Search NASA⌕ Search

SEARCH · Search NASA

Results for “data modeling”

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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Integrating Wind Profiling Radars and Radiosonde Observations with Model Point Data to Develop a Decision Support Tool to Assess Upper-Level Winds for Space Launch

On the day of launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds for their launch customers. During launch operations, the payload/launch team sometimes asks the LWOs if they expect the upper-level winds to change during the countdown. The LWOs used numerical weather prediction model point forecasts to provide the information, but did not have the capability to quickly retrieve or adequately display the upper-level observations and compare them directly in the same display to the model point forecasts to help them determine which model performed the best. The LWOs requested the Applied Meteorology Unit (AMU) develop a graphical user interface (GUI) that will plot upper-level wind speed and direction observations from the Cape Canaveral Air Force Station (CCAFS) Automated Meteorological Profiling System (AMPS) rawinsondes with point forecast wind profiles from the National Centers for Environmental Prediction (NCEP) North American Mesoscale (NAM), Rapid Refresh (RAP) and Global Forecast System (GFS) models to assess the performance of these models. The AMU suggested adding observations from the NASA 50 MHz wind profiler and one of the US Air Force 915 MHz wind profilers, both located near the Kennedy Space Center (KSC) Shuttle Landing Facility, to supplement the AMPS observations with more frequent upper-level profiles. Figure 1 shows a map of KSC/CCAFS with the locations of the observation sites and the model point forecasts.

Bauman, William H., III↗
NASA NTRS1998

Penetration Electric Fields and Inner Magnetosphere Dynamics: A Model and Data Comparison

Significant progress has been made on the analysis of the June, 1991 storm. We have completed the analysis of the CRRES and DMSP data sets. We have been able to follow the evolution during the storm of the inner edge of the electric field patterns in the dusk sector and its relation to electron boundaries, ion boundaries and the ring current. Whereas the CRRES data provide radial cuts out through the plasmasphere every I 0 hours, DMSP provides snapshots every orbit of the potential and particle boundaries. Electric field and energetic particle measurements from CRRES and ion drifts and energetic particle measurements from DMSP provide the principal data sets. The combined data set provides high resolution definition of the boundary motions over the phases of the storm. L shell differences between the DMSP and CRRES particle boundaries are easily explained by inflation of the dusk sector by the ring current. The electric field boundaries are typically associated with the ion boundary and the inner edge of the ring current except during rapid increases in the cross-polar cap potential. A significant fraction of the dusk cell potential is found inside the plasmasheet electron inner edge. Twice during the storm this potential exceeded 60 kV. A paper (copy attached) has been prepared (Burke et al., 1998) and submitted to JGR. An invited paper will be presented by Burke at the spring AGU meeting. An abstract has also been submitted to the COSPAR meeting for presentation by Maynard. New simulations have been carried out with the Rice Convection Model for the magnetic storm of June 4-5, 1991, and comparisons have been made with CRRES and DMSP data.

Maynard, Nelson C.↗
DOE OSTI2021

Validation and Verification for INL Modelica-based TEDS models Via Experimental Results

This report provides an overview on the verification and validation (V&V) of the Thermal Energy Distribution System (TEDS) model developed in the Modelica process modeling ecosystem using experimental data. Model development has led to the creation of a dynamic process model of the experimental TEDS facility housed within the Energy Systems Laboratory (ESL) at Idaho National Laboratory (INL). The model was then used during the preconstruction phase of the experimental effort to inform experimental design (e.g., insulation requirements, bypass line placement, expected performance of components) and to test innovative control schemes prior to the initial operation. The TEDS model developed in Modelica includes the primary components of the TEDS experimental unit: a 200kW Chromalox heater; a single-tank packed-bed thermal energy storage system filled with 0.125-inch alumina (Al2O3) beads; an ethylene-glycol-to-Therminol-66 heat exchanger; system piping; five control valves; and all associated temperature, pressure, and volumetric flow sensors. Using the Institute of Electrical and Electronics Engineers (IEEE) V&V methodologies, considered the gold standard in the engineering field, the model was verified using a combination of static analysis, spatial convergence, and regression tests. Then using dynamic time warping (DTW) initial runs to validate and tune the TEDS model versus the experiment were conducted. This tuning method was accomplished using the INL Risk Analysis Virtual ENvironment (RAVEN) software package. Tuning is required to account for physical phenomena that are less understood within the empirical heat transfer correlations. Through the commencement of this work, a systems-level model of TEDS with associated control systems, sensors, piping diameters, and component capabilities has been created. This model was utilized in the pre-experimental phase to inform system design, insulation thicknesses, and potential control schemes to operate the system effectively and safely. Then, initial experimental startup and operational data were used to demonstrate the validation and tuning methodology. This process demonstrates the classical two-step approach of a model informing experimental design followed by the experiment validation and tuning the model.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

