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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

Molthan, Andrew↗
DOE OSTI2025

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study

ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Kulyashov, M. A.↗
DOE OSTI2016

Structurally Controlled Geothermal Systems in the Central Cascades Arc-Backarc Regime, Oregon

The goal of this project has been to analyze available magnetotelluric (MT) geophysical surveys, structural geology based on mapping and LiDAR, and fluid geochemical data, to identify high-temperature fluid upwellings, critically stressed rock volumes, and other evidence of structurally-controlled geothermal resources. Data were to be integrated to create conceptual models of volcanic-hosted geothermal resources along the Central Cascades arc segment, especially in the vicinity of Mt. Jefferson to Three Sisters. LiDAR data sets available at Oregon State University (OSU) allowed detailed structural geology modeling through forest canopy. Copious spring and well fluid chemistries, including isotopes, were modeled using Geo-T and TOUGHREACT software.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Nasa Genelab - Knowledge Graph Fabric Enables Deep Biomedical Analysis of Multi-Omics Datasets

The limited number of astronauts and human samples from long-duration space missions pose significant challenges for studying the health risks associated with spaceflight and developing new treatments. As a result, much of our understanding of the biological impact of space travel relies on samples from model organisms. NASA GeneLab, integrated into Open Science Data Repository (OSDR) is a centralized multi-omics resource containing almost 1000 datasets from over 500 space-related studies from human and model organism samples. Previous studies have demonstrated that human phenotypes and physiological changes caused by spaceflight can be identified by connecting gene expression data from model organisms flown in space to a biomedical knowledge graph (SPOKE). In this work, we present a data fabric connecting OSDR datasets to SPOKE that empowers biomedical analyses through the GeneLab visualization portal. This collaboration is funded by NSF’s Proto-OKN program.

data fabric↗
NASA NTRS1992

Integration of satellite and surface data using a radiative-convective oceanic boundary-layer model

A mixing-line boundary-layer model is used to retrieve cloud-top height from satellite-derived cloud-top temperatures, using 700-hPa National Meteorological Center (NMC) analyses and the Comprehensive Ocean and Atmosphere Data Set (COADS) surface data as supporting datasets. Results are compared with the fixed-lapse-rate method of retrieving boundary-layer depth from sea surface temperatures (SST) and cloud-top temperatures. A radiative-convective equilibrium boundary-layer model is used to retrieve boundary-layer structure given SST and surface wind, satellite cloud-top temperatures and cloud fraction, and the 700-hPa NMC thermodynamic analyses. Good agreement is found between the COADS data and the model solutions for low-level temperature and moisture. This suggests that equilibrium boundary-layer models may be of use over remote oceans in the retrieval of boundary-layer structure.

Betts, Alan K.↗
NASA NTRSDate not supplied

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today, as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017 in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating the rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Son Do↗
NASA NTRS2023

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While the InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model’s accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017, in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Urban flooding↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
NASA NTRS2021

Pluto’s Sputnik Planitia: Composition of geological units from infrared spectroscopy

We have compared spectroscopic data of Sputnik Planitia on Pluto, as acquired by New Horizons’ Linear Etalon Imaging Spectral Array (LEISA) instrument, to the geomorphology as mapped by White et al. (2017) using visible and panchromatic imaging acquired by the LOng-Range Reconnaissance Imager (LORRI) and the Multi-spectral Visible Imaging Camera (MVIC). We have focused on 13 of the geologic units identified by White et al. (2017), which include the plains and mountain units contained within the Sputnik basin. We divided the map of Sputnik Planitia into 15 provinces, each containing one or more geologic units, and we use LEISA to calculate the average spectra of the units inside the 15 provinces. Hapke-based modeling was then applied to the average spectra of the units to infer their surface composition, and to determine if the composition resulting from the modeling of LEISA spectra reflects the geomorphologic analyses of LORRI data, and if areas classified as being the same geologically, but which are geographically separated, share a similar composition. We investigated the spatial distribution of the most abundant ices on Pluto’s surface - CH4, N2, CO, H2O, and a non-ice component presumed to be a macromolecular carbon-rich material, termed a tholin, that imparts a positive spectral slope in the visible spectral region and a negative spectral slope longward of ~1.1 μm. Because the exact nature of the non-ice component is still debated and because the negative spectral slope of the available tholins in the near infrared does not perfectly match the Pluto data, for spectral modeling purposes we reference it generically as the negative spectral slope endmember (NSS endmember). We created maps of variations in the integrated band depth (from LEISA data) and areal mass fraction (from the modeling) of the components. The analysis of correlations between the occurrences of the endmembers in the geologic units led to the observation of an anomalous suppression of the strong CH4 absorption bands in units with compositions that are dominated by H2O ice and the NSS endmember. Exploring the mutual variation of the CH4 and N2 integrated band depths with the abundance of crystalline H2O and NSS endmember revealed that the NSS endmember is primarily responsible for the suppression of CH4 absorptions in mountainous units located along the western edge of Sputnik Planitia. Our spectroscopic analyses have provided additional insight into the geological processes that have shaped Sputnik Planitia. A general increase in volatile abundance from the north to the south of Sputnik Planitia is observed. Such an increase first observed and interpreted by Protopapa et al., 2017 and later confirmed by climate modeling (Bertrand et al., 2018) is expressed geomorphologically in the form of preferential deposition of N2 ice in the upland and mountainous regions bordering the plains of southern Sputnik Planitia. Relatively high amounts of pure CH4 are seen at the southern Tenzing Montes, which are a natural site for CH4 deposition owing to their great elevation and the lower insolation they are presently receiving. The NSS endmember correlates the existence of tholins within certain units, mostly those coating the low-latitude mountain ranges that are co-latitudinal with the tholin-covered Cthulhu Macula. The spectral analysis has also revealed compositional differences between the handful of occurrences of northern non-cellular plains and the surrounding cellular plains, all of which are located within the portion of Sputnik Planitia that is presently experiencing net sublimation of volatiles, and which do not therefore exhibit a surface layer of bright, freshly-deposited N2 ice. The compositional differences between the cellular and non-cellular plains here hint at the effectiveness of convection in entraining and trapping tholins within the body of the cellular plains, while preventing the spread of such tholins to abutting non-cellular plains.

