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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Laws in Order: An Inventory of State Renewable Energy Siting Policies

This report identifies which government entity or entities in each state or territory have the jurisdictional authority to make siting and permitting decisions about large scale wind and solar projects. The report also covers established timelines for siting and permitting processes, requirements for public involvement in those processes, and the availability of permitting guides and model ordinances designed to assist local jurisdictions. It details state renewable energy siting policies and permitting authorities across the United States, profiling all 50 states plus Puerto Rico. The report release also includes an interactive map that allows users to easily explore each state’s authorities and policy features. The map, hosted by DOE, includes high-level information on each state’s siting and permitting processes and link directly to the profiles in the report.

14 SOLAR ENERGY↗
DOE OSTI2023

Code for the manuscript titled "Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces"

We would like to disclose two scripts, written in Jupyter notebook, in which we implement the "Blackout Diffusion Process" described in the manuscript "Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces" (LA-UR-23-20509), to be submitted to the International Conference of Machine Learning (ICML). The abstract of the manuscript is append below. == Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, which occur in many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward diffusion process. We relate the theory to the existing continuous-state Gaussian diffusion and identify the corresponding reverse-time stochastic process and score function in the continuous-time setting, and the reverse-time mapping in the discrete-time setting. As an example of this framework, we introduce "Blackout Diffusion", which learns to produce samples from an empty image instead of from noise. Numerical experiments on the CIFAR-10 dataset confirm the feasibility of generative diffusion modeling in a discrete space. Generalizing from specific (Gaussian) forward processes to a more general framework also sheds light on how to interpret generative diffusion models and their mathematical structure, which we comment on.

Lin, Yen Ting↗
DOE OSTI2016

SDSS-IV/MaNGA: SPECTROPHOTOMETRIC CALIBRATION TECHNIQUE

Mapping Nearby Galaxies at Apache Point Observatory (MaNGA), one of three core programs in the Sloan Digital Sky Survey-IV, is an integral-field spectroscopic survey of roughly 10,000 nearby galaxies. It employs dithered observations using 17 hexagonal bundles of 2″ fibers to obtain resolved spectroscopy over a wide wavelength range of 3600–10300 Å. To map the internal variations within each galaxy, we need to perform accurate spectral surface photometry, which is to calibrate the specific intensity at every spatial location sampled by each individual aperture element of the integral field unit. The calibration must correct only for the flux loss due to atmospheric throughput and the instrument response, but not for losses due to the finite geometry of the fiber aperture. This requires the use of standard star measurements to strictly separate these two flux loss factors (throughput versus geometry), a difficult challenge with standard single-fiber spectroscopy techniques due to various practical limitations. Therefore, we developed a technique for spectral surface photometry using multiple small fiber-bundles targeting standard stars simultaneously with galaxy observations. We discuss the principles of our approach and how they compare to previous efforts, and we demonstrate the precision and accuracy achieved. MaNGA's relative calibration between the wavelengths of Hα and Hβ has an rms of 1.7%, while that between [N ii] λ6583 and [O ii] λ3727 has an rms of 4.7%. Using extinction-corrected star formation rates and gas-phase metallicities as an illustration, this level of precision guarantees that flux calibration errors will be sub-dominant when estimating these quantities. The absolute calibration is better than 5% for more than 89% of MaNGA's wavelength range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

The preliminary results: Seismic ambient noise Rayleigh wave tomography around Merapi volcano, central Java, Indonesia

