Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model data volumes”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Model aerodynamic test results for two variable cycle engine coannular exhaust systems at simulated takeoff and cruise conditions. Comprehensive data report. Volume 3: Graphical data book 1

A graphical presentation of the aerodynamic data acquired during coannular nozzle performance wind tunnel tests is given. The graphical data consist of plots of nozzle gross thrust coefficient, fan nozzle discharge coefficient, and primary nozzle discharge coefficient. Normalized model component static pressure distributions are presented as a function of primary total pressure, fan total pressure, and ambient static pressure for selected operating conditions. In addition, the supersonic cruise configuration data include plots of nozzle efficiency and secondary-to-fan total pressure pumping characteristics. Supersonic and subsonic cruise data are given.

Nelson, D. P.↗
DOE OSTI2022

GIS-Based Graphical User Interface Tools for Analyzing Solar Thermal Desalination Systems & High-Potential Implementation

This project developed a user-friendly, open-source, software that enables a comparative evaluation of solar thermal desalination technology options and employs geospatial data layers to identify regions of high-potential for solar thermal desalination. This was accomplished by integrating solar models with desalination models and enhancing their utility by providing GIS-based data inputs. The developed Solar Energy Desalination Analysis Tool (SEDAT) enables techno-economical evaluation of desalination technologies and selection of regions with the highest potential for using solar energy to power desalination plants. It simplifies the planning, design, and valuation of solar thermal and solar hybrid desalination systems in the U.S. and worldwide. SEDAT uses Dash for integrating various layers of large volumes of GIS data with Python-based models of solar energy generation and desalination technologies. It derives time-series of energy generation and water production, with details of plant performance and suggestions for improving the solar-desalination coupling. It is a one of-a-kind tool of analysis representing a definitive advancement in the state-of-the-art. Of solar desalination modeling This report summarizes the various phases of the tool’s development, and presents examples of the results.

14 SOLAR ENERGY↗
DOE OSTI2022

A fusion relevant data-driven engineering void swelling model for 9Cr tempered martensitic steels

The UCSB database on cavity evolution in 9-12Cr tempered martensitic steels (TMS), includes the results for both dual heavy and helium ion (DII), and High Flux Isotope Reactor (HFIR) in situ helium injection (ISHI) neutron irradiations at 500°C. These results were combined with literature single ion and fission neutron irradiation data to derive a model for the void volume fraction, f v , as a function of displacements per atom (dpa) and transmutant helium concentrations in atomic parts per million (appm). The scientific foundation for the paper is described in a companion paper entitled “Cavity Evolution and Void Swelling in Dual Ion Irradiated Tempered Martensitic Steels”. Here, in this study, we show that f v (dpa, He/dpa) is described by the incubation dose, dpa i , for the onset of void growth, and the post-incubation growth rate, f v ’(%/dpa). Both dpa i and f v ’ decrease with increasing He/dpa at > ~ 5. The dpa i is also lower for the ISHI neutron irradiations at the same He/dpa. Single heavy ion and fission reactor neutron irradiations, with low He/dpa ratios, have a much larger dpa i . Based on a combined analysis of DII, single ion, ISHI and fission neutron data, we further show that the post-incubation f v data analyzed here have a common empirical curve shape, with f v ’ reaching up to ~ 0.2%/dpa at very high dpa. We also show that f v ’ can be predicted based on a physical model of defect partitioning between evolving sinks. At 500°C and fusion relevant He/dpa ≈ 10, the best-fit model predicts nominal swelling, S = f v /(1-f v ), of ~ 1.1, 4.9 and 16% at 50, 100 and 200 dpa, respectively. The physically motivated, data-driven model includes estimated uncertainties for both dpa i and f v ’.

