Search NASA⌕ Search

SEARCH · Search NASA

Results for “Processing maps”

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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

ARE THERE ECHOES FROM THE PRE-BIG-BANG UNIVERSE? A SEARCH FOR LOW-VARIANCE CIRCLES IN THE COSMIC MICROWAVE BACKGROUND SKY

In a recent analysis of Wilkinson Microwave Anisotropy Probe (WMAP) seven-year temperature maps, Gurzadyan and Penrose claim to find concentric circular patterns in the sky with anomalously low variances. These circles are presented as observational evidence for violent processes in a universe preceding our big bang as predicted by Penrose's Conformal Cyclic Cosmology. We reassess the statistical significance of the detection of the claimed concentric low-variance circles by comparing the WMAP data with Monte Carlo simulations of the cosmic microwave background (CMB) sky plus realistic modeling of WMAP's anisotropic noise. We find no anomaly in the variances compared with the {Lambda}CDM cosmological model. The observed variances in the data are consistent with a Gaussian CMB sky as predicted by the inflationary cosmology model at better than 3{sigma}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

BAYESIAN NOISE ESTIMATION FOR NON-IDEAL COSMIC MICROWAVE BACKGROUND EXPERIMENTS

We describe a Bayesian framework for estimating the time-domain noise covariance of cosmic microwave background (CMB) observations, typically parameterized in terms of a 1/f frequency profile. This framework is based on the Gibbs sampling algorithm, which allows for exact marginalization over nuisance parameters through conditional probability distributions. In this paper, we implement support for gaps in the data streams and marginalization over fixed time-domain templates, and also outline how to marginalize over confusion from CMB fluctuations, which may be important for high signal-to-noise experiments. As a by-product of the method, we obtain proper constrained realizations, which themselves can be useful for map making. To validate the algorithm, we demonstrate that the reconstructed noise parameters and corresponding uncertainties are unbiased using simulated data. The CPU time required to process a single data stream of 100,000 samples with 1000 samples removed by gaps is 3 s if only the maximum posterior parameters are required, and 21 s if one also wants to obtain the corresponding uncertainties by Gibbs sampling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

On the reach of perturbative methods for dark matter density fields

We study the mapping from Lagrangian to Eulerian space in the context of the Effective Field Theory (EFT) of Large Scale Structure. We compute Lagrangian displacements with Lagrangian Perturbation Theory (LPT) and perform the full non-perturbative transformation from displacement to density. When expanded up to a given order, this transformation reproduces the standard Eulerian Perturbation Theory (SPT) at the same order. However, the full transformation from displacement to density also includes higher order terms. These terms explicitly resum long wavelength motions, thus making the resulting density field better correlated with the true non-linear density field. As a result, the regime of validity of this approach is expected to extend that of the Eulerian EFT, and match that of the IR-resummed Eulerian EFT. This approach thus effectively enables a test of the IR-resummed EFT at the field level. We estimate the size of stochastic, non-perturbative contributions to the matter density power spectrum. We find that in our highest order calculation, at redshift z = 0 the power spectrum of the density field is reproduced with an accuracy of 1% (10%) up to k = 0.25 hMpc{sup −1} (k = 0.46 hMpc{sup −1}). We believe that the dominant source of the remaining error is the stochastic contribution. Unfortunately, on these scales the stochastic term does not yet scale as k{sup 4} as it does in the very low k regime. Thus, modeling this contribution might be challenging.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

$\rm {H\,\small {I}}$ constraints from the cross-correlation of eBOSS galaxies and Green Bank Telescope intensity maps

