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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A Probabilistic Reasoner Based on Bayes Risk for Damage Detection in Structural Systems

Structural health monitoring (SHM) systems are used to inform operation of structural systems subject to loads and environments that may affect their integrity. SHM systems rely on continuous monitoring of the structure to determine its health state. These systems are often coupled with a model of the deployed structure to determine the consequences of changes in the system by forecasting the response to future states. These models, which may be thought of as digital twins, need to be updated to reflect the latest state of the structural system. This work makes use of an uncertainty-aware machine learning model that enforces distance preservation of the original input space to determine deviations from the training data input space distributions. This workflow enables domain shift detection to determine whether damage is present in the structure. The uncertainty metrics generated by this network are then used in a Bayes risk framework to design an optimal damage detector given cost and risk considerations. The approach is demonstrated on a computational example with simulated damage.

Najera-Flores, David [ATA Engineering, Inc.]↗
DOE OSTI2024

Machine Learning-Based Identification of the Interface Regions for Coupling Local and Nonlocal Models

Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process can be challenging, requiring expertise to identify the interface between local and nonlocal regions. Here, this study introduces a machine learning-based approach to automatically detect the regions in which the local and nonlocal models should be used. The method uses loading functions evaluated at grid points to decide the model selection at those points. Training of the networks is based on datasets provided by classes of loading functions for which reference coupling configurations are computed using accurate coupled solutions, where accuracy is measured in terms of the relative error between the solution to the coupling approach and the solution to the nonlocal model. We study two approaches that vary in data structure. The first, the full-domain input data approach, uses the entire load vector and outputs a complete label vector, performing a global classification. The second, a window-based approach, processes loads into windows and addresses the problem as a node-wise classification where each window's central point is classified individually. The classification problems are solved via deep learning algorithms based on convolutional neural networks. The performance of these approaches is studied on one-dimensional numerical examples using F1-scores and accuracy metrics. Notably, the windowing approach achieves an accuracy of 0.96 and an F1-score of 0.97, highlighting its potential to automate coupling processes effectively and enhance computational efficiency in material science applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Blockchain based Communication Architectures with Applications to Private Security Networks

Existing communication protocols in high consequence security networks are highly centralized. While this naively makes the controls easier to physically secure, external actors require fewer resources to disrupt the system because there are fewer points in the system can be destroyed or interrupted without the entire system failing. We present a solution to this problem using a proof-of-work-based blockchain implementation built on MultiChain. We construct a test-bed network containing two types of data input: visual imagers and microwave sensor information. These data types are ubiquitous in perimeter intrusion detection security systems and allow a realistic representation of a real-world network architecture. The cameras in this system use an object detection algorithm to nd important targets in the scene. The raw data from the camera and the outputs from the detection algorithm are then placed in a transaction on the distributed ledger. Similarly, microwave data is used to detect relevant events and are placed in a transaction. These transactions are then bundled into blocks and broadcast to the rest of the network using the Bitcoin-based MultiChain protocol. We develop five tests to examine the security metrics of our network. We performed the five security metric test using different sized networks from 7 to 39 nodes to determine how the metrics scale with respect to size. We nd that when compared to a centralized architecture our implementation provides a resiliency increase that is expected from a blockchain-based protocol without slowing the system so much that a human operator would notice. Furthermore, our approach is able to detect tampering in real time. Based on these results, we theorize that security networks in general could use a blockchain-based approach in a meaningful way.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1973

Investigation using data from ERTS-1 to develop and implement utilization of living marine resources

The author has identified the following significant results. This 15-month ERTS-1 investigation produced correlations between satellite, aircraft, menhaden fisheries, and environmental sea truth data from the Mississippi Sound. Selected oceanographic, meteorological, and biological parameters were used as indirect indicators of the menhaden resource. Synoptic and near real time sea truth, fishery, satellite imagery, aircraft acquired multispectral, photo and thermal IR information were acquired as data inputs. Computer programs were developed to manipulate these data according to user requirements. Preliminary results indicate a correlation between backscattered light with chlorophyll concentration and water transparency in turbid waters. Eight empirical menhaden distribution models were constructed from combinations of four fisheries-significant oceanographic parameters: water depth, transparency, color, and surface salinity. The models demonstrated their potential for management utilization in areas of resource assessment, prediction, and monitoring.

