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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.

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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Use of speckle for determining the response characteristics of Doppler imaging radars

An optical model is developed for imaging optical radars such as the SAR on Seasat and the Shuttle Imaging Radar (SIR-B) by analyzing the Doppler shift of individual speckles in the image. The signal received at the spacecraft is treated in terms of a Fresnel-Kirchhoff integration over all backscattered radiation within a Huygen aperture at the earth. Account is taken of the movement of the spacecraft along the orbital path between emission and reception. The individual points are described by integration of the point source amplitude with a Green's function scattering kernel. Doppler data at each point furnishes the coordinates for visual representations. A Rayleigh-Poisson model of the surface scattering characteristics is used with Monte Carlo methods to generate simulations of Doppler radar speckle that compare well with Seasat SAR data SIR-B data.

Tilley, D. G.↗
NASA NTRS2004

Representation of the Geosynchronous Plasma Environment for Spacecraft Charging Calculations

Historically, our ability to predict and postdict surface charging has suffered from both a lack of reliable secondary emission and backscattered electron yields and poor characterization of the plasma environment. One difficulty lies in the common practice of fitting the plasma data to a Maxwellian or Double Maxwellian distribution function, which may not represent the data well for charging purposes. For 13 years Los Alamos National Laboratory (LANL) has been accumulating measurements of electron and proton spectra from Magnetospheric Plasma Analyzer (MPA) instruments aboard a series of geosynchronous satellites. These data provide both a plasma characterization and the potential of the instrument ground. We use electron and ion flux spectra measured by the LANL MPA to examine how the use of different spectral representations of the charged particle environment in computations of spacecraft potentials during magnetospheric substorms affects the accuracy of the results. We calculate the spacecraft potential using both the measured fluxes and several different fits to these fluxes. These flux measurements and fits have been corrected for the difference between the measured and calculated potential. The potentials computed using the measured fluxes, the best available material properties of graphite carbon, and a secondary electron escape fraction of 81%, are within a factor of three of the measured potential for nearly all the data. Using a Kappa fit to the electron distribution function and a Maxwellian fit to the ion distribution function gives agreement similar to the calculations using the actual data. Alternative spectral representations, including Maxwellian and double Maxwellian for both species, lead to less satisfactory agreement between predicted and measured potentials.

Davis, V. A.↗
DOE OSTI2017

Gradient-based Optimization for Regression in the Functional Tensor-Train Format

We consider the task of low-multilinear-rank functional regression, i.e., learning a low-rank parametric representation of functions from scattered real-valued data. Our first contribution is the development and analysis of gradient-based optimization procedures, including stochastic gradient descent and quasi-Newton methods, for learning the parameters of a functional tensor-train (FT) that yields improved accuracy over standard alternating least squares methods. The functional tensor-train uses the tensor-train (TT) representation of low-rank arrays as an ansatz for a class of low-multilinear-rank functions. The FT is represented by a set of matrix-valued functions that contain a set of univariate functions, and the regression task is to learn the parameters of these univariate functions. Our second contribution demonstrates that using nonlinearly parameterized univariate functions, e.g., symmetric kernels with moving centers, within each core can outperform the standard approach of using a linear expansion of basis functions. Our final contributions are new rank adaptation and group-sparsity regularization procedures to minimize overfitting. We use several benchmark problems to demonstrate at least an order of magnitude lower accuracy with gradient-based optimization methods than ALS in the low-sample number regime. We also demonstrate an order of magnitude reduction in accuracy on a test problem resulting from nonlinear parameterizations over linear parameterizations. Finally we compare regression performance with 22 other nonparametric and parametric regression methods on 10 real-world data sets. We achieve top-five accuracy for seven of the data sets and best accuracy for two of the data sets. These rankings are the best amongst parametric models and competetive with the best non-parametric methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis

We present a novel unsupervised machine learning method for quantitative representation of scanning electron micrographs and its applications and performance for nuclear forensic analysis of uranium ore concentrates. The method uses a vector quantizing variational autoencoder followed by a histogram operation to encode a micrograph into a single dimensional representation, called the latent vector. The method requires no extant labeling of the data and can be applied over large datasets of micrographs with minimal human interaction. The representations generated are broadly descriptive of each micrograph and the microstructure of the material imaged. In the case of uranium ore concentrate analysis, the representations were amenable to processing reagent and ore concentrate species classification with accuracy of 81:8%, which is competitive with state-of-the-art supervised networks. The representations were also used to classify previously unseen processing routes, were able to classify imaging parameters such as magnification (to 76:0% accuracy), were able to classify fine grained process parameters such as calcining temperature (to 74:4% accuracy), and their informatic properties indicate that they are generally descriptive of the image represented. This method can be applied across microstructure analysis fields to perform quantitative analysis without the need for labor intensive and possibly biased human analysis.

