Search NASA⌕ Search

SEARCH · Search NASA

Results for “Data model integration”

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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Machine Learning for NASA Advanced Information Systems

NASA's Advanced Information Systems Technology (AIST) Program is one of several Technology programs managed by the Earth Science Technology Office (ESTO) in the Earth Science Division (ESD). The AIST Program focuses on advanced information systems and novel computer science technologies that will be needed by NASA Earth Science in the next 5 to 10 years. The three main thrusts of the AIST Program deal with Novel Observing Strategies (NOS), Analytic Collaborative Frameworks (ACF) and Earth System Digital Twins (ESDT). For all these thrusts, Machine Learning (ML) is increasingly being used in multiple aspects of Earth science systems, e.g., for onboard autonomy and decision making, for the analysis of massive and diverse datasets as well as more recently for developing surrogate models that will represent one of the main components of future Digital Twins of the Earth. Particularly, ESDT technologies developed by the AIST Program will allow to develop integrated Earth Science frameworks that will mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. These information systems will be used for supporting near- and long-term science and policy decisions. ESDT frameworks will build on previously developed AIST capabilities and technologies to integrate interconnected models with continuous streams of observations, data analytics, data assimilation, simulations, advanced visualizations and the ability to conduct "what-if" scenarios. This talk will describe the three thrusts of the AIST Program with a special focus on Machine Learning and how it is being used at all steps of the Earth Science data lifecycle.

Mathematical and Computer Sciences (General)↗
DOE OSTI2024

Tools Assessing Performance (TAP) 2.0

Dmitry Duplyakin will be presenting on the latest research and results in the Tools Assessing Performance (TAP) 2.0 project. This presentation will include updates on the latest data the group has produced, integration of obstacle models in the computational pipeline for distributed wind siting, and the plans for the near-term analysis and validation efforts. The talk will acknowledge the work of collaborators from NREL and three other national labs - ANL, LANL, and PNNL - all contributing to this multi-year project.

distributed wind↗
DOE OSTI2026

Development of Digital Twin-Informed Predictive Maintenance for Critical Components in Advanced Reactors

Small modular reactors (SMRs) and microreactors, along with other advanced reactor (AR) technologies, are key to the future of nuclear energy. For these systems to achieve low operating costs, high reliability, and flexibility across applications, their operation and maintenance must be optimized. Digital twin (DT) technology is one of the technologies that enables real-time (or faster than real-time) monitoring and prognosis of critical components which are vital for operational efficiency, low costs, and enhanced safety of ARs, accelerating their deployment. DT technology provides dynamic virtual representation of physical assets by integrating real-time data, physics-based models, and advanced analytics, which is critical to optimizing the performance of the entire energy system throughout the life cycle. DTs empower engineers and operators to virtually explore different scenarios, configurations, and control strategies, allowing for the identification of optimal solutions that maximize reactor efficiency, safety, and economics.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

REFSafE: A RAG-Enabled Framework for Predictive Risk Analysis and Automated Safety Report Generation in Mission-Critical Environments

Operational safety in mission-critical environments requires AI systems that are accurate, interpretable, and resistant to hallucination. We present an agentic Retrieval-Augmented Generation (RAG) framework, REFSafe, for grounded hazard analysis and automated safety report generation. The system integrates Large Language Models (LLMs) with structured operational data, historical incident repositories, policy documents, and external authoritative sources. Through iterative agentic reasoning, the framework retrieves, verifies, and synthesizes evidence prior to generation, enforcing citation-backed outputs with explicit source attribution (documents, links, and prior events) to ensure traceability and trust. To mitigate hallucinations and unsupported claims, all risk assessments and forecasts are constrained to retrieved evidence, with confidence signals derived from retrieval relevance and source consistency. A transparent pipeline enables subject matter experts (SMEs) to validate predictions, and provide structured feedback, forming a continuous performance calibration loop. Preliminary deployment demonstrates improved reliability in hazard detection and safety/vulnerability report generation. This work advances trustworthy, evidence-grounded AI for predictive safety intelligence in mission-critical operations.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗
NASA NTRS1982