We present the joint analysis of Neutral Hydrogen ($\rm {H\,\small {I}}$) Intensity Mapping observations with three galaxy samples: the Luminous Red Galaxy (LRG) and Emission Line Galaxy (ELG) samples from the eBOSS survey, and the WiggleZ Dark Energy Survey sample. The $\rm {H\,\small {I}}$ intensity maps are Green Bank Telescope observations of the redshifted $21\rm cm$ emission on $100 \, {\rm deg}^2$ covering the redshift range 0.6 < z < 1.0. We process the data by separating and removing the foregrounds present in the radio frequencies with FastI ICA. We verify the quality of the foreground separation with mock realizations, and construct a transfer function to correct for the effects of foreground removal on the $\rm {H\,\small {I}}$ signal. We cross-correlate the cleaned $\rm {H\,\small {I}}$ data with the galaxy samples and study the overall amplitude as well as the scale dependence of the power spectrum. We also qualitatively compare our findings with the predictions by a semianalytical galaxy evolution simulation. The cross-correlations constrain the quantity $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm opt}}$ at an effective scale k eff , where $\Omega _\rm {H\,\small {I}}$ is the $\rm {H\,\small {I}}$ density fraction, $b_\rm {H\,\small {I}}$ is the $\rm {H\,\small {I}}$ bias, and $r_{\rm {H\,\small {I}},{\rm opt}}$ the galaxy–hydrogen correlation coefficient, which is dependent on the $\rm {H\,\small {I}}$ content of the optical galaxy sample. At $k_{\rm eff}=0.31 \, h\,{\rm Mpc^{-1}}$ we find $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm Wig}} = [0.58 \pm 0.09 \, {\rm (stat) \pm 0.05 \, {\rm (sys)}}] \times 10^{-3}$ for GBT-WiggleZ, $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm ELG}} = [0.40 \pm 0.09 \, {\rm (stat) \pm 0.04 \, {\rm (sys)}}] \times 10^{-3}$ for GBT-ELG, and $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm LRG}} = [0.35 \pm 0.08 \, {\rm (stat) \pm 0.03 \, {\rm (sys)}}] \times 10^{-3}$ for GBT-LRG, at z ≃ 0.8. We also report results at $k_{\rm eff}=0.24$ and $k_{\rm eff}=0.48 \, h\,{\rm Mpc^{-1}}$. With little information on $\rm {H\,\small {I}}$ parameters beyond our local Universe, these are amongst the most precise constraints on neutral hydrogen density fluctuations in an underexplored redshift range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

ISAVS: Interactive Scalable Analysis and Visualization System

Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability. We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. Here, we demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Shortest Path and Neighborhood Subgraph Extraction on a Spiking Memristive Neuromorphic Implementation

Spiking neuromorphic computers (SNCs) are promising as a post Moore's law technology partly because of their potential for very low power computation. SNCs have primarily been demonstrated on machine learning and neural network applications, but they can also be used for applications beyond machine learning that can leverage SNC properties such as massively parallel computation and collocated processing and memory. Here, we demonstrate two graph problems (shortest path and neighborhood subgraph extraction) that can be solved using SNCs. We discuss the approach for mapping these applications to an SNC. We also estimate the performance of a memristive SNC for these applications on three real-world graphs.

Schuman, Catherine↗
DOE OSTI2021

Hybrid Simulation Framework

HYBRID is a modeling toolset to assess the economic viability of Nuclear-Renewable Integrated Energy Systems (N-R IES). The frameworks enabling this toolset are INL’s RAVEN, its CashFlow plugin and the Modelica language. The toolset includes sample RAVEN workflows performing economic assessments. These workflows consist of: generation of stochastic time series and application of probabilistic analysis and optimization algorithms (RAVEN); a library of Modelica models representing the physical behavior of N-R IES; and the CashFlow plugin mapping physical performance to economic performance. The toolset allows assembling existing and new models such as nuclear reactors, renewable energy sources, energy storage, gas turbines, industrial processes, etc. into an N-R IES. The toolset workflows evaluate the dynamics of the N-R IES responding to stochastic conditions (electricity demand, price, etc.) and optimize the dispatch economics as well as N-R IES capacity planning.