Ambient noise tomography is relatively a new method for imaging the shallow structure of the Earth subsurface. We presents the application of this method to produce a Rayleigh wave group velocity maps around the Merapi Volcano, Central Java. Rayleigh waves group velocity maps were reconstructed from the cross-correlation of ambient noise recorded by the DOMERAPI array which consists 43 broadband seismometers. In the processing stage, we first filtered the observation data to separatethe noise from the signal that dominated by the strong volcanic activities. Next, we cross-correlate the filtered data and stack to obtain the Green’s function for all possible station pairs. Then we carefully picked the peak of each Green’s function to estimate the dispersion trend and appliedMultiple Filter Technique to obtain the dispersion curve. Inter-station group velocity curvesare inverted to produceRayleigh wave group velocity maps for periods 1 to 10 s. The resulted Rayleigh group velocity maps show the interesting features around the Merapi Volcano which generally agree with the previous studies. Merapi-Lawu Anomaly (MLA) is emerged as a relatively low anomaly in our group velocity maps.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Rapid Identification of Synthetic Routes to Functional Metastable Phases Using X-ray Probed Laser Anneal Mapping (XPLAM) Time-Temperature Quench Maps

Many material systems have known or predicted functional phases that are metastable at standard temperature and pressure. While substantial advances have been made in the high-throughput and combinatorial synthesis of materials with a range of stoichiometries, investigation of thermal processing remains largely the domain of iterative uniform anneals or static gradients. Here we develop X-ray probed laser anneal mapping (XPLAM), a high throughput technique coupling spatially resolved X-ray diffraction with microsecond to millisecond laser gradient anneals to produce temperature–dwell–transformation (TDT) diagrams of the phase as a function of quench time and temperature. In addition to showing regimes where specific metastable phases form preferentially, TDT diagrams provide insight into the submillisecond kinetics of solid–solid phase transitions. This is a unique tool for mapping reaction pathways for metastable phases. As a first demonstration of XPLAM, we study Bi2O3, which has a rich set of polytypes, including the d-phase with an exceptionally high oxygen ion conductivity. We demonstrate the first annealing-driven synthesis of room temperature d-Bi2O3. We expect XPLAM to prove a powerful technique for rapid identification of synthetic routes to metastable phases and to generate the exhaustive data sets required for machine learning-guided exploration of materials processing.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Anticipating global terrestrial ecosystem state change using FLUXNET

Ecosystems can be characterized as complex systems that traverse a variety of functional and structural states in response to changing bioclimatic forcings. A central challenge of global change biology is the robust empirical description of these states and state transitions. An ecosystem's functional state can be empirically described using Process Networks (PN) that use timeseries observations to determine the strength of process-level functional couplings between ecosystem components. A globally extensive source of in-situ observations of terrestrial ecosystem dynamics is the FLUXNET eddy-covariance network that provides standardized observations of micrometeorology and carbon, water, and energy flux dynamics. We employ the LaThuile FLUXNET synthesis dataset to delineate each month's functional state for 204 sites, yielding the LaThuile PN version 1.0 database that describes the strength of an ecosystem's functional couplings from air temperature and precipitation to carbon fluxes during each site-month. Then we calculate the elasticity of these couplings to seasonal scale forcings: air temperature, precipitation, solar radiation, and phenophase. Finally, we train artificial neural networks to extrapolate these elasticities from 204 sites to the globe, yielding maps of the estimated functional elasticity of every terrestrial ecosystem's functional states to changing seasonal bioclimatic forcings. These maps provide theoretically novel resource that can be used to anticipate ecological state transitions in response to climate change and to validate process-based models of ecological change. These elasticity maps show that each ecosystem can be expected to respond uniquely to changing forcings. Tropical forests, hot deserts, savannas, and high elevations are most elastic to climate change, and elasticity of ecosystems to seasonal air temperature is on average an order of magnitude higher than elasticity to other bioclimatic forcings. We also observe a reasonable amount of moderate relationships between functional elasticity and structural state change across different ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Observations Show Charge Density of Volcanic Plumes is Higher Than Thunderstorms

