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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Feasibility of automated dropsize distributions from holographic data using digital image processing techniques

An automated technique for measuring particle diameters and their spatial coordinates from holographic reconstructions is being developed. Preliminary tests on actual cold-flow holograms of impinging jets indicate that a suitable discriminant algorithm consists of a Fourier-Gaussian noise filter and a contour thresholding technique. This process identifies circular as well as noncircular objects. The desired objects (in this case, circular or possibly ellipsoidal) are then selected automatically from the above set and stored with their parametric representations. From this data, dropsize distributions as a function of spatial coordinates can be generated and combustion effects due to hardware and/or physical variables studied.

Feinstein, S. P.↗
NASA NTRS2009

Multi-Mission Automated Task Invocation Subsystem

Multi-Mission Automated Task Invocation Subsystem (MATIS) is software that establishes a distributed data-processing framework for automated generation of instrument data products from a spacecraft mission. Each mission may set up a set of MATIS servers for processing its data products. MATIS embodies lessons learned in experience with prior instrument- data-product-generation software. MATIS is an event-driven workflow manager that interprets project-specific, user-defined rules for managing processes. It executes programs in response to specific events under specific conditions according to the rules. Because requirements of different missions are too diverse to be satisfied by one program, MATIS accommodates plug-in programs. MATIS is flexible in that users can control such processing parameters as how many pipelines to run and on which computing machines to run them. MATIS has a fail-safe capability. At each step, MATIS captures and retains pertinent information needed to complete the step and start the next step. In the event of a restart, this information is retrieved so that processing can be resumed appropriately. At this writing, it is planned to develop a graphical user interface (GUI) for monitoring and controlling a product generation engine in MATIS. The GUI would enable users to schedule multiple processes and manage the data products produced in the processes. Although MATIS was initially designed for instrument data product generation,

Cheng, Cecilia S.↗
NASA NTRS2016

DebriSat Fragment Characterization System and Processing Status

The DebriSat project is a continuing effort sponsored by NASA and DoD to update existing break-up models using data obtained from hypervelocity impact tests performed to simulate on-orbit collisions. After the impact tests, a team at the University of Florida has been working to characterize the fragments in terms of their mass, size, shape, color and material content. The focus of the post-impact effort has been the collection of 2 mm and larger fragments resulting from the hypervelocity impact test. To date, in excess of 125K fragments have been recovered which is approximately 40K more than the 85K fragments predicted by the existing models. While the fragment collection activities continue, there has been a transition to the characterization of the recovered fragments. Since the start of the characterization effort, the focus has been on the use of automation to (i) expedite the fragment characterization process and (ii) minimize the effects of human subjectivity on the results; e.g., automated data entry processes were developed and implemented to minimize errors during transcription of the measurement data. At all steps of the process, however, there is human oversight to ensure the integrity of the data. Additionally, repeatability and reproducibility tests have been developed and implemented to ensure that the instrumentations used in the characterization process are accurate and properly calibrated.

Rivero, M.↗
NASA NTRS1999

SIRTF Science Operations System Design

SIRTF Science Operations System Design William B. Green Manager, SIRTF Science Center California Institute of Technology M/S 310-6 1200 E. California Blvd., Pasadena CA 91125 (626) 395 8572 Fax (626) 568 0673 bgreen@ipac.caltech.edu. The Space Infrared Telescope Facility (SIRTF) will be launched in December 2001, and perform an extended series of science observations at wavelengths ranging from 20 to 160 microns for five years or more. The California Institute of Technology has been selected as the home for the SIRTF Science Center (SSC). The SSC will be responsible for evaluating and selecting observation proposals, providing technical support to the science community, performing mission planning and science observation scheduling activities, instrument calibration during operations and instrument health monitoring, production of archival quality data products, and management of science research grants. The science payload consists of three instruments delivered by instrument Principal Investigators located at University of Arizona, Cornell, and Harvard Smithsonian Astrophysical Observatory. The SSC is responsible for design, development, and operation of the Science Operations System (SOS) which will support the functions assigned to the SSC by NASA. The SIRTF spacecraft, mission profile, and science instrument design have undergone almost ten years of refinement. SIRTF development and operations activities are highly cost constrained. The cost constraints have impacted the design of the SOS in several ways. The Science Operations System has been designed to incorporate a set of efficient, easy to use tools which will make it possible for scientists to propose observation sequences in a rapid and automated manner. The use of highly automated tools for requesting observations will simplify the long range observatory scheduling process, and the short term scheduling of science observations. Pipeline data processing will be highly automated and data-driven, utilizing a variety of tools developed at JPL, the instrument development teams, and Space Telescope Science Institute to automate processing. An incremental ground data system development approach has been adopted, featuring periodic deliveries that are validated with the flight hardware throughout the various phases of system level development and testing. This approach minimizes development time and decreases operations risk. This paper will describe the top level architecture of the SOS and the basic design concepts. A summary of the incremental development approach will be presented. Examples of the unique science user tools now under final development prior to the first proposal call scheduled for mid-2000 will be shown.