Pluto's Sputnik Planitia↗
DOE OSTI2014

Bell Creek Test Site: 3-D Seismic and Characterization Report

The Plains CO 2 Reduction (PCOR) Partnership, led by the Energy & Environmental Research Center (EERC), is working with Denbury Onshore (Denbury) to evaluate the effectiveness of large-scale injection of carbon dioxide (CO 2 ) into the Bell Creek oil field for CO 2 enhanced oil recovery (EOR) and to study long-term incidental CO 2 storage. A technical team that includes Denbury, the EERC, and others are conducting activities to determine the baseline reservoir characteristics for development of a geologic model for predictive simulations and to serve as a comparison to time-lapse data as they are acquired. One of the activities was the acquisition and interpretation of a baseline three-dimensional (3-D) surface seismic survey acquired over a major portion of the field which is the subject of this report. The interpretation will be used to advance the field characterization effort. The geophysical data will be integrated with the EERC’s geologic model to improve its accuracy. In the future, when paired with at least one subsequent 3-D surface seismic survey, the data difference will provide a direct indication of where the CO 2 has migrated within the reservoir and aid in monitoring, verification, and accounting goals.

02 PETROLEUM↗
DOE OSTI2024

A Data-driven approach to Core Power distribution reconstruction in a Nuclear Reactor

This report presents the initial development of a data-driven approach for reconstructing the core power distribution in a nuclear reactor (power shape synthesis) using ex-core sensors. Traditional techniques rely on deploying a large number of detectors throughout the reactor core. However, this approach is not feasible for innovative reactor concepts like Advanced Reactors and Microreactors. First, the tight lattice pitch, designed to maximize power density, limits the space available for sensors. Secondly, the harsh operating conditions are not compatible with commercially available detectors. The method proposed in this work integrates high-fidelity modeling with data-driven techniques to accurately reconstruct power distribution across various reactor types, thereby reducing the reliance on in-core sensors. Purdue University Reactor One (PUR-1) was selected as the test case. The CAD model representing the latest configuration of the PUR-1 core was imported into the OpenMC simulation framework, and the model was built. Additionally, the previously developed MCNP6 model was updated. The two models were assessed against the data collected during an experimental campaign conducted in July 2024. Thirty gold foils were placed in three Irradiation Assemblies in PUR-1 core. Using the measured activity of the irradiated foils, the neutron flux at different core locations was estimated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification

This report summarizes a joint effort between Argonne National Laboratory, Idaho National Laboratory, and Los Alamos National Laboratory to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first employs a phenomenological viscoplastic model for which parameters have been calibrated based on experimental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allows for an engineering-scale model to take into account the characteristics of the underlying microstructure, while only incurring a reasonable computational expense. Both of these approaches have different strengths, and are applicable for different parts of the design/analysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-of-concept uncertainty quantification analyses of a simple component under prototypical conditions. The built- in stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.