36 MATERIALS SCIENCE↗
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

3-D Geological Modeling for Numerical Flow Simulation Studies of Gas Hydrate Reservoirs at the Kuparuk State 7-11-12 Pad in the Prudhoe Bay Unit on the Alaska North Slope

Accurate reservoir evaluation requires reliable three-dimensional (3-D) geological models. Here, this study conducted 3-D geological modeling for numerical flow simulation of the B1 sand gas hydrate reservoir at the Kuparuk State 7-11-12 pad, Prudhoe Bay Unit, Alaska North Slope. The model integrates well logs, core, and seismic data to address spatial heterogeneity in geological structures and reservoir properties. Two modeling types were performed: structural framework modeling and petrophysical property modeling. For structural framework modeling, seismic data and well log markers were used to reproduce subsurface structures characterized by a normal fault system. A volume-based modeling algorithm and stair-stepping grid were applied. The resulting 3-D model comprised 2,640,000 grid cells across 264 layers, including seven fault grids. For petrophysical property modeling, total porosity was initially modeled using sequential Gaussian simulation with collocated cokriging. To reproduce the upward coarsening of the B1 sand, upscaled log-derived total porosity and a three-dimensional (3-D) trend depicting total porosity variation were used as primary and secondary data, respectively. Gas hydrate saturation distribution was modeled similarly, with secondary data from estimated porosity distribution and seismic-derived acoustic impedance map enhancing accuracy. Results indicate higher gas hydrate saturation in the upper part of the B1 sand and areas with higher acoustic impedance. Intrinsic permeability was modeled from the total porosity and clay-bound water volume, and effective permeability was derived from the gas hydrate saturation and intrinsic permeability distributions based on the “Tokyo model”. Effective permeability distributions were influenced by the total porosity, gas hydrate saturation, and intrinsic permeability. Within the same layer, higher gas hydrate saturation leads to decreased effective permeability. In total, 100 sets of multiple scenarios were prepared, providing input data for dynamic flow simulations to evaluate the effects of lateral heterogeneity in reservoir properties and the hydraulic characteristics of faults on production behavior for preassessment before the long-term production test.

58 GEOSCIENCES↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 2: Orbiter configuration

Archived wind tunnel test data are available for flyback booster or other alternative recoverable configurations as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle. Considerable wind tunnel data was acquired by the competing contractors and the NASA centers for an extensive variety of configurations with an array of wing and body planforms. All contractor and NASA wind tunnel test data acquired in the Phase B development have been compiled into a data base and are available for applying to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Data Base is structured by vehicle component and configuration type. Basic components include the booster, the orbiter, and the launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retro-glide and twin body. Orbiter configuration types include straight and delta wings, lifting body, drop tanks, and double delta wings. Launch configuration types include booster and orbiter components in various stacked and tandem combinations.

Glynn, J. L.↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 2: Orbiter configuration

Archived wind tunnel test data are available for flyback booster or other alternate recoverable configurations as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle. Considerable wind tunnel data was acquired by the competing contractors and the NASA centers for an extensive variety of configurations with an array of wing and body planforms. All contractor and NASA wind tunnel test data acquiredin the Phase B development have been compiled into a database and are available for applying to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Database is structured by vehicle component and configuration type. Basic components include the booster, the orbiter, and the launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retroglide, and twin body. Orbiter configuration types include straight and delta wings, lifting body, drop tanks, and double delta wings. Launch configration types include booster and orbiter components in various stacked and tandom combinations. The digital database consists of 220 files of data containing basic tunnel recorded data.

Glynn, J. L.↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 1: Booster configuration

Archived wind tunnel test data are available for flyback booster or other alternative recoverable configurations as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle. Considerable wind tunnel data was acquired by the competing contractors and the NASA Centers for an extensive variety of configurations with an array of wing and body planforms. All contractor and NASA wind tunnel test data acquired in the Phase B development have been compiled into a database and are available for application to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Database is structured by vehicle component and configuration type. Basic components include the booster, the orbiter and the launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retroglide and twin body. Orbiter configuration types include straight and delta wings, lifting body, drop tanks, and double delta wings. Launch configurations include booster and orbiter components in various stacked and tandem combinations. This is Volume 1 (Part 1) of the report -- Booster Configuration.

