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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Building a Database for a Quantitative Model

A database can greatly benefit a quantitative analysis. The defining characteristic of a quantitative risk, or reliability, model is the use of failure estimate data. Models can easily contain a thousand Basic Events, relying on hundreds of individual data sources. Obviously, entering so much data by hand will eventually lead to errors. Not so obviously entering data this way does not aid linking the Basic Events to the data sources. The best way to organize large amounts of data on a computer is with a database. But a model does not require a large, enterprise-level database with dedicated developers and administrators. A database built in Excel can be quite sufficient. A simple spreadsheet database can link every Basic Event to the individual data source selected for them. This database can also contain the manipulations appropriate for how the data is used in the model. These manipulations include stressing factors based on use and maintenance cycles, dormancy, unique failure modes, the modeling of multiple items as a single "Super component" Basic Event, and Bayesian Updating based on flight and testing experience. A simple, unique metadata field in both the model and database provides a link from any Basic Event in the model to its data source and all relevant calculations. The credibility for the entire model often rests on the credibility and traceability of the data.

Kahn, C. Joseph↗
NASA NTRS2000

Hybrid concatenated codes and iterative decoding

Several improved turbo code apparatuses and methods. The invention encompasses several classes: (1) A data source is applied to two or more encoders with an interleaver between the source and each of the second and subsequent encoders. Each encoder outputs a code element which may be transmitted or stored. A parallel decoder provides the ability to decode the code elements to derive the original source information d without use of a received data signal corresponding to d. The output may be coupled to a multilevel trellis-coded modulator (TCM). (2) A data source d is applied to two or more encoders with an interleaver between the source and each of the second and subsequent encoders. Each of the encoders outputs a code element. In addition, the original data source d is output from the encoder. All of the output elements are coupled to a TCM. (3) At least two data sources are applied to two or more encoders with an interleaver between each source and each of the second and subsequent encoders. The output may be coupled to a TCM. (4) At least two data sources are applied to two or more encoders with at least two interleavers between each source and each of the second and subsequent encoders. (5) At least one data source is applied to one or more serially linked encoders through at least one interleaver. The output may be coupled to a TCM. The invention includes a novel way of terminating a turbo coder.

Divsalar, Dariush↗
NASA NTRS1990

Neural network approaches versus statistical methods in classification of multisource remote sensing data

Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.

Benediktsson, Jon A.↗
NASA NTRS1974

Galactic X-ray sources

Data on galactic X-ray sources from rocket and balloon experiments and the UHURU satellite are reported. The outline emphasizes general discrete source models and begins with some a priori considerations about X-ray emission mechanisms and their applicability, after which contemporary data are interpreted according to these models. Topics covered include the production of X-rays by various mechanisms, source regimes, observational parameters, stellar distributions, individual stars, flare stars, supernovae and pulsars, the Crab nebula, old and young remnants, binary systems, and others.

Holt, S. S.↗
NASA NTRS1990

Statistical methods and neural network approaches for classification of data from multiple sources

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Benediktsson, Jon Atli↗
NASA NTRS1990

Management and display of four-dimensional environmental data sets using McIDAS

Over the past four years, great strides have been made in the areas of data management and display of 4-D meteorological data sets. A survey was conducted of available and planned 4-D meteorological data sources. The data types were evaluated for their impact on the data management and display system. The requirements were analyzed for data base management generated by the 4-D data display system. The suitability of the existing data base management procedures and file structure were evaluated in light of the new requirements. Where needed, new data base management tools and file procedures were designed and implemented. The quality of the basic 4-D data sets was assured. The interpolation and extrapolation techniques of the 4-D data were investigated. The 4-D data from various sources were combined to make a uniform and consistent data set for display purposes. Data display software was designed to create abstract line graphic 3-D displays. Realistic shaded 3-D displays were created. Animation routines for these displays were developed in order to produce a dynamic 4-D presentation. A prototype dynamic color stereo workstation was implemented. A computer functional design specification was produced based on interactive studies and user feedback.

Hibbard, William L.↗
NASA NTRS2011

Zero-Copy Objects System

Zero-Copy Objects System software enables application data to be encapsulated in layers of communication protocol without being copied. Indirect referencing enables application source data, either in memory or in a file, to be encapsulated in place within an unlimited number of protocol headers and/or trailers. Zero-copy objects (ZCOs) are abstract data access representations designed to minimize I/O (input/output) in the encapsulation of application source data within one or more layers of communication protocol structure. They are constructed within the heap space of a Simple Data Recorder (SDR) data store to which all participating layers of the stack must have access. Each ZCO contains general information enabling access to the core source data object (an item of application data), together with (a) a linked list of zero or more specific extents that reference portions of this source data object, and (b) linked lists of protocol header and trailer capsules. The concatenation of the headers (in ascending stack sequence), the source data object extents, and the trailers (in descending stack sequence) constitute the transmitted data object constructed from the ZCO. This scheme enables a source data object to be encapsulated in a succession of protocol layers without ever having to be copied from a buffer at one layer of the protocol stack to an encapsulating buffer at a lower layer of the stack. For large source data objects, the savings in copy time and reduction in memory consumption may be considerable.

