Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “hierarchical data”

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 181 records · Page 10

GPU-Accelerated Solution of the Bethe–Salpeter Equation for Large and Heterogeneous Systems

We present a massively parallel GPU-accelerated implementation of the Bethe–Salpeter equation (BSE) for the calculation of the vertical excitation energies (VEEs) and optical absorption spectra of condensed and molecular systems, starting from single-particle eigenvalues and eigenvectors obtained with density functional theory. The algorithms adopted here circumvent the slowly converging sums over empty and occupied states and the inversion of large dielectric matrices through a density matrix perturbation theory approach and a low-rank decomposition of the screened Coulomb interaction, respectively. Further computational savings are achieved by exploiting the nearsightedness of the density matrix of semiconductors and insulators to reduce the number of screened Coulomb integrals. We scale our calculations to thousands of GPUs with a hierarchical loop and data distribution strategy. The efficacy of our method is demonstrated by computing the VEEs of several spin defects in wide-band-gap materials, showing that supercells with up to 1000 atoms are necessary to obtain converged results. We discuss the validity of the common approximation that solves the BSE with truncated sums over empty and occupied states. In conclusion, we then apply our GW-BSE implementation to a diamond lattice with 1727 atoms to study the symmetry breaking of triplet states caused by the interaction of a point defect with an extended line defect.

Absorption spectra↗

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Observations of the two components of the Abell 98 cluster of galaxies

X-ray and optical observations of A98 show that the cluster is composed of two subclusters. From an estimate of the mass and velocity differences, the two components may merge in about a billion years. These data support the hierarchical clustering model in which clusters occur on all size scales, with larger clusters being formed from mergers of smaller ones.

Henry, J. P.↗

Simulation of complex three-dimensional flows

The concept of splitting is used extensively to simulate complex three dimensional flows on modern computer architectures. Used in all aspects, from initial grid generation to the determination of the final converged solution, splitting is used to enhance code vectorization, to permit solution driven grid adaption and grid enrichment, to permit the use of concurrent processing, and to enhance data flow through hierarchal memory systems. Three examples are used to illustrate these concepts to complex three dimensional flow fields: (1) interactive flow over a bump; (2) supersonic flow past a blunt based conical afterbody at incidence to a free stream and containing a centered propulsive jet; and (3) supersonic flow past a sharp leading edge delta wing at incidence to the free stream.

Diewert, G. S.↗

Integration of geometric modeling and advanced finite element preprocessing

The structure to a geometry based finite element preprocessing system is presented. The key features of the system are the use of geometric operators to support all geometric calculations required for analysis model generation, and the use of a hierarchic boundary based data structure for the major data sets within the system. The approach presented can support the finite element modeling procedures used today as well as the fully automated procedures under development.

Shephard, Mark S.↗

IDEF4 technical report, version 1.0

A language for the representation of object oriented software designs is described. IDEF4, a methodology for object-oriented design, is being developed as a design tool for software designers who use such object-oriented languages. Such languages include the Common LISP Object System, Flavors, C++, Smalltalk, Objective C, and others. Since effective usage of the object-oriented paradigm requires a different thought process than that used with conventional procedural or database languages, standard methodologies such as structure charts, data flow diagrams, and traditional data design models (hierarchical, relational, and network) are not sufficient. IDEF4 seeks to provide the necessary facilities to support the object-oriented design decision making process. Specifically, the two primary design goals of IDEF4 are: (1) to provide support for creating object oriented designs whose implementations will exhibit desirable life cycle qualities and reduce total implementation development time; and (2) to make it easy to evaluate object oriented code to determine whether or not the delivered product both conforms to the design and exhibits the desired life cycle qualities. The application of IDEF4 in the life cycle of a software development project is intended to be focused on those activities after a decision has been made to employ object oriented programming technology, but prior to detailed code specification.

