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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Stability of Hydrated Methylamine: Structural Characteristics and H2N···H–O Hydrogen Bonds

Methylamine is the simplest aliphatic amine found in human urine, blood, and tissues. It is thought to play a significant part in central nervous system disturbances observed during renal and hepatic disease. In this work we have investigated the methylamine hydration clusters using a basin hopping (BH) algorithm with the density functional theory (DFT). The results presented herein yield a detailed understanding of the structure and stability for a system consisting of one methylamine molecule and up to seven waters: the most stable geometries arise from a fusion of tetramer or pentamer rings; by the geometrical parameters and topological parameters analysis, the strengths of the H2N···H–O hydrogen bonds of the global minima increase as the sizes of clusters increase, except for n = 5 where there is a slight fluctuation. This work may shed light on the form mechanism of methylamine existing in organisms and the hydration structures of larger molecules containing amino functional groups and their interaction with the water molecules nearby.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2016

A HOT FLUX ROPE OBSERVED BY SDO/AIA

A filament eruption was observed on 2010 October 31 in the images recorded by the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamic Observatory (SDO) in its Extreme Ultra-Violet (EUV) channels. The filament showed a slow-rise phase followed by a fast rise and was classified to be an asymmetric eruption. In addition, multiple localized brightenings which were spatially and temporally associated with the slow-rise phase were identified, leading us to believe that the tether-cutting mechanism initiated the eruption. An associated flux rope was detected in high-temperature channels of AIA, namely 94 and 131 Å, corresponding to 7 and 11 MK plasma respectively. In addition, these channels are also sensitive to cooler plasma corresponding to 1–2 MK. In this study, we have applied the algorithm devised by Warren et al. to remove cooler emission from the 94 Å channel to deduce only the high-temperature structure of the flux rope and to study its temporal evolution. We found that the flux rope was very clearly seen in the clean 94 Å channel image corresponding to Fe xviii emission, which corresponds to a plasma at a temperature of 7 MK. This temperature matched well with that obtained using Differential Emission Measure analysis. This study provides important constrains in the modeling of the thermodynamic structure of the flux ropes in coronal mass ejections.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

SU-E-J-218: Evaluation of CT Images Created Using a New Metal Artifact Reduction Reconstruction Algorithm for Radiation Therapy Treatment Planning

Purpose: Metal in patients creates streak artifacts in CT images. When used for radiation treatment planning, these artifacts make it difficult to identify internal structures and affects radiation dose calculations, which depend on HU numbers for inhomogeneity correction. This work quantitatively evaluates a new metal artifact reduction (MAR) CT image reconstruction algorithm (GE Healthcare CT-0521-04.13-EN-US DOC1381483) when metal is present. Methods: A Gammex Model 467 Tissue Characterization phantom was used. CT images were taken of this phantom on a GE Optima580RT CT scanner with and without steel and titanium plugs using both the standard and MAR reconstruction algorithms. HU values were compared pixel by pixel to determine if the MAR algorithm altered the HUs of normal tissues when no metal is present, and to evaluate the effect of using the MAR algorithm when metal is present. Also, CT images of patients with internal metal objects using standard and MAR reconstruction algorithms were compared. Results: Comparing the standard and MAR reconstructed images of the phantom without metal, 95.0% of pixels were within ±35 HU and 98.0% of pixels were within ±85 HU. Also, the MAR reconstruction algorithm showed significant improvement in maintaining HUs of non-metallic regions in the images taken of the phantom with metal. HU Gamma analysis (2%, 2mm) of metal vs. non-metal phantom imaging using standard reconstruction resulted in an 84.8% pass rate compared to 96.6% for the MAR reconstructed images. CT images of patients with metal show significant artifact reduction when reconstructed with the MAR algorithm. Conclusion: CT imaging using the MAR reconstruction algorithm provides improved visualization of internal anatomy and more accurate HUs when metal is present compared to the standard reconstruction algorithm. MAR reconstructed CT images provide qualitative and quantitative improvements over current reconstruction algorithms, thus improving radiation treatment planning accuracy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