Epiney, Aaron↗
DOE OSTI2021

Model-Data for Joint Estimation of Biogeochemical Model Parameters from Multiple Experiments: A Bayesian Approach Applied to Mercury Methylation

This modeling archive supports the manuscript submitted for publication in the Environmental Modeling and Software. This study is supported by ORNL-SFA and IDEAS-Watershed. This study aims to improve calibration of complex biogeochemical models using datasets from multiple experiments targeting specific subprocesses. The proposed Bayesian joint-fitting scheme calibrates the entire biogeochemical model in one go using all the available datasets and estimate parameter uncertainties using Markov Chain Monte Carlo (MCMC). This allows for complete propagation of uncertainties and utilization of the information shared between different datasets. Mapping joint distribution of parameters guides model improvement by identifying null spaces in the parameter space. This archive contains files used to perform MCMC, post-process outputs and visualize results.

East Fork Poplar Creek↗
DOE OSTI2021

In-situ digital image correlation and thermal monitoring in directed energy deposition additive manufacturing

As in welding, directed energy deposition (DED) additive manufacturing (AM) generates complex residual stresses and distortions commensurate with the complexity of the scan pattern used for deposition. To date, measuring DED distortions on complex geometries has only been achieved post process, discarding the complex thermomechanical history that leads to that final material state. In this work, surround stereo digital image correlation (DIC) is used to 3D map surfaces and strain tensors in-situ in a powder-blown laser DED system. Infrared thermography is then projected onto these surfaces to record the full thermomechanical history of printed parts. DIC presents a unique challenge to DED AM, as no part exists at the beginning of deposition, which (a) prevents application of an appropriate speckle pattern and (b) denies the user a zero strain reference frame. Solutions to these problems are proposed and their limitations explored herein. In sum, this work presents a relatively low-cost solution to monitoring and optimizing the unique temporal artifacts induced by complex scan strategies that was previously unobtainable.

42 ENGINEERING↗
DOE OSTI2022

Towards Automated/Semiautomated Extraction of Faults from Lidar Data

The Pajarito fault system is a complex zone of deformation and a seismically active region nestled within the Rio Grande rift in north-central New Mexico. Numerous laterally discontinuous faults and associated folds and fractures interact in a manner that has important implications for seismic hazards and risk mitigation. Previous efforts have established a foundation for the location of lineaments and structures in the Pajarito fault system; however, ensuring the completeness of the current lineament mapping is required for identifying areas for field validation, evaluating the potential for future seismic activity, and better understanding fault interaction. Assistance with this fault-mapping task via automated or semiautomated techniques as applied to lidar data over a large area of interest is highly desirable. A proof-of-concept processing flow which transforms lidar point-cloud data into a raster of surficial fault candidates is described and illustrated herein. We report these initial results hold great promise toward achieving our ultimate goal.

58 GEOSCIENCES↗
DOE OSTI2021

GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Task 2 Report - A GIS-Based Technical Potential Assessment of Domestic Energy Resources for Electricity Generation

