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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction

In many university and healthcare projects, models are built for very different data types such as tables, institutional time series, and medical images, but they are deployed as separate applications. In this work, that separation made testing and maintenance difficult because each module had its own pipeline and runtime requirements. This paper presents an integrated AI lakehouse-style implementation that runs three model pipelines inside one containerized backend. For medical imaging, we used MRI datasets from IEEE DataPort: a four-class classification set with 7012 images (5708 train/1304 test) and a segmentation set with 3063 image–mask pairs. The classification model (ResNet50 transfer learning) is evaluated using a proper train–validation–test protocol across multiple splits (80/10/10, 70/10/20, 60/10/30, and 10/30/60), achieving a test accuracy of 99.00% under the standard 80/10/10 split. Additionally, a patient-level evaluation is conducted using an external glioma dataset to provide a more realistic assessment without data leakage. The segmentation model (DeepLabV3-ResNet50) achieved 83.09% validation mIoU and 88.79% Dice score. For university KPI forecasting, we used annual IPEDS and NSF HERD data from 2010 to 2023 for three universities (BSU, EOU, and UAB). To examine the effect of preprocessing on forecasting performance, two case studies are conducted. In the first case, linear interpolation is applied to generate semester-level data. In the second case, the original annual data is used directly without interpolation. Random Forest regression and ARIMA models are evaluated using MAE, RMSE, MAPE, and R 2 . The results showed that interpolation improved apparent forecasting performance due to smoothing, while evaluation on the original annual data provided a more realistic assessment of model behavior. To further validate the framework on a larger dataset, an additional case study is conducted using a student dropout dataset. For water potability, we trained and compared multiple tabular classifiers on a large dataset (1,048,575 samples). A Random Forest model (100 trees, max depth 10) achieved 85.86% test accuracy and high recall for unsafe samples (0.8447). All modules are served via FastAPI and deployed together using Docker, with workflow automation routing requests to the correct endpoint. System-level benchmarking indicates that the backend maintains stable throughput and latency under concurrent requests.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Grand challenges in the digitalisation of wind energy

The availability of large amounts of data is starting to impact how the wind energy community works. From turbine design to plant layout, construction, commissioning, and maintenance and operations, new processes and business models are springing up. This is the process of digitalisation, and it promises improved efficiency and greater insight, ultimately leading to increased energy capture and significant savings for wind plant operators, thus reducing the levelised cost of energy. Digitalisation is also impacting research, where it is both easing and speeding up collaboration, as well as making research results more accessible. This is the basis for innovations that can be taken up by end users. But digitalisation faces barriers. This paper uses a literature survey and the results from an expert elicitation to identify three common industry-wide barriers to the digitalisation of wind energy. Comparison with other networked industries and past and ongoing initiatives to foster digitalisation show that these barriers can only be overcome by wide-reaching strategic efforts, and so we see these as “grand challenges” in the digitalisation of wind energy. They are, first, creating FAIR data frameworks; secondly, connecting people and data to foster innovation; and finally, enabling collaboration and competition between organisations. The grand challenges in the digitalisation of wind energy thus include a mix of technical, cultural, and business aspects that will need collaboration between businesses, academia, and government to solve. Working to mitigate them is the beginning of a dynamic process that will position wind energy as an essential part of a global clean energy future.

17 WIND ENERGY↗
DOE OSTI2024

DMTN-102: LSST Alerts: Key Numbers

A quantitative review of the key numbers associated with the Legacy Survey of Space and Time (LSST) Alert Stream.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1982

Uranus science with space telescope

The Space Telescope Observatory, scheduled for launch in 1985, is described. The advantages of the space environment and the consequent features of ST performance are given, with Uranus observations as examples. The first generation instruments, including two cameras, two spectrographs and a high speed photometer, are discussed. The Space Telescope Science Institute, which will manage the Observatory, is discussed briefly. The potential scientific interaction with the Voyager 2 encounter of Uranus is also considered.

Caldwell, J.↗
NASA NTRS1982

NASA-ESA Spacelab systems and programs; Proceedings of the Seminar, Washington, DC, April 23, 24, 1981

Topics discussed include the development status of the Space Shuttle and Spacelab, with attention to Spacelab subsystem performance capabilities and Shuttle-Spacelab flight operations; Spacelab data management and software for science applications, ESA and Space Shuttle pointing systems, and the Space Shuttle's Office of Space and Terrestrial Applications (OSTA)-1 payload. Also covered are the lessons learned from the first Spacelab mission, the pallet-only mode verification flight of the second Spacelab mission, the objectives of the Spacelab mission D1, its role in the German space program, and its implementation, the impact of Spacelab on space-based life sciences research, the high resolution, large area modular reflector array X-ray telescope to be used by Spacelab, and Space Shuttle contamination effects on UV coronagraphic observations.

