing integration with the data repository and search functions developed during the prior phases. The final product will be an open-access system where users can individually or collaboratively publish, search, integrate, analyze, and visualize omics data.

genome↗

NASA NTRS2018

NASA's GeneLab: An Integrated Omics Data Commons and Workbench

GeneLab (http://genelab.nasa.gov) is a NASA initiative designed to accelerate "open science" biomedical research in support of the human exploration of space and the improvement of life on earth. The GeneLab Data Systems (GLDS) were developed to help investigators corroborate findings from "omics" (genomics, transcriptomics, proteomics, and metabolomics) assays and translate them into systems biology knowledge and, eventually, therapeutics, including countermeasures to support life in space. Phase I of the project (completed) emphasized developing key capabilities for submission, curation, storage, search, and retrieval of omics data from biomedical research in and of space environments. The development focus for Phase II (completed) was federated data search and retrieval of these kinds of data from other open-access repositories. The last phase of the project (in work) entails developing an omics analysis tool set, and a portal to visualize processed omics data, emphasizing integration with the data repository and search functions developed during the prior phases. The final product will be an open-access system where users can individually or collaboratively publish, search, integrate, analyze, and visualize omics data.

genome↗

NASA NTRS2017

NASA's GeneLab: An Integrated Omics Data Commons and Workbench

Berrios, Daniel C.↗

NASA NTRS1990

Hot gas ingestion test results of a two-poster vectored thrust concept with flow visualization in the NASA Lewis 9- x 15-foot low speed wind tunnel

A 9.2 percent scale Short Takeoff and Vertical Landing (STOVL) hot gas ingestion model was designed and built by McDonnell Douglas Corporation (MCAIR) and tested in the Lewis Research Center 9 x 15 foot Low Speed Wind Tunnel (LSWT). Hot gas ingestion, the entrainment of heated engine exhaust into the inlet flow field, is a key development issure for advanced short takeoff and vertical landing aircraft. Flow visualization from the Phase 1 test program, which evaluated the hot ingestion phenomena and control techniques, is covered. The Phase 2 test program evaluated the hot gas ingestion phenomena at higher temperatures and used a laser sheet to investigate the flow field. Hot gas ingestion levels were measured for the several forward nozzle splay configurations and with flow control/life improvement devices (LIDs) which reduced the hot gas ingestion. The model support system had four degrees of freedom - pitch, roll, yaw, and vertical height variation. The model support system also provided heated high-pressure air for nozzle flow and a suction system exhaust for inlet flow. The test was conducted at full scale nozzle pressure ratios and inlet Mach numbers. Test and data analysis results from Phase 2 and flow visualization from both Phase 1 and 2 are documented. A description of the model and facility modifications is also provided. Headwind velocity was varied from 10 to 23 kn. Results are presented over a range of nozzle pressure ratios at a 10 kn headwind velocity. The Phase 2 program was conducted at exhaust nozzle temperatures up to 1460 R and utilized a sheet laser system for flow visualization of the model flow field in and out of ground effects. The results reported are for nozzle exhaust temperatures up to 1160 R. These results will contain the compressor face pressure and temperature distortions, the total pressure recovery, the inlet temperature rise, and the environmental effects of the hot gas. The environmental effects include the ground plane contours, the model airframe heating, and the location of the ground flow separation.