Stevenson, W. H.↗
NASA NTRS1974

Vibration and stress analysis of soft-bonded shuttle insulation tiles. Modal analysis with compact widely space stringers

An efficient iterative procedure is described for the vibration and modal stress analysis of reusable surface insulation (RSI) of multi-tiled space shuttle panels. The method, which is quite general, is rapidly convergent and highly useful for this application. A user-oriented computer program based upon this procedure and titled RESIST (REusable Surface Insulation Stresses) has been prepared for the analysis of compact, widely spaced, stringer-stiffened panels. RESIST, which uses finite element methods, obtains three dimensional tile stresses in the isolator, arrestor (if any) and RSI materials. Two dimensional stresses are obtained in the tile coating and the stringer-stiffened primary structure plate. A special feature of the program is that all the usual detailed finite element grid data is generated internally from a minimum of input data. The program can accommodate tile idealizations with up to 850 nodes (2550 degrees-of-freedom) and primary structure idealizations with a maximum of 10,000 degrees-of-freedom. The primary structure vibration capability is achieved through the development of a new rapid eigenvalue program named ALARM (Automatic LArge Reduction of Matrices to tridiagonal form).

Ojalvo, I. U.↗
NASA NTRS1981

NASA/BLM Applications Pilot Test (APT), phase 2. Volume 1: Executive summary

Data from LANDSAT, low altitude color aerial photography, and ground visits were combined and used to produce vegetation cover maps and to estimate productivity of range, woodland, and forest resources in northwestern Arizona. A planning session, two workshops, and four status reviews were held to assist technology transfer from NASA. Computer aided digital classification of LANDSAT data was selected as a major source of input data. An overview is presented of the data processing, data collection, productivity estimation, and map verification techniques used. Cost analysis and digital LANDSAT digital products are also considered.

Source record↗
NASA NTRS1990

PATRAN-STAGS translator (PATSTAGS)

A a computer program used to translate PATRAN finite element model data into Structural Analysis of General Shells (STAGS) input data is presented. The program supports translation of nodal, nodal constraints, element, force, and pressure data. The subroutine UPRESS required for the readings of live pressure data into STAGS is also presented.

Otte, Neil↗
NASA NTRS2003

Vector-Ordering Filter Procedure for Data Reduction

The vector-ordering filter (VOF) technique involves a procedure for sampling a large population of data vectors to select a subset of data vectors that fully characterize the state space of the large population. The VOF technique enables a large reduction of the volume of data that must be handled in the automated monitoring system and method discussed in the two immediately preceding articles. In so doing, the VOF technique enables the development of data-driven mathematical models of a monitored asset from sets of data that would otherwise exceed the memory capacities of conventional engineering computers. Data-driven mathematical models have been shown to offer high fidelity for purposes of control and monitoring of assets. In practice, a collection of asset-operating observations is acquired with the intention that the collection contain observations characteristic of the full dynamic range of operation of the asset. Often, such a collection contains an extremely large number of observations, many of which are redundant. The VOF technique fills the need for a means to extract, from the original collection of observational data, a reduced data matrix that excludes redundant data while maintaining the full statistical character and dynamic range of the original data. The reduced data matrix can then be used as the input data for development of a mathematical model of the monitored asset, or as training data for a neural-network substitute for an explicit mathematical model of the asset. Alternatively, the reduced data matrix can, itself, be used directly as a mathematical model of the monitored asset, as is commonly done in multivariate state-estimation techniques. The original data are collected from the asset over a range of operating states and are put in matrix form. Each column vector in the original data matrix represents the signal values acquired at a particular operational state of the asset. Thus, the number of columns of the original data matrix equals the number of observed states and the number of rows in this matrix equals the number of signals acquired at each observation. In the VOF technique, one extracts the reduced data matrix from the original data matrix through the selection of a representative subset of the column (state) vectors.

Bickford, Randall L.↗
NASA NTRS2013

GRAIL Refinements to Lunar Seismic Structure

Joint interpretation of disparate geophysical datasets helps to reduce drawbacks that can result from analyzing them individually. The Apollo seismic network was situated on the lunar nearside surface in a roughly equilateral triangle having sides approximately 1000 km long, with stations 12/14 nearly co‐located at one corner. Due to this limited geographical extent, near‐surface ray coverage from moonquakes is low, but increases with depth. In comparison, gravity surveys and their resulting gravity anomaly maps have traditionally offered optimal resolution at crustal depths. Gravimetric maps and seismic data sets are therefore well suited to joint inversion, since the complementary information reduces inherent model ambiguity. Previous joint inversions of the Apollo seismic data (seismic phase arrival times) and Clementine‐ or Lunar Prospector‐derived gravity data (mass and moment of inertia) attempted to recover the subsurface structure of the Moon by focusing on hypothetical lunar compositions that explore the density/velocity relationship. These efforts typically search for the best fitting thermodynamically calculated velocity/density model, allowing variables like core size, velocity, and/or composition to vary freely. Seismic velocity profiles previously derived from the Apollo seismic data through inversion of travel times vary both in the depth of the crust and mantle layers, and the seismic velocities and densities assigned to those layers. The lunar mass and moment of inertia likewise only constrain gross variations in the density profile beyond that of a uniform density sphere. As a result, composition and structure models previously obtained by jointly inverting these data retain the original uncertainties inherent in the input data sets. We will perform a joint inversion of Apollo seismic delay times and gravity data collected by the GRAIL lunar gravity mission, in order to recover seismic velocities and density as a function of latitude, longitude, and depth within the Moon. We will relate density to seismic velocity using a linear relationship that is allowed to be depth‐dependent. The corresponding coefficient (B) can reflect a variety of material properties that vary with depth, including temperature and composition. The inversion seeks to recover the set of density, velocity, and B‐coefficient perturbations that minimize (in a least‐squares sense) the difference between the observed and calculated data.

