Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data virtualization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Investigation of the Influence of Microgravity on Transport Mechanisms in a Virtual Spaceflight Chamber: A Ground-Based Program

The IML-1 Spaceflight produced over 1000 holograms of a well-defined particle field in the low g Spacelab environment; each containing as much as 1000 megabytes of information. This project took advantage of these data and the concept of holographic "virtual" spaceflight to advance the understanding of convection in the space shuttle environment, g-jitter effects on crystal growth, and complex transport phenomena in low Reynolds number flows. The first objective of the proposed work was to advance the understanding of microgravity effects on crystal growth. This objective was achieved through the use of existing holographic data recorded during the IML-1 Spaceflight. The second objective was to design a spaceflight experiment that exploits the "virtual space chamber concept" in which holograms of space chambers can provide a virtual access to space. This led to a flight definition project, which is now underway under a separate contract known as SHIVA, Spaceflight Holography Investigation in a Virtual Apparatus.

Trolinger, James D.↗
NASA NTRS1999

NASA's Virtual Product Laboratory Overview

The Virtual Product Laboratory (VPL) developed at Stennis Space Center is a tool that enables design and verification of remote sensing systems within a software (virtual) environment. The VPL can serve NASA's industry, government, and university partners by providing a means to conduct system trade studies, visual modeling, and data product simulations entirely in a virtual environment. The VPL can serve as a complete end-to-end simulation tool capable of producing system-level compatability assessments, performance characterizations, and simulated data products. In this paper, we present an overview of the VPL's current functions and planned capabilities. The VPL functional areas include Requirements, Design and Analysis, Simulation, Project Management, and Knowledge Base. A description of each function, along with the tools and techniques used to accomplish these functions, is provided.

Gasser, Gerald↗
DOE OSTI2021

Utilizing the Dynamic Networks Data Processing and Analysis Experiment (DNE18) to Establish Methodologies for the Comparison of Automatic Infrasonic Signal Detectors

The Dynamic Networks Experiment 2018 (DNE18) was a collaborative effort between Los Alamos National Laboratory (LANL), Sandia National Laboratories (SNL), Lawrence Livermore National Laboratory (LLNL) and Pacific Northwest National Laboratory (PNNL) designed to evaluate methodologies for multi-modal data ingestion and processing. One component of this virtual experiment was a quantitative assessment of current capabilities for infrasound data processing, beginning with the establishment of a baseline for infrasound signal detection. To produce such baselines, SNL and LANL exploited a common dataset of infrasound data recorded across a regional network in Utah from December 2010 through February 2011. We utilize two automated signal detectors, the Adaptive F-Detector (AFD) and the Multivariate Adaptive Learning Detector (MALD) to produce automated signal detection catalogs and an analyst-produced catalog. Comparisons indicate that automatic detectors may be able to identify small amplitude, low SNR events that cannot be identified by analyst review. We document detector performance in terms of precision and recall, demonstrating that the AFD is more precise, but the MALD has higher recall. We use a synthetic dataset of signals embedded in pink noise in order to highlight shortcomings in assessing detection algorithms for low signal to noise ratio signals which are commonly of interest to the nuclear monitoring community. For comparisons utilizing the synthetic dataset, the AFD has higher recall while precision is equal for both detectors. These results indicate that both detectors perform well across a variety of background noise environments; however, both detectors fail to identify repetitive, short duration signals arriving from similar backazimuths. These failures represent specific scenarios that could be targeted for further detector development.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

Working Group Reports and Presentations: Virtual Worlds and Virtual Exploration

Scientists and engineers are continually developing innovative methods to capitalize on recent developments in computational power. Virtual worlds and virtual exploration present a new toolset for project design, implementation, and resolution. Replication of the physical world in the virtual domain provides stimulating displays to augment current data analysis techniques and to encourage public participation. In addition, the virtual domain provides stakeholders with a low cost, low risk design and test environment. The following document defines a virtual world and virtual exploration, categorizes the chief motivations for virtual exploration, elaborates upon specific objectives, identifies roadblocks and enablers for realizing the benefits, and highlights the more immediate areas of implementation (i.e. the action items). While the document attempts a comprehensive evaluation of virtual worlds and virtual exploration, the innovative nature of the opportunities presented precludes completeness. The authors strongly encourage readers to derive additional means of utilizing the virtual exploration toolset.