Moore, J. W.↗
NASA NTRS1986

Processes and process development in Taiwan

Silicon material research in the Republic of China (ROC) parallels its development in the electronic industry. A brief outline of the historical development in ROC silicon material research is given. Emphasis is placed on the recent Silane Project managed by the National Science Council, ROC, including project objectives, task forces, and recent accomplishments. An introduction is also given to industrialization of the key technologies developed in this project.

Hwang, H. L.↗
NASA NTRS1984

The role of scientists in developing Hubble Space Telescope

The objective of the Hubble Space Telescope Program is to build and operate a high-performance, fully-instrumented and long-lived optical observatory in earth orbit. Key Program elements are (1) a logistic strategy of Shuttle launch/maintenance and TDRSS communications, (2) overall responsibility at the MSFC Project for developing both the flight and ground systems, and (3) scientific participation in ST development and responsibility after launch for conducting the observing program. The ST Project is a management structure with a budget and schedule for delivering an ST system capable of achieving its established scientific objectives. The Project distributes through contracts the responsibility for supplying particular components, but retains the responsibility for system-level integrity. Scientists have set the performance objectives and now support the Project during the development stage by providing (1) PI management of the five Science Instruments, (2) continuing translations of scientific requirements into engineering specifications, (3) oversight of designs and performance tests, and (4) specific expertise on technical concerns.

Brown, R. A.↗
NASA NTRS1991

Requirements management: A CSR's perspective

The following subject areas are covered: customer service overview of network service request processing; Customer Service Representative (CSR) responsibility matrix; extract from a sample Memorandum of Understanding; Network Service Request Form and its instructions sample notification of receipt; and requirements management in the NASA Science Internet.

Thompson, Joanie↗
NASA NTRS1992

The National Space Science Data Center: An operational perspective

The National Space Science Data Center (NSSDC) manages over 110,000 data tapes with over 4,000 data sets. The size of the digital archive is approximately 6,000 GBytes and is expected to grow to more than 28,000 GBytes by 1995. The NSSDC is involved in several initiatives to better serve the scientific community and improve the management of current and future data holdings. These initiatives address the need to manage data to ensure ready access by the user and manage the media to ensure continuing accessibility and integrity of the data. This paper will present an operational view of the NSSDC, outlining current policies and procedures that were implemented to ensure the effective use of available resources to support service and mission goals, and maintain compliance with prescribed data management directives.

Blitstein, Ronald↗
NASA NTRS1991

The National Space Science Data Center: An operational perspective

The National Space Science Data Center (NSSDC) manages over 110,000 data tapes with over 4,000 data sets. The size of the digital archive is approximately 6,000 GBytes and is expected to grow to more than 28,000 GBytes by 1995. The NSSDC is involved in several initiatives to better serve the scientific community and improve the management of current and future data holdings. These initiatives address the need to manage data to ensure ready access by the user and manage the media to ensure continuing accessibility and integrity of the data. An operational view of the NSSDC, outlining current policies and procedures that have been implemented to ensure the effective use of available resources to support service and mission goals, and maintain compliance with prescribed data management directives is presented.

Blitstein, Ronald↗
NASA NTRS1994

Influence of flow on interface shape stability in low gravity

The objectives are to: (1) Understand the influence in low gravity of flow on interface shape. For example, document and control the influence of axial flow on the Plateau-Rayleigh instability of a liquid bridge; and (2) Extend the ground-based density-matching technique of low gravity simulation to situations with flow; that is, develop Plateau chamber experiments for which flow can be controlled. Containerless containment of liquid by surface tension has broad importance in low gravity. For space vehicles, the behavior of liquid/gas interfaces is crucial to successful liquid management systems. In microgravity science, free interfaces are exploited in various applications. Examples include float-zone crystal growth, phase separation near the critical point of liquid mixtures (spinoidal decomposition) and quenching of miscibility gap molten metal alloys. In some cases, it is desired to stabilize the capillary instability while in others it is desired to induce capillary breakup. In all cases, understanding the stability of interface shape in the presence of liquid motion is central.

Steen, Paul H.↗
NASA NTRS1994

An interactive environment for the analysis of large Earth observation and model data sets

Envision is an interactive environment that provides researchers in the earth sciences convenient ways to manage, browse, and visualize large observed or model data sets. Its main features are support for the netCDF and HDF file formats, an easy to use X/Motif user interface, a client-server configuration, and portability to many UNIX workstations. The Envision package also provides new ways to view and change metadata in a set of data files. It permits a scientist to conveniently and efficiently manage large data sets consisting of many data files. It also provides links to popular visualization tools so that data can be quickly browsed. Envision is a public domain package, freely available to the scientific community. Envision software (binaries and source code) and documentation can be obtained from either of these servers: ftp://vista.atmos.uiuc.edu/pub/envision/ and ftp://csrp.tamu.edu/pub/envision/. Detailed descriptions of Envision capabilities and operations can be found in the User's Guide and Reference Manuals distributed with Envision software.

