Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data requirements”

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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

The Geostationary Operational Satellite R Series SpaceWire Based Data System Architecture

The GOES-R program selected SpaceWire as the best solution to satisfy the desire for simple and flexible instrument to spacecraft command and telemetry communications. Data generated by GOES-R instruments is critical for meteorological forecasting, public safety, space weather, and other key applications. In addition, GOES-R instrument data is provided to ground stations on a 24/7 basis. GOES-R requires data errors be detected and corrected from origin to final destination. This paper describes GOES-R developed strategy to satisfy this requirement

Krimchansky, Alexander↗
NASA NTRS2008

Interoperability of Heliophysics Virtual Observatories

If you'd like to find interrelated heliophysics (also known as space and solar physics) data for a research project that spans, for example, magnetic field data and charged particle data from multiple satellites located near a given place and at approximately the same time, how easy is this to do? There are probably hundreds of data sets scattered in archives around the world that might be relevant. Is there an optimal way to search these archives and find what you want? There are a number of virtual observatories (VOs) now in existence that maintain knowledge of the data available in subdisciplines of heliophysics. The data may be widely scattered among various data centers, but the VOs have knowledge of what is available and how to get to it. The problem is that research projects might require data from a number of subdisciplines. Is there a way to search multiple VOs at once and obtain what is needed quickly? To do this requires a common way of describing the data such that a search using a common term will find all data that relate to the common term. This common language is contained within a data model developed for all of heliophysics and known as the SPASE (Space Physics Archive Search and Extract) Data Model. NASA has funded the main part of the development of SPASE but other groups have put resources into it as well. How well is this working? We will review the use of SPASE and how well the goal of locating and retrieving data within the heliophysics community is being achieved. Can the VOs truly be made interoperable despite being developed by so many diverse groups?

Thieman, J.↗
DOE OSTI2021

FY21 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1993

A parallel data management system for large-scale NASA datasets

The past decade has experienced a phenomenal growth in the amount of data and resultant information generated by NASA's operations and research projects. A key application is the reprocessing problem which has been identified to require data management capabilities beyond those available today (PRAT93). The Intelligent Information Fusion (IIF) system (ROEL91) is an ongoing NASA project which has similar requirements. Deriving our understanding of NASA's future data management needs based on the above, this paper describes an approach to using parallel computer systems (processor and I/O architectures) to develop an efficient parallel database management system to address the needs. Specifically, we propose to investigate issues in low-level record organizations and management, complex query processing, and query compilation and scheduling.

Srivastava, Jaideep↗
DOE OSTI2020

RODeO (Revenue Operation and Device Optimization Model) [SWR 20-67]

The Revenue, Operation, and Device Optimization (RODeO) model explores optimal system design and operation considering different levels of grid integration, equipment cost, operating limitations, financing, and credits and incentives. RODeO is a price-taker model formulated as a mixed-integer linear programming (MILP) model in the GAMS modeling platform. The objective is to maximizes the net revenue for a collection of equipment at a given site. The equipment includes generators (e.g., gas turbine, steam turbine, solar, wind, hydro, fuel cells, etc.), storage systems (batteries, pumped hydro, gas-fired compressed air energy storage, long-duration systems, hydrogen), and flexible loads (e.g., electric vehicles, electrolyzers, flexible building loads). The input data required by RODeO can be classified into three bins: 1) utility service data, which refers to retail utility rate information (meter cost, energy and demand charges), 2) electricity market data, which include energy and reserve prices, 3) other inputs, which refer to additional electrical demand, product output demand, technological assumptions, financial properties, and operational parameters.

Guerra Fernandez, Omar Jose↗
NASA NTRS1977

Programmable data communications controller requirements

The design requirements for a Programmable Data Communications Controller (PDCC) that reduces the difficulties in attaching data terminal equipment to a computer are presented. The PDCC is an interface between the computer I/O channel and the bit serial communication lines. Each communication line is supported by a communication port that handles all line control functions and performs most terminal control functions. The port is fabricated on a printed circuit board that plugs into a card chassis, mating with a connector that is joined to all other card stations by a data bus. Ports are individually programmable; each includes a microprocessor, a programmable read-only memory for instruction storage, and a random access memory for data storage.

Source record↗
NASA NTRS1978

Environmental observations from space present and future

Spaceborne systems for measuring and predicting environmental conditions have achieved a level of development that justifies a look at the total environmental system. There are common requirements in all the disciplines, and many of the same measurements are needed to satisfy various discipline needs. It is this factor - the commonality of needs and measurements - that will be used in our future missions. The needs and requirements in the future plan will be faced by combining mission and data requirements that may be satisfied from orbiting spacecraft. There are also similarities in information requirements, as a result of which there is a need for developing an integrated data/information system where information will be produced in a format that will be of immediate utility to the users.