LAmoreaux, Claudia↗
NASA NTRS2014

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

Data Analytics↗
DOE OSTI2025

Mapping and Synthesis of International Biomass Supply Assessments

This report, Mapping and Synthesis of International Biomass Supply Assessments (or Global Biomass Resource Assessment) is the first step in a long-term process to assemble data from around the globe into a virtual repository that can be updated and provide user-friendly access to the data. The Clean Energy Ministerial (CEM) Biofuture Platform Initiative recommended that research be completed to “address the need for internationally accepted benchmarks quantifying sustainable biomass feedstock supplies.” To act upon the CEM Biofuture recommendation, in 2024, the U.S. Department of Energy (DOE) commissioned Oak Ridge National Laboratory (ORNL) to prepare this report as the primary deliverable for a one-year assignment to assemble data into a citable form that could help resolve the persistent question presented related to bioenergy policy, “Is there enough sustainable biomass?” In response to that query, this report includes information received by August 2024 from national CEM representatives, collaborators, and public sources on current and future sustainable biomass supplies in 62 nations,and subsequently documents (a) the approach used by ORNL to analyze and categorize the information received in a manner that enables aggregation and comparability; and (b) recommendations for next steps and guidelines to help others update and harmonize future assessments of global sustainable biomass supplies.

09 BIOMASS FUELS↗
NASA NTRS2005

A Testbed Demonstration of an Intelligent Archive in a Knowledge Building System

The last decade's influx of raw data and derived geophysical parameters from several Earth observing satellites to NASA data centers has created a data-rich environment for Earth science research and applications. While advances in hardware and information management have made it possible to archive petabytes of data and distribute terabytes of data daily to a broad community of users, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications in order to realize the full potential of these valuable datasets. In examining what is needed to enable this progress in the data provider environment that exists today and is expected to evolve in the next several years, we arrived at the concept of an Intelligent Archive in context of a Knowledge Building System (IA/KBS). Our prior work and associated papers investigated usage scenarios, required capabilities, system architecture, data volume issues, and supporting technologies. We identified six key capabilities of an IA/KBS: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting. Among these capabilities, large-scale data mining is perceived by many in the community to be an area of technical risk. One of the main reasons for this is that standard data mining research and algorithms operate on datasets that are several orders of magnitude smaller than the actual sizes of datasets maintained by realistic earth science data archives. Therefore, we defined a test-bed activity to implement a large-scale data mining algorithm in a pseudo-operational scale environment and to examine any issues involved. The application chosen for applying the data mining algorithm is wildfire prediction over the continental U.S. This paper reports a number of observations based on our experience with this test-bed. While proof-of-concept for data mining scalability and utility has been a major goal for the research reported here, it was not the only one. The other five capabilities of an WKBS named above have been considered as well, and an assessment of the implications of our experience for these other areas will also be presented. The lessons learned through the testbed effort and presented in this paper will benefit technologists, scientists, and system operators as they consider introducing IA/KBS capabilities into production systems.