Johns, Albert L.↗

DOE OSTI2023

Online Analytics for Remedy Support at DOE Environmental Management Sites

Environmental data is important for managing environmental restoration/waste site remediation, planning of monitoring efforts, addressing climate resilience, and engaging with stakeholders and regulators. A major challenge is how to manage the many different types and the large volume of environmental data in a way that allows practitioners and site managers to understand data implications and support decisions. The Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites (SOCRATES, https://www.pnnl.gov/projects/socrates) is a web application that provides data access, visualization, and rapid analytics to help make sense of environmental data, support remedy decisions, and communicate information. Development of SOCRATES has been funded through the DOE Richland Operations Office (RL) to support communication and decision making for the Hanford Site, thus is only tied into Hanford environmental data. However, the capabilities of SOCRATES are more broadly applicable to DOE-EM sites engaged in environmental remediation and management. This report describes the work to develop mechanisms for bringing non-Hanford data into SOCRATES so that other DOE-EM sites could make use of the visualization and analysis capabilities to support communication and decision making related to managing environmental restoration/waste site remediation, optimization/exit strategies for pump-and-treat systems, planning monitoring efforts, addressing climate resilience, and/or engaging with stakeholders and regulators. The background, approach, data transfer formats, examples, and next steps for this new SOCRATES-EM software are described in this report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

SNF Data Visualization on the Centralized Used Fuel Resource for Information Exchange (CURIE) Website

To better inform stakeholders regarding the nature of spent nuclear fuel (SNF) at utility sites, an online data visualization system has been created for the U.S. Department of Energy's Office of Nuclear Energy (DOENE). The data visualization system is part of the Centralized Used Fuel Resource for Information Exchange (CURIE) website, hosted by the Oak Ridge National Laboratory (ORNL) for DOE-NE. The visualization employs a database that is part of an analysis tool known as the Used Nuclear Fuel Storage, Transportation and Disposal Analysis Resource and Data System (UNF-ST[and]DARDS). Because many of the previous document management and sharing tools related to SNF have been terminated (or are inaccessible), a new tool, the CURIE, was developed for DOE-NE and launched on March 1, 2013 at http://curie.ornl.gov. In addition to supporting DOE SNF programs, CURIE is a national resource that is accessible to industry, vendor, federal, and laboratory partners, as well as other stakeholders. CURIE's architecture was initially based on previous work that supported DOE's Bioenergy Technologies Office on the Bioenergy Knowledge Discovery Framework (KDF). The site uses an open-source search engine, Apache Solr, which provides intuitive search results based on a faceted search method similar to that used by Amazon.com{sup R}. This algorithm helps to ensure that finding documents and data is straightforward. SNF data visualization capability was added to the CURIE website which provides a capability to post and access documents and data related to the management of SNF. CURIE currently maintains over 4,100 documents and the Siting Experience Database (SED), which was developed in response to a near-term recommendation by the Blue Ribbon Commission on America's Nuclear Future (BRC). The SED is a database of the experience that has been gained and relevant documentation that has been produced in efforts to site nuclear waste facilities, both in the US and abroad. Most of these documents are publicly accessible. In addition, CURIE maintains a current calendar of related events, an SNF image gallery, featured documents, and external links to related databases and websites. CURIE also maintains private groups (or communities) to facilitate collaborative development through sharing and editing documents that are not publicly available. There are more than 250 registered users in 23 different communities within CURIE.ORNL has also been developing UNF-ST[and]DARDS to provide a controlled source of technical data for various waste management system analysis/evaluation tools, access to key technical data, and analysis capabilities for characterizing the SNF inventory. UNF-ST[and]DARDS also provides enhanced capabilities to assess safety, risk, and uncertainty throughout the integrated waste management system. This paper describes the UNF-ST[and]DARDS data available for visualization and the associated visualization techniques used by CURIE to display this data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2002

Description of Data Archiving Associated With the NASA Goddard Grant NAGS-9590

Data restoration and archiving activities for this project have resulted in the restoration of 100% of the original Mariner 9 raw data set as well as many of the secondary analysis data sets. These data sets have been submitted to the Planetary Data System (PDS) Atmospheric Node, along with their PDS labels and descriptive metadata. In addition, a useful visualization and analysis tool has also been developed which allows the user to compare these Mariner 1971 Ultraviolet spectral data with several choices of related data sets: Mariner 9 images, USGS geologic data, MGS MOLA topography, Viking images (Viking MDIM) and thermal inertia data (MGS TES).