We analyze slow electric field change and lightning mapping measurements to provide insight into the characteristics of volcanic lightning and the associated implications on charging processes and the charge structure of a Vulcanian eruption plume. Data were obtained during a multi-instrumental field campaign at Sakurajima volcano in 2015 when the Showa crater was active. We combine the electric field change and lightning mapping data from one explosive eruption on June 6, 2015 to identify individual flashes. From this, we interpret the flash type and polarity. In addition, the long-time constant of the electric field change instrument allowed measurement of the quasi-static field associated with charge separation in the eruption plume. We find that both intracloud and cloud-to-ground discharges occurred, and the polarity of cloud-to-ground discharges were all negative. The quasi-static field measurement showed the plume carried a net negative charge. We calculate both the total charge transferred by cloud-to-ground discharges and the net charge density of the eruption plume. We find that cloud-to-ground discharges transfer an average of –0.41C per flash and the net charge density was –33C/ km 3 . Here, the percent error is at least 200%, due to uncertainty in the antenna gain. We show that these estimates are consistent with lightning that is 100 m in length. Further, the average flash rate during the first 8 s following the onset of eruption was five flashes per second. After that time, the flash rate abruptly decreased, which may be related to the end of gas-thrust forcing.

58 GEOSCIENCES↗
DOE OSTI2024

New approach to QCD final-state evolution in processes with massive partons

We present an algorithm for massive parton evolution which is based on the differentially accurate simulation of soft-gluon radiation by means of a nontrivial azimuthal angle dependence of the splitting functions. The kinematics mapping is chosen such as to reflect the symmetry of the final state in soft-gluon radiation and collinear splitting processes. We compute the counterterms needed for a fully differential next-to-leading order matching and discuss the analytic structure of the parton shower in the next-to-leading logarithmic limit. We implement the new algorithm in the numerical code Alaric and present a first comparison to experimental data. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

In-situ electron microscopy mapping of an order-disorder transition in a superionic conductor

Solid-solid phase transitions are processes ripe for the discovery of correlated atomic motion in crystals. Here, we monitor an order-disorder transition in real-time in nanoparticles of the super-ionic solid, Cu 2-x Se. The use of in-situ high-resolution transmission electron microscopy allows the spatiotemporal evolution of the phase transition within a single nanoparticle to be monitored at the atomic level. The high spatial resolution reveals that cation disorder is nucleated at low co-ordination, high energy sites of the nanoparticle where cationic vacancy layers intersect with surface facets. Time-dependent evolution of the reciprocal lattice of individual nanoparticles shows that the initiation of cation disorder is accompanied by a ~3% compression of the anionic lattice, establishing a correlation between these two structural features of the lattice. The spatiotemporal insights gained here advance understanding of order-disorder transitions, ionic structure and transport, and the role of nanoparticle surfaces in phase transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2015

AN ATLAS OF CORONAL ELECTRON DENSITY AT 5R{sub ⊙}. I. DATA PROCESSING AND CALIBRATION

Tomography of the solar corona can provide cruicial constraints for models of the low corona, unique information on changes in coronal structure and rotation rates, and a valuable boundary condition for models of the heliospheric solar wind. This is the first of a series of three papers which aim to create a set of maps of the coronal density over an extended period (1996-present). The papers will describe the data processing and calibration (this paper), the tomography method (Paper II), and the resulting atlas of coronal electron density at a height of 5 R{sub ⊙} between years 1996–2014 (Paper III). This first paper presents a detailed description of data processing and calibration for the Large-Angle and Spectrometric Coronagraph (LASCO) C2 instrument on board the Solar and Heliospheric Observatory (SOHO) and the COR2 instruments of the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) package on board the Solar Terrestial Relations Observatory (STEREO) A and B spacecraft. The methodology includes noise suppression, background subtraction, separation of large dynamic events, conversion of total brightness to K-coronal brightness, and simple functions for cross-calibration between C2/LASCO and COR2/SECCHI. Comparison of the brightness of stars between LASCO C2 total and polarized brightness (pB) observations provide in-flight calibration factors for the pB observations, resulting in considerable improved agreement between C2 and COR2 A, and elimination of curious artifacts in the C2 pB images. The cross-calibration between LASCO C2 and the STEREO coronagraphs allows, for the first time, the potential use of multi-spacecraft coronagraph data for tomography and for coronal mass ejection analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Scientific Foundations and Approaches for Qualification of Additively Manufactured Structural Components

