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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Complexation of Curium(III) with DTPA at 10–70 °C: Comparison with Eu(III)–DTPA in Thermodynamics, Luminescence, and Coordination Modes

Separation of trivalent actinides (An(III)) from trivalent lanthanides (Ln(III)) is a challenging task because of their nearly identical chemical properties. Diethylenetriaminepentaacetate (DTPA), a key reagent used in the TALSPEAK process that effectively separates An(III) from Ln(III), is believed to play a critical role in the An(III)/Ln(III) separation. However, the underlying principles for the separation based on the difference in the complexation of DTPA with An(III) and Ln(III) remain unclear. In this work, the complexation of DTPA with Cm(III) at 10-70 ºC was investigated by spectrophotometry, luminescence spectroscopy, and microcalorimetry, in conjunction with computational methods. The binding strength, the enthalpy of complexation, the coordination modes, and the luminescence properties are compared between the Cm(III)-DTPA and Eu(III)-DTPA systems. The experimental and computational data have demonstrated that the difference between Cm(III) and Eu(III) in the binding strength with DTPA can be attributed to the stronger covalence bonding between Cm(III) and the nitrogen donors of DTPA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Shock compression response of cold-rolled Ni/Al multilayer composites

Uniaxial strain, plate-on-plate impact experiments were performed on cold-rolled Ni/Al multilayer composites and the resulting Hugoniot was determined through time-resolved measurements combined with impedance matching. The experimental Hugoniot agreed with that previously predicted by two dimensional (2D) meso-scale calculations. Additional 2D meso-scale simulations were performed using the same computational method as the prior study to reproduce the experimentally measured free surface velocities and stress profiles. Finally, these simulations accurately replicated the experimental profiles, providing additional validation for the previous computational work.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Standardized description of scientific evidence using the Evidence Ontology (ECO)

The Evidence Ontology (ECO) is a structured, controlled vocabulary for capturing evidence in biological research. ECO includes diverse terms for categorizing evidence that supports annotation assertions including experimental types, computational methods, author statements and curator inferences. Using ECO, annotation assertions can be distinguished according to the evidence they are based on such as those made by curators versus those automatically computed or those made via high-throughput data review versus single test experiments. Originally created for capturing evidence associated with Gene Ontology annotations, ECO is now used in other capacities by many additional annotation resources including UniProt, Mouse Genome Informatics, Saccharomyces Genome Database, PomBase, the Protein Information Resource and others. Information on the development and use of ECO can be found at http://evidenceontology.org. The ontology is freely available under Creative Commons license (CC BY-SA 3.0), and can be downloaded in both Open Biological Ontologies and Web Ontology Language formats at http://code.google.com/p/evidenceontology. Also at this site is a tracker for user submission of term requests and questions. ECO remains under active development in response to user-requested terms and in collaborations with other ontologies and database resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Extracting E Versus K Effective Band Structure from Supercell Calculations on Alloys and Impurities

The supercell approach to defects and alloys has circumvented the limitations of those methods that insist on using artificially high symmetry, yet this step usually comes at the cost of abandoning the language of E versus k band dispersion. Here we describe a computational method that maps the energy eigenvalues obtained from large supercell calculations into an effective band structure (EBS) and recovers an approximate E(k) for alloys. Making use of supercells allows one to model a random alloy A{sub 1-x}B{sub x}C by occupying the sites A and B via a coin-toss procedure, affording many different local environments (polymorphic description) to occur. We present the formalism and implementation details of the method and apply it to study the evolution of the impurity band appearing in the dilute GaN:P alloy. We go beyond the perfectly random case, realizing that many alloys may have nonrandom microstructures, and investigate how their formation is reflected in the EBS. It turns out that the EBS is extremely sensitive in determining the critical disorder level for which delocalized states start to appear in the intermediate band. In addition, the EBS allows us to identify the role played by atomic relaxation in the positioning of the impurity levels.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities.

14 SOLAR ENERGY↗
DOE OSTI2022

Mechanistic insights into metal, nitrogen doped carbon catalysts for oxygen reduction: progress in computational modeling

We report metal and nitrogen doped carbon materials (denoted as M–N–C) synthesized through high-temperature pyrolysis have been found to exhibit activity for oxygen reduction reaction (ORR) approaching that of Pt and electrochemical stability higher than previous MN 4 -containing macrocyclic molecular catalysts. Tremendous efforts have thus been devoted to the advancement of M–N–C catalysts as an economical alternative to Pt-based catalysts for proton exchange membrane fuel cell cathodes with a focus on simultaneously improving activity and stability. To this end, novel computational modeling techniques have been developed and applied to acquire knowledge crucial for accelerating the pace of M–N–C catalyst development. In this review, recent progress in computational method development, as well as the predictions of chemical structure of active sites, reaction pathways, ORR kinetics, and catalyst stability in electrochemical environments, are critically surveyed. Moreover, the crucial role of computational modeling to elucidate the functional mechanism of M–N–C catalysts for ORR in acid media and enable rational design of M–N–C catalysts is discussed with a visionary outlook for the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

