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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Inventory Management Improvement Project

The Lawrence Livermore National Laboratory (LLNL) conducts research and development for the National Nuclear Security Agency (NNSA) and its affiliates. The Polymer AM team at LLNL conducts research and development in 3D printing, specifically direct-ink-write, in accordance with LLNL and NNSA missions. The team is composed of machine operators, chemists, testing engineers, and project engineers across multiple lab spaces with one shared storage area at LLNL. The operations use a variety of different consumables and hardware to conduct research applications for LLNL and NNSA. These items are critical to performing operation tasks; if an item is out of stock, operations associated with the respective item could be suspended for weeks. As such, the Polymer AM team manages an inventory of spare consumables and hardware to ensure these items are always available. However, the current management system, an excel sheet managed by the project engineer group, is not intuitive in providing inventory information despite the high labor utilization needed to maintain the system. As such, the team is looking to improve their inventory management that can send notifications regarding inventory needs, accessible to other team members, provide all relevant information to a specific item, and store historical information for budget and operation planning. The proposed solution is a Computer Maintenance Management System (CMMS): a web-based management software that stores inventory information and provides automated notifications. The system will store information on each item including quantity in stock, technical information, supplier information, costs, lead times, and expiration date. The system has a notifications function that can send notifications to the respective team member’s when an item needs to be counted, item inventory is low, or an item is approaching their expiration date. Information for each item can be tracked over time creating data to be used for budget planning and process improvement purposes. Due to its web-based source, the system can be accessed by the respective team member viewing technical information, quantity, and purchasing information.

42 ENGINEERING↗
DOE OSTI2021

A modular methodology for time-domain stochastic seismic wave propagation

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. In this work, the first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM). Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.

58 GEOSCIENCES↗
NASA NTRS2018

Spreading Sequence System for Full Connectivity Relay Network

Fully connected uplink and downlink fully connected relay network systems using pseudo-noise spreading and despreading sequences subjected to maximizing the signal-to-interference-plus-noise ratio. The relay network systems comprise one or more transmitting units, relays, and receiving units connected via a communication network. The transmitting units, relays, and receiving units each may include a computer for performing the methods and steps described herein and transceivers for transmitting and/or receiving signals. The computer encodes and/or decodes communication signals via optimum adaptive PN sequences found by employing Cholesky decompositions and singular value decompositions (SVD). The PN sequences employ channel state information (CSI) to more effectively and more securely computing the optimal sequences.

Kwon, Hyuck M.↗
DOE OSTI2025

Cyber-Informed Engineering Briefing for ABET

Cyber-Informed Engineering (CIE) is an emerging method to integrate cybersecurity considerations into the conception, design, development, and operation of any physical system, energy or otherwise, to mitigate or even eliminate avenues for cyber-enabled attacks.?CIE concepts use design decisions and engineering controls to prioritize defense against the worst possible consequences of cyberattacks facing critical infrastructure systems and asset owners. These slides offer a deep dive into Cyber-Informed Engineering for engineering educators.

42 - ENGINEERING↗
NASA NTRS1992

Updating and expanding the library of materials on NASA Spacelink electronic information system

NASA Spacelink, a proven resource medium, may be accessed over telephone lines or via the Internet by teachers or anyone with a computer or modem. It is a collection of historical and current information on NASA programs and activities. Included in this library is information on a variety of NASA programs, updates on Shuttle status, news releases, aeronautics, space exploration, classroom materials, NASA Educational Services, and computer programs and graphics. The material stored in Spacelink has found widespread use by teachers and others, and is being used to stimulate students, particularly in the area of aerospace science.

Blake, Jean A.↗
DOE OSTI2025

Equation-Free Coarse Control of Distributed Parameter Systems via Local Neural Operators

The control of high-dimensional distributed parameter systems (DPS) remains a challenge when explicit coarse-grained equations are unavailable. Classical equation-free (EF) approaches rely on fine-scale simulators treated as black-box timesteppers. However, repeated simulations for steady-state computation, linearization, and control design are often computationally prohibitive, or the microscopic timestepper may not even be available, leaving us with data as the only resource. We propose a data-driven alternative that uses local neural operators, trained on spatiotemporal microscopic/mesoscopic data, to obtain efficient short-time solution operators. These surrogates are employed within Krylov subspace methods to compute coarse steady and unsteady-states, while also providing Jacobian information in a matrix-free manner. Krylov-Arnoldi iterations then approximate the dominant eigenspectrum, yielding reduced models that capture the open-loop slow dynamics without explicit Jacobian assembly. Both discrete-time Linear Quadratic Regulator (dLQR) and pole-placement (PP) controllers are based on this reduced system and lifted back to the full nonlinear dynamics, thereby closing the feedback loop.

93B52, 93C20, 47N70, 65J15, 65M32, 68T07, 68T20, 6↗
DOE OSTI2021

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive-due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Quasiparticle Couplings in Transport of Heat, Charge, and Spin for Novel Energy Materials. Final report

Understanding the microscopic processes involved in the transport and conversion of energy from the atomic-scale to the meso-scale is critical for the development of next-generation materials for energy conversion and information processing. The goal of this program was to elucidate the couplings between microscopic degrees-of-freedom of atomic vibrations, spins, and electrons, which underpin heat, charge, and spin transport. This project systematically investigated these quasi-particle interactions, using state-of-the-art neutron scattering techniques, combined with first-principles computer simulations. This research program uncovered new information and understanding, which helps to rationalize materials properties based on the interplay of their atomistic degrees of freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. Here we present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

The design and use of an error correction information system for NASTRAN

Error Correction Information System (ECIS) is a system for a two-way transmittal of NASTRAN maintenance information via a data base stored on a nationwide accessible computer. ECIS consists of two data bases. The first data base is used for comments, reporting NASTRAN Software Problem Reports (SPR's) and bookkeeping information which can be updated by the user or the NASTRAN Office. The second data base is used by the NSMO to store all SPR information and updates. The hardware needed by an accessing user is any desktop computer terminal and a telephone to communicate with the central computer. The instruction format is an engineering oriented language and requires less than an hour to obtain a working knowledge of its functions.