Mayer, Richard J.↗

NASA Tech Briefs, June 2006

Topics covered include: Magnetic-Field-Response Measurement-Acquisition System; Platform for Testing Robotic Vehicles on Simulated Terrain; Interferometer for Low-Uncertainty Vector Metrology; Rayleigh Scattering for Measuring Flow in a Nozzle Testing Facility; "Virtual Feel" Capaciflectors; FETs Based on Doped Polyaniline/Polyethylene Oxide Fibers; Miniature Housings for Electronics With Standard Interfaces; Integrated Modeling Environment; Modified Recursive Hierarchical Segmentation of Data; Sizing Structures and Predicting Weight of a Spacecraft; Stress Testing of Data-Communication Networks; Framework for Flexible Security in Group Communications; Software for Collaborative Use of Large Interactive Displays; Microsphere Insulation Panels; Single-Wall Carbon Nanotube Anodes for Lithium Cells; Tantalum-Based Ceramics for Refractory Composites; Integral Flexure Mounts for Metal Mirrors for Cryogenic Use; Templates for Fabricating Nanowire/Nanoconduit- Based Devices; Measuring Vapors To Monitor the State of Cure of a Resin; Partial-Vacuum-Gasketed Electrochemical Corrosion Cell; Theodolite Ring Lights; Integrating Terrain Maps Into a Reactive Navigation Strategy; Reducing Centroid Error Through Model-Based Noise Reduction; Adaptive Modeling Language and Its Derivatives; Stable Satellite Orbits for Global Coverage of the Moon; and Low-Cost Propellant Launch From a Tethered Balloon

Source record↗

Predicting Patterns of Solar Energy Buildout to Identify Opportunities for Biodiversity Conservation

The construction of solar energy facilities can have positive or negative impacts on biodiversity depending on siting and associated land use transitions. We identified drivers of solar siting and quantified patterns of buildout in states surrounding the Chesapeake Bay watershed – a biodiversity hotspot with numerous ecosystem services. Using a convolutional neural network, we mapped the footprints of ground-mounted solar arrays present in satellite imagery annually from 2017 to 2021 in Delaware, Maryland, Pennsylvania, New York, Virginia, and West Virginia. As of 2021, we identified 958 solar arrays covering 52.3 km2 built primarily on previously cultivated land, while avoiding natural landcover. We fit a binomial-Weibull model to these solar timeseries data in a hierarchical, Bayesian framework to quantify the relationship between geospatial covariates and rate of solar development. Solar array construction rate increased in cultivated areas, areas of lower agricultural suitability, lower slope, lower forest cover, lower biodiversity protection, and greater distances from roads. We also estimated changes in the rate of solar construction over time and found differences among states: acceleration in Virginia and deceleration in New York. We used parameter estimates to map the relative likelihood of future solar development across the study area. This methodology can be used to anticipate where solar is likely to be built in different landscapes and how these patterns align with conservation goals. Around the Chesapeake Bay watershed, the selection of lower quality agricultural areas for solar energy minimizes removal of important habitat and provides opportunities for native plant and pollinator restoration.

Artificial Intelligence↗

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.↗

Rdesign: A data dictionary with relational database design capabilities in Ada

Data Dictionary is defined to be the set of all data attributes, which describe data objects in terms of their intrinsic attributes, such as name, type, size, format and definition. It is recognized as the data base for the Information Resource Management, to facilitate understanding and communication about the relationship between systems applications and systems data usage and to help assist in achieving data independence by permitting systems applications to access data knowledge of the location or storage characteristics of the data in the system. A research and development effort to use Ada has produced a data dictionary with data base design capabilities. This project supports data specification and analysis and offers a choice of the relational, network, and hierarchical model for logical data based design. It provides a highly integrated set of analysis and design transformation tools which range from templates for data element definition, spreadsheet for defining functional dependencies, normalization, to logical design generator.