Additive manufacturing (AM) maintains a wide process window that enables complex designs otherwise unattainable via conventional production technologies. However, the lack of confidence in qualifying AM parts that leverage AM process–structure–property–performance (PSPP) relationships stymies design optimization and adoption of AM. While continuing efforts to map fundamental PSPP relationships that cover the potential design space, we first need pragmatic and then long-term solutions that overcome challenges associated with qualifying AM-designed parts. Two pragmatic solutions include: (1) AM material specifications to substantiate process reproducibility, and (2) component risk categorization to associate system risk relative to part performance and required part quality. A novel qualification paradigm under development involves efficient prediction of part performance over wide-ranging PSPP relationships through targeted testing and computational simulation. Here this paper describes projects at Sandia National Laboratories on PSPP relationship discovery, these pragmatic approaches, and the novel qualification approach.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide (MAPbX 3 ) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX 3 compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 MAPbX 3 compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. Finally, this accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning-based discovery of molecular descriptors that control polymer gas permeation

While machine learning has found increasing use in predicting the properties of polymeric materials with only a knowledge of chain architecture, determining the molecular factors underpinning properties (“interpretable AI”) has remained less well explored. We show that encoding chain chemistry in commonly employed formats, e.g., binary-valued fingerprints, leads to uniqueness issues during the hashing process to save storage space. This is because the hashing algorithm can map several chemical moieties into the same bit. These issues carry over into the ML algorithms, especially for “inverse” design and interpretable AI, and cannot be avoided by changing the length of the fingerprint. Using MACCS key featurizations of monomer repeats resolves some of these issues, and we show that a few substructures consistently appear in top features for maximizing permeability across several gases and ML models. These are carbon–carbon double bonds (as in polyacetylenes) especially when they are associated with methyl groups (found in branching architectures). Here these results, derived from the limited data set of ~ 500 polymers with experimental gas permeation data, are in agreement with physical insight and thus provide a robust foundation which could further enable study of these material classes through detailed experiments and simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2016

High cycle fatigue in the transmission electron microscope

One of the most common causes of structural failure in metals is fatigue induced by cyclic loading. Historically, microstructure-level analysis of fatigue cracks has primarily been performed post mortem. However, such investigations do not directly reveal the internal structural processes at work near micro- and nanoscale fatigue cracks and thus do not provide direct evidence of active microstructural mechanisms. In this paper, the tension–tension fatigue behavior of nanocrystalline Cu was monitored in real time at the nanoscale by utilizing a new capability for quantitative cyclic mechanical loading performed in situ in a transmission electron microscope (TEM). Controllable loads were applied at frequencies from one to several hundred hertz, enabling accumulations of 10 6 cycles within 1 h. The nanometer-scale spatial resolution of the TEM allows quantitative fatigue crack growth studies at very slow crack growth rates, measured here at ~10 –12 m·cycle –1 . This represents an incipient threshold regime that is well below the tensile yield stress and near the minimum conditions for fatigue crack growth. Evidence of localized deformation and grain growth within 150 nm of the crack tip was observed by both standard imaging and precession electron diffraction orientation mapping. Finally, these observations begin to reveal with unprecedented detail the local microstructural processes that govern damage accumulation, crack nucleation, and crack propagation during fatigue loading in nanocrystalline Cu.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Empirical Dynamic Modeling Reveals Complexity of Methane Fluxes in a Temperate Salt Marsh