Green, William↗
NASA NTRS2013

CFD Extraction Tool for TecPlot From DPLR Solutions

This invention is a TecPlot macro of a computer program in the TecPlot programming language that processes data from DPLR solutions in TecPlot format. DPLR (Data-Parallel Line Relaxation) is a NASA computational fluid dynamics (CFD) code, and TecPlot is a commercial CFD post-processing tool. The Tec- Plot data is in SI units (same as DPLR output). The invention converts the SI units into British units. The macro modifies the TecPlot data with unit conversions, and adds some extra calculations. After unit conversions, the macro cuts a slice, and adds vectors on the current plot for output format. The macro can also process surface solutions. Existing solutions use manual conversion and superposition. The conversion is complicated because it must be applied to a range of inter-related scalars and vectors to describe a 2D or 3D flow field. It processes the CFD solution to create superposition/comparison of scalars and vectors. The existing manual solution is cumbersome, open to errors, slow, and cannot be inserted into an automated process. This invention is quick and easy to use, and can be inserted into an automated data-processing algorithm.

Norman, David↗
NASA NTRS2003

Automating the Processing of Earth Observation Data

NASA s vision for Earth science is to build a "sensor web": an adaptive array of heterogeneous satellites and other sensors that will track important events, such as storms, and provide real-time information about the state of the Earth to a wide variety of customers. Achieving this vision will require automation not only in the scheduling of the observations but also in the processing of the resulting data. To address this need, we are developing a planner-based agent to automatically generate and execute data-flow programs to produce the requested data products.

Golden, Keith↗
DOE OSTI2025

White Paper: Scalable Digital Twin Capabilities for Aging and Surveillance of Engineered Systems

This white paper presents a multi-year initiative to develop practical, secure, and scalable digital twin capabilities for engineered systems in aging and surveillance contexts—an approach pioneered at the National Nuclear Security Administration (NNSA) Lawrence Livermore National Laboratory (LLNL) that maps directly onto the needs and ambitions of the Navy for ship- and fleet-level digital twins. LLNL’s work in building part- and process-level digital twins for advanced manufacturing, with a vision to scale up to entire factory floors and, ultimately, enterprise-wide digital twins, offers an adaptable pathway for the Navy as it seeks to modernize lifecycle management, readiness, and predictive maintenance across ships and fleets. For our application, we integrate physics-based modeling with automated data ingestion, processing, and AI-driven calibration, creating hybrid models that are both interpretable and data responsive. We modernized legacy workflows, established centralized data infrastructure, automated experimental pipelines, and demonstrated end-to-end coupling of accelerated aging data with finite element simulations via optimization and surrogate modeling. The result is a generalizable framework that supports part-level digital twins today and lays the groundwork for future system-level twins suitable for Navy applications.

36 MATERIALS SCIENCE↗
NASA NTRS2016

Preliminary Characterization Results from the DebriSat Project

The DebriSat project is a continuing effort sponsored by NASA and DoD to update existing break-up models using data obtained from two separate hypervelocity impact tests used to simulate on-orbit collisions. To protect the fragments resulting from the impact tests, "soft-catch" arenas made of polyurethane foam panels were utilized. After each impact test, the test chamber was cleaned and debris resulting from the catastrophic demise of the test article were collected and shipped to the University of Florida for post-impact processing. The post-impact processing activities include collecting, characterizing, and cataloging of the fragments. Since the impact tests, a team of students has been working to characterize the fragments in terms of their mass, size, shape, color and material content. The focus of the 20 months since the impact tests has been on the collection of 2 millimeters- and larger fragments resulting from impact test on the 56 kilogram-representative LEO (Low Earth Orbit) satellite referred to as DebriSat. To date we have recovered in excess of 115,000 fragments, 30,000 more than the prediction of 85,000 fragments from the existing model. We continue to collect fragments but have transitioned to the characterization phase of the post-impact activities. Since the start of the characterization phase, the focus has been to utilize automation to (i) expedite fragment characterization process and (ii) minimize human-in-the- loop. We have developed and implemented such automated processes; e.g., we have automated the data entry process to reduce operator errors during transcription of the measurement data. However, at all steps of the process, there is human oversight to ensure the integrity of the data. Additionally, we have developed and implemented repeatability and reproducibility tests to ensure that the instrumentation used in the characterization process is accurate and properly calibrated. In this paper, the implemented processes are described and preliminary results presented. Additionally, lessons learned from the implemented automations and their impacts on the integrity of the results are discussed.