Weber, Renee C.↗
NASA NTRS2014

GRAIL Refinements to Lunar Seismic Structure

Joint interpretation of disparate geophysical datasets helps reduce drawbacks that can result from analyzing them individually. The Apollo seismic network was situated on the lunar nearside surface in a roughly equilateral triangle having sides approximately 1000 km long, with stations 12/14 nearly co-located at one corner. Due to this limited geographical extent, near-surface ray coverage from moonquakes is low, but increases with depth. In comparison, gravity surveys and their resulting gravity anomaly maps have traditionally offered optimal resolution at crustal depths. Gravimetric maps and seismic data sets are therefore well suited to joint inversion, since the complementary information reduces inherent model ambiguity. Previous joint inversions of the Apollo seismic data (seismic phase arrival times) and Clementine- or Lunar Prospector-derived gravity data (mass and moment of inertia) attempted to recover the subsurface structure of the Moon by focusing on hypothetical lunar compositions that explored the density/velocity relationship. These efforts typically searched for the best fitting thermodynamically calculated velocity/density model, and allowed variables like core size, velocity, and/or composition to vary freely. Seismic velocity profiles derived from the Apollo seismic data through travel time inversion vary both in the depth of the crust and mantle layers, and the seismic velocities and densities assigned to those layers. The lunar mass and moment of inertia likewise only constrain gross variations in the density profile beyond that of a uniform density sphere. As a result, composition and structure models previously obtained by jointly inverting these data retain the original uncertainties inherent in the input data sets. We perform a joint inversion of Apollo seismic delay times and gravity data collected by the GRAIL lunar gravity mission, in order to recover seismic velocity and density as a function of latitude, longitude, and depth within the Moon. We relate density (p) to seismic velocity (v) using a depth-dependent linear relationship. The corresponding coefficient (B) can reflect a variety of material properties, including temperature and composition. The inversion seeks to recover the set of p, v, and B perturbations that minimize (in a least-squares sense) the difference between the observed and calculated data.

Weber, Renee C.↗
NASA NTRS1974

Analytical and experimental investigation of a 1/8-scale dynamic model of the shuttle orbiter. Volume 3B: Supporting data

The NASA Structural Analysis System (NASTRAN) Model 1 finite element idealization, input data, and detailed analytical results are presented. The data presented include: substructuring analysis for normal modes, plots of member data, plots of symmetric free-free modes, plots of antisymmetric free-free modes, analysis of the wing, analysis of the cargo doors, analysis of the payload, and analysis of the orbiter.

Mason, P. W.↗
DOE OSTI2024

Updates to the ATLAS Data Carousel Project

The High Luminosity upgrade to the LHC (HL-LHC) is expected to deliver scientific data at the multi-exabyte scale. In order to address this unprecedented data storage challenge, the ATLAS experiment launched the Data Carousel project in 2018. Data Carousel is a tape-driven workflow whereby bulk production campaigns with input data resident on tape are executed by staging and promptly processing a sliding window to disk buffer such that only a small fraction of inputs are pinned on disk at any one time. Data Carousel is now in production for ATLAS in Run3. In this paper, we provide updates on recent Data Carousel R&D projects, including data-on-demand and tape smart writing. Data-on-demand removes from disk data that has not been accessed for a predefined period, when users request them, they will be either staged from tape or recreated by following the original production steps. Tape smart writing employs intelligent algorithms for file placement on tape in order to retrieve data back more efficiently, which is our long term strategy to achieve optimal tape usage in Data Carousel.