Ramapriyan, Hampapuram↗
NASA NTRS2008

The Heliophysics Data Environment Today

Driven by the nature of the research questions now most critical to further progress in heliophysics science, data-driven research has evolved from a model once centered on individual instrument Principal investigator groups and a circle of immediate collaborators into a more inclusive and open environment where data gathered ay great public cost must then be findable and useable throughout the broad national and international research community. In this paper and as an introduction to this special session, we will draw a picture of existing and evolving resources throughout the heliophyscs community, the capabilities and data now available to end users, and the relationships and complementarity of different elements in the environment today. We will cite the relative roles of mission and instrument data centers and resident archives, multi-mission data centers, and the growing importance of virtual discipline observatories and cross-cutting services including the evolution of a common data dictionary. We will briefly summarize our view of the most important challenges still faced by users and providers, and our vision in ow the efforts today can evolve into a more and more enabling data framework for the global research community to tap the widest range of existing missions and their data to address a full range of critical science questions from the scale of microphysics to the heliospheric system as a whole.

Fung, Shing F.↗
DOE OSTI2024

Large-Scale Visualization of 3D Unstructured Groundwater Model Using Cave Automated Virtual Environment

The immersive three-dimensional (3D) virtual reality (VR) visualization of groundwater models allows us to deepen our understanding of aquifer systems and provide better solutions to present groundwater-related problems, such as groundwater recharge, water quality, and sustainability. Visualization assists in accurately developing groundwater models and revealing important subsurface features, including faulting, folding, and unconformity. However, assessing model accuracy poses challenges due to the complexity of geology and groundwater systems. This research demonstrates a workflow to visualize and analyze raw 3D unstructured groundwater model data using an immersive Cave Automated Virtual Environment (CAVE). To visualize the unstructured groundwater model data, the raw dataset is converted into interactive CAVE-compatible formats utilizing a set of tools: ParaView, Blender, and Unity. This enables researchers to immerse themselves in the data, identifying influential patterns and relationships. e resulting insights can inform the development of sophisticated machine-learning models for groundwater level prediction. The CAVE’s immersive capabilities allow intuitive exploration from various perspectives, providing a more holistic understanding of the factors affecting groundwater levels. These insights are crucial to improve predictive models. The CAVE results also facilitate collaborative analysis and have potential applications in training and education. is research demonstrates the value of immersive VR tools such as the CAVE for unraveling intricacies within high-dimensional scientific data to drive real-world forecasting and modeling applications.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Virtual Planetary Analysis Environment for Remote Science

All of the data for NASA's current planetary missions and most data for field experiments are collected via orbiting spacecraft, aircraft, and robotic explorers. Mission scientists are unable to employ traditional field methods when operating remotely. We have developed a virtual exploration tool for remote sites with data analysis capabilities that extend human perception quantitatively and qualitatively. Scientists and mission engineers can use it to explore a realistic representation of a remote site. It also provides software tools to "touch" and "measure" remote sites with an immediacy that boosts scientific productivity and is essential for mission operations.

Keely, Leslie↗
NASA NTRS2016

Providing Comprehensive and Consistent Access to Astronomical Observatory Archive Data: The NASA Archive Model

Since the turn of the millennium a constant concern of astronomical archives have begun providing data to the public through standardized protocols unifying data from disparate physical sources and wavebands across the electromagnetic spectrum into an astronomical virtual observatory (VO). In October 2014, NASA began support for the NASA Astronomical Virtual Observatories (NAVO) program to coordinate the efforts of NASA astronomy archives in providing data to users through implementation of protocols agreed within the International Virtual Observatory Alliance (IVOA). A major goal of the NAVO collaboration has been to step back from a piecemeal implementation of IVOA standards and define what the appropriate presence for the US and NASA astronomy archives in the VO should be. This includes evaluating what optional capabilities in the standards need to be supported, the specific versions of standards that should be used, and returning feedback to the IVOA, to support modifications as needed. We discuss a standard archive model developed by the NAVO for data archive presence in the virtual observatory built upon a consistent framework of standards defined by the IVOA. Our standard model provides for discovery of resources through the VO registries, access to observation and object data, downloads of image and spectral data and general access to archival datasets. It defines specific protocol versions, minimum capabilities, and all dependencies. The model will evolve as the capabilities of the virtual observatory and needs of the community change.