Rosser, D. C., Jr.↗
DOE OSTI2020

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

An investigation of the information propagation and entropy transport aspects of Stirling machine numerical simulation

Aspects of the information propagation modeling behavior of integral machine computer simulation programs are investigated in terms of a transmission line. In particular, the effects of pressure-linking and temporal integration algorithms on the amplitude ratio and phase angle predictions are compared against experimental and closed-form analytic data. It is concluded that the discretized, first order conservation balances may not be adequate for modeling information propagation effects at characteristic numbers less than about 24. An entropy transport equation suitable for generalized use in Stirling machine simulation is developed. The equation is evaluated by including it in a simulation of an incompressible oscillating flow apparatus designed to demonstrate the effect of flow oscillations on the enhancement of thermal diffusion. Numerical false diffusion is found to be a major factor inhibiting validation of the simulation predictions with experimental and closed-form analytic data. A generalized false diffusion correction algorithm is developed which allows the numerical results to match their analytic counterparts. Under these conditions, the simulation yields entropy predictions which satisfy Clausius' inequality.

Goldberg, Louis F.↗
NASA NTRS1993

Managing Information On Technical Requirements

Technical Requirements Analysis and Control Systems/Initial Operating Capability (TRACS/IOC) computer program provides supplemental software tools for analysis, control, and interchange of project requirements so qualified project members have access to pertinent project information, even if in different locations. Enables users to analyze and control requirements, serves as focal point for project requirements, and integrates system supporting efficient and consistent operations. TRACS/IOC is HyperCard stack for use on Macintosh computers running HyperCard 1.2 or later and Oracle 1.2 or later.

Mauldin, Lemuel E., III↗
DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1983

User's guide for the Solar Backscattered Ultraviolet (SBUV) and the Total Ozone Mapping Spectrometer (TOMS) RUT-S and RUT-T data sets: October 31, 1978 to November 1, 1980

Raw data from the Solar Backscattered Ultrviolet/Total Ozone Mapping Spectrometer (SBUV/TOMS) Nimbus 7 operation are available on computer tape. These data are contained on two separate sets of RUTs (Raw Units Tapes) for SBUV and TOMS, labelled RUT-S and RUT-T respectively. The RUT-S and RUT-T tapes contain uncalibrated radiance and irradiance data, housekeeping data, wavelength and electronic calibration data, instrument field-of-view location and solar ephemeris information. These tapes also contain colocated cloud, terrain pressure and snow/ice thickness data, each derived from an independent source. The "RUT User's Guide" describes the SBUV and TOMS experiments, the instrument calibration and performance, operating schedules, and data coverage, and provides an assessment of RUT-S and -T data quality. It also provides detailed information on the data available on the computer tapes.

Fleig, A. J.↗
NASA NTRS1987

Intelligent interfaces to aircraft systems

An approach for developing intelligent aiding systems for airline flight crews is proposed. This approach proceeds in four cycles: (1) National Traffic Safety Board data and pilot interviews are used to determine the areas in which pilots require aid; (2) a pilot information processing model is designed; (3) an intelligent pilot-aiding model is developed; and (4) systems for implementing aiding functions are defined. This approach is utilized to design a general situation-response pilot-aiding system. The situation-response model and the structure of a general situation-response pilot-aiding system are described. The computer implementation of the situation-response information processing model is examined.

Schudy, Robert↗
NASA NTRS2011

Software Reuse Methods to Improve Technological Infrastructure for e-Science

Social computing has the potential to contribute to scientific research. Ongoing developments in information and communications technology improve capabilities for enabling scientific research, including research fostered by social computing capabilities. The recent emergence of e-Science practices has demonstrated the benefits from improvements in the technological infrastructure, or cyber-infrastructure, that has been developed to support science. Cloud computing is one example of this e-Science trend. Our own work in the area of software reuse offers methods that can be used to improve new technological development, including cloud computing capabilities, to support scientific research practices. In this paper, we focus on software reuse and its potential to contribute to the development and evaluation of information systems and related services designed to support new capabilities for conducting scientific research.

Marshall, James J.↗
NASA NTRS1972

An input adaptive, pursuit tracking model of the human opertor

Developed and evaluated is a simple model of the input adaptive behavior of the human operator (HO) in a pursuit tracking task in which the plant controlled consists of a pure gain. If it is assumed that the HO is approximately an optimal predictor using only position and velocity information, then there is a simple method of computing the values of the model parameters in terms of the autocorrelation function of the input signal. Experimental evidence indicates that the ability of the HO to use velocity information decreases with increasing signal velocity indicating that a biased estimator of the velocity weighting should be used. A suitable approximation is derived which has rapid convergence and low variance. The model thus derived is compared to actual subject transfer functions and is found to be in close agreement. In addition to tracking random processes the model can adapt to and track deterministic signals, such as sine waves, up to approximately the frequency at which human operators begin to track precognitively.

Ware, J. R.↗