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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

All sources NASA NTRS DOE OSTI

NASA NTRS1982

Yet another method for triangulation and contouring for automated cartography

An algorithm is presented for hierarchical subdivision of a set of three-dimensional surface observations. The data structure used for obtaining the desired triangulation is also singularly appropriate for extracting contours. Some examples are presented, and the results obtained are compared with those given by Delaunay triangulation. The data points selected by the algorithm provide a better approximation to the desired surface than do randomly selected points.

De Floriani, L.↗

NASA NTRS1991

Void statistics of the CfA redshift survey

Clustering properties of two samples from the CfA redshift survey, each containing about 2500 galaxies, are studied. A comparison of the velocity distributions via a K-S test reveals structure on scales comparable with the extent of the survey. The void probability function (VPF) is employed for these samples to examine the structure and to test for scaling relations in the galaxy distribution. The galaxy correlation function is calculated via moments of galaxy counts. The shape and amplitude of the correlation function roughly agree with previous determinations. The VPFs for distance-limited samples of the CfA survey do not match the scaling relation predicted by the hierarchical clustering models. On scales not greater than 10/h Mpc, the VPFs for these samples roughly follow the hierarchical pattern. A variant of the VPF which uses nearly all the data in magnitude-limited samples is introduced; it accounts for the variation of the sampling density with velocity in a magnitude-limited survey.

Vogeley, Michael S.↗

NASA NTRS2018

ESIP Documentation Cluster Session: GCMD Keyword Update

The Global Change Master Directory (GCMD) Keywords are a hierarchical set of controlled Earth Science vocabularies that help ensure Earth science data and services are described in a consistent and comprehensive manner and allow for the precise searching of collection-level metadata and subsequent retrieval of data and services. Initiated over twenty years ago, the GCMD Keywords are periodically analyzed for relevancy and will continue to be refined and expanded in response to user needs. This talk explores the current status of the GCMD keywords, the value and usage that the keywords bring to different tools/agencies as it relates to data discovery, and how the keywords relate to SWEET (Semantic Web for Earth and Environmental Terminology) Ontologies.

data discover↗

NASA NTRSDate not supplied

Generation of Continental Scale Percent Tree Cover Product Using Deep-learning and Multi-scale Remote Sensing Data

Spatially explicit percent tree cover (TC) estimation is critical for mapping forest aboveground biomass and its dynamics. While various TC products have been developed, there has not been a generalized framework that can be applied to diverse terrestrial ecosystems due to underlain extreme complexities. Deep learning algorithms can learn a spatial pattern and radiometric characteristics of tree canopy as a robust approximation of physical or empirical models, and thus have emerged as promising and efficient tools for large-scale TC mapping. In this study, we synergistically use very high-resolution aerial imageries (National Agriculture Imagery Program, NAIP) and medium resolution Landsat data to map continental-scale TC (CONUS and Mexico) through a hierarchical deep learning approach (Convolutional Neural Network), i.e., NAIP TC generated from a NAIP model is utilized to train a Landsat model. The produced TC product (hereafter, NEX-TC) is able to capture the spatial pattern of TC distribution and its changes driven by natural disturbance and human land management. We further explore and analyze the reliability and potential uncertainty of the NEX-TC by comparing it to lidar- (lidar-TC), National Land Cover Database (NLCD-TC), and MODIS Vegetation Continuous Field (MODIS-TC). This evaluation practice reveals that TC products based on passive optical sensors tend to underestimate TC across all land cover types while Landsat-based TCs (i.e., NEX-TC & NLCD-TC) perform better than the coarser MODIS TC estimate. Our results show that the NEX-TC is generally comparable to NLCD-TC but it particularly outperforms NLCD-TC and MODIS-TC over the dense forests where lidar-TC indicates >80% TC. These results indicate that our hierarchical deep learning approach and TC product will be effective and useful for characterizing large-scale tree cover and possibly associated carbon dynamics.