42 ENGINEERING↗
NASA NTRS2003

XML: James Webb Space Telescope Database Issues, Lessons, and Status

This paper will present the current concept using extensible Markup Language (XML) as the underlying structure for the James Webb Space Telescope (JWST) database. The purpose of using XML is to provide a JWST database, independent of any portion of the ground system, yet still compatible with the various systems using a variety of different structures. The testing of the JWST Flight Software (FSW) started in 2002, yet the launch is scheduled for 2011 with a planned 5-year mission and a 5-year follow on option. The initial database and ground system elements, including the commands, telemetry, and ground system tools will be used for 19 years, plus post mission activities. During the Integration and Test (I&T) phases of the JWST development, 24 distinct laboratories, each geographically dispersed, will have local database tools with an XML database. Each of these laboratories database tools will be used for the exporting and importing of data both locally and to a central database system, inputting data to the database certification process, and providing various reports. A centralized certified database repository will be maintained by the Space Telescope Science Institute (STScI), in Baltimore, Maryland, USA. One of the challenges for the database is to be flexible enough to allow for the upgrade, addition or changing of individual items without effecting the entire ground system. Also, using XML should allow for the altering of the import and export formats needed by the various elements, tracking the verification/validation of each database item, allow many organizations to provide database inputs, and the merging of the many existing database processes into one central database structure throughout the JWST program. Many National Aeronautics and Space Administration (NASA) projects have attempted to take advantage of open source and commercial technology. Often this causes a greater reliance on the use of Commercial-Off-The-Shelf (COTS), which is often limiting. In our review of the database requirements and the COTS software available, only very expensive COTS software will meet 90% of requirements. Even with the high projected initial cost of COTS, the development and support for custom code over the 19-year mission period was forecasted to be higher than the total licensing costs. A group did look at reusing existing database tools and formats. If the JWST database was already in a mature state, the reuse made sense, but with the database still needing to handing the addition of different types of command and telemetry structures, defining new spacecraft systems, accept input and export to systems which has not been defined yet, XML provided the flexibility desired. It remains to be determined whether the XML database will reduce the over all cost for the JWST mission.

Detter, Ryan↗
NASA NTRS1993

A petabyte size electronic library using the N-Gram memory engine

A model library containing petabytes of data is proposed by Triada, Ltd., Ann Arbor, Michigan. The library uses the newly patented N-Gram Memory Engine (Neurex), for storage, compression, and retrieval. Neurex splits data into two parts: a hierarchical network of associative memories that store 'information' from data and a permutation operator that preserves sequence. Neurex is expected to offer four advantages in mass storage systems. Neurex representations are dense, fully reversible, hence less expensive to store. Neurex becomes exponentially more stable with increasing data flow; thus its contents and the inverting algorithm may be mass produced for low cost distribution. Only a small permutation operator would be recalled from the library to recover data. Neurex may be enhanced to recall patterns using a partial pattern. Neurex nodes are measures of their pattern. Researchers might use nodes in statistical models to avoid costly sorting and counting procedures. Neurex subsumes a theory of learning and memory that the author believes extends information theory. Its first axiom is a symmetry principle: learning creates memory and memory evidences learning. The theory treats an information store that evolves from a null state to stationarity. A Neurex extracts information data without a priori knowledge; i.e., unlike neural networks, neither feedback nor training is required. The model consists of an energetically conservative field of uniformly distributed events with variable spatial and temporal scale, and an observer walking randomly through this field. A bank of band limited transducers (an 'eye'), each transducer in a bank being tuned to a sub-band, outputs signals upon registering events. Output signals are 'observed' by another transducer bank (a mid-brain), except the band limit of the second bank is narrower than the band limit of the first bank. The banks are arrayed as n 'levels' or 'time domains, td.' The banks are the hierarchical network (a cortex) and transducers are (associative) memories. A model Neurex was built and studied. Data were 50 MB to 10 GB samples of text, data base, and images: black/white, grey scale, and high resolution in several spectral bands. Memories at td, S(m(sub td)), were plotted against outputs of memories at td-1. S(m(sub td)) was Boltzman distributed, and memory frequencies exhibited self-organized criticality (SOC); i.e., 'l/f(sup beta)' after long exposures to data. Whereas output signals from level n may be encoded with B(sub output) = O(-log(2)f(sup beta)) bits, and input data encoded with B(sub input) = O((S(td)/S(td-1))(sup n)), B(sup output)/B(sub input) is much less than 1 always, the Neurex determines a canonical code for data and it is a lossless data compressor. Further tests are underway to confirm these results with more data types and larger samples.

Bugajski, Joseph M.↗
NASA NTRS1976

Inputs requested from earth resources remote sensing data users regarding LANDSAT-C mission requirements and data needs

Inputs from prospective LANDSAT-C data users are requested to aid NASA in defining LANDSAT-C mission and data requirements and in making decisions regarding the scheduling of satellite operations and ground data processing operations. Design specifications, multispectral band scanner performance characteristics, satellite schedule operations, and types of available data products are briefly described.

Source record↗
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↗