Virtual observatory; data archives; standards; IVO↗
NASA NTRS2008

Modifying the Heliophysics Data Policy to Better Enable Heliophysics Research

The Heliophysics (HP) Science Data Management Policy, adopted by HP in June 2007, has helped to provide a structure for the HP data lifecycle. It provides guidelines for Project Data Management Plans and related documents, initiates Resident Archives to maintain data services after a mission ends, and outlines a route to the unification of data finding, access, and distribution through Virtual observatories. Recently we have filled in missing pieces that assure more coherence and a home for the VxOs (through the 'Heliophsyics Data and Model Consortium'), and provide greater clarity with respect to long term archiving. In particular, the new policy which has been vetted with many community members, details the 'Final Archives' that are to provide long-term data access. These are distinguished from RAs in that they provide little additional service beyond servicing data, but critical to their success is that the final archival materials include calibrated data in useful formats such as one finds in CDAWeb and various ASCII or FITS archives. Having a clear goal for legacy products, to be detailed as part of the Mission Archives Plans presented at Senior Reviews, will help to avoid the situation so common in the past of having archival products that preserve bits well but not readily usable information. We hope to avoid the need for the large numbers of 'data upgrade' projects that have been necessary in recent years.

Hayes, Jeffrey↗
NASA NTRS2015

Bundle Data Approach at GES DISC Targeting Natural Hazards

Severe natural phenomena such as hurricane, volcano, blizzard, flood and drought have the potential to cause immeasurable property damages, great socioeconomic impact, and tragic loss of human life. From searching to assessing the Big, i.e., massive and heterogeneous scientific data (particularly, satellite and model products) in order to investigate those natural hazards, it has, however, become a daunting task for Earth scientists and applications researchers, especially during recent decades. The NASA Goddard Earth Sciences Data and Information Service Center (GES DISC) has served Big Earth science data, and the pertinent valuable information and services to the aforementioned users of diverse communities for years. In order to help and guide our users to online readily (i.e., with a minimum effort) acquire their requested data from our enormous resource at GES DISC for studying their targeted hazard event, we have thus initiated a Bundle Data approach in 2014, first targeting the hurricane event topic. We have recently worked on new topics such as volcano and blizzard. The bundle data of a specific hazard event is basically a sophisticated integrated data package consisting of a series of proper datasets containing a group of relevant (knowledge--based) data variables readily accessible to users via a system-prearranged table linking those data variables to the proper datasets (URLs). This online approach has been developed by utilizing a few existing data services such as Mirador as search engine; Giovanni for visualization; and OPeNDAP for data access, etc. The online Data Cookbook site at GES DISC is the current host for the bundle data. We are now also planning on developing an Automated Virtual Collection Framework that shall eventually accommodate the bundle data, as well as further improve our management in Big Data.

GES DISC↗
DOE OSTI2020

Leveraging 3D CAD and Virtual Reality in Design of the Calcine Disposition Project - 20512

Fluor Idaho's Calcine Disposition Project (CDP) is leveraging the power of three-dimensional (3D) visualization, light detection and ranging (Lidar), and virtual reality to improve designs and worker safety. With the use of computer-aided design (CAD) modeling software, the CDP has created 3D models of the facility and systems to aid in the design, development, and operation of its retrieval and transfer system. This by itself is nothing new and has been done in industry for years. The CDP project however is taking this a few steps farther with the use of Lidar and virtual reality software. The CRP is tasked with removing a radioactive granular material from stainless steel bins located in concrete vaults that were constructed in the late 1950's and early 1960's. While construction drawings are available, it is not certain that the drawings are as-built or how accurate they truly are. Part of the project requires precise placement of equipment on to bins around multiple pipe, electrical lines, and bin stiffeners. Relying solely on the existing drawings and models created from these drawings is highly risky and prone to failure. The first step to determine the precise location and orientation of all obstructions in the vault the project will use a Lidar system to scan the vault structure and precisely locate all vault and bin components. Once this information is obtained, the data will be integrated into the CAD model. The model will then be verified, and a true as-built model developed. Precise component location, size, and orientation will be used for final design and placement of retrieval components. The second step will be to take the verified 3D model and scan data and import it into virtual reality software creating a virtual world. While some work on the project requires all work to be completed remotely due to high radiation fields, some areas have low enough radiation levels that personnel can enter and perform work. With the use of a virtual reality headset, operators and technicians will be able to enter our virtual world and become familiar with the surroundings and perform work prior to ever entering the radiation environment. This training is expected to pay dividends by improving worker efficiency, reducing errors, and improving confidence that the work can be performed as expected. It will play a key aspect in as low as reasonably achievable (ALARA) principles. An additional aspect in using the Lidar scans and virtual environment, is that it will give engineers and managers an opportunity to revisit the facility, especially radiation areas, at any time to obtain design information and measurements that may be costly or impossible to obtain once the facility transitions into radiation operations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1996