The Transportation Leapfrog: Using Smart Phones to Collect Driving Data and Model Fuel Economy in India

Car ownership in India is expected to skyrocket in the coming decades, strongly driven by rising incomes. This phenomenon provides unprecedented opportunities for automakers and equally unprecedented social and environmental challenges. Policymakers, urban planners and civil society see this car boom leading to an explosion in problems related to congestion, infrastructure, air pollution, safety, higher oil imports and climate change. For all these stakeholders to take effective action, good data on how people use their cars, their demand for mobility and their behavior in mobility is essential. Unfortunately, there is very little data on the Indian transport sector as a whole and virtually none on real-world vehicle performance and use. The rapid development of high quality mobile telecommunications infrastructure provides India with the opportunity to leapfrog the West in cheaply collecting vast amounts of useful data from transportation. In this paper, we describe a pilot project in which we use commercial smart phone apps to collect per second car driving data from the city of Pune, instantly upload it through 3G and prepare it for analysis using advanced noise filtering algorithms for less than $1 per day per car. We then use our data in an Autonomie simulation to show that India’s currently planned fuel economy test procedures will result in over-estimates of fuel economy of approximately 35% for a typical Indian car when it is operated in real world conditions. Supporting better driving cycle development is just one of many applications for smart phone derived data in Indian transportation.

25 ENERGY STORAGE↗
DOE OSTI2021

Interferometric SAR modelling of near surface data to improve geological model in the Surat Basin, Australia

Here we present a study on geophysical inverse modelling for subsurface structural properties of an unconventional hydrocarbon site that was monitored previously by Interferometric Synthetic Aperture Radar (InSAR) technology for surface deformation. A static three-dimensional geomodel along with extracted property maps replicates the depth of each underlying stratigraphic unit and structural feature with the density of each geological layer. We examine the hypothesis that integration of elastic properties of each formation layer with InSAR observations in a stratified elastic medium will lead to a viscoelastic geophysical inverse problem that can be solved to estimate fractional volume change at the reservoir level. Moreover, we examine synthetic scenarios in which the elastic properties of the formations are perturbed before determining the resulting impact on the rate of surface deformation. The results show that although the slope of underlying formations, their density and depth can define the extent and pattern of a deformation signal, their properties have a marginal impact on volumetric change compared to the dense network of shallow depth Coal Seam Gas (CSG) mining wells. Besides, it is also demonstrated that the inversion of InSAR deformation maps can resolve the uncertainties associated with low-resolution seismic interpretation as well as filling the data gaps within seismic acquisitions. A significant contribution of this investigation to the geological basin modelling involves a) introducing a remote and non-invasive technology such as InSAR to improve geophysical mapping of subsurface structures such as faults in areas with sparse or no reflective seismic information, and b) applying a multi-layer viscoelastic geophysical source model for an unconventional hydrocarbon reservoir such as CSG.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Exploring data-driven modeling of boundary layer transition