Landsat↗

NASA NTRSDate not supplied

Keywords for All People: How Keyword Governance and Coordination with the NASA ESDIS Standards Coordination Office (ESCO) Improves GCMD Keywords for Discuvery and Use

The Global Change Master Directory (GCMD) Keywords, initiated over twenty years ago, are a hierarchical set of controlled Earth Science vocabularies that help ensure Earth science data, services, and variables are described in a consistent and comprehensive manner and allow for the precise searching of metadata and subsequent retrieval of data, services, and variables. GCMD keywords are periodically analyzed for relevancy and will continue to be refined and expanded in response to user needs. The periodic analysis is a result of successful coordination with the ESDIS Standards Coordination Office (ESCO), which is responsible for standards activities across ESDIS, and assists with providing valuable stakeholder and subject matter expert (SME) feedback on GCMD vocabularies. The ESCO is also turning to the GCMD to discuss how the keyword review process can be improved and more streamlined. In addition to the ESCO, keyword requests and feedback are also received through the GCMD Keyword Forum.

Tyler Stevens↗

NASA NTRSDate not supplied

MAPL3: A Hierarchical Framework Based on ESMF

MAPL is a hierarchical, ESMF-based, coupling framework developed to support NASA's GEOS data assimilation system, but also now supports some external applications from partner institutions. The framework continues to evolve in response novel coupling requirements and is currently undergoing a substantial re-engineering effort to improve extensibility and maintainability. Some of the major new features that will be provided in the upcoming release of MAPL3 include the following: (1) "weak" NUOPC compliance that expresses a MAPL hierarchy as single NUOPC model, (2) component extensions that represent an improvement over conventional component couplers in many contexts, (3) an expansion of the concept of import and export to include component "services" such as advection, turbulence, etc., (4) automated OpenMP threading of column-based component subtrees, (5) and (5) improved I/O scalability.

GEOS↗

DOE OSTI2020

Mobility, Energy, and Electric Vehicle Typology for New York State

Mobility patterns, technology adoption and associated energy outcomes vary tremendously across settlement types. This paper explores how a highly geographically resolved exploration of the social, economic, techno-infrastructural and environmental domains of New York State is key to understanding observed variations in transportation technology adoption and associated mobility and energy outcomes. Current socioeconomic and mobility data sets at the census block group level are integrated in a hierarchical clustering approach to show how variations in mobility and energy outcomes are shaped by these domains - which may enhance progress on investments, or effectively inform planning and other decisions for state-wide goals. The clustering produces four settlement types to predict dependent variables of electric vehicle (EV) adoption rates, commute mode, vehicle fuel economy, and vehicles per household. This typology shows EV adoption rates among the core urban population, which is wealthy and highly-educated, are high - 3 EVs/1000 vehicles versus 1 EV/1000 vehicles among the urban working class. Commuting mode is closely linked with population and employment density - more than 90% of core urbanites use transit or active modes, compared with only 22% of suburbanites and 17% of rural residents. Household vehicle ownership also varies, with 2 vehicles per household in rural areas and only 0.5 in core urban settings. Important findings on differences among the rural, suburban, urban, and urban core settlement types suggest a need to explore how best to manage and anticipate very different types of services that may be supportive in achieving energy-efficient and affordable mobility systems state-wide.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data

The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Report for the ASCR Workshop on the Management and Storage of Scientific Data

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Design of a supervisory control system for autonomous operation of advanced reactors

Advanced reactors to be deployed in the coming decades will face deregulated energy markets, and may adopt flexible operation to boost profitability. To aid in the transition from baseload to flexible operation paradigm, autonomous operation is sought. This work focuses on the control aspect of autonomous operation. Specifically, a hierarchical control system is designed to support constraint enforcement during routine operational transients. Within the system, data-driven modeling, physics-based state observation, and classical control algorithms are integrated to provide an adaptable and robust solution. A 320 MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design basis for demonstrating the proposed control system. The hierarchical control system consists of a supervisory layer and low-level layer. The supervisory layer receives requests to change the system's operating conditions (e.g., the current reactor power to meet a load -follow), and accepts or rejects them based on constraints that have been assigned. Constraints are issued to keep the plant within an optimal operating region. The low-level layer interfaces with the actuators of the system to fulfill requested changes, while maintaining tracking and regulation duties. Further, to accept requests at the supervisory layer, the Reference Governor algorithm was adopted. To model the dynamics of the reactor, a system identification algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution of process variables that cannot be directly measured (e.g., the propagation of delayed neutron precursors), the Unscented Kalman Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The composition of these algorithms led to a numerical demonstration of constraint enforcement during a 40% power drop transient (at a rate of 5 %/min). Uncontrolled secondary-side temperatures were successfully constrained. Adaptability of the proposed system was demonstrated by modifying the constraint values, and enforcing them during the transient. Robustness was also demonstrated by enforcing constraints under noisy environments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS2020