An assessment of the FGGE satellite observing system during SOP-1

The sensitivity of a Goddard Laboratory for Atmospheric Sciences global objective analysis cycle to the addition of FGGE level II-b data is assessed. The GOAS system comprises a predictive continuity provided by a model first-guess forecast integrated from a previous forecast and updated by data gathered in the interim. FGGE data originated in the Jan.-Mar. 1979 period and were acquired by rawinsondes, pilot balloons, surface stations, satellites, ships, and drifting buoys deployed during SOP-1. Focussing on 2-5 and 8-day forecasts, comparisons were made of the 6 hr forecast error at the 300 mb height in three experiments using all, no-satellite (NOSAT), and without rawinsondes or pilot balloons modes. Larger errors occurred in the case of NOSAT, while significant corrections to the GOAS predictions were noted using all the FGGE data. It was concluded that all forecasts were improved by inclusion of full FGGE data sets, including forecasting beyond one week.

Halem, M.↗
DOE OSTI2012

Overview of the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES)

Substantial uncertainties still exist in the scientific understanding of the possible interactions between urban and natural (biogenic) emissions in the production and transformation of atmospheric aerosol and the resulting impact on climate change. The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Carbonaceous Aerosol and Radiative Effects Study (CARES) carried out in June 2010 in Central Valley, California, was a comprehensive effort designed to improve this understanding. The primary objective of the field study was to investigate the evolution of secondary organic and black carbon aerosols and their climate-related properties in the Sacramento urban plume as it was routinely transported into the forested Sierra Nevada foothills area. Urban aerosols and trace gases experienced significant physical and chemical transformations as they mixed with the reactive biogenic hydrocarbons emitted from the forest. Two heavily-instrumented ground sites - one within the Sacramento urban area and another about 40 km to the northeast in the foothills area - were set up to characterize the evolution of meteorological variables, trace gases, aerosol precursors, aerosol size, composition, and climate-related properties in freshly polluted and 'aged' urban air. On selected days, the DOE G-1 aircraft was deployed to make similar measurements upwind and across the evolving Sacramento plume in the morning and again in the afternoon. The NASA B-200 aircraft, carrying remote sensing instruments, was also deployed to characterize the vertical and horizontal distribution of aerosols and aerosol optical properties within and around the plume. This overview provides: a) the scientific background and motivation for the study, b) the operational and logistical information pertinent to the execution of the study, c) an overview of key observations and initial results from the aircraft and ground-based sampling platforms, and d) a roadmap of planned data analyses and focused modeling efforts that will facilitate the integration of new knowledge into improved representations of key aerosol processes in climate models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Fourier functional analysis for unsteady aerodynamic modeling

A method based on Fourier analysis is developed to analyze the force and moment data obtained in large amplitude forced oscillation tests at high angles of attack. The aerodynamic models for normal force, lift, drag, and pitching moment coefficients are built up from a set of aerodynamic responses to harmonic motions at different frequencies. Based on the aerodynamic models of harmonic data, the indicial responses are formed. The final expressions for the models involve time integrals of the indicial type advocated by Tobak and Schiff. Results from linear two- and three-dimensional unsteady aerodynamic theories as well as test data for a 70-degree delta wing are used to verify the models. It is shown that the present modeling method is accurate in producing the aerodynamic responses to harmonic motions and the ramp type motions. The model also produces correct trend for a 70-degree delta wing in harmonic motion with different mean angles-of-attack. However, the current model cannot be used to extrapolate data to higher angles-of-attack than that of the harmonic motions which form the aerodynamic model. For linear ramp motions, a special method is used to calculate the corresponding frequency and phase angle at a given time. The calculated results from modeling show a higher lift peak for linear ramp motion than for harmonic ramp motion. The current model also shows reasonably good results for the lift responses at different angles of attack.