SPARTAN (Scalable Probabilistic Application Reconfigurable Tensor Autonomous Network)

The technical founder of Ludwig Computing Inc has been competitively selected for support by Cyclotron Road, a U.S. Department of Energy (DOE) Advanced Manufacturing Office (AMO) Lab-Embedded Entrepreneurship Program (LEEP) through an approved merit review process. Ludwig Computing Inc, supported by the U.S. Department of Energy's Advanced Manufacturing Office through the Cyclotron Road program, has investigated the advantages of probabilistic computing for real-world compute-intensive applications. This research adds to the understanding of alternative computing paradigms by exploring a unique hardware-software co-design that integrates quantum computing methods with nature-inspired problem-solving techniques. The project's focus on areas such as combinatorial optimization, graph analytics, and machine learning demonstrates the potential for significant advancements in computational efficiency and performance. By harnessing natural randomness to streamline large circuits into fewer devices, Ludwig's approach enables massive parallelism, potentially offering higher throughput, speed, and energy efficiency compared to conventional hardware solutions. This work benefits the public by paving the way for more efficient computing solutions that could address complex real-world problems while potentially reducing energy consumption in data-intensive industries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

Abstract Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization (BO) strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a standard univariate test function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the BO framework.

42 ENGINEERING↗
DOE OSTI2013

COMPUTING THE CONTINUUM POLARIZATION FROM THOMSON SCATTERING IN GASEOUS CIRCUMSTELLAR DISKS

We investigate the computation of the intrinsic continuum linear polarization from electron scattering in optically thin and thick circumstellar disks of gas. We present the use of a non-LTE radiative transfer code, along with two different computational methods for obtaining the Stokes parameters, to reproduce the polarization levels that arise from disks of classical Be stars. Since the pioneering work of Poeckert and Marlborough, numerous improvements and refinements have been incorporated into computational radiative transfer models of classical Be stars. We present an assessment of the effect of several improvements on Poeckert and Marlborough's technique for calculating the polarization levels of the classical Be star {gamma} Cas. We find that improvements to the sampling of the disk density and the inclusion of a non-isothermal structure for the gas in the disk yield polarization levels that differ from the levels expected by Poeckert and Marlborough. Principally, the inclusion of the self-consistent calculation of the thermal structure of the disk has a significant impact on the resulting polarization. In addition, we assess the importance of the inclusion of multiple scattering calculations in predicting the continuum polarization in classical Be stars. We confirm that multiple scattering calculations are necessary for studying the linear polarization levels from optically thick gaseous disks around classical Be stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling

We report that carbon fiber reinforced plastics (CFRPs) are attracting growing attention in industry because of their enhanced properties. Preforming of thermoset carbon fiber prepregs is one of the most common production techniques of CFRPs. To simulate preforming, several computational methods have been developed. Most of these methods, however, obtain the material properties directly from experiments such as uniaxial tension and bias-extension where the coupling effect between tension and shear is not considered. Neglecting this coupling effect deteriorates the prediction accuracy of simulations. To address this issue, we develop a Bayesian model calibration and material characterization approach in a multiscale finite element preforming simulation framework that utilizes mesoscopic representative volume element (RVE) to account for the tension-shear coupling. A new geometric modeling technique is first proposed to generate the RVE corresponding to the close-packed uncured prepreg. This RVE model is then calibrated with a modular Bayesian approach to estimate the yarn properties, test its potential biases against the experiments, and fit a stress emulator. Finally, the predictive capability of this multiscale approach is further demonstrated by employing the stress emulator in the macroscale preforming simulation which shows that this approach can provide accurate predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2017

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK —a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks—aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed—the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. As a result, we show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Accelerating catalytic advancements through the precision of high-throughput experiments & calculations

The growing demand for energy-efficient processes to support a sustainable future drives the need for research to rapidly explore chemical and material space through accelerated catalyst discovery initiatives. Recent breakthroughs in high-throughput experimental and computational methods are transforming the catalysis field, surpassing traditional approaches to manipulating variables in catalytic processes. Key advancements in innovation include the integration of machine learning for efficient catalyst screening, high-throughput experimentation, data-driven methodologies employing comprehensive databases, and in situ and in operando techniques for realistic observations. This progress has undoubtedly been intertwined with a collaborative framework across disciplines, reshaping catalyst discovery methods in both industry and academia. This Opinion article presents a multifaceted perspective from coauthors with expertise spanning various stages of the Technology Readiness Level spectrum, highlighting both opportunities and persistent challenges in integrating computational and experimental approaches in catalysis. These challenges span from obtaining high-quality experimental data, scaling simulations to industrially relevant materials and process conditions to navigating the complexity and predictive accuracy of computational models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Anomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover’s Algorithm on a Quantum Computing Simulator

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. In principle, gamma radiation sources can be detected and identified by their unique spectral lines. However, detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. In recent prior work, we have developed a Hopfield Neural Network (HNN) in conjunction with an image processing algorithm to detect a weak signal anomaly hidden among the highly fluctuating background spectra. The objective of this work is to explore quantum computing methods to increase the speed of HNN. The approach is based on the Grover’s search algorithm in conjunction with a 3-SAT problem formalism. The Grover’s algorithm is implemented on a quantum computing simulator using Qiskit software. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign, where the anomaly is a subset of measurements containing a 137 Cs source. Results indicate that using Grover’s algorithm on a quantum simulator reduces runtime of HNN by two orders of magnitude.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

Lightning Talks 2015: Theoretical Division

This document is a compilation of slides from a number of student presentations given to LANL Theoretical Division members. The subjects cover the range of activities of the Division, including plasma physics, environmental issues, materials research, bacterial resistance to antibiotics, and computational methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pathways for the Formation of C 2+ Products under Alkaline Conditions during the Electrochemical Reduction of CO 2

Establishing how Cu facilitates the electrochemical CO 2 reduction reaction (CO2RR) to C 2+ products remains a critical challenge. Under typical reaction conditions, the pH near the electrode is considerably more alkaline than that in the bulk due to mass transport limitations. Challenges with probing alkaline pathways using computational methods have limited understanding of the CO2RR under experimentally relevant conditions. In this work, using the Volmer reaction on Cu (100), we demonstrate that predicted activation barriers can substantially differ between acidic and alkaline pathways. We compute reaction energetics for alkaline *CO protonation and find that, while the formation of *CHO is preferred thermodynamically, the formation of *COH is favored kinetically at high overpotential. However, we find formation of *CHO via reaction of *H and *CO feasible at room temperature. Further, we report potential-dependent energetics for forming the first C-C bond in CO2RR and find that CO dimerization likely dominates. Finally, we investigate how long-range van der Waals interactions impact our results by comparing to the meta-GGA B97M-rV.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Quantum state-resolved, bulk gas energetics: Comparison of theory and experiment

Until very recently, the computational model of state-to-state energy transfer in large gas mixtures, introduced by the author and co-workers, has had little experimental data with which to assess the accuracy of its predictions. In a novel experiment, Alghazi et al. [Chem. Phys. 448, 76 (2015)] followed the equilibration of highly vibrationally excited CsH(D) in baths of H{sub 2}(D{sub 2}) with simultaneous time- and quantum state-resolution. Modal temperatures of vibration, rotation, and translation for CsH(D) were obtained and presented as a function of pump-probe delay time. Here the data from this study are used as a test of the accuracy of the computational method, and in addition, the consequent changes in bath gas modal temperatures, not obtainable in the experiment, are predicted. Despite large discrepancies between initial CsH(D) vibrational states in the experiment and those available using the computational model, the quality of agreement is sufficient to conclude that the model’s predictions constitute at least a very good representation of the overall equilibration that, for some measurements, is very accurate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Novel Maximum-based Timing Acquisition for Spread-Spectrum Communications

This paper proposes and analyzes a new packet detection and timing acquisition method for spread spectrum systems. The proposed method provides an enhancement over the typical thresholding techniques that have been proposed for direct sequence spread spectrum (DS-SS). The effective implementation of thresholding methods typically require accurate knowledge of the received signal-to-noise ratio (SNR), which is particularly difficult to estimate in spread spectrum systems. Instead, we propose a method which utilizes a consistency metric of the location of maximum samples at the output of a filter matched to the spread spectrum waveform to achieve acquisition, and does not require knowledge of the received SNR. Through theoretical study, we show that the proposed method offers a low probability of missed detection over a large range of SNR with a corresponding probability of false alarm far lower than other methods. Computer simulations that corroborate our theoretical results are also presented. Although our work here has been motivated by our previous study of a filter bank multicarrier spread-spectrum (FB-MC-SS) system, the proposed method is applicable to DS-SS systems as well.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

Theoretical and experimental physical methods of neutron-capture therapy

This review is based to a substantial degree on our priority developments and research at the IR-8 reactor of the Russian Research Centre Kurchatov Institute. New theoretical and experimental methods of neutron-capture therapy are developed and applied in practice; these are: A general analytical and semi-empiric theory of neutron-capture therapy (NCT) based on classical neutron physics and its main sections (elementary theories of moderation, diffuse, reflection, and absorption of neutrons) rather than on methods of mathematical simulation. The theory is, first of all, intended for practical application by physicists, engineers, biologists, and physicians. This theory can be mastered by anyone with a higher education of almost any kind and minimal experience in operating a personal computer. Methods: of phantom experimental studies for the semi-empiric theory of NCT using epithermal neutrons. Experimental methods for measuring and monitoring the spectral composition of therapeutic, research, and analytical neutron beams for NCT and other investigations with neutrons. Experimental methods of remote neutron dosimetry of biological objects with intentional modification of the element composition of tissues in NCT. Methods: of mass investigations in pharmacokinetics and biologic efficiency of novel dose-forming preparations. Methods: for tailoring of therapeutic, research, and analytical neutron beams for NCT and other investigations with neutrons. Methods: of capillary neutron optics application for NCT and other fundamental and applied studies with neutrons.

62 RADIOLOGY AND NUCLEAR MEDICINE↗