Bayesian Statistical Models for Community Annoyance Survey Data

This paper demonstrates the use of two Bayesian statistical models to analyze single-event sonic boom exposure and human annoyance data from community response surveys. Each model is fit to data from a NASA pilot study.Unlike many community noise surveys, this study used a panel sample to collect multiple observations per participant instead of a single observation. Thus, a multilevel (also known as hierarchical or mixed-effects) model is used to account for the within-subject correlation in the panel sample data. This paper describes a multilevel logistic regression model and a multilevel ordinal regression model. The paper also proposes a method for calculating a summary dose-response curve from the multilevel models that represents the population. The two models’ summary dose-response curves are visually similar. However, their estimates differ when calculating the noise dose at a fixed percent highly annoyed.

Musical instruments↗

DOE OSTI2024

SEAS Communication Engine: An Extensible, Flexible Wrapper for Co-Simulation Agents

When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.

co-simulation↗

DOE OSTI2025

Hot Droughts and Forest Tree Dynamics in the Amazon - Statistical Models, Scripts, Data, and Outputs

This package contains data, outputs, equations, and R scripts for analyses for manuscript entitled "Hot droughts in the Amazon: A window to a future hypertropical climate" by J. Chambers et al., in particular it contains statistical models and analyses for the INPA BIONTE tree mortality study. The Models folder contains details for all statistical models in PDF files. The Scripts folder contains the R scripts for Bayesian Hierarchical Models (two text files) and SEMs (one text file) are separate and reasonably annotated. All data associated with these scripts are in the data folder. The Data folder contains two of the three CSV files used for the analyses and are called by the R scripts. Two of them are part of published datasets (`BIONTE_mortality-rates.csv` from Lima et al. 2024, DOI:10.15486/ngt/1898910 and `SPEI.csv` from Pastorello et al. 2023 DOI:10.15486/ngt/1958257) and also provided in this package for convenience (please see the corresponding datasets for usage and citation terms). The third dataset (`BIONTE_gapfilled_wd.csv`) contains sensitive information and can be obtained by contacting the manuscript lead author. The Outputs folder contains the two output files that provide extra information about the analyses. The file `figuresFeb2025d.pdf` contains all the figures from the manuscript - captions are in the manuscript. The file `ChambersMS.pdf` contains primary results from Bayesian statistical models, regression analyses, and validation steps applied to the tree mortality data from the INPA experiments. The document includes visual summaries, model diagnostics, and leave-one-out (LOO) validation results. A breakdown of file contents can be found in the README file that is part of this package.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Investigating the influence of particle distribution on force and torque statistics using hierarchical machine learning

An accurate representation of hydrodynamic force and torque experienced by every particle in a distribution can be obtained from particle resolved (PR) simulations. These unique quantities are influenced by the deterministic position of surrounding particles. However, systems simulated with this methodology are typically limited to particles due to the involved computational cost. This resource requirement is a major bottleneck in analyzing the effect of variations in particle distribution. Here, this article attempts to address this bottleneck by availing relatively inexpensive deep learning models. The surrogate models that we employ in this article use a physics‐based hierarchical framework and symmetry‐preserving neural networks to achieve robustness with limited training data. This article first performs additional generalizability tests on PR data of distinct distributions that are not involved in the training process. The models are then deployed on several different particle distributions. Impact of clustering and structure on the observed statistics are investigated.

42 ENGINEERING↗

DOE OSTI2020

Learning Hidden Structure in Multi-Fidelity Information Sources for Efficient Uncertainty Quantification (LDRD 218317)

This report summarizes the work done under the Laboratory Directed Research and Development (LDRD) project entitled "Learning Hidden Structure in Multi-Fidelity Information Sources for Efficient Uncertainty Quantification". In this project we investigated multi-fidelity strategies for fusing data from information sources of varying cost and accuracy. Most existing strategies exploit hierarchical relationships between models, for example that occur when different models are generated by refining a numerical discretization parameter. In this work we focused on encoding the relationships between information sources using directed acyclic graphs. The multi-fidelity networks can have general structure and represent a significantly greater variety of modeling relationships than recursive networks used in the current state literature. Numerical results show that a non-hierarchical multi-fidelity Monte Carlo strategy can reduce the cost of estimating uncertainty in predictions of a model of plasma expanding in a vacuum by almost two orders of magnitude.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Verification and Validation Activities for the Multi-Fidelity Toolkit