Prediction of laminar-turbulent transition in boundary layer flows is an important component of predicting the aerodynamic performance of a number of aerospace configurations. According to the CFD Vision 2030 [1], transition modeling represents acriticalarea in CFD simulation capability that will remain a pacing item for the foreseeable future. The fact thattransition can take placevia either one of a myriad possible paths adds to the challenges inreliable transition predictions, despite a limited knowledge of the relevant input parameters. In the low disturbance environments typical of flight applications, transition is often initiated by small amplitude disturbances in the form of linear instability waves of the laminar boundary layer. These disturbances amplify linearly at first and eventually undergo a sequence of nonlinear interactions that result in transition to turbulence. Because the nonlinear phase is rather rapid, the amplification of boundary layer instabilities is governed by the linearstability theory over a majority of the distance leading up to the onset of transition. Semi-empirical transition correlations based on the linear stability theory have been successful in explaining the observed trends in transition location within a broad class of flows. However, the application of stability theory is highly non-robust and often requires a significant domain expertise. Recent work at the NASA Langley Research Center has beenaimed at bridging the gap between physics based transition analyses such as those based on linear stability theory and practical applications that require transition prediction by users that may not be well versed in transition physics. The applications of deep learning have been at the center of these efforts. This presentation will focus on the progress achieved thus far, highlighting the applications of neural networks to selectedtransition scenarios across a range of Mach numbers and flow configuration, as well as the lessons learnedand remaining challengeswithrespect to the selection of training data and neural networks architectures, hyperparameter tuning, and the physical insights distilled from the otherwise black-box models.

M. R. Malik↗
DOE OSTI2019

Charged-particle angular correlations in XeXe collisions at s N N = 5.44 TeV

Azimuthal correlations of charged particles in xenon-xenon collisions at a center-of-mass energy per nucleon pair of $\sqrt{s_{NN}}=5.44$ TeV are studied. The data were collected by the CMS experiment at the LHC with a total integrated luminosity of 3.42μb -1 . The collective motion of the system formed in the collision is parametrized by a Fourier expansion of the azimuthal particle density distribution. The azimuthal anisotropy coefficients $v_2$, $v_3$, and $v_4$ are obtained by the scalar-product, two-particle correlation, and multiparticle correlation methods. Within a hydrodynamic picture, these methods have different sensitivities to noncollective and fluctuation effects. The dependence of the Fourier coefficients on the size of the colliding system is explored by comparing the xenon-xenon results with equivalent lead-lead data. Model calculations that include initial-state fluctuation effects are also compared to the experimental results. The observed angular correlations provide new constraints on the hydrodynamic description of heavy ion collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2022

An Advanced Open-Source Platform for Air Quality Analysis, Visualization, and Prediction

Ambient air pollution is the largest environmental health risk factor, leading to several million premature deaths globally per year. The challenge of combating poor air quality is exacerbated by growing urban populations, changing emissions, and a warming climate. While there have been many advances monitoring and modeling of atmospheric composition, reflected in the dramatic increase in archived Earth Observations, there is no single measurement or method that alone can provide an accurate depiction of the entire atmosphere. The rapidly growing collections of observational and modeling data require us to be smarter about what data to include, and how such data is used. In recent years, NASA has invested significantly in advancing the concepts for Analytics Collaborative Framework (ACF) [5] and New Observing Strategies (NOS) [4] to tackle our software infrastructure need for harmonized data management and dynamic acquisition of diverse measurements for on-demand, interactive, multivariate analysis, and access [3]. It is not enough to have a big data, standalone analytics solution; it is critical that we start integrating data from remote sensing, modeling, and in-situ networks in a harmonized manner that enables timely and data-driven decision-making for air quality management. This work presents the design and development of an Air Quality Analytics Collaborative Framework (AQ ACF), as part of NASA’s Advanced Information Systems Technology (AIST) effort, to establish a data, machine-learning, and numerically driven platform for air quality analysis, visualization, and prediction.

Liu, Qian↗
DOE OSTI2018

FACE-MDS Phase 2: Model Output

Model output from Phase 2 of the Free Air CO2 Enrichment Model Data Synthesis (FACE-MDS) project. Results from 12 models simulating six elevated CO2 experiments using the protocols, met data, and other data supplied in the accompanying dataset: Phase 2 Free Air CO2 Enrichment Model-Data Synthesis: Meteorological Data and Protocols. File naming convention can be found in the file 'facemds-file-table-modeldata.xml' and information about variable naming convention can be found in the file 'facemds-standard-name-table-modvars.xml'. We followed the ALMA v3 convention for meteorological variables and developed a hierarchical naming scheme for ecosystem variables. Additional info can be found in the protocols in the accompanying meteorological dataset (doi:10.15485/1480328). We replaced the original naming convention mentioned in the protocol with the one described in 'facemds-standard-name-table-modvars.xml'.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Meld: Exploring the Feasibility of a Framework-less Framework