Lan, C. Edward↗
NASA NTRS1991

Fourier functional analysis for unsteady aerodynamic modeling

A method based on Fourier analysis is developed to analyze the force and moment data obtained in large amplitude forced oscillation tests at high angles of attack. The aerodynamic models for normal force, lift, drag, and pitching moment coefficients are built up from a set of aerodynamic responses to harmonic motions at different frequencies. Based on the aerodynamic models of harmonic data, the indicial responses are formed. The final expressions for the models involve time integrals of the indicial type advocated by Tobak and Schiff. Results from linear two- and three-dimensional unsteady aerodynamic theories as well as test data for a 70-degree delta wing are used to verify the models. It is shown that the present modeling method is accurate in producing the aerodynamic responses to harmonic motions and the ramp type motions. The model also produces correct trend for a 70-degree delta wing in harmonic motion with different mean angles-of-attack. However, the current model cannot be used to extrapolate data to higher angles-of-attack than that of the harmonic motions which form the aerodynamic model. For linear ramp motions, a special method is used to calculate the corresponding frequency and phase angle at a given time. The calculated results from modeling show a higher lift peak for linear ramp motion than for harmonic ramp motion. The current model also shows reasonably good results for the lift responses at different angles of attack.

Chin, Suei↗
NASA NTRS2019

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has been hypothesized by local ecologists to result in the conversion of forest to grassland and subsequent increased fire danger. This hypothesis stands in contrast to empirical studies in the continental US which suggested that beetle mortality has only a negligible effect on fire danger. In response, we conducted a study using Landsat data and modeling techniques to map land cover change in the Kenai Peninsula and to integrate change maps with other geospatial data to predictively map fire danger for the same region. We collected Landsat imagery to map land cover change at roughly five-year intervals following a severe, mid-1990s beetle infestation to the present. Landcover classification was performed at each time step and used to quantify grassland encroachment patterns over time. The maps of land cover change along with digital elevation models (DEMs),temperature, and historical fire data were used to map and assess wildfire danger across the study area. Results indicate the highest wildfire danger tended to occur in herbaceous and black spruce land cover types, suggesting that the relationship between spruce beetle damage and wildfire danger in costal Alaskan forested ecosystems differs from the relationship between the two in the forests of the coterminous United States. These change detection analyses and fire danger predictions provide the Kenai National Wildlife Refuge (KENWR) ecologists and other forest managers a better understanding of the extent and magnitude of grassland conversion and subsequent change in fire danger following the 1990s spruce beetle outbreak.

wildfire↗
DOE OSTI2026

Calibration of urban building energy model using smart meter data for district peak load prediction

Urban building energy modeling (UBEM) is a powerful approach to assessing baseline building energy performance and retrofits with new technologies across building stocks in cities. However, the accuracy of UBEM is often constrained by the limited availability of reliable data about building characteristics and operations, such as envelope efficiency levels, HVAC system performance, and end-use load patterns. Existing research has performed UBEM calibration using annual or monthly energy consumption data, which falls short when higher-resolution time series applications are needed, such as peak load prediction for utility operation planning. This study presents a new framework for calibrating building energy models at urban scale using smart meter data, targeting the accurate prediction of summer peak electricity loads to support robust grid planning. The framework first integrates various data sources to enhance baseline input assumptions for building models, and then calibrates the baseline models through a pattern-matching approach. A case study using CityBES and two years of AMI data from over 9000 residential customers in Portland, Oregon, demonstrated the workflow and its effectiveness. The calibrated models achieved a daily peak load mean absolute percentage error of 2.6 % during the heatwave in the calibration year, and 2.0 % in the validation year using another year of AMI data. Using the calibrated models, we analyzed the demand flexibility potential of the district building stock as an application of UBEM calibration. The findings affirm the appropriate use of UBEM for peak electric load forecasting and demand side management at the utility distribution system level.