The Multi-Fidelity Toolkit (MFTK) is a simulation tool being developed at Sandia National Laboratories for aerodynamic predictions of compressible flows over a range of physics fidelities and computational speeds. These models include the Reynolds-Averaged-Navier-Stokes (RANS) equations, the Euler equations, and modified Newtonian aerodynamics (MNA) equations, and they can be invoked independently or coupled with hierarchical Kriging to interpolate between high-fidelity simulations using lower-fidelity data. However, as with any new simulation capability, verification and validation are necessary to gather credibility evidence. This work describes formal code- and solution-verification activities as well as model validation with uncertainty considerations. Code verification is performed on the MNA model by comparing with an analytical solution for flat-plate and inclined-plate geometries. Solution-verification activities include grid-refinement studies of HIFiRE-1 wind tunnel measurements, which are used for validation, for all model fidelities. A thorough treatment of the validation comparison with prediction error and validation uncertainty is also presented.

42 ENGINEERING↗

DOE OSTI2023

Expanded verification and validation studies of hypersonic aerodynamics with multiple physics-fidelity models

Hypersonic aerothermodynamics is an important domain of modern multiphysics simulation. The Multi-Fidelity Toolkit is a simulation tool being developed at Sandia National Laboratories to predict aerodynamic properties for compressible flows from a range of physics fidelities and computational speeds. These models include the Reynolds-averaged Navier–Stokes (RANS) equations, the Euler equations with momentum-energy integral technique (MEIT), and modified Newtonian aerodynamics with flat-plate boundary layer (MNA+FPBL) equations, and they can be invoked independently or coupled with hierarchical Kriging to interpolate between high-fidelity simulations using lower-fidelity data. However, as with any new simulation capability, verification and validation are necessary to gather credibility evidence. This work describes formal code- and solution-verification activities, as well as model validation with uncertainty considerations. Code verification activities on the MNA+FPBL model build on previous work by focusing on the viscous portion of the model. Viscous quantities of interest are compared against those from an analytical solution for flat-plate, inclined-plate, and cone geometries. The code verification methodology for the MEIT model is also presented. Test setup and results of code verification tests on the laminar and turbulent models within MEIT are shown. Solution-verification activities include grid-refinement studies on simulations that model the HIFiRE-1 wind tunnel experiments. These experiments are used for validation of all model fidelities. A thorough validation comparison with prediction error and uncertainty is also presented. Three additional HIFiRE-1 experimental runs are simulated in this study, and the solution verification and validation work examines the effects of the associated parameter changes on model performance. Finally, a study is presented that compares the computational costs and fidelities from each of the different models.

42 ENGINEERING↗

DOE OSTI2026

Bridging Control and Deployment: A Cross-Layer Analysis of Scalable Building Cluster Control

Building cluster control has emerged as a promising approach for enabling flexible and coordinated operation of distributed building systems, yet its transition from pilot demonstrations to routine grid-interactive operation remains limited. This paper argues that this gap cannot be explained by control algorithms alone. Instead, it arises from interacting barriers in communication infrastructure, data and semantic interoperability, uncertainty management, stakeholder participation, market design, and policy support. Accordingly, the paper reviews both technical and non-technical barriers to building cluster control. Technical challenges include heterogeneous devices and protocols, communication latency and reliability, distributed decision-making, and uncertainty propagation across aggregated loads. Non-technical barriers include user participation, stakeholder coordination, incentive allocation, and data governance. Existing solution approaches are synthesized, including semantic interoperability frameworks, edge and hierarchical communication architectures, distributed and transactive control strategies, uncertainty-aware optimization, policy mechanisms, and market reforms. Based on this analysis, two research directions are identified: testing infrastructures that can evaluate control performance under realistic multi-building conditions, and abstraction methods that allow building clusters to interact with other energy sectors through standardized flexibility representations. Overall, the paper provides a structured review of how building cluster control can move from isolated demonstrations toward reproducible, market-compatible, and grid-relevant implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