Scanning Electron Microscopy, Vector Quantizing Va↗
NASA NTRS2020

Reaching Broad Audiences from a Large Agency Setting

NASA’s missions, engineering accomplishments, and scientific findings have inspired generations and advanced our understanding of the world we live in. NASA’s Earth science data are acquired by various sources, including satellites, aircraft, and field measurements. Captured data, their by-products, and their visual representations developed by research teams become available within few hours after satellite overpass or processing through a variety of NASA’s imaging, mapping services, and portals. Such online services as the Global Imagery Browse Services (GIBS), Worldview, LANCE, and LAADS DAAC are freely and openly available thanks to NASA’s Earth-Observing Satellite Data and Information Systems (EOSDIS). These services provide access to products created over the last 30 years, support a broad range of users from the scientific community to the general public, and cover a multitude of applications such as basic and applied scientific research, natural hazard and disaster monitoring, and social and educational outreach. In order to illustrate the significance of the overall work, the visualization products, and the broad range of users, we present three case studies: NASA’s Black Marble Product Suite, the Global Imagery Browse Services (GIBS) and Worldview, and the scientific visualization production process to communicate results to the scientific community and the general public.

Helen-Nicole Kostis↗
NASA NTRS2001

Short-Arc Analysis of Intersatellite Tracking Data in a Gravity Mapping Mission

A technique for the analysis of low-low intersatellite range-rate data in a gravity mapping mission is explored. The technique is based on standard tracking data analysis for orbit determination but uses a spherical coordinate representation of the 12 epoch state parameters describing the baseline between the two satellites. This representation of the state parameters is exploited to allow the intersatellite range-rate analysis to benefit from information provided by other tracking data types without large simultaneous multiple data type solutions. The technique appears especially valuable for estimating gravity from short arcs (e.g., less than 15 minutes) of data. Gravity recovery simulations which use short arcs are compared with those using arcs a day in length. For a high-inclination orbit, the short-arc analysis recovers low-order gravity coefficients remarkably well, although higher order terms, especially sectorial terms, are less accurate. Simulations suggest that either long or short arcs of GRACE data are likely to improve parts of the geopotential spectrum by orders of magnitude.

Rowlands, David D.↗
DOE OSTI2021

Artificial generation of representative single Li-ion electrode particle architectures from microscopy data

Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling. Information is drawn from multimodal microscopy techniques to artificially generate LiNi 0.5 Mn 0.3 Co 0.2 O 2 particles with full sub-particle grain detail. Statistical representations of particle architectures are derived from X-ray nano-computed tomography data supporting an ‘outer shell’ model, and sub-particle grain representations are derived from focused-ion beam electron backscatter diffraction data supporting a ‘grain’ model. A random field model used to characterize and generate the outer shells, and a random tessellation model used to characterize and generate grain architectures, are combined to form a multi-scale model for the generation of virtual electrode particles with full-grain detail. This work demonstrates the possibility of generating representative single electrode particle architectures for modeling and characterization that can guide synthesis approaches of particle architectures with enhanced performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Bridging 20 Years of Soil Organic Matter Frameworks: Empirical Support, Model Representation, and Next Steps

Abstract In the past few decades, there has been an evolution in our understanding of soil organic matter (SOM) dynamics from one of inherent biochemical recalcitrance to one deriving from plant‐microbe‐mineral interactions. This shift in understanding has been driven, in part, by influential conceptual frameworks which put forth hypotheses about SOM dynamics. Here, we summarize several focal conceptual frameworks and derive from them six controls related to SOM formation, (de)stabilization, and loss. These include: (a) physical inaccessibility; (b) organo‐mineral and ‐metal stabilization; (c) biodegradability of plant inputs; (d) abiotic environmental factors; (e) biochemical reactivity and diversity; and (f) microbial physiology and morphology. We then review the empirical evidence for these controls, their model representation, and outstanding knowledge gaps. We find relatively strong empirical support and model representation of abiotic environmental factors but disparities between data and models for biochemical reactivity and diversity, organo‐mineral and ‐metal stabilization, and biodegradability of plant inputs, particularly with respect to SOM destabilization for the latter two controls. More empirical research on physical inaccessibility and microbial physiology and morphology is needed to deepen our understanding of these critical SOM controls and improve their model representation. The SOM controls are highly interactive and also present some inconsistencies which may be reconciled by considering methodological limitations or temporal and spatial variation. Future conceptual frameworks must simultaneously refine our understanding of these six SOM controls at various spatial and temporal scales and within a hierarchical structure, while incorporating emerging insights. This will advance our ability to accurately predict SOM dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Extraction of Tumor Site from Cancer Pathology Reports using Deep Filters