Greenwood, L. R.↗
NASA NTRS1994

Comparison of TOMS and AVHRR imagery of volcanic clouds

This project consisted of merging volcanic cloud data from initial studies from TOMS and AVHRR sensors. Because of the different data acquisition platforms and processing algorithms and formats, the combination of the two data required special consideration. Once the registration technique was perfected, several eruptions with data from both sensors were studied. The conclusion showed that, from the initial work, the positions of the volcanic clouds as detected by the two sensors are very similar. Because the AVHRR algorithm is now capable of retrieving masses of ash and sulfate aerosol, the capability of comparing data from the two sensors now allows changes in SO2, H2SO4, and silicated ash in drifting clouds to be described. The results of this study culminated in several presentations, and a master's thesis, which in turn was converted into a paper submitted to the Journal of Geophysical Research. Abstracts of two presentations are attached.

Rose, William I.↗
NASA NTRS2011

Unintended Consequences: How Qualification Constrains Innovation

The development and implementation of new materials and manufacturing processes for aerospace application is often hindered by the high cost and long time span associated with current qualification procedures. The data requirements necessary for material and process qualification are extensive and often require millions of dollars and multiple years to complete. Furthermore, these qualification data can become obsolete for even minor changes to the processing route. This burden is a serious impediment to the pursuit of revolutionary new materials and more affordable processing methods for air vehicle structures. The application of integrated computational materials engineering methods to this problem can help to reduce the barriers to rapid insertion of new materials and processes. By establishing predictive capability for the development of microstructural features in relation to processing and relating this to critical property characteristics, a streamlined approach to qualification is possible. This paper critically examines the advantages and challenges to a modeling-assisted qualification approach for aerospace structural materials. An example of how this approach might apply towards the emerging field of additive manufacturing is discussed in detail.

Brice, Craig A.↗
NASA NTRSDate not supplied

High Performance Access to Archival Data Stored in HDF4 and HDF5 on Cloud Object Stores Without Reformatting the Files

Cloud computing offers numerous advantages for users of extensive Earth science data collections. These benefits encompass direct online access to data files and granules from any location, scalable access supporting parallel computing workflows, and flexible computing tools enabling innovative experimentation with processing techniques. However, older archival file formats designed for distinct computing systems hinder efficient access to decade-long time-series data when compared to data stored in modern cloud-optimized formats like Web Object Stores (WOS), exemplified by Amazon Web Services’ Simple Storage Service (S3). We describe DMR++ (Dataset Metadata Response plus plus), a technology facilitating efficient access to HDF5 (Hierarchical Data Format, version 5) and HDF4 files stored on WOS systems without requiring data reformatting. DMR++ achieves performance comparable to technologies like Zarr while preserving the original file structure, a substantial benefit considering the vast quantity of archival files held by organizations such as NASA. Moreover, DMR++ typically outperforms cloud-optimized versions of HDF5. Essentially an XML (Extensible Markup Language) document usually stored alongside the described data, DMR++ can also be generated on-the-fly but is generally created during data staging to the WOS. Archival files that use HDF4/5 often store large arrays of numerical data. The data in these files is often compressed, typically reducing their size by a factor of four or more. To achieve efficient access to portions of those arrays, they are 'chunked' into smaller sub-arrays, each individually compressed. The chunk size is a compromise, where spinning disks can efficiently access data in smaller chunks while S3 favors larger chunks. A simple optimization of aggregating smaller chunks that are stored adjacently, transferring them in a single access and then individually decompressing them will improve performance. NASA data pose an additional challenge: special Application Programmer Interface (API) libraries are often needed to compute some variables. These libraries are incompatible with WOS environments. Our solution involves storing computed values in the DMR++ document or a companion file, making them accessible like other variables and eliminating the need for specialized APIs. We outline specific optimizations for both satellite grid and swath data stored in HDF4-EOS2 (Earth Observing System).

James Gallagher↗
DOE OSTI2023

Out-of-distribution generalization for learning quantum dynamics

Abstract Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Out-of-distribution generalization for learning quantum dynamics

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2014

Interrelationships Between Receiver/Relative Operating Characteristics Display, Binomial, Logit, and Bayes' Rule Probability of Detection Methodologies

Unknown risks are introduced into failure critical systems when probability of detection (POD) capabilities are accepted without a complete understanding of the statistical method applied and the interpretation of the statistical results. The presence of this risk in the nondestructive evaluation (NDE) community is revealed in common statements about POD. These statements are often interpreted in a variety of ways and therefore, the very existence of the statements identifies the need for a more comprehensive understanding of POD methodologies. Statistical methodologies have data requirements to be met, procedures to be followed, and requirements for validation or demonstration of adequacy of the POD estimates. Risks are further enhanced due to the wide range of statistical methodologies used for determining the POD capability. Receiver/Relative Operating Characteristics (ROC) Display, simple binomial, logistic regression, and Bayes' rule POD methodologies are widely used in determining POD capability. This work focuses on Hit-Miss data to reveal the framework of the interrelationships between Receiver/Relative Operating Characteristics Display, simple binomial, logistic regression, and Bayes' Rule methodologies as they are applied to POD. Knowledge of these interrelationships leads to an intuitive and global understanding of the statistical data, procedural and validation requirements for establishing credible POD estimates.