Rivero, M.↗
NASA NTRS1971

Apollo contamination analysis

The Apollo 12 photographic observations of clouds produced by venting liquids into space were reduced and analyzed. The number of Baker-Nunn photographs required that film measurement and data processing be automated, and the microdensitometer was adapted to automatic digital operation. Computer programs were prepared to process the image-density measurements. A substantial selection of Apollo 12 data was processed; both hydrogen and oxygen clouds are represented. Some limited data were obtained from Apollo 13 and 14 missions. During the Apollo 12 data analysis, the theory and models of cloud phenomena were refined because the models were initially applied to inferior oxygen data from earlier missions. The reported observations of water dumps from Apollo 12 are also discussed, along with the use of the oxygen and hydrogen data from Apollo 12 photographs with the cloud models.

Source record↗
NASA NTRS1993

HP upgrade operational streamlining

New computer technology and resources must be successfully integrated into CDSLR station operations to manage new complex operational tracking requirements, support the on site production of new data products, support ongoing station performance improvements, and to support new station communication requirements. The NASA CDSLR Network is in the process of upgrading station computer resources with HP UNIX workstations, designed to automate a wide range of operational station requirements. The primary HP upgrade objective was to relocate computer intensive data system tasks from the controller computer to a new advanced computer environment designed to meet the new data system requirements. The HP UNIX environment supports fully automated real time data communications, data management, data processing, and data quality control. Automated data compression procedures are used to improve the efficiency of station data communications. In addition, the UNIX environment supports a number of semi-automated technical and administrative operational station tasks. The x window user interface generates multiple simultaneous color graphics displays, providing direct operator visibility and control over a wide range of operational station functions.

Edge, David R.↗
NASA NTRS1993

The application of automated operations at the Institutional Processing Center

The JPL Institutional and Mission Computing Division, Communications, Computing and Network Services Section, with its mission contractor, OAO Corporation, have for some time been applying automation to the operation of JPL's Information Processing Center (IPC). Automation does not come in one easy to use package. Automation for a data processing center is made up of many different software and hardware products supported by trained personnel. The IPC automation effort formally began with console automation, and has since spiraled out to include production scheduling, data entry, report distribution, online reporting, failure reporting and resolution, documentation, library storage, and operator and user education, while requiring the interaction of multi-vendor and locally developed software. To begin the process, automation goals are determined. Then a team including operations personnel is formed to research and evaluate available options. By acquiring knowledge of current products and those in development, taking an active role in industry organizations, and learning of other data center's experiences, a forecast can be developed as to what direction technology is moving. With IPC management's approval, an implementation plan is developed and resources identified to test or implement new systems. As an example, IPC's new automated data entry system was researched by Data Entry, Production Control, and Advance Planning personnel. A proposal was then submitted to management for review. A determination to implement the new system was made and elements/personnel involved with the initial planning performed the implementation. The final steps of the implementation were educating data entry personnel in the areas effected and procedural changes necessary to the successful operation of the new system.

Barr, Thomas H.↗
DOE OSTI2020

Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. Furthermore, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data is becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this paper, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-minute-interval. The proposed model is motivated by the recent advancement of Long-Short-Term-Memory (LSTM) networks and AutoEncoder (AE), which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model (PM) is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations

42 ENGINEERING↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗
DOE OSTI2020

Phase evolution and amorphous stability upon solid-state reaction in superlattice like Ge–Sb–Te combinatorial thin-film

In this paper, the superlattice-like (SLL) Ge–Sb–Te combinatorial thin films were prepared by using a high-throughput ion beam sputtering system. The phase evolution and amorphous stability of such films undergoing heat treatment as a function of the coating sequence and modulation period were systematically studied. The composition structure diagram was constructed via an automated process of data obtained by high-throughput synchrotron micro-X-ray diffraction and lab-based micro-X-ray fluorescence. Furthermore, the element distribution and microstructure in the depth direction of the SLL thin films were characterized with time-of-flight secondary ion mass spectrometry and transmission electron microscopy, respectively. These studies demonstrated that the coating sequence has a significant effect on the element distribution in the as-deposited SLL thin films and the structure of the final product upon solid-state reaction. Reducing the modulation period of the SLL thin film improves the stability of the amorphous Ge–Sb–Te phase. This work lays a solid foundation for the rational design of SLL Ge–Sb–Te thin films to improve their performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Abstract Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Exploring Secondary Markets to Improve Circularity: A Comparative Case Study of Photovoltaics and Hard-Disk Drives

Each year renewable energy generation increases notably with solar panel installations, but these panels have a limited lifespan and will produce between 2 and 4 million metric tons of waste by 2040. Similarly, there are currently between 20 to 70 million hard-disk drives (HDDs) reaching end-of-life (EOL) annually. The circular economy (CE) strives to recycle and reuse materials that are rare and expensive to obtain, minimizing waste. However, studying the potential circularity of photovoltaics (PV) and HDDs requires various data, for instance, on the maturity of the secondhand markets. In this context, the objective of the present study is to identify the current state of secondhand PV and HDD markets. After conducting a literature review, an automated data collection process was set up for that purpose. The analysis of the literature and collected data assess the maturity of the secondhand PV modules and HDDs markets and highlight differences between them.

agent-based modelling↗
DOE OSTI2026

Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)

Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.

42 ENGINEERING↗