COE Task Order 4. Final Report: Automated Threat Recognition

Lawrence Livermore National Laboratory (LLNL) was tasked by the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) with developing an automated target recognition algorithm for finding objects of interest in checked baggage at airports. The project used x-ray computed tomography (CT) data collected on a medical scanner of various simulated checked bags. Target objects of interest include clay, rubber, and saline in various concentrations. The final results are 93.9% probability of detection (P D ) with 11.9% probability of false alarm (PFA) on all targets and 100% detection for pseudo target sheets. This result is achieved by using a three-stage algorithm made up of segmentation, post-processing, and feature extraction and object classification. The segmentation proved to be the most important stage and it involves merging two distinct segmentation results followed by a set of algorithms for cleaning up the merged segmentations. The first segmentation algorithm operates on planar 10x10 voxel slabs (two- dimensional segments) in the three spatial planes. The segmenter calculates a probability that each slab in the image is a part of a target, determines if the probability was above a threshold, and then merges all connected slabs together. The second segmentation algorithm is an ensemble segmenter, which generates a set of possible segmentations through random permutations and computes an average behavior. The distinct properties of these segmenters results in a merged segmentation with superior results than either segmenter alone. The merged segmentation is “cleaned up” using post processing techniques to achieve the final labeled images. The final classification stage made use of a random forest classifier algorithm to discriminate targets from non-targets based on a set of features of the segmented objects partially from the voxel slabs and partially from the individual voxels. Features include standard statistics such as mean, median, and standard deviations of the density, features capturing some aspects of texture, and some features about the surrounding bag structure. In addition to the detection and false alarm results above, we show how the algorithms can be trained to “overfit” to the limited data set provided and achieve near perfect results (P D =100% and P FA =0%). This over-training is not recommended for long-term operation, but instead is meant to demonstrate how test design is an important factor in aviation security. Possible improvements to the algorithm and the testing methodology (including blind testing) are also presented.

42 ENGINEERING↗
DOE OSTI2017

Coalescence Prevention Algorithm for Level Set Method

An algorithm to prevent or delay bubble coalescence for the level set (LS) method is presented. This novel algorithm uses the LS method field to detect when bubbles are in close proximity, indicating a potential coalescence event, and applies a repellent force to simulate the unresolved liquid drainage force. The model is introduced by locally modifying the surface tension force near the liquid film drainage area. The algorithm can also simulate the liquid drainage time of the thin film by controlling the length of time the increased surface tension has been applied. Thus, a new method of modeling bubble coalescence has been developed. Several test cases were designed to demonstrate the capabilities of the algorithm. The simulations, including a mesh study, confirmed the abilities to identify and prevent coalescence as well as implement the time tracking portion, with an additional 10–25% computational cost. Ongoing tests aim to verify the algorithm's functionality for simulations with different flow conditions, a ranging number of bubbles, and both structured and unstructured computational mesh types. Specifically, a bubble rising toward a free surface provides a test of performance and demonstrates the ability to consistently prevent coalescence. In addition, a two bubble case and a seven bubble case provide a more complex demonstration of how the algorithm performs for larger simulations. These cases are compared to much more expensive simulations capable of resolving the liquid film drainage (through very high local mesh resolution) to investigate how the algorithm replicates the liquid film drainage process.

Engineering↗
DOE OSTI2014

SU-E-T-477: An Efficient Dose Correction Algorithm Accounting for Tissue Heterogeneities in LDR Brachytherapy

Purpose: Seed brachytherapy is currently used for adjuvant radiotherapy of early stage prostate and breast cancer patients. The current standard for calculation of dose surrounding the brachytherapy seeds is based on American Association of Physicist in Medicine Task Group No. 43 (TG-43 formalism) which generates the dose in homogeneous water medium. Recently, AAPM Task Group No. 186 emphasized the importance of accounting for tissue heterogeneities. This can be done using Monte Carlo (MC) methods, but it requires knowing the source structure and tissue atomic composition accurately. In this work we describe an efficient analytical dose inhomogeneity correction algorithm implemented using MIM Symphony treatment planning platform to calculate dose distributions in heterogeneous media. Methods: An Inhomogeneity Correction Factor (ICF) is introduced as the ratio of absorbed dose in tissue to that in water medium. ICF is a function of tissue properties and independent of source structure. The ICF is extracted using CT images and the absorbed dose in tissue can then be calculated by multiplying the dose as calculated by the TG-43 formalism times ICF. To evaluate the methodology, we compared our results with Monte Carlo simulations as well as experiments in phantoms with known density and atomic compositions. Results: The dose distributions obtained through applying ICF to TG-43 protocol agreed very well with those of Monte Carlo simulations as well as experiments in all phantoms. In all cases, the mean relative error was reduced by at least 50% when ICF correction factor was applied to the TG-43 protocol. Conclusion: We have developed a new analytical dose calculation method which enables personalized dose calculations in heterogeneous media. The advantages over stochastic methods are computational efficiency and the ease of integration into clinical setting as detailed source structure and tissue segmentation are not needed. University of Toronto, Natural Sciences and Engineering Research Council of Canada.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Mapping nanocrystal orientations via scanning Laue diffraction microscopy for multi-peak Bragg coherent diffraction imaging

The recent commissioning of a movable monochromator at the 34-ID-C endstation of the Advanced Photon Source has vastly simplified the collection of Bragg coherent diffraction imaging (BCDI) data from multiple Bragg peaks of sub-micrometre scale samples. Laue patterns arising from the scattering of a polychromatic beam by arbitrarily oriented nanocrystals permit their crystal orientations to be computed, which are then used for locating and collecting several non-co-linear Bragg reflections. The volumetric six-component strain tensor is then constructed by combining the projected displacement fields that are imaged using each of the measured reflections via iterative phase retrieval algorithms. Complications arise when the sample is heterogeneous in composition and/or when multiple grains of a given lattice structure are simultaneously illuminated by the polychromatic beam. Here, