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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Measurement of sound emitted by flying projectiles with aeroacoustic sources

Training projectiles with axisymmetric ring cavities that produce intense tones in an airstream were shot in a straight-line trajectory. A ground-based microphone was used to obtain the angular distribution of sound intensity produced from the flying projectile. Data reduction required calculation of Doppler and attenuation factors. Also, the directional sensitivity of the ground-mounted microphone was measured and used in the data reduction. A rapid angular variation of sound intensity produced from the projectile was found that can be used to plot an intensity contour map on the ground. A full-scale field test confirmed the validity of the aeroacoustic concept of producing a relatively intense whistle from the projectile, and the usefulness of short-range flight tests that yield acoustic data free of uncertainties associated with diffraction, reflection, and refraction at jet boundaries in free-jet tests.

Cho, Y. I.↗
NASA NTRS1992

Structural analysis for preliminary design of High Speed Civil Transport (HSCT)

In the preliminary design environment, there is a need for quick evaluation of configuration and material concepts. The simplified beam representations used in the subsonic, high aspect ratio wing platform are not applicable for low aspect ratio configurations typical of supersonic transports. There is a requirement to develop methods for efficient generation of structural arrangement and finite element representation to support multidisciplinary analysis and optimization. In addition, empirical data bases required to validate prediction methods need to be improved for high speed civil transport (HSCT) type configurations.

Bhatia, Kumar G.↗
NASA NTRS2012

Exploratory Climate Data Visualization and Analysis Using DV3D and UVCDAT

Earth system scientists are being inundated by an explosion of data generated by ever-increasing resolution in both global models and remote sensors. Advanced tools for accessing, analyzing, and visualizing very large and complex climate data are required to maintain rapid progress in Earth system research. To meet this need, NASA, in collaboration with the Ultra-scale Visualization Climate Data Analysis Tools (UVCOAT) consortium, is developing exploratory climate data analysis and visualization tools which provide data analysis capabilities for the Earth System Grid (ESG). This paper describes DV3D, a UV-COAT package that enables exploratory analysis of climate simulation and observation datasets. OV3D provides user-friendly interfaces for visualization and analysis of climate data at a level appropriate for scientists. It features workflow inte rfaces, interactive 40 data exploration, hyperwall and stereo visualization, automated provenance generation, and parallel task execution. DV30's integration with CDAT's climate data management system (COMS) and other climate data analysis tools provides a wide range of high performance climate data analysis operations. DV3D expands the scientists' toolbox by incorporating a suite of rich new exploratory visualization and analysis methods for addressing the complexity of climate datasets.

Maxwell, Thomas↗
NASA NTRS2014

Space Link Extension (SLE) Emulation for High-Throughput Network Communication

As the data rate requirements for space communications increases, signicant stressis placed not only on the wireless satellite communication links, but also on the groundnetworks which forward data from end-users to remote ground stations. These wide areanetwork (WAN) connections add delay and jitter to the end-to-end satellite communicationlink, eects which can have signicant impacts on the wireless communication link. It isimperative that any ground communication protocol can react to these eects such that theground network does not become a bottleneck in the communication path to the satellite.In this paper, we present our SCENIC Emulation Lab testbed which was developed to testthe CCSDS SLE protocol implementations proposed for use on future NASA communica-tion networks. Our results show that in the presence of realistic levels of network delay,high-throughput SLE communication links can experience signicant data rate throttling.Based on our observations, we present some insight into why this data throttling happens,and trace the probable issue back to non-optimal blocking communication which is sup-ported by the CCSDS SLE API recommended practices. These issues were presented aswell to the SLE implementation developers which, based on our reports, developed a newrelease for SLE which we show xes the SLE blocking issue and greatly improves the pro-tocol throughput. In this paper, we also discuss future developments for our end-to-endemulation lab and how these improvements can be used to develop and test future spacecommunication technologies.