Glynn, J. L.↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 1: Booster configuration

Archived wind tunnel test data are available for flyback booster or other alternative recoverable configurations as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle. Considerable wind tunnel data was acquired by the competing contractors and the NASA Centers for an extensive variety of configurations with an array of wing and body planforms. All contractor and NASA wind tunnel test data acquired in the Phase B development have been compiled into a database and are available for application to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Database is structured by vehicle component and configuration type. Basic components include the booster, the orbiter, and the launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retroglide and twin body. Orbiter configuration types include straight and delta wings, lifting body, drop tanks and double delta wings. Launch configurations include booster and orbiter components in various stacked and tandem combinations. This is Volume 1 (Part 2) of the report -- Booster Configuration.

Glynn, J. L.↗
NASA NTRS1988

Space shuttle phase B wind tunnel model and test information. Volume 3: Launch configuration

Archived wind tunnel test data are available for flyback booster or other alternate recoverable configuration as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle, including contractor data for an extensive variety of configurations with an array of wing and body planforms. The test data have been compiled into a database and are available for application to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Database is structured by vehicle component and configuration. Basic components include booster, orbiter, and launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retroglide and twin body. Orbiter configurations include straight and delta wings, lifting body, drop tanks and double delta wings. Launch configurations include booster and orbiter components in various stacked and tandem combinations. The digital database consists of 220 files containing basic tunnel data. Database structure is documented in a series of reports which include configuration sketches for the various planforms tested. This is Volume 3 -- launch configurations.

Glynn, J. L.↗
NASA NTRS2018

Progress Towards Integrating the Finite-Volume Cubed-Sphere (FV3) Dynamical Core Tangent Linear and Adjoint Models into JEDI

The Joint Effort for Data assimilation Integration (JEDI) -- led by the Joint Center for Satellite Data Assimilation (JCSDA) -- is an inter-organizational endeavor to develop a common framework for performing data assimilation. This extensive framework will ultimately provide solvers, observation operators, interpolation and model interfaces using object oriented modeling. Two partners involved in JEDI use or plan to use the Finite Volume Cubed-Sphere (FV3) dynamical core to produce weather forecasts; these are NASA's Global Modeling and Assimilation Office and NOAA's National Center for Environment Prediction. In this work we present an update on ongoing efforts to integrate the FV3 tangent linear and adjoint models into the prototype JEDI framework. We setup and run a simple cycled data assimilation experiment using 4DVAR on the cubed sphere grid and with the FV3 tangent linear and adjoint models. Development of the observation operators for JEDI is separately underway. Instead of using real observations a simplified set of simulated observations will be used. We discuss the steps required to bring the FV3 linearized model into the object oriented framework and consider what would be the computational requirements of running this configuration for an operational system. FV3 uses a small time-step to ensure that small scales are well resolved, however this presents design challenges when running 4DVAR with the adjoint. An approach to storing the FV3 model trajectory has been developed that maintains the flexibility of using automatic differentiation. We discuss how this approach is incorporated into the framework. Other important uses of adjoint models include computing observation impacts and singular vectors, we consider how these tools can be included in JEDI.

Holdaway, Daniel↗
NASA NTRS1975

Results of heat transfer tests of an 0.0175-scale space shuttle vehicle model 22 OTS in the NASA-Ames 3.5-foot hypersonic wind tunnel (IH3), volume 4

Heat-transfer data for the 0.0175-scale Space Shuttle Vehicle 3 are presented. Interference heating effects were investigated by a model build-up technique of Orbiter alone, tank alone, second, and first stage configurations. The test program was conducted in the NASA-Ames 3.5-Foot Hypersonic Wind Tunnel at Mach 5.3 for nominal free-stream Reynolds number per foot values of 1.5 x 1,000,000 and 5.0 x 1,000,000.

Foster, T. F.↗
NASA NTRS2020

Beyond Ecosystem Modeling: A Roadmap to Community Cyberinfrastructure for Ecological Data-Model Integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.