Burleigh, Scott C.↗
NASA NTRS1999

Uniform Data Access Using GXD

This paper gives an overview of GXD, a framework facilitating publication and use of data from diverse data sources. GXD defines an object-oriented data model designed to represent a wide range of things including data, its metadata, resources and query results. GXD also defines a data transport language. a dialect of XML, for representing instances of the data model. This language allows for a wide range of data source implementations by supporting both the direct incorporation of data and the specification of data by various rules. The GXD software library, proto-typed in Java, includes client and server runtimes. The server runtime facilitates the generation of entities containing data encoded in the GXD transport language. The GXD client runtime interprets these entities (potentially from many data sources) to create an illusion of a globally interconnected data space, one that is independent of data source location and implementation.

Vanderbilt, Peter↗
NASA NTRS1976

User data dissemination concepts for earth resources

Domestic data dissemination networks for earth-resources data in the 1985-1995 time frame were evaluated. The following topics were addressed: (1) earth-resources data sources and expected data volumes, (2) future user demand in terms of data volume and timeliness, (3) space-to-space and earth point-to-point transmission link requirements and implementation, (4) preprocessing requirements and implementation, (5) network costs, and (6) technological development to support this implementation. This study was parametric in that the data input (supply) was varied by a factor of about fifteen while the user request (demand) was varied by a factor of about nineteen. Correspondingly, the time from observation to delivery to the user was varied. This parametric evaluation was performed by a computer simulation that was based on network alternatives and resulted in preliminary transmission and preprocessing requirements. The earth-resource data sources considered were: shuttle sorties, synchronous satellites (e.g., SEOS), aircraft, and satellites in polar orbits.

Davies, R.↗
NASA NTRS1976

Forestry applications project/timber resource. Sam Houston National forest inventory and development of a survey planning model

The Forestry Applications Project has been directed towards solving the problem of meeting informational needs of the resource managers utilizing remote sensing data sources including satellite data, conventional aerial photography, and direct measurement on the ground in such combinations as needed to best achieve these goals. It is recognized that sampling plays an important role in generating relevant information for managing large geographic populations. The central problem, therefore, is to define the kind and amount of sampling and the place of remote sensing data sources in that sampling system to do the best possible job of meeting the manager's informational needs.

Colwell, R. N.↗
NASA NTRS1994

Optimality Of Variable-Length Codes

Report presents analysis of performances of conceptual Rice universal noiseless coders designed to provide efficient compression of data over wide range of source-data entropies. Includes predictive preprocessor that maps source data into sequence of nonnegative integers and variable-length-coding processor, which adapts to varying entropy of source data by selecting whichever one of number of optional codes yields shortest codeword.

Yeh, Pen-Shu↗
NASA NTRSDate not supplied

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗
DOE OSTI2025

High-Fidelity, Large-Scale, Realistic Dataset Development

The final report summarizes the work performed for supporting the ARPA-E Grid Optimization Competition (Challenge 2 and Challenge 3) within the stated period. Challenge 2 For the challenge period, the main responsibility of the team is to investigate, gen- erate, and deliver parts of the data sets for the competition, based on the competition model for Challenge 2, existing data sets from Challenge 1, and data source supplied by other data set teams. Challenge 3 For the challenge period, the main responsibility of the team is to propose, create, deliver, and maintain the data format during the competition period. The data format will specify how the benchmark data will be represented and communicated to competitors. It will also specify how competitors should report back the solutions. The data format will be closely aligned with the problem formulation (maintained by the formulation team) and the solution validation process (maintained by the validation team). Our team is also responsible in investigating, generating, and delivering parts of the data sets for the competition. The data sets will be created based on the competition model for Challenge 3, existing data sets from Challenge 1 and Challenge 2, and data source supplied by other data set teams.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1995

Visualization of Computational Fluid Dynamics

Scientific visualization serves the dual purpose of exploration and exposition of the results of numerical simulations of fluid flow. Along with the basic visualization process which transforms source data into images, there are four additional components to a complete visualization system: Source Data Processing, User Interface and Control, Presentation, and Information Management. The requirements imposed by the desired mode of operation (i.e. real-time, interactive, or batch) and the source data have their effect on each of these visualization system components. The special requirements imposed by the wide variety and size of the source data provided by the numerical simulation of fluid flow presents an enormous challenge to the visualization system designer. We describe the visualization system components including specific visualization techniques and how the mode of operation and source data requirements effect the construction of computational fluid dynamics visualization systems.

Gerald-Yamasaki, Michael↗
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗
DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗
NASA NTRS1980

Synergistic relationships among remote-sensing and geophysical media: Geological and hydrological applications

The synergistic relationships among LANDSAT imagery, Skylab photographs, and aerial photographs were useful for establishing areas of near surface bedrock. Lineaments were located on LANDSAT imagery and aerial photographs during 1978 and near surface water tables were to be located during 1980. Both of these subjects can be identified by remote sensing methods more reliably than individual outcrops, which are small and occur in a wide variety of environments with a wide range of responses. Bedrock outcrops themselves could not be resolved by any of the data sources used, nor did any combination of data sources specifically identify rock at the ground surface. The data sources could not simply be combined mathematically to produce a visual image of probable areas of near surface bedrock. Outcrops and near surface bedrock had to be verified visually at the site. Despite these drawbacks, a procedure for locating areas of near surface bedrock within which actual surface outcrops may occur was developed.

Goebel, J. E.↗