HEP data-processing frameworks are essential ingredients in getting from raw data to physics results. But they are often tricky to use well, and they present a significant learning barrier for the beginning HEP physicist. In addition, existing frameworks typically support rigid, collider-based data models, which do not map well to neutrino-physics experiments like DUNE. Neutrino physicists thus expend significant effort working around framework limitations instead of using a framework that directly supports their needs. Presented here is Meld, a Fermilab R&D project, which intends to address these limitations. By leveraging modern C++ capabilities, state-of-the-art concurrency libraries, and a flexible data model, it is possible for beginning (and seasoned) HEP physicists to execute framework programs easily and efficiently, with minimal coupling to framework-specific constructs. Meld aims to directly support the frameworks needs of neutrino experiments like DUNE as well as the more common collider-based experiments.

Knoepfel, Kyle J.↗
NASA NTRS2012

Using Science Data and Models for Space Weather Forecasting - Challenges and Opportunities

Space research, and, consequently, space weather forecasting are immature disciplines. Scientific knowledge is accumulated frequently, which changes our understanding or how solar eruptions occur, and of how they impact targets near or on the Earth, or targets throughout the heliosphere. Along with continuous progress in understanding, space research and forecasting models are advancing rapidly in capability, often providing substantially increases in space weather value over time scales of less than a year. Furthermore, the majority of space environment information available today is, particularly in the solar and heliospheric domains, derived from research missions. An optimal forecasting environment needs to be flexible enough to benefit from this rapid development, and flexible enough to adapt to evolving data sources, many of which may also stem from non-US entities. This presentation will analyze the experiences obtained by developing and operating both a forecasting service for NASA, and an experimental forecasting system for Geomagnetically Induced Currents.

Hesse, Michael↗
DOE OSTI2016

Validation Data and Model Development for Fuel Assembly Response to Seismic Loads

Vibrations are inherently present in nuclear reactors, especially in cores and steam generators of pressurized water reactors (PWR). They can have significant effects on local heat transfer and wear and tear in the reactor and often set safety margins. The simulation of these multiphysics phenomena from first principles requires the coupling of several codes, which is one the most challenging tasks in modern computer simulation. Here an ambitious multiphysics multidisciplinary validation campaign is conducted. It relied on an integrated team of experimentalists and code developers to acquire benchmark and validation data for fluid-structure interaction codes. Data are focused on PWR fuel bundle behavior during seismic transients.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2004

Combining Satellite Data, Trajectory Modeling and Surface Insolation Measurements to Deduce the Direct Radiative Effect of Smoke Aerosol

In this paper, we have introduced a method of inferring the radiative effect of smoke aerosols using a technique that combines satellite remote sensing with trajectory modeling. The results shown here clearly show large flux biases between theoretical and measured radiative fluxes correlate with the arrival of smoke aerosol to the area. Further analysis is required to convincingly demonstrate that the reason for these differences is the radiative effect of the smoke aerosols. To do this, the estimated fluxes taken from the ERA-15 will be recomputed every 3 hours using International Satellite Cloud Climatology Project (ISCCP) data set entitled DX gridded to a 1o equal angle resolution (see paper 7B.2 for details). Surface radiometric and ancillary data for several more Canadian surface sites are being obtained at minute temporal resolution. The ultimate purpose of this research is to derive aerosol smoke maps for fire events such as this to be included in an aerosol climatology and be incorporated in the computation of the earth's surface radiation budget to better understand the radiative effect of aerosols.