AMI data↗
NASA NTRSDate not supplied

TPSAS-NF1676L-16833-DND

Semantic Infrastructure is central to realizing the first goal of the ASDC's Strategic Plan: expanding the ASDC's customer base by improving access to ASDC data. ASDC data comprises a widely heterogeneous set of complex products which presents two significant challenges in data access: Helping customers discover, among many available options, the most suitable data products for their purpose; and Guiding customers to easily and appropriately use products. Data products differ significantly in terms of how the data was collected and processed, even with similar subject matter. Understanding differences is critical to using data effectively. To reach a broader customer range, the ASDC must provide prospective users with enough information to quickly and meaningfully compare and evaluate data products. Data formats and structures also differ among products. Applications displaying and analyzing data need access to federated and semantically disambiguated data. Semantic technologies offer functionality for addressing this issue. Ontologies can provide robust, stable domain models serving as common schema for discovering, evaluating, comparing, and integrating data from disparate products. Reasoning engines and triple stores can leverage ontologies to support intelligent search applications allowing users to discover, query, retrieve, and easily reformat data from a broad spectrum of sources.

Beth Huffer↗
NASA NTRS2024

Asheville Urban Development II: Mapping Urban Heat to Support Cooling Initiatives and Climate Resilience Planning in the Greater Asheville Area

Asheville, North Carolina experiences the urban heat island effect, where temperatures in the city are higher than in surrounding rural areas. This effect intensifies with increased urbanization and less vegetative cover. Asheville’s urban heat island was exacerbated by population increases and tree cover decline, escalating the need for heat mitigation. We partnered with the City of Asheville’s Sustainability Department and Asheville GreenWorks whose actions prioritize sustainable city planning and equitable climate resilience. Using NASA Earth observations and ancillary datasets we spatially mapped urban heat, heat vulnerability, and cooling and adaptive capacity from 2019-2023. To map urban heat, we used Landsat 8 and 9 Operational Land Imager and Thermal Infrared Sensor for land surface temperature and albedo data and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station for evapotranspiration data. We assessed heat vulnerability using the urban heat data andthe Centers for Disease Control and Prevention’s Social Vulnerability Index. To evaluate cooling and adaptive capacity we used the InVEST Urban Cooling Model, integrating our heat vulnerability analysis with land use and cover data from Sentinel-1 Synthetic Aperture Rada rand Sentinel-2 Multispectral Instrument. Our results revealed distinct spatial patterns of urban heat, heat vulnerability, and cooling and adaptive capacity in Asheville with downtown as the focal hotspot and an outward decreasing radial pattern. These findings highlight targeted need for interventions to reduce heat impacts, address environmental injustices, and enhance climate resilience. Our project provided research to local organizations that can be used for heat mitigation in the greater Asheville area.

Authors not in NED but are confirmed contractors. ↗
DOE OSTI2019

Execution of an Alternative Modeling Strategy for Closure of the Rainier Mesa Corrective Action Unit at the Nevada National Security Site - 19099

The Underground Test Area (UGTA) Project of the U.S Department of Energy (DOE) is investigating radionuclide contaminant movements in groundwater derived from a decades-long period of underground nuclear testing at the Nevada National Security Site (NNSS) to support regulatory corrective action and closure requirements. The project is built around an integrated campaign of drilling and sampling, data interpretation, computer modeling, and broader monitoring activities conducted under a joint Federal Facilities Agreement and Consent Order (1996 as amended) (FFACO) between the State of Nevada, the DOE, and the U.S. Department of Defense. The FFACO strategy is applied to individual Corrective Action Units (CAUs) that roughly envelop disparate geographic testing areas at the NNSS. The Rainier Mesa/Shoshone Mountain (RMSM) CAU is a geologically and hydrologically complex system lying in a remote up-gradient section of the NNSS. It was the site of 67 underground tests conducted between 1957 and 1992, 61 at Rainier Mesa proper, and 6 at Shoshone Mountain, isolated to its south. The original strategy to close the RMSM CAU required a simulation-based investigation to define the evolution of contaminated volumes of groundwater (in 3D) under uncertainty as a means to define a surface-projected contaminant boundary and to serve as a basis for longer term monitoring activities. Early models developed under this strategy proved to be quite complex and difficult to calibrate. Additional costs required to better characterize the system were judged to be out of balance with the low risks associated with this particular CAU. As a result, an Alternative Modeling Strategy (AMS) was adopted. The AMS removes the requirement for developing ensembles of 3D simulations and replaces it with a simpler one requiring the generation of multiple 1D streamline models. These are meant to encompass a range of alternative, yet plausible release and transport scenarios as a means to bound the overall lateral extent of contaminant transport. This paper will review the modeling approach developed under the AMS and its application to the Rainier Mesa portion of the CAU. Preliminary model results made under different parametric assumptions and boundary condition scenarios will be presented. Additional refinements of the model and its further application will be discussed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1993