Purpose: Pathology reports are the primary source of information concerning the millions of cancer cases across the United States. Cancer registries manually process the pathology reports to extract the pertinent information including primary tumor site, behavior, histology, laterality, and grade. Processing a large volume of the pathology reports in a timely manner is a continuing challenge for cancer registries. The purpose of this study is to develop an information extraction pipeline to reliably and efficiently extract reportable information. Method: We have developed a novel inverse-regression (IR) based information extraction pipeline. The inverse-regression based supervised filter has been successfully applied to many application domains. However, its application to the information extraction from unstructured text is hindered primarily by the extreme high-dimensionality of n-gram representations of text. In this study, we attempt to overcome the obstacles by a novel bootstrapping strategy. First, we use an information-theoretic mutual information based filter to discard the excessive and redundant n-gram features. This step reduces the size and improves the condition number of the sample covariance matrix, thus reducing the computational cost and improving the numerical stability of the subsequent inverse-regression step. Then we use localized sliced inverse-regression (LSIR) to learn a low-dimensional discriminatory subspace for information inference. In particular, we use the k-nearest neighbors of an unlabeled pathology report in the learned representation to infer the desired information from the labeled data in a supervised manner. Results: The experiments were conducted on a set of de-identified pathology reports with human expert labels as the ground truth. Our pipeline consistently performed better than or comparable to the best performing state-of-the-art methods while reducing the training and inference times substantially. Conclusion: Our results demonstrate the potential of inverse-regression based information extraction pipeline for reliable and efficient information extraction from unstructured text. The information extracted from the pathology reports can be used along with clinical information, medical imaging, and genomic information to instigate discoveries in cancer research.

Dubey, Abhishek↗
DOE OSTI2020

Experimental investigation and thermodynamic modeling of the binary Pt–Sn system

Here, the thermodynamics of the Pt–Sn system is revisited with attention to phase equilibria involving PtSn 4 , a topological quantum material with interesting physical properties. The phase relationship in the Sn-rich region of the Pt–Sn binary system was investigated by a combination of differential scanning calorimetry and equilibration experiments. Based on the present experiments and a critical evaluation of the available thermodynamic and phase diagram data reported in the literature, thermodynamic assessments of the entire Pt–Sn system were performed using the CALPHAD approach. Two sets of thermodynamic parameters were optimized, where the liquid phase was described using the Bragg-Williams random mixing model and the associate solution model, respectively. The modeling results are compared with the reliable experimental data and are shown to provide good representations of the phase equilibria and thermodynamic properties in this system.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-level optimization with the koopman operator for data-driven, domain-aware, and dynamic system security

Cyber-Physical Systems (CPSs) like the power grid are critically important but also increasingly vulnerable; ensuring reliable system operation in the face of disruptions is becoming more and more challenging. Multi-Level Optimization (MLO) is a powerful way to model adversarial interactions, which naturally makes it applicable to studying CPS security. However, MLO typically does not address underlying system dynamics, and incorporating nonlinear dynamics is generally infeasible. In this paper, we show how to combine MLO with the Koopman Operator (KO) to remedy this. The KO maps nonlinear dynamics to a lifted space in which those dynamics are linear, thus making it ideal for use with MLO. Moreover, the structure of the KO also provides convenient ways to incorporate domain knowledge into the data-driven process of learning the KO representation of a given system. Here we then demonstrate the use of MLO-KO on a small example problem taken from the power grid domain, discuss the scalability and computational cost of MLO-KO, and identify future research directions for this work.