Istem Fer↗
DOE OSTI2026

Rapid Evaluation Framework for the CMIP7 Assessment Fast Track

As Earth system models (ESMs) grow in complexity and in volume of output data, there is an increasing need for rapid, comprehensive evaluation of their scientific performance. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require expeditious response for model analyses designed to inform and drive integrated Earth system assessments. To meet this challenge, the Rapid Evaluation Framework (REF), a community-driven platform for benchmarking and performance assessment of ESMs, was designed and developed. The initial implementation of the REF, constructed to meet the near-term needs of the CMIP7 Assessment Fast Track, builds upon four disparate community evaluation and benchmarking tools that are coupled together using the Coordinated Model Evaluation Capabilities (CMEC) framework. The REF runs within a containerized workflow for portability and reproducibility and is aimed at generating and organizing diagnostics covering a variety of model variables. The REF leverages well documented observational datasets to provide assessments of model fidelity across a collection of diagnostics. All diagnostics were identified and selected with community involvement and consultation. Operational integration with the Earth System Grid Federation (ESGF) will permit automated execution of the REF for selected diagnostics as soon as model output data are published on ESGF by the originating modeling centers. The REF is designed to be portable across a range of current computational platforms to facilitate use by modeling centers for assessing the evolution of model versions or gauging the relative performance of CMIP simulations before being published on ESGF. When integrated into production simulation workflows, results from the REF provide immediate quantitative feedback that allows model developers and scientists to quickly identify model biases and performance issues. After the REF is released to the community, its subsequent development and support will be prioritized by an international consortium of scientists and engineers, enabling a broader impact across Earth science disciplines. For instance, the REF will facilitate improvements to models and will enhance confidence in model projections through process-based selection of models based on their performance with respect to observations. Production of reproducible diagnostics and community-based assessments are key features of the REF. Furthermore, providing interoperability with existing evaluation packages assures that contributions from previous community efforts will be available for use in future model intercomparison projects.

Hoffman, Forrest [ORNL] (ORCID:0000000158024134)↗
NASA NTRSDate not supplied

Tools for Performing SBG hyperspectral Observing System Simulation Experiment

One of NASA’s Decadal Survey mission, Surface Biology and Geology (SBG), will include a hyperspectral remote sensing imager, which has a very high spatial resolution and a wide spectral coverage (from UV to Near IR). Unprecedented large data volumes will be generated by the SBG hyperspectral instrument. Before the launch of the new satellite, an Observing System Simulation Experiment (OSSE) can be used to study different designs of the new satellite system. One of the key components in an OSSE study is a radiative transfer model (RTM) or forward model. In this presentation, we will describe a Principal Component-based Radiative Transfer Model (PCRTM) which is capable of simulating atmospheric (TOA) radiance or reflectance spectra from far IR to visible and UV spectral regions (50 wavenumber to 30000 wavenumber) quickly and accurately. Multiple scattering from multiple layers of clouds/aerosols are included in the model. The PCRTM has a very good accuracy relative to reference line-by-line radiative transfer models (LBLRTM), and it saves 3-4 orders of magnitude computational time relative to LBLRTM or MODTRAN. The PCRTM model has been successfully used to analyze large volumes of data from hyperspectral sensors such as AIRS, CrIS, and IASI. It has also been used to perform OSSE studies for the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission. Another useful tool for the OSSE is surface Bidirectional Reflectance Distribution Function (BRDF) database. It is very crucial for the SBG OSSE to include realistic BRDF spectra. Currently, most of the surface reflectance spectra such as those in the ECOSIS and ECOSTRESS are measured at specific observation geometries. We have developed a hyperspectral bidirectional reflectance (HSBR) model which combines Ross-Li BRDF model with the existing reflectance spectral libraries using a principal component analysis. This HSBR model can provide realistic BRDF spectra under various observation conditions. It can also be used to generate realistic BRDF spectra using measurements from multi-band imagers or spectrometers such as MODIS or VIIRS.

Xu Liu↗