Bowman, Kenneth P.↗
NASA NTRS2001

NASA Exhibits

A series of NASA presentations for the Supercomputing 2001 conference are summarized. The topics include: (1) Mars Surveyor Landing Sites "Collaboratory"; (2) Parallel and Distributed CFD for Unsteady Flows with Moving Overset Grids; (3) IP Multicast for Seamless Support of Remote Science; (4) Consolidated Supercomputing Management Office; (5) Growler: A Component-Based Framework for Distributed/Collaborative Scientific Visualization and Computational Steering; (6) Data Mining on the Information Power Grid (IPG); (7) Debugging on the IPG; (8) Debakey Heart Assist Device: (9) Unsteady Turbopump for Reusable Launch Vehicle; (10) Exploratory Computing Environments Component Framework; (11) OVERSET Computational Fluid Dynamics Tools; (12) Control and Observation in Distributed Environments; (13) Multi-Level Parallelism Scaling on NASA's Origin 1024 CPU System; (14) Computing, Information, & Communications Technology; (15) NAS Grid Benchmarks; (16) IPG: A Large-Scale Distributed Computing and Data Management System; and (17) ILab: Parameter Study Creation and Submission on the IPG.

Deardorff, Glenn↗
NASA NTRS2003

Aging Mechanisms and Control: Specialists' Meeting on Life Management Techniques for Aging Air Vehicles

The costs of maintaining ageing aircraft are draining the existing budgets. The Specialist Meeting provided guidance on strategies for the development and implementation of technologies and logistic management processes to reduce this economic burden. The emphasis was on military aircraft, but many of the principles could be applied to other defence systems. The papers covered the entire range of ageing problems including structural integrity, corrosion, avionics, mechanical subsystems, structures and wiring as well as the role of information management. Forty-two papers addressed the safety and economic implications such as fatigue cracking, corrosion, wear and material degradation. Key technologies were discussed, including non-destructive inspection, repair, modifications, prevention analysis, and health management. The shortcomings of current were highlighted and the investment required was identified. The need for research and development was clearly identified.

aging tests (materials)↗
NASA NTRS2002

NASA Scientific Data Purchase Project: From Collection to User

NASA's Scientific Data Purchase (SDP) project is currently a $70 million operation managed by the Earth Science Applications Directorate at Stennis Space Center. The SDP project was developed in 1997 to purchase scientific data from commercial sources for distribution to NASA Earth science researchers. Our current data holdings include 8TB of remote sensing imagery consisting of 18 products from 4 companies. Our anticipated data volume is 60 TB by 2004, and we will be receiving new data products from several additional companies. Our current system capacity is 24 TB, expandable to 89 TB. Operations include tasking of new data collections, archive ordering, shipment verification, data validation, distribution, metrics, finances, customer feedback, and technical support. The program has been included in the Stennis Space Center Commercial Remote Sensing ISO 9001 registration since its inception. Our operational system includes automatic quality control checks on data received (with MatLab analysis); internally developed, custom Web-based interfaces that tie into commercial-off-the-shelf software; and an integrated relational database that links and tracks all data through operations. We've distributed nearly 1500 datasets, and almost 18,000 data files have been downloaded from our public web site; on a 10-point scale, our customer satisfaction index is 8.32 at a 23% response level. More information about the SDP is available on our Web site.

Nicholson, Lamar↗
NASA NTRS2005

A Collaborative Decision Environment for UAV Operations

NASA is developing Intelligent Mission Management (IMM) technology for science missions employing long endurance unmanned aerial vehicles (UAV's). The IMM groundbased component is the Collaborative Decision Environment (CDE), a ground system that provides the Mission/Science team with situational awareness, collaboration, and decisionmaking tools. The CDE is used for pre-flight planning, mission monitoring, and visualization of acquired data. It integrates external data products used for planning and executing a mission, such as weather, satellite data products, and topographic maps by leveraging established and emerging Open Geospatial Consortium (OGC) standards to acquire external data products via the Internet, and an industry standard geographic information system (GIs) toolkit for visualization As a Science/Mission team may be geographically dispersed, the CDE is capable of providing access to remote users across wide area networks using Web Services technology. A prototype CDE is being developed for an instrument checkout flight on a manned aircraft in the fall of 2005, in preparation for a full deployment in support of the US Forest Service and NASA Ames Western States Fire Mission in 2006.

D'Ortenzio, Matthew V.↗