The purpose of this report is to support the Lao Ministry of Energy and Mines (MEM) in assessing the technical potential of domestic energy resources for utility scale electricity generation in the Lao PDR. Specifically, this work provides assessments of technical potential, and associated maps of developable areas, for energy technologies of interest. This report details the methodology, assumptions, and datasets employed in this analysis to provide a transparent, replicable process for future analyses. The methodology and results presented are intended to be a fundamental input to subsequent decision making and energy planning-related analyses. This report is the second output of the Energy Alternatives Study for the Lao PDR (Energy Alternatives Study), a collaboration led by MEM and the United States Agency for International Development under the auspices of the Smart Infrastructure for the Mekong program.1 The Energy Alternatives Study is composed of five successive tasks that collectively support the project’s goals as shown in FIGURE ES-1. This work is focused on Task 2 – Assess technical potential of domestic energy resources for electricity generation . The work was carried out by a team from the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) in collaboration with MEM and other Lao power sector stakeholders. This work concentrates on domestic energy resources for utility-scale electricity generation and considers solar photovoltaic (PV), wind, biomass, and coal resources. This work does not consider potentially imported energy resources (e.g., natural gas) or domestic energy resources that are not present in sufficient quantity for utility-scale generation (e.g., geothermal resources). A technical potential assessment of hydropower resources is currently not feasible due to the absence of required data including site-level assessments of multiple characteristics (e.g., geology environment and access) as well as spatial data on estimated non-exploited hydropower resources (Lee et al. 2018). Previous studies estimated total hydropower resource potential to be between 23,000 and 26,000 MW, of which approximately 3,894 MW has been developed (IES and MKE 2016; MEM 2015b, 2011). MEM and Électricité du Laos have gained significant experience in the evaluation and development of hydropower resources and expect to develop close to 10,034 MW of additional capacity by 2025 (MEM 2017).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

CUBIT: Geometry and Mesh Generation Toolkit (15.1 User Documentation)

CUBIT is a full-featured software toolkit for robust generation of two- and three-dimensional finite element meshes (grids) and geometry preparation. Its main goal is to reduce the time to generate meshes, particularly large hex meshes of complicated, interlocking assemblies. It is a solid-modeler based preprocessor that meshes volumes and surfaces for finite element analysis. Mesh generation algorithms include quadrilateral and triangular paving, 2D and 3D mapping, hex sweeping and multi-sweeping, tetrahedral meshing, and various special purpose primitives. CUBIT contains many algorithms for controlling and automating much of the meshing process, such as automatic scheme selection, interval matching, sweep grouping, and also includes state-of-the-art smoothing algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

White Paper: Scalable Digital Twin Capabilities for Aging and Surveillance of Engineered Systems

This white paper presents a multi-year initiative to develop practical, secure, and scalable digital twin capabilities for engineered systems in aging and surveillance contexts—an approach pioneered at the National Nuclear Security Administration (NNSA) Lawrence Livermore National Laboratory (LLNL) that maps directly onto the needs and ambitions of the Navy for ship- and fleet-level digital twins. LLNL’s work in building part- and process-level digital twins for advanced manufacturing, with a vision to scale up to entire factory floors and, ultimately, enterprise-wide digital twins, offers an adaptable pathway for the Navy as it seeks to modernize lifecycle management, readiness, and predictive maintenance across ships and fleets. For our application, we integrate physics-based modeling with automated data ingestion, processing, and AI-driven calibration, creating hybrid models that are both interpretable and data responsive. We modernized legacy workflows, established centralized data infrastructure, automated experimental pipelines, and demonstrated end-to-end coupling of accelerated aging data with finite element simulations via optimization and surrogate modeling. The result is a generalizable framework that supports part-level digital twins today and lays the groundwork for future system-level twins suitable for Navy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2017

The Grism Lens-Amplified Survey from Space (GLASS). X. Sub-kiloparsec Resolution Gas-phase Metallicity Maps at Cosmic Noon behind the Hubble Frontier Fields Cluster MACS1149.6+2223