Networking↗
NASA NTRSDate not supplied

Icing Physics Studies Using the 3D SIDRM Test Article: Aerodynamic and Supercooled Liquid Icing Analysis

In-flight icing is an important safety issue and is a factor that affects aircraft design and performance. Newer regulations are driving a need for improvements in airframe and engine icing simulation capability. Experimental data is required for development of icing physics models and simulation validation. To that end, this paper presents the analysis of the supercooled liquid icing data subset from tests conducted in 2022 at the NASA Icing Research Tunnel that studied both supercooled water and ice-crystal icing. The test article that was utilized replicated 3D geometrical features of an inter-compressor duct and strut region of a turbofan engine. The surfaces of the Simulated Inter-compressor Duct Research Model (SIDRM) can be heated to simulate the warm surfaces of the turbofan inter-compressor duct. The test article is instrumented with pressure taps, heaters, heat flux gauges, and thermocouples, while a 3D laser scanner, cameras, and a scale to measure ice mass were utilized to characterize the icing behavior. The aim of these tests was to generate ice accretions on the SIDRM test article under well-characterized supercooled liquid icing and ice crystal icing conditions. This paper discusses measurements related to aerodynamic testing and supercooled liquid icing tests that were conducted. Aerodynamic measurements were analyzed and compared to computational simulations and were found to be in good agreement for the range of airspeeds (50 to 230 knots) and angles of attack (0 to 4°) tested. Various parametric sweeps were conducted during the supercooled liquid icing portion of the test entry (cloud median volumetric diameter ranged from 15 to 90 µm, total air temperature from -3 to -17 °C, angle of attack from 0 to 4°, and accretion time from 5 to 20 min). These sweeps were performed to measure that parameter’s impact on ice accretion size, location (icing extent), characteristics (such as glaze/rime ice and shedding behavior), and test article surface temperature. Analysis of the test data showed that clouds composed of larger drops, colder air temperatures, smaller angles of attack, and longer spray times were the primary parameters that resulted in accretions with greater ice mass. Test article angle of attack and cloud droplet size influenced the location of ice accretion as these two parameters directly impact collection efficiency. With respect to icing characteristics, total air temperature dictated icing type, and smaller cloud drop size along with warmer air temperatures resulted in greater amounts of ice shedding. Surface temperature increased during ice accretion from the release of latent (fusion) heat, where total air temperature and cloud drop size impacted the amount of surface temperature change. The icing measurements collected during the SIDRM tests will be used to develop and validate 3D computational engine icing tools, such as GlennICE, that predictively assesses the onset and growth of ice. One of the goals of the sponsoring NASA project is to develop simulation models and tools that can assist in the design and certification of engines for flight in icing conditions in a cost effective way.

supercooled liquid icing↗
NASA NTRSDate not supplied

VEDA Visualization Exploration & Data Analysis

Why? - Interdisciplinary science depends on large amount of Earth science data and computational resources - Working with these datasets is non-trivial - Big data science requires advanced distributed computing knowledge What? VEDA is an open platform that brings key Earth science datasets next to open source tools for data processing, analysis, visualization, and exploration in a managed and more accessible computing environment.

Manil Maskey↗
NASA NTRSDate not supplied

Modern Scientific Data Governance Framework

Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies and stewardship best practices tend to be disconnected from operational data management practices and enforcement and mainly exist in well-meaning documents or reports. These governance policies are, at best, partially implemented and rarely monitored or audited. In addition, existing governance policies keep adding additional data management steps that require a human, ‘a data steward’, in the loop, and the cost of data management can no longer scale proportionately with the current and future increased data volume and complexity. The goal for developing an updated data governance framework is to modernize scientific data governance to the reality of Big data and align it with the current technology trends such as cloud computing and AI. The goals of this framework are two folds. One is to ensure thoroughness that the governance adequately covers the entire data life cycle. Two, provide a practical approach that offers a consistent and repeatable process for different projects. Three core principles ground this framework. First, focus on just enough governance and prevent data governance from becoming a roadblock toward the scientific process. Remove any unnecessary processes and steps. Second, automate data management steps where possible. Actively remove steps that require ‘human in the loop’ within the management process to be efficient and scale with increasing data. Third, all the processes should continually be optimized using quantified metrics to streamline the monitoring and auditing workflows.

Rahul Ramachandran↗
DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗
DOE OSTI2024

Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems

Abstract Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.