Simmons, K. E.↗

DOE OSTI2017

Characterizing Time Series Data Diversity for Wind Forecasting

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the important information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.

big data visualization↗

DOE OSTI2017

Automated X-Ray Diffraction of Irradiated Materials

Synchrotron-based X-ray diffraction (XRD) and small-angle Xray scattering (SAXS) characterization techniques used on unirradiated and irradiated reactor pressure vessel steels yield large amounts of data. Machine learning techniques, including PCA, offer a novel method of analyzing and visualizing these large data sets in order to determine the effects of chemistry and irradiation conditions on the formation of radiation induced precipitates. In order to run analysis on these data sets, preprocessing must be carried out to convert the data to a usable format and mask the 2-D detector images to account for experimental variations. Once the data has been preprocessed, it can be organized and visualized using principal component analysis (PCA), multi-dimensional scaling, and k-means clustering. In conclusion, from these techniques, it is shown that sample chemistry has a notable effect on the formation of the radiation induced precipitates in reactor pressure vessel steels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2000

Numerical Zooming Between a NPSS Engine System Simulation and a One-Dimensional High Compressor Analysis Code

Within NASA's High Performance Computing and Communication (HPCC) program, NASA Glenn Research Center is developing an environment for the analysis/design of aircraft engines called the Numerical Propulsion System Simulation (NPSS). NPSS focuses on the integration of multiple disciplines such as aerodynamics, structures, and heat transfer along with the concept of numerical zooming between zero-dimensional to one-, two-, and three-dimensional component engine codes. In addition, the NPSS is refining the computing and communication technologies necessary to capture complex physical processes in a timely and cost-effective manner. The vision for NPSS is to create a "numerical test cell" enabling full engine simulations overnight on cost-effective computing platforms. Of the different technology areas that contribute to the development of the NPSS Environment, the subject of this paper is a discussion on numerical zooming between a NPSS engine simulation and higher fidelity representations of the engine components (fan, compressor, burner, turbines, etc.). What follows is a description of successfully zooming one-dimensional (row-by-row) high-pressure compressor analysis results back to a zero-dimensional NPSS engine simulation and a discussion of the results illustrated using an advanced data visualization tool. This type of high fidelity system-level analysis, made possible by the zooming capability of the NPSS, will greatly improve the capability of the engine system simulation and increase the level of virtual test conducted prior to committing the design to hardware.

Follen, Gregory↗

DOE OSTI2018

A generative graph model for electrical infrastructure networks

We propose a generative graph model for electrical infrastructure networks that accounts for heterogeneity in both node and edge type. To inform the design of this model, we analyze the properties of power grid graphs derived from the U.S. Eastern Interconnection, Texas Interconnection, and Poland transmission system power grids. Across these datasets, we find subgraphs induced by nodes of the same voltage level exhibit shared structural properties atypical to small-world networks, including low local clustering, large diameter and large average distance. On the other hand, we find subgraphs induced by transformer edges linking nodes of different voltage types contain a more limited structure, consisting mainly of small, disjoint star graphs. The goal of our proposed model is to match both these inter and intra-network properties by proceeding in two phases: we first generate subgraphs for each voltage level and then generate transformer edges that connect these subgraphs. The first phase of the model adapts the Chung-Lu random graph model, taking desired vertex degrees and desired diameter as inputs, while the second phase of the model is based on a simpler random star graph generation process. We test the performance of our model by comparing its output across many runs to the aforementioned real data. In nearly all categories tested, we find our model is more accurate in reproducing the unusual mixture of properties apparent in the data than the Chung-Lu model. We also include graph visualization comparisons, as well as a brief analysis of edge-deletion resiliency. Ultimately, our model may be used to generate synthetic graph data, test hypotheses and algorithms at different scales, and serve as a baseline model on top of which further electrical network properties, device models, and interdependencies to other networks, may be appended.

24 POWER TRANSMISSION AND DISTRIBUTION↗