We combine deep Hubble Space Telescope grism spectroscopy with a new Bayesian method to derive maps of gas-phase metallicity for 10 star-forming galaxies at high redshift (1.2≲z≲2.3). Exploiting lensing magnification by the foreground cluster MACS1149.6+2223, we reach sub-kiloparsec spatial resolution and push the limit of stellar mass associated with such high-z spatially resolved measurements below 10{sup 8} M{sub ⊙} for the first time. Our maps exhibit diverse morphologies, indicative of various effects such as efficient radial mixing from tidal torques, rapid accretion of low-metallicity gas, and other physical processes that can affect the gas and metallicity distributions in individual galaxies. Based upon an exhaustive sample of all existing sub-kiloparesec resolution metallicity gradient measurements at high z, we find that predictions given by analytical chemical evolution models assuming a relatively extended star-formation profile in the early disk-formation phase can explain the majority of observed metallicity gradients, without involving galactic feedback or radial outflows. We observe a tentative correlation between stellar mass and metallicity gradients, consistent with the “downsizing” galaxy formation picture that more massive galaxies are more evolved into a later phase of disk growth, where they experience more coherent mass assembly at all radii and thus show shallower metallicity gradients. In addition to the spatially resolved analysis, we compile a sample of homogeneously cross-calibrated integrated metallicity measurements spanning three orders of magnitude in stellar mass at z ∼ 1.8. We use this sample to study the mass–metallicity relation (MZR) and find that the slope of the observed MZR can rule out the momentum-driven wind model at a 3σ confidence level.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

AURORA: Automated Refinement of Coarse-Grained Reconfigurable Accelerators

Coarse-grained reconfigurable arrays (CGRAs), loosely defined as arrays of functional units interconnected through a network-on-chip (NoC), provide higher flexibility than domain-specific ASIC accelerators while offering increased hardware efficiency with respect to fine-grained reconfigurable devices, such as Field Programmable Gate Arrays (FPGAs). Un-fortunately, designing a CGRA for a specific application domain involves enormous software/hardware engineering effort (e.g., designing the CGRA, map operations onto the CGRA, etc) and requires the exploration on a large design space (e.g., applying appropriate loop transformation on each application, specializing the reconfigurable processing elements of the CGRA, refining the network topology, deciding the size of the data memory, etc). Int his paper, we propose AURORA – a software/hardware co-design framework to automatically synthesize optimal CGRA given a set of applications of interest

Tan, Cheng↗
DOE OSTI2013

Middlesex FUSRAP Site - A Path to Site-Wide Closure - 13416

The road-map to obtaining closure of the Middlesex Sampling Plant FUSRAP site in Middlesex, New Jersey (NJ) has required a multi-faceted approach, following the CERCLA Process. Since 1998, the US ACE, ECC, and other contractors have completed much of the work required for regulatory acceptance of site closure with unrestricted use. To date, three buildings have been decontaminated, demolished, and disposed of. Two interim storage piles have been removed and disposed of, followed by the additional removal and disposal of over 87,000 tons of radiologically and chemically-impacted subsurface soils by the summer of 2008. The US ACE received a determination from the EPA for the soils Operable Unit, (OU)-1, in September 2010 that the remedial excavations were acceptable, and meet the criteria for unrestricted use as required by the 2004 Record of Decision (ROD) for OU-1. Following the completion of OU-1, the project delivery team performed additional field investigation of the final Operable Unit for Middlesex, OU-2, Groundwater. As of December 2012, the project delivery team has completed a Supplemental Remedial Investigation, which will be followed with a streamlined Feasibility Study, Proposed Plan, and ROD. Several years of historical groundwater data was available from previous investigations and the FUSRAP Environmental Surveillance Program. Historical data indicated sporadic detections of Volatile Organic Compounds (VOCs), primarily trichloroethylene (TCE), carbon tetrachloride (CT), and methyl tert-butyl ether (MTBE), with no apparent trend or pattern indicating extent or source of the VOC impact. In 2008, the project delivery team initiated efforts to re-assess the Conceptual Site Model (CSM) for groundwater. The bedrock was re-evaluated as a leaky multi-unit aquifer, and a plan was developed for additional investigations for adequate bedrock characterization and delineation of groundwater contaminated primarily by CT, TCE, and tetrachloroethene (PCE). The investigation was designed to accumulate multiple lines of evidence to determine the source and to delineate the extent of contamination, as required to complete the CERCLA Process and gain regulatory acceptance. Investigative techniques included in-well vertical flow tracing, borehole geophysics and packer testing of temporary test holes to characterize contamination in the bedrock fractures beneath the site, and to delineate likely source areas. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