RAID Unbound: Storage Fault Tolerance in a Distributed Environment

Mirroring, data replication, backup, and more recently, redundant arrays of independent disks (RAID) are all technologies used to protect and ensure access to critical company data. A new set of problems has arisen as data becomes more and more geographically distributed. Each of the technologies listed above provides important benefits; but each has failed to adapt fully to the realities of distributed computing. The key to data high availability and protection is to take the technologies' strengths and 'virtualize' them across a distributed network. RAID and mirroring offer high data availability, which data replication and backup provide strong data protection. If we take these concepts at a very granular level (defining user, record, block, file, or directory types) and them liberate them from the physical subsystems with which they have traditionally been associated, we have the opportunity to create a highly scalable network wide storage fault tolerance. The network becomes the virtual storage space in which the traditional concepts of data high availability and protection are implemented without their corresponding physical constraints.

Ritchie, Brian↗
NASA NTRS2004

Turbomachine Sealing and Secondary Flows - Part 3: Review of Power-Stream Support, Unsteady Flow Systems, Seal and Disk Cavity Flows, Engine Externals, and Life and Reliability Issues - Part 3

The issues and components supporting the engine power stream are reviewed. It is essential that companies pay close attention to engine sealing issues, particularly on the high-pressure spool or high-pressure pumps. Small changes in these systems are reflected throughout the entire engine. Although cavity, platform, and tip sealing are complex and have a significant effect on component and engine performance, computational tools (e.g., NASA-developed INDSEAL, SCISEAL, and ADPAC) are available to help guide the designer and the experimenter. Gas turbine engine and rocket engine externals must all function efficiently with a high degree of reliability in order for the engine to run but often receive little attention until they malfunction. Within the open literature statistically significant data for critical engine components are virtually nonexistent; the classic approach is deterministic. Studies show that variations with loading can have a significant effect on component performance and life. Without validation data they are just studies. These variations and deficits in statistical databases require immediate attention.

Hendricks, R. C.↗
NASA NTRS2011

Scheduling Onboard Processing for the Proposed HyspIRI Mission

The proposed Hyspiri mission is evaluating a X-band Direct Broadcast (DB) capability that would enable data to be delivered to ground stations virtually as it is acquired. However the HyspIRI VSWIR and TIR instruments will produce 1 Gbps data while the DB capability is 15 M bps for a ~60x oversubscription. In order to address this data volume mismatch a DB concept has been developed thatdetermines which data to downlink based on both: 1. The type of surface the spacecraft is overflying and 2. Onboard processing of the data to detect events. For example when the spacecraft is overflying polar regions it might downlink a snow/ice product. Additionally the onboard software will search for thermal signatures indicative of a volcanic event or wild fire and downlink summary information (extent, spectra) when detected. The process of determining which products to generate when, based on request prioritization and onboard processing and downlink constraints is inherently a prioritized scheduling problem - we describe work to develop an automated solution to this problem.

visible shortwave infrared (VSWIR)↗