Search NASA⌕ Search

SEARCH · Search NASA

Results for “data 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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO 2 and a Gradient of Experimental Warming

The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon–flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux– versus pool–based carbon cycle variables and (2) the time points when temperature and CO 2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data–model integration. EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO 2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux–related variables than model parameters. However, the parameter uncertainty primarily contributes to the uncertainty in forecasting C pool–related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast–turnover pools to various CO 2 and warming treatments were observed sooner than slow–turnover pools. In conclusion, our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An integrated online radioassay data storage and analytics tool for nEXO

Large-scale low-background detectors are increasingly used in rare-event searches as experimental collaborations push for enhanced sensitivity. However, building such detectors, in practice, creates an abundance of radioassay data especially during the conceptual phase of an experiment when hundreds of materials are screened for radiopurity. A tool is needed to manage and make use of the radioassay screening data to quantitatively assess detector design options. We have developed a Materials Database Application for the nEXO experiment to serve this purpose. Furthermore, this paper describes this database application, explains how it functions, and discusses how it streamlines the design of the experiment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Integrating geophysical monitoring data into multiphase fluid flow reservoir simulation

Simulation of multiphase flow systems are of critical importance in managing hydrological systems. Flow simulations are affected by a number of factors including structure and flow properties including porosity and permeability as well as the anisotropy and heterogeneity of these properties. In many cases traditional hydrological and reservoir data are highly affected by these parameters, but are not directly sensitive to them. As such modellers often adjust these parameters in an ad hoc manner until solutions numerically converge. Simulation models are generally based on structural data from reflection seismics whose physical flow properties are then populated using geostatistical extrapolation techniques utilizing a sparse number of borehole logs and core analysis. In multiphase systems including enhanced oil recovery and carbon capture and sequestration uncertainties regarding phase-dependent physical properties confounds this challenge further. Geophysical methods provide a means by which to gain an improved understanding of phase distributions in the subsurface. In this paper we will look at applications from active carbon capture and sequestration and enhanced oil recovery applications, as well as synthetic examples. Geophysical data including electromagnetic and gravity are inverted using structural constraints from the reservoir model. Inversions are then mapped into flow properties using calibrated relations such as Archie’s Equation. The coupled models can then be used to both verify and improve on the reservoir flow model which improves it’s predictive power and utility as a management tool.

02 PETROLEUM↗
DOE OSTI2014

Semantic search integration to climate data

In this paper we present how research projects at Oak Ridge National Laboratory are using Semantic Search capabilities to help scientists perform their research. We will discuss how the Mercury metadata search system, with the help of the semantic search capability, is being used to find, retrieve, and link climate change data. DOI: 10.1109/CTS.2014.6867639

Devarakonda, Ranjeet↗
DOE OSTI2013

Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint

As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecasts relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.

17 WIND ENERGY↗
DOE OSTI2015

Methodologies for Reservoir Characterization Using Fluid Inclusion Gas Chemistry

The purpose of this project was to: 1) evaluate the relationship between geothermal fluid processes and the compositions of the fluid inclusion gases trapped in the reservoir rocks; and 2) develop methodologies for interpreting fluid inclusion gas data in terms of the chemical, thermal and hydrological properties of geothermal reservoirs. Phase 1 of this project was designed to conduct the following: 1) model the effects of boiling, condensation, conductive cooling and mixing on selected gaseous species; using fluid compositions obtained from geothermal wells, 2) evaluate, using quantitative analyses provided by New Mexico Tech (NMT), how these processes are recorded by fluid inclusions trapped in individual crystals; and 3) determine if the results obtained on individual crystals can be applied to the bulk fluid inclusion analyses determined by Fluid Inclusion Technology (FIT). Our initial studies however, suggested that numerical modeling of the data would be premature. We observed that the gas compositions, determined on bulk and individual samples were not the same as those discharged by the geothermal wells. Gases discharged from geothermal wells are CO 2 -rich and contain low concentrations of light gases (i.e. H 2 , He, N, Ar, CH4). In contrast many of our samples displayed enrichments in these light gases. Efforts were initiated to evaluate the reasons for the observed gas distributions. As a first step, we examined the potential importance of different reservoir processes using a variety of commonly employed gas ratios (e.g. Giggenbach plots). The second technical target was the development of interpretational methodologies. We have develop methodologies for the interpretation of fluid inclusion gas data, based on the results of Phase 1, geologic interpretation of fluid inclusion data, and integration of the data. These methodologies can be used in conjunction with the relevant geological and hydrological information on the system to create fluid models for the system. The hope is that the methodologies developed will allow bulk fluid inclusion gas analysis to be a useful tool for estimating relative temperatures, identifying the sources and origins of the geothermal fluids, and developing conceptual models that can be used to help target areas of enhanced permeability.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Integrated Multi Scale Data Analytics and Machine Learning for the Grid; Benchmarking Algorithms and Data Quality Analysis

Machine Learning and Data Analytics are terms which are utilized now ubiquitously throughout grid analytics forums, as a potential solution to the growing big data challenges for the distribution grid. As sensors become inexpensive and common, and communication networks grow, the analytics themselves must adapt and enable consumers and providers to visualize and utilize the data provided effectively. Machine learning is one of the techniques proposed in order to manage this data deluge. This report is a subset of the work in this activity, funded through the DOE Grid Modernization Initiative, to summarize the benchmarking and review of data quality impact on the algorithm sets being developed within each use case area.

42 ENGINEERING↗
DOE OSTI2019

Extending the Blockchain: Ensuring Transactional Integrity in Relational Data via Blockchain Technology

The idea of combining a relational database with concepts associated with blockchain technology is being examined. The use case outlined herein concurrently utilizes relational and blockchain data structures to facilitate the sharing of complex data among untrusted users without the participation of a trusted third party. By capturing the transactions that result in data being stored in a relational database and placing those transactions in a blockchain structure, users can be assured that the information has not been created, altered, or destroyed without the knowledge of all users of the data. Capturing relational database transactions via a blockchain structure presents several challenges. Among them are increased storage capacity requirements, transaction ordering prior to placement on the blockchain within a multiuser database, use of existing or creation of new consensus mechanisms, and timely data verification. Addressing these challenges, although not unsurmountable, will introduce trade-offs (e.g., increased system sizing, slower transaction execution) that might or might not be acceptable based upon the envisioned use. If these trade-offs are acceptable, then this concept of extending the blockchain is a powerful method to provide both applications and their users immutable, trusted, well-defined, and consistent data.

Ray, Brian↗