Diao, Xiaoxu (ORCID:0000000346726352)↗
DOE OSTI2025

Eureka: Enabling Fine-Grained Access and Range Queries on Compressed Scientific Data via Data-Index Co-Compression

Handling large-scale scientific data in high-performance computing (HPC) environments poses significant challenges, including excessive I/O, high storage costs, and slow query performance. Traditional approaches often require full data decompression and scans, making them impractical for real-time or interactive analysis. To address these limitations, we introduce Eureka, a unified data-index co-compression framework that enables fine-grained access and efficient range queries on compressed scientific datasets. Eureka integrates spatial domain decomposition with block-wise error-bounded lossy compression to support selective decompression. It constructs a hierarchical AVL-tree index during compression to capture block-level value ranges, enabling fast pruning during query execution. To reduce metadata overhead, the index itself is also compressed while ensuring recall-preserving results. Experiments on six diverse HPC simulation datasets show that Eureka achieves up to 25x data compression and over 300x index compression, surpassing state-of-the-art compressors such as SZ3 and ZFP in rate-distortion performance. Additionally, Eureka delivers over 30x speedup for low-selectivity range queries, making it a scalable and efficient solution for modern scientific data analysis.

Yan, Ning↗
DOE OSTI2020

Identifying Suspect Instrument Intervals Using Midnight Noise Time Histories

With an intense work ethic, and high levels of devotion to their craft, seismologists continue to battle the elements and wrestle with complex logistics in their efforts to field instrumentation over the entire globe. We are fortunate to see data volumes accelerating in size, and quality continuing to improve; for example, universal timing issues can now be considered rare. However, instrument response issues are still common, and can hinder research that seeks to understand amplitudes of seismic wavefields. We are interested in using the accumulations of seismic data to develop models that will predict high frequency (0.2-20 Hz, and higher) signal amplitudes for explosion monitoring purposes over broad areas, which requires large data sets and extensive quality control. To identify response and station health issues, we have collected noise time histories for global seismic data, focusing on measurements near (but not restricted to) midnight to eliminate diurnal variations, and have manually determined time intervals that appear inconsistent with background behavior. We assign descriptive labels, but do not attempt to diagnose causes. We use these intervals to discard data. To date, we have examined 39,260 channels from 11,105 stations, heavily weighted toward IRIS holdings, dates through 2017 (depending on station, roughly the date we started our manual review), bands between 1 and 8 Hz, finding 24,733 anomalous time intervals. The great majority (90%) of these intervals appear to be shifts of constant offset, often bounded by times of known instrument changes, likely the result of poor documentation of response parameters at one of many stages between the field and the plotter. We hope these results can be of use to our colleagues, and would encourage community efforts to diagnose anomalous behavior, and fix poor responses. We also hope these results will support automation efforts, including application of supervised learning techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗
NASA NTRS1977

Telemetry and data relay for manned space programs

Previously and currently planned methods of transferring or relaying telemetered information from a remote receiving station to the Mission Control Center, at the National Aeronautics and Space Administration Lyndon B. Johnson Space Center, are discussed. The data flow techniques of the Apollo Program (fixed formats), the Skylab Program (data compression), and the Space Shuttle Program (data throughput) are defined and analyzed according to their advantages and deficiencies from an operations viewpoint. Excluded from the data flow technique discussions are command data, tracking data, television data, and Teletype and Datafax messages. Voice transmissions are addressed in the Space Shuttle Program discussion because they form a part of the telemetry data stream. The tradeoffs between actual data flow requirements and existing communications limitations are addressed for each of these programs. Finally, a short discussion of current problem areas in the planned ground data transmission system for the Space Shuttle Program is presented.

Anson, K. W.↗
NASA NTRS1983

Space Station needs, attributes and architectural options. Volume 2, book 2, part 1: Mission implementation concepts

The overall configuration and modules of the initial and evolved space station are described as well as tended industrial and polar platforms. The mass properties that are the basis for costing are summarized. User friendly attributes (interfaces, resources, and facilities) are identified for commercial; science and applications; industrial park; international participation; national security; and the external tank option. Configuration alternates studied to determine a baseline are examined. Commonality for clustered 3-man and 9-man stations are considered as well as the use of tethered platforms. Requirements are indicated for electrical, communication and tracking; data management Subsystem requirements for electrical, data management, communication and tracking, environment control/life support system; and guidance navigation and control subsystems are identified.

Source record↗
NASA NTRS1983

Development of the JSC Thematic Mapper quick-look preprocessing capability

The development of a preprocessing unit for Landsat Thematic Mapper (TM) data for the Earth Observations Data Laboratory at Johnson Space Center is reported. The background of the project is sketched, including the greatly increased data-handling requirements compared to MSS, the influence of the JPL VICAR system on the system design, and the completeness of the GSFC SCROUNGE (LASLIB) TM data tapes. The design approach and realization are discussed, and the performance and transportability of the preprocessor programs (totaling about 2000 lines of source code in FORTRAN and IBM Assembly languages) are indicated. The system is able to read the TM image tapes, extract areas of interest to particular studies, and register the extracted imagery to suitable references. Ancillary programs include image enhancement, rotation, filtering and pixel-size modification.