Stackhouse, Paul W., Jr.↗
DOE OSTI2019

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely topology aware datacubes, we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Energy Reporting: Device Demonstration, Communication Protocols, and Codes and Standards

Energy reporting is the principle that all energy-using devices in buildings should be able to track their own energy use and report this to the local network. Energy reporting can provide building owners with easy access to highly granular energy use data. This report makes the case that energy reporting should become a free basic feature of all devices, and reports on a project intended to move us towards that goal. The project collected a set of demonstration devices with energy reporting features, including products that were modified by the project team or the manufacturer, or are already available for sale. To show these devices operating live at meetings and conferences, the team created a management system that queries the energy reporting devices for their data, stores the data, and displays it in compelling visualizations. The devices covered a wide range, including heating, ventilation, and air conditioning (thermostat and air purifier); lighting (individual bulb, task light, and auto-dimming overhead light); a vehicle charger; a water heater; electronics (notebook personal computer and universal serial bus charger); and three external meters (one integral with a dimming light switch). The demonstration uses a variety of communication protocols. The report reviews existing communication protocols that support energy reporting and describes how to use them with a proposed reference data model for energy reporting. It also assesses ways that energy codes and standards processes can be leveraged to drive energy reporting technology into the market. Energy reporting could ultimately save California on the order of 2.5 terawatt-hours per year and about $0.8 billion per year. Energy reporting is a highly practical technology with minimal (sometimes no) cost to consumers and manufacturers. This report discusses creation of the energy reporting devices themselves, analysis and recommendations for data models and protocols for energy reporting, and energy codes and standards implications of energy reporting technology. While energy reporting does not directly save energy, it provides information for better decision-making to save energy in changing equipment operation, maintenance, and replacement.

devices↗
NASA NTRS1985

Using a data base management system for modelling SSME test history data

The usefulness of a data base management system (DBMS) for modelling historical test data for the complete series of static test firings for the Space Shuttle Main Engine (SSME) was assessed. From an analysis of user data base query requirements, it became clear that a relational DMBS which included a relationally complete query language would permit a model satisfying the query requirements. Representative models and sample queries are discussed. A list of environment-particular evaluation criteria for the desired DBMS was constructed; these criteria include requirements in the areas of user-interface complexity, program independence, flexibility, modifiability, and output capability. The evaluation process included the construction of several prototype data bases for user assessement. The systems studied, representing the three major DBMS conceptual models, were: MIRADS, a hierarchical system; DMS-1100, a CODASYL-based network system; ORACLE, a relational system; and DATATRIEVE, a relational-type system.

Abernethy, K.↗
NASA NTRS2015

Technical Report Series on Global Modeling and Data Assimilation, Volume 41 : GDIS Workshop Report

The workshop "An International Global Drought Information System Workshop: Next Steps" was held on 10-13 December 2014 in Pasadena, California. The more than 60 participants from 15 countries spanned the drought research community and included select representatives from applications communities as well as providers of regional and global drought information products. The workshop was sponsored and supported by the US National Integrated Drought Information System (NIDIS) program, the World Climate Research Program (WCRP: GEWEX, CLIVAR), the World Meteorological Organization (WMO), the Group on Earth Observations (GEO), the European Commission Joint Research Centre (JRC), the US Climate Variability and Predictability (CLIVAR) program, and the US National Oceanic and Atmospheric Administration (NOAA) programs on Modeling, Analysis, Predictions and Projections (MAPP) and Climate Variability & Predictability (CVP). NASA/JPL hosted the workshop with logistical support provided by the GEWEX program office. The goal of the workshop was to build on past Global Drought Information System (GDIS) progress toward developing an experimental global drought information system. Specific goals were threefold: (i) to review recent research results focused on understanding drought mechanisms and their predictability on a wide range of time scales and to identify gaps in understanding that could be addressed by coordinated research; (ii) to help ensure that WRCP research priorities mesh with efforts to build capacity to address drought at the regional level; and (iii) to produce an implementation plan for a short duration pilot project to demonstrate current GDIS capabilities. See http://www.wcrp-climate.org/gdis-wkshp-2014-objectives for more information.

GEO↗
NASA NTRS1987

Analysis of photopole data reduction models

An analysis of the total impulse obtained from a buried explosive charge can be calculated from displacement versus time points taken from successive film frames of high speed motion pictures of the explosive event. The indicator of that motion is a pole and baseplate (photopole), which is placed on or within the soil overburden. Here, researchers are concerned with the precision of the impulse calculation and ways to improve that precision. Also examined here is the effect of each initial condition on the curve fitting process. It is shown that the zero initial velocity criteria should not be applied due to the linear acceleration versus time character of the cubic power series. The applicability of the new method to photopole data records whose early time motions are obscured is illustrated.

Cheek, James B.↗