42 ENGINEERING↗
NASA NTRSDate not supplied

Characterization of Urban Air Mobility Vehicle Operational Noise and Community Noise Impact

This presentation focuses on the process for predicting urban air mobility (UAM) vehicle noise at the source and how that can be used to estimate and mitigate the impact on the community. Following conceptual design, in which the vehicle is appropriately sized for its intended mission, a comprehensive analysis must be performed for a range of operating conditions spanning the flight envelope to determine the corresponding configurations of the vehicle, that is, the trimmed states. Many UAM vehicles have redundant controls, so the trimmed state for any particular operating condition may not be unique, with some states producing more noise than others. For each trimmed state, the noise produced by each source, for example, steady and unsteady rotor noise, inclusive of propulsion airframe aeroacoustic effects, may be computed and so-called source noise (hemi)spheres generated. The data may be used for analyses supporting noise certification or serve as input to auralizations (turning the numerical data into an audible sound) that can subsequently be used as part of a perception-influenced acoustic design process. Land use planning tools, including those using simulation and integrated modeling approaches, also use these (or derived) data to generate noise exposure maps on the ground. Finally, the data may be used to support development of low noise flight operations using an acoustic flight simulator or acoustically aware flight control.

urban air mobility↗
DOE OSTI2026

Differential Privacy in Grid Kitchen: Implementation & Software Documentation

Sharing of power grid feeder models faces significant challenges due to the potential risk of exposing sensitive operational information. Traditional anonymization techniques have shown notable limitations in other sensitive domains, as evidenced by documented re-identification attacks that combine supposedly anonymized datasets with auxiliary information, raising concerns that similar vulnerabilities could affect power grid data. Consequently, there is a pressing need for a more rigorous privacy protection strategy that not only delivers formal mathematical guarantees but also preserves the analytical value of the shared models. To address this challenge, we have enhanced the Grid Kitchen framework by implementing differential privacy mechanisms within the distribution model dehydration pipeline. This implementation carefully calibrates and applies noise to sensitive attributes in feeder models according to configurable privacy levels—low, moderate, and high—each offering different balances between data utility and privacy protection. Our approach uses established noise functions (Gaussian for continuous data and Discrete Laplace for integer values) with parameters carefully calibrated so that the impact of individual data points is effectively masked in the final output. The integration leverages our Noise Catalog, which we developed to categorize feeder model properties by component type, data type, and sensitivity. This catalog guides the application of appropriate noise functions and privacy parameters ($\varepsilon$ and $\delta$) to each attribute, ensuring consistent privacy protection across the model while maintaining its structural integrity and analytical usefulness. This implementation also includes evaluation tools that allow model owners to assess the impact of privacy-preserving transformations before sharing data with external parties. This report provides documentation for the differential privacy capabilities added to the Grid Kitchen project. It includes a primer on differential privacy concepts and their importance in modern data sharing, details the architecture of our implementation, explains the privacy modes and parameter configurations, and offers practical guidance on using the code for applying differential privacy to grid feeder models. Through examples and code snippets, we demonstrate the effective application of these privacy-enhancing technologies, enabling utility operators and researchers to confidently share grid data while protecting sensitive information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

The introspective may achieve more: Enhancing existing Geoscientific models with native-language emulated structural reflection

Geoscientific models manage myriad and increasingly complex data structures as trans -disciplinary models are integrated. They often incur significant redundancy with cross-cutting tasks. Reflection, the ability of a program to inspect and modify its structure and behavior at runtime, is known as a powerful tool to improve code reusability, abstraction, and separation of concerns. Reflection is rarely adopted in high-performance Geoscientific models, especially with Fortran, where it was previously deemed implausible. Practical constraints of language and legacy often limit us to feather-weight, native-language solutions. We demonstrate the usefulness of a structural-reflection-emulating, dynamically-linked metaObjects, gd . We show real-world examples including data structure self-assembly, effortless save/restart and upgrade to parallel I/O, recursive actions and batch operations. We share gd and a derived module that reproduces MATLAB-like structure in Fortran and C++. We suggest that both a gd representation and a Fortran-native representation are maintained to access the data, each for separate purposes. In conclusion, embracing emulated reflection allows generically-written codes that are highly re-usable across projects.

58 GEOSCIENCES↗
DOE OSTI2022

CityBES v2021

City Buildings, Energy, and Sustainability (CityBES) is a web-based data and computing platform, focusing on energy modeling and analysis of a city's building stock to support district or city-scale building energy efficiency programs. CityBES uses an international open data standard, CityGML, to represent and exchange 3D city models. CityBES employs EnergyPlus to simulate building energy use and savings from energy efficient retrofits. Other CityBES features include energy benchmarking, district heating and cooling system modeling, rooftop PV analysis, building performance visualization, heat resilience modeling, as well as urban scale mapping of microclimate and heat vulnerability at census tract level. Different from other tools, CityBES uses integrated open and standard 3D city building data and models each individual building using EnergyPlus. CityBES can be used by urban planners, city energy managers, building owners, utilities, energy consultants and researchers.

Hong, Tianzhen↗