Generazio, Edward R.↗
NASA NTRS2006

Efficient and Robust Data Collection Using Compact Micro Hardware, Distributed Bus Architectures and Optimizing Software

Future In-Space propulsion systems for exploration programs will invariably require data collection from a large number of sensors. Consider the sensors needed for monitoring several vehicle systems states of health, including the collection of structural health data, over a large area. This would include the fuel tanks, habitat structure, and science containment of systems required for Lunar, Mars, or deep space exploration. Such a system would consist of several hundred or even thousands of sensors. Conventional avionics system design will require these sensors to be connected to a few Remote Health Units (RHU), which are connected to robust, micro flight computers through a serial bus. This results in a large mass of cabling and unacceptable weight. This paper first gives a survey of several techniques that may reduce the cabling mass for sensors. These techniques can be categorized into four classes: power line communication, serial sensor buses, compound serial buses, and wireless network. The power line communication approach uses the power line to carry both power and data, so that the conventional data lines can be eliminated. The serial sensor bus approach reduces most of the cabling by connecting all the sensors with a single (or redundant) serial bus. Many standard buses for industrial control and sensor buses can support several hundreds of nodes, however, have not been space qualified. Conventional avionics serial buses such as the Mil-Std-1553B bus and IEEE 1394a are space qualified but can support only a limited number of nodes. The third approach is to combine avionics buses to increase their addressability. The reliability, EMI/EMC, and flight qualification issues of wireless networks have to be addressed. Several wireless networks such as the IEEE 802.11 and Ultra Wide Band are surveyed in this paper. The placement of sensors can also affect cable mass. Excessive sensors increase the number of cables unnecessarily. Insufficient number of sensors may not provide adequate coverage of the system. This paper also discusses an optimal technique to place and validate sensors.

micro hardware↗
NASA NTRS1997

Efficient GO2/GH2 Injector Design: A NASA, Industry and University Cooperative Effort

Developing new propulsion components in the face of shrinking budgets presents a significant challenge. The technical, schedule and funding issues common to any design/development program are complicated by the ramifications of the continuing decrease in funding for the aerospace industry. As a result, new working arrangements are evolving in the rocket industry. This paper documents a successful NASA, industry, and university cooperative effort to design efficient high performance GO2/GH2 rocket injector elements in the current budget environment. The NASA Reusable Launch Vehicle (RLV) Program initially consisted of three vehicle/engine concepts targeted at achieving single stage to orbit. One of the Rocketdyne propulsion concepts, the RS 2100 engine, used a full-flow staged-combustion cycle. Therefore, the RS 2100 main injector would combust GO2/GH 2 propellants. Early in the design phase, but after budget levels and contractual arrangements had been set the limitations of the current gas/gas injector database were identified. Most of the relevant information was at least twenty years old. Designing high performance injectors to meet the RS 2100 requirements would require the database to be updated and significantly enhanced. However, there was no funding available to address the need for more data. NASA proposed a teaming arrangement to acquire the updated information without additional funds from the RLV Program. A determination of the types and amounts of data needed was made along with test facilities with capabilities to meet the data requirements, budget constraints, and schedule. After several iterations a program was finalized and a team established to satisfy the program goals. The Gas/Gas Injector Technology (GGIT) Program had the overall goal of increasing the ability of the rocket engine community to design efficient high-performance, durable gas/gas injectors relevant to RLV requirements. First, the program would provide Rocketdyne with data on preliminary gas/gas injector designs which would enable discrimination among candidate injector designs. Secondly, the program would enhance the national gas/gas database by obtaining high-quality data that increases the understanding of gas/gas injector physics and is suitable for computational fluid dynamics (CFD) code validation. The third program objective was to validate CFD codes for future gas/gas injector design in the RLV program.

Tucker, P. K.↗
DOE OSTI2024

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Electrical Load Forecasting Over Multihop Smart Metering Networks With Federated Learning

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting, but require data sharing, which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. Here, this article presents a novel personalized FL (PFL) method for high-quality load forecasting in metering networks. A meta-learning-based strategy is developed to address data heterogeneity at local SMs in the collaborative training of local load forecasting models. Moreover, to minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs. A theoretical convergence analysis is also conducted to provide insights into FL design for federated load forecasting. Extensive simulations from real-world datasets show that our method outperforms existing approaches regarding better load forecasting and reduced operational latency costs.

Rahman, Ratun [Univ. of Alabama, Huntsville, AL (U↗
DOE OSTI2025

MRCI Subtask 2.3: Developing Industrial Partnerships and Regional Technical Collaboration Final Technical Summary Report

Under the objective of regional data collection and helping accelerate deployment, MRCI collaborated with industrial stakeholders in their project planning, characterization, and analysis. Some examples of these collaborations are given below. The data and information shared by the industrial collaborations added to the regional CCS framework development and were incorporated into the overall datasets, while addressing any proprietary data requirements. Three examples of collaborative partnerships with industry that have provided geologic characterization data relevant and beneficial to the MRCI program are discussed below, including: the UIC Class II Injection Facility in Eastern Ohio, the Core Energy CO2-EOR (enhanced oil recovery) operation in Otsego County Michigan, and the Marquis ethanol plant in Hennepin Illinois.

CCS,CCUS,MRCI,Midwest USA,Technical Challenges,inj↗