Lekkos, Anthony A.↗

A Novel Framework for Multi-Path Data Fusion in Earth Observation and New Observing Strategies: Applications to Predicting Forest Canopy Height

Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.

Mark Moussa↗

Isosurface Extraction in Time-Varying Fields Using a Temporal Hierarchical Index Tree

Many high-performance isosurface extraction algorithms have been proposed in the past several years as a result of intensive research efforts. When applying these algorithms to large-scale time-varying fields, the storage overhead incurred from storing the search index often becomes overwhelming. this paper proposes an algorithm for locating isosurface cells in time-varying fields. We devise a new data structure, called Temporal Hierarchical Index Tree, which utilizes the temporal coherence that exists in a time-varying field and adoptively coalesces the cells' extreme values over time; the resulting extreme values are then used to create the isosurface cell search index. For a typical time-varying scalar data set, not only does this temporal hierarchical index tree require much less storage space, but also the amount of I/O required to access the indices from the disk at different time steps is substantially reduced. We illustrate the utility and speed of our algorithm with data from several large-scale time-varying CID simulations. Our algorithm can achieve more than 80% of disk-space savings when compared with the existing techniques, while the isosurface extraction time is nearly optimal.

Shen, Han-Wei↗

Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network

This research is focused on the identification of cracking mechanisms for cement paste using acoustic emission data, recorded from compression and notched four-point bending tests. A procedure is developed for analyzing the data by employing an agglomerative hierarchical clustering method, an artificial neural network, and a ray-tracing source location algorithm. An agglomerative hierarchical clustering method is utilized to cluster the AE data from a compression test using frequency-dependent features. A neural network is trained using the compression test data and applied to the AE data emitted during the four-point bending test. The clustered data from the four-point bending test is localized using a ray-tracing algorithm. Based on the occurrence and locations of the clustered events and signal feature analyses, potential cracking mechanisms are identified and assigned.

36 MATERIALS SCIENCE↗

Equalization in redundant channels

A miscomparison between a channel's configuration data base and a voted system configuration data base in a redundant channel system having identically operating, frame synchronous channels triggers autoequalization of the channel's historical signal data bases in a hierarchical, chronological manner with that of a correctly operating channel. After equalization, symmetrization of the channel's configuration data base with that of the system permits upgrading of the previously degraded channel to full redundancy. An externally provided equalization command, e.g., manually actuated, can also trigger equalization.

Tulpule, Bhalchandra R.↗

Distance and organization in multifunction displays

One approach to organizing information in a multifunction display (MFD) is to place related screens of information closer to each other. This study identified three metrics that could be used to operationalize the concept of distance in an MFD. The proposed distance metrics - navigational (the number of choice points lying between two screens), organizational (the hierarchical structure of the data base), and cognitive (the user's perception of relationships among screens) - were empirically examined by using a simulated, hierarchically arranged, menu-driven MFD in an aviation context. Subjects engaged in two tasks that required them to access different target screens from various starting screens in a 290-screen MFD. The tasks differed in the navigational mechanisms subjects were allowed to use to navigate around the MFD and the relationships between the starting and target screens. The results suggest that the three distance metrics are meaningful within the context of a multifunction display.

Seidler, Karen S.↗

Evaluation of the Radiometric Quality of the TM Data Using Clustering, Linear Transformations and Multispectral Distance Measures

The radiometric quality of LANDSAT 4 TM data for the classification and identification of Earth surface features was evaluated. Techniques employed in the evaluation included clustering, data compression (linear transformations), multispectral distance measures, and hierarchical classification methods. TM and MSS data for the Chicago, Illinois test site were studied. In order to determine the radiometric quality of the TM thermal data for temperature mapping of surface water, a test site was selected within the area covered by the TM scene (Scene ID: 40101-16025) gathered over Illinois. This site was chosen because it includes a surface water body with a large range of temperatures, i.e., a cooling pond for the Dresden nuclear power plant and the junction of two rivers.

Bartolucci, L. A.↗