Abstract Methane dynamics within salt marshes are complex because vegetation types, temperature, oscillating water levels, and changes in salinity and redox conditions influence CH 4 production, consumption, oxidation, and emissions. These non‐linear and complex interactions among variables affect the traditionally expected functional relationships and present challenges for interpreting and developing process‐based models. We employed empirical dynamic modeling (EDM) and convergent cross mapping (CCM) as a novel approach for characterizing seasonal/multiday and diurnal CH 4 dynamics by inferring causal variables, lags, and interconnections among multiple biophysical variables within a temperate salt marsh using 5 years of eddy covariance data. EDM/CCM is a nonparametric approach capable of quantifying the coupling between variables while determining time scales where variable interactions are the most relevant. We found that gross primary productivity, tidal creek dissolved oxygen, and temperature were important for seasonal/multiday dynamics (rho = 0.73–0.80), while water level was most important for diurnal dynamics during both the growing and dormancy phenoperiods (rho = 0.72 and 0.56, respectively). Lags for the top‐ranked variables (i.e., gross primary productivity, dissolved oxygen, temperature, water level) occurred between 1 and 5 weeks at the seasonal scale and 1–24 hr at the diurnal scale. The EDM had high prediction capabilities for intra‐/inter‐seasonal patterns and annual CH 4 sums but had limitations in representing large, infrequent fluxes. Results highlight the importance of non‐linearity, drivers, lag times, and interconnections among multiple biophysical variables that regulate CH 4 fluxes in tidal wetlands. This research introduces a novel approach to examining CH 4 fluxes, which will aid in evaluating current paradigms in wetlands and other ecosystems.

Environmental Sciences & Ecology↗
DOE OSTI2023

A large expert-curated cryo-EM image dataset for machine learning protein particle picking

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.

42 ENGINEERING↗
DOE OSTI2018

Probing charge transfer dynamics in a single iron tetraphenylporphyrin dyad adsorbed on an insulating surface

Although the dynamics of charge transfer (CT) processes can be probed with ultimate lifetime resolution, the inability to control CT at the nanoscale is one of the most important roadblocks to revealing some of its deep fundamental aspects. In this work, we present an investigation of CT dynamics in a single iron tetraphenylporphyrin (Fe-TPP) donor/acceptor dyad adsorbed on a CaF 2 /Si(100) insulating surface. The tip of a scanning tunneling microscope (STM) is used to create local ionic states in one fragment of the dyad. The CT process is monitored by imaging subsequent changes in the neighbor acceptor molecule and its efficiency is mapped, revealing the influence of the initial excited state in the donor molecule. In the validation of the experiments, simulations based on density functional theory show that holes have a higher donor–acceptor CT rate compared to electrons and highlight a noticeable initial state dependence on the CT process. We leverage the unprecedented spatial resolution achieved in our experiments to show that the CT process in the dyad is governed via molecule–molecule coherent tunneling with negligible surface-mediated character.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Information system of rice planting calendar based on ten-day (Dasarian) rainfall prediction

This paper describes information system of rice planting calendar to help farmers in determining the time for rice planting. The information includes rainfall prediction in ten days (dasarian) scale overlaid to map of rice field to produce map of rice planting in village level. The rainfall prediction was produced by stochastic modeling using Fast Fourier Transform (FFT) and Non-Linier Least Squares methods to fit the curve of function to the rainfall data. In this research, the Fourier series has been modified become non-linear function to follow the recent characteristics of rainfall that is non stationary. The results have been also validated in 4 steps, including R-Square, RMSE, R-Skill, and comparison with field data. The development of information system (cyber extension) provides information such as rainfall prediction, prediction of the planting time, and interactive space for farmers to respond to the information submitted. Interfaces for interactive response will be critical to the improvement of prediction accuracy of information, both rainfall and planting time. The method used to get this information system includes mapping on rice planting prediction, converting the format file, developing database system, developing website, and posting website. Because of this map was overlaid with the Google map, the map files must be converted to the .kml file format.

60 APPLIED LIFE SCIENCES↗