42 ENGINEERING↗
DOE OSTI2023

The Intermetallic Reactivity Database: Compiling Chemical Pressure and Electronic Metrics toward Materials Design and Discovery

Here, the advent of high-throughput Density Functional Theory (DFT) calculations has supported the creation of large databases containing the quantitative output necessary for constructing theoretical phase diagrams and predicting physical properties. In this Article, we present a complementary resource, the Intermetallic Reactivity Database (IRD), focused on the chemical bonding features of solid-state structures and indicators of potential structural transformations. Each IRD entry augments common features, such as band structures and density of states (DOS) distributions, with chemically motivated information including DFT-Chemical Pressure (CP) schemes and visualizable representations of the atomic charges. Together, these data types enable the rationalization and prediction of potential structural phenomena encountered in intermetallic chemistry, as we illustrate with four examples: the origins of the Y 2 Ni 2 Mg structure in terms of CP features of its parent structures, the anticipation of intergrowth phases from the net atomic CPs collected in Al-containing binary phases, the correlation between trends in the CP schemes of CaCu 5 -type phases and experimentally observed structural variations, and finally, the development of theoretical methodology with the testing of a streamlined method generating DFT-CP schemes. Altogether, these examples highlight how the IRD supports the creation of models of structural chemistry that extend beyond the bounds of its entries.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Adsorption and Capillary Condensation-Induced Imbibition in Nanoporous Media

Multiphase flow phenomena in nanoporous media are encountered in many science and engineering applications. Shales, for example, possessing complex nanopore networks, have considerable importance as source rocks for unconventional oil and gas production and as low-permeability seals for geologic carbon sequestration or nuclear waste disposal. This work presents a theoretical investigation of the processes controlling adsorption, capillary condensation, and imbibition in such nanoporous media, with a particular focus on understanding the effects of fluid-fluid and fluid-pore wall interaction forces in the interconnected nanopore space. Building on a new theoretical framework, we developed a numerical model for the multiphase nanoporous flow and tested it against water vapor uptake measurements conducted on a shale core sample. The model, which is based on the density functional approach, explicitly includes the relevant interaction forces among fluids and solids while allowing for a continuum representation of the porous medium. The experimental data include gravimetrically measured mass changes in an initially dry core sample exposed to varying levels of relative humidity, starting with a low relative humidity (rh = 0.31) followed by a period of a higher relative humidity (rh = 0.81). During this process, water vapor uptake in the dry core is recorded as a function of time. Our model suggests that, under low rh conditions, the flow within the shale sample is controlled by adsorption- and diffusion-type processes. After increasing the rh to 0.81, the uptake of water vapor becomes more significant, and according to our model, this can be explained by capillary condensation followed by immiscible displacement in the core sample. It appears that strong fluid-pore wall attractive forces cause condensation near the inlet, which then induces water imbibition further into sample.

58 GEOSCIENCES↗
DOE OSTI2019

Diatom Physiology Controls Silicic Acid Leakage in Response to Iron Fertilization

We explore how the iron dependence of the Si:P uptake ratio R Si:P of diatoms controls the response of the global silicon cycle and phytoplankton community structure to Southern Ocean iron fertilization. We use a data‐constrained model of the coupled Si‐P‐Fe cycles that features a mechanistic representation of nutrient colimitations for three phytoplankton classes and that is embedded in a data‐assimilated global ocean circulation model. We consider three parameterizations of the iron dependence of R Si:P , all of which are consistent with the available field data and allow equally good fits to the observed nutrient climatology but result in very different responses to iron fertilization: Depending on how sharply R Si:P decreases with increasing iron concentration, iron fertilization can either cause enhanced silicic acid leakage from the Southern Ocean or strengthened Southern Ocean silicon trapping. Enhanced silicic acid leakage occurs if decreases in R Si:P win over increases in diatom growth, while the converse causes strengthened Southern Ocean silicon trapping. Silicic acid leakage drives a floristic shift in favor of diatoms in the subtropical gyres and stimulates increased low‐latitude opal export. The diatom contribution to global phosphorus export increases, but the lower diatom silicon requirement under iron‐replete conditions reduces the global opal export. Regardless of R Si:P parameterization, the global response of the biological phosphorus and silicon pumps is dominated by the Southern Ocean. The Si isotope signature of opal flux becomes systematically lighter with increasing iron‐induced silicic acid leakage, consistent with sediment records from iron‐rich glacial periods.