On the Accuracy of CFD-Based Pressure Drop Predictions for Right-Angle Ducts

The predictive capability of computational fluid dynamics (CFD) codes for turbulent flow through curved ducts is of significant importance to the design and performance analysis of modern rocket engine flowpaths. Code calibration and validation studies for this class of flow are desireable to estimate the performance margin and operating range of components designed using Navier-Stokes methods. Parametric experimental studies such as that of Weske (NACA ARR W-39) provided a wealth of performance data for the design of single- and compound elbow configurations with various cross-sections, curvature and aspect ratios at varying Reynolds numbers. In that work, the majority of data is presented in the form of loss coefficients, characterizing pressure losses due to duct curvature, and including losses due to wall friction. Using measured friction coefficients, losses of equivalent straight lengths of duct are subtracted, resulting in performance curves useful for design computations. These data are currently used in a CFD-based parametric study covering a broad range of operating conditions. Of particular interest for the accuracy of CFD predictions are the effects on pressure loss due to inlet boundary layer thickness (dependent on upstream development length), and the wall treatment for the turbulence equations (conventional wall functions vs. wall integration using a two-layer model). The experimental data are reassessed in the form of an error analysis, and are compared with CFD predictions for 18 computational cases. Grid-independence, grid spacing, and convergence requirements of the cases are discussed. Conclusions regarding the relative importance of the parametric variables will be presented.

Brankovic, Andreja↗
DOE OSTI2011

The Effects of Disturbance and Climate on Carbon Storage and the Exchanges of CO 2 Water Vapor and Energy Exchange of Evergreen Coniferous Forests in the Pacific Northwest: Integration of Eddy Flux, Plant and Soil Measurements at a Cluster of Supersites. Final report