Gilbert, J. R.↗
NASA NTRS1984

Land remote sensing commercialization: A status report

The current offer by the United States Department of Commerce to transfer the U.S. land remote sensing program to the private sector is described. A Request for Proposals (RFP) was issued, soliciting offers from U.S. firms to provide a commercial land remote sensing satellite system. Proposals must address a complete system including satellite, communications, and ground data processing systems. Offerors are encouraged to propose to take over the Government LANDSAT system which consists of LANDSAT 4 and LANDSAT D'. Also required in proposals are the market development procedures and plans to ensure that commercialization is feasible and the business will become self-supporting at the earliest possible time. As a matter of Federal Policy, the solicitation is designed to protect both national security and foreign policy considerations. In keeping with these concerns, an offeror must be a U.S. Firm. Requirements for data quality, quantity, distribution and delivery are met by current operational procedures. It is the Government's desire that the Offeror be prepared to develop and operate follow-on systems without Government subsidies. However, to facilitate rapid commercialization, an offeror may elect to include in his proposal mechanisms for short term government financial assistance.

Bishop, W. P.↗
NASA NTRS2006

Fast Longwave and Shortwave Radiative Flux (FLASHFlux) Products from CERES and MODIS Measurements

The Clouds and the Earth s Radiant Energy Systems (CERES) project is currently producing world-class climatological data products derived from measurements taken aboard the Terra and Aqua spacecrafts (Wielicki et al., 1996). While of exceptional fidelity, these data products require a considerable amount of processing to assure quality and verify accuracy and precision. Obtaining such high quality assurance, however, means that the CERES data is typically released more than six months after the acquisition of the initial measurements. For climate studies, such delays are of little consequence, especially considering the improved quality of the released data products. There are, however, many uses for the CERES data products on a near real-time basis. These include: CERES instrument calibration and subsystem quality checks, CLOUDSAT operations, seasonal predictions, agricultural and ocean assimilations, support of field campaigns, and outreach programs such as S'Cool. The FLASHflux project was envisioned as a conduit whereby CERES data could be provided to the community within a week of the initial measurements, with the trade-off that some degree of fidelity would be exacted to gain speed. In this paper, we will report on some very encouraging initial results from the FLASHflux project in which we compared the FLASHflux instantaneous surface fluxes to the CERES surface-only flux algorithm data products.

Stackhouse, Paul W., Jr.↗
NASA NTRS1992

Lunar Observer Laser Altimeter observations for lunar base site selection

One of the critical datasets for optimal selection of future lunar landing sites is local- to regional-scale topography. Lunar base site selection will require such data for both engineering and scientific operations purposes. The Lunar Geoscience Orbiter or Lunar Observer is the ideal precursory science mission from which to obtain this required information. We suggest that a simple laser altimeter instrument could be employed to measure local-scale slopes, heights, and depths of lunar surface features important to lunar base planning and design. For this reason, we have designed and are currently constructing a breadboard of a Lunar Observer Laser Altimeter (LOLA) instrument capable of acquiring contiguous-footprint topographic profiles with both 30-m and 300-m along-track resolution. This instrument meets all the severe weight, power, size, and data rate limitations imposed by Observer-class spacecraft. In addition, LOLA would be capable of measuring the within-footprint vertical roughness of the lunar surface, and the 1.06-micron relative surface reflectivity at normal incidence. We have used airborne laser altimeter data for a few representative lunar analog landforms to simulate and analyze LOLA performance in a 100-km lunar orbit. We demonstrate that this system in its highest resolution mode (30-m diameter footprints) would quantify the topography of all but the very smallest lunar landforms. At its global mapping resolution (300-m diameter footprints), LOLA would establish the topographic context for lunar landing site selection by providing the basis for constructing a 1-2 km spatial resolution global, geodetic topographic grid that would contain a high density of observations (e.g., approximately 1000 observations per each 1 deg by 1 deg cell at the lunar equator). The high spatial and vertical resolution measurements made with a LOLA-class instrument on a precursory Lunar Observer would be highly synergistic with high-resolution imaging datasets, and will allow for direct quantification of critical slopes, heights, and depths of features visible in images of potential lunar base sites.

Garvin, James B.↗