Holzer, Mark↗
DOE OSTI2015

Simulated evolution of fluorophores for light emitting diodes

Organic light emitting diodes based on fluorophores with a propensity for thermally activated delayed fluorescence (TADF) are able to circumvent limitations imposed on device efficiency by spin statistics. Molecules with a propensity for TADF necessarily have two properties: a small gap between the lowest lying singlet and triplet excited states and a large transition dipole moment for fluorescence. In this work, we demonstrate the use of a genetic algorithm to search a region of chemical space for molecules with these properties. This algorithm is based on a flexible and intuitive representation of the molecule as a tree data structure, in which the nodes correspond to molecular fragments. Our implementation takes advantage of hybrid parallel graphics processing unit accelerated computer clusters to allow efficient sampling while retaining a reasonably accurate description of the electronic structure (in this case, CAM-B3LYP/6-31G{sup ∗∗}). In total, we have identified 3792 promising candidate fluorophores from a chemical space containing 1.26 × 10{sup 6} molecules. This required performing electronic structure calculations on only 7518 molecules, a small fraction of the full space. Several novel classes of molecules which show promise as fluorophores are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Sparse maps—A systematic infrastructure for reduced-scaling electronic structure methods. I. An efficient and simple linear scaling local MP2 method that uses an intermediate basis of pair natural orbitals

In this work, a systematic infrastructure is described that formalizes concepts implicit in previous work and greatly simplifies computer implementation of reduced-scaling electronic structure methods. The key concept is sparse representation of tensors using chains of sparse maps between two index sets. Sparse map representation can be viewed as a generalization of compressed sparse row, a common representation of a sparse matrix, to tensor data. By combining few elementary operations on sparse maps (inversion, chaining, intersection, etc.), complex algorithms can be developed, illustrated here by a linear-scaling transformation of three-center Coulomb integrals based on our compact code library that implements sparse maps and operations on them. The sparsity of the three-center integrals arises from spatial locality of the basis functions and domain density fitting approximation. A novel feature of our approach is the use of differential overlap integrals computed in linear-scaling fashion for screening products of basis functions. Finally, a robust linear scaling domain based local pair natural orbital second-order Möller-Plesset (DLPNO-MP2) method is described based on the sparse map infrastructure that only depends on a minimal number of cutoff parameters that can be systematically tightened to approach 100% of the canonical MP2 correlation energy. With default truncation thresholds, DLPNO-MP2 recovers more than 99.9% of the canonical resolution of the identity MP2 (RI-MP2) energy while still showing a very early crossover with respect to the computational effort. Based on extensive benchmark calculations, relative energies are reproduced with an error of typically <0.2 kcal/mol. The efficiency of the local MP2 (LMP2) method can be drastically improved by carrying out the LMP2 iterations in a basis of pair natural orbitals. While the present work focuses on local electron correlation, it is of much broader applicability to computation with sparse tensors in quantum chemistry and beyond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO 2 interferometer on DIII-D

Abstract A Machine-Learning (ML) based detection scheme that automatically detects Alfvén Eigenmodes (AE) in a labelled DIII-D database is presented here. Controlling AEs is important for the success of planned burning plasma devices such as ITER, since resonant fast ions can drive AEs unstable and degrade the performance of the plasma or damage the first walls of the machine vessel. Artificial Intelligence could be useful for real-time detection and control of AEs in steady-state plasma scenarios by implementing ML-based models into control algorithms that drive actuators for mitigation of AE impacts. Thus, the objective is to compare differences in performance between using two different recurrent neural network systems (Reservoir Computing Network and Long Short Term Memory Network) and two different representations of the C O 2 phase data (simple and crosspower spectrograms). All C O 2 interferometer chords are used to train both models, but only one is processed during each training step. The results from the model and data comparison show higher performance for the RCN model (True Positive Rate = 90% and False Positive Rate = 14%), and that using simple magnitude spectrograms is sufficient to detect AEs. Also, the vertical C O 2 interferometer chord passing near the center is better for ML-based detection of AEs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Free and interacting short-range entangled phases of fermions: Beyond the tenfold way

Here, we extend the periodic table of phases of free fermions in the tenfold way symmetry classes to a classification of free fermionic phases protected by an arbitrary on-site unitary symmetry $\hat{G}$ in an arbitrary dimension. The classification is described as a function of the real representation theory of $\hat{G}$ and the data of the original periodic table. We also systematically study in low dimensions the relationship between the free invariants and the invariants of short-range entangled interacting phases of fermions. Namely we determine whether a given symmetry protected phase of free fermions is destabilized by sufficiently strong interactions or it remains stable even in the presence of interactions. Finally, we also determine which interacting fermionic phases cannot be realized by free fermions. Examples of both destabilized free phases and intrinsically interacting phases are common in all dimensions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