This is the final technical report containing a summary of all findings with regard to the following objectives of the project: (1) To quantify and understand the effects of wildfire on carbon storage and the exchanges of energy, CO2, and water vapor in a chronosequence of ponderosa pine (disturbance gradient); (2) To investigate the effects of seasonal and interannual variation in climate on carbon storage and the exchanges of energy, CO2, and water vapor in mature conifer forests in two climate zones: mesic 40-yr old Douglas-fir and semi-arid 60-yr old ponderosa pine (climate gradient); (3) To reduce uncertainty in estimates of CO2 feedbacks to the atmosphere by providing an improved model formulation for existing biosphere-atmosphere models; and (4) To provide high quality data for AmeriFlux and the NACP on micrometeorology, meteorology, and biology of these systems. Objective (1): A study integrating satellite remote sensing, AmeriFlux data, and field surveys in a simulation modeling framework estimated that the pyrogenic carbon emissions, tree mortality, and net carbon exchange associated with four large wildfires that burned ~50,000 hectares in 2002-2003 were equivalent to 2.4% of Oregon statewide anthropogenic carbon emissions over the same two-year period. Most emissions were from the combustion of the forest floor and understory vegetation, and only about 1% of live tree mass was combusted on average. Objective (2): A study of multi-year flux records across a chronosequence of ponderosa pine forests yielded that the net carbon uptake is over three times greater at a mature pine forest compared with young pine. The larger leaf area and wetter and cooler soils of the mature forest mainly caused this effect. A study analyzing seven years of carbon and water dynamics showed that interannual and seasonal variability of net carbon exchange was primarily related to variability in growing season length, which was a linear function of plant-available soil moisture in spring and early summer. A multi-year drought (2001-2003) led to a significant reduction of net ecosystem exchange due to carry-over effects in soil moisture and carbohydrate reserves in plant-tissue. In the same forest, the interannual variability in the rate carbon is lost from the soil and forest floor is considerable and related to the variability in tree growth as much as it is to variability in soil climatic conditions. Objective (3): Flux data from the mature ponderosa pine site support a physical basis for filtering nighttime data with friction velocity above the canopy. An analysis of wind fields and heat transport in the subcanopy at the mesic 40-year old Douglas site yielded that the non-linear structure and behavior of spatial temperature gradients and the flow field require enhanced sensor networks to estimate advective fluxes in the subcanopy of forest to close the surface energy balance in forests. Reliable estimates for flux uncertainties are needed to improve model validation and data assimilation in process-based carbon models, inverse modeling studies and model-data synthesis, where the uncertainties may be as important as the fluxes themselves. An analysis of the time scale dependence of the random and flux sampling error yielded that the additional flux obtained by increasing the perturbation timescale beyond about 10 minutes is dominated by random sampling error, and therefore little confidence can be placed in its value. Artificial correlation between gross ecosystem productivity (GEP) and ecosystem respiration (Re) is a consequence of flux partitioning of eddy covariance flux data when GEP is computed as the difference between NEE and computed daytime Re (e.g. using nighttime Re extrapolated into daytime using soil or air temperatures). Tower-data must be adequately spatially averaged before comparison to gridded model output as the time variability of both is inherently different. The eddy-covariance data collected at the mature ponderosa pine site and the mesic Douglas fir site were used to develop and evaluate a new method to extract the signal of ecosystem respiration directly from daytime net ecosystem exchange. This approach may help reducing uncertainty in carbon budgets by providing direct measurements of ecosystem respiration during daylight conditions by replacing modeled estimates. Objective (4): We submitted our flux and biological and ancillary data to the AmeriFlux web site and to Fluxnet. This includes atmospheric carbon, water, and heat fluxes, soil fluxes, NPP, carbon stocks, LAI, and disturbance history. Fluxnet is updating the original La Thuile files and will include the more recent years of data. They will be using a new approach to compute GPP, following discussions within the network about the need to improve GPP methodology

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Managed Development Environment Successes for MSFC's VIPA Team

This paper outlines the best practices of the Vehicle Design Team for VIPA. The functions of the VIPA Vehicle Design (VVD) discipline team are to maintain the controlled reference geometry and provide linked, simplified geometry for each of the other discipline analyses. The core of the VVD work, and the approach for VVD s first task of controlling the reference geometry, involves systems engineering, top-down, layout-based CAD modeling within a Product Data Manager (PDM) development environment. The top- down approach allows for simple control of very large, integrated assemblies and greatly enhances the ability to generate trade configurations and reuse data. The second VVD task, model simplification for analysis, is handled within the managed environment through application of the master model concept. In this approach, there is a single controlling, or master, product definition dataset. Connected to this master model are reference datasets with live geometric and expression links. The referenced models can be for drawings, manufacturing, visualization, embedded analysis, or analysis simplification. A discussion of web based interaction, including visualization, between the design and other disciplines is included. Demonstrated examples are cited, including the Space Launch Initiative development cycle, the Saturn V systems integration and verification cycle, an Orbital Space Plane study, and NASA Exploration Office studies of Shuttle derived and clean sheet launch vehicles. The VIPA Team has brought an immense amount of detailed data to bear on program issues. A central piece of that success has been the Managed Development Environment and the VVD Team approach to modeling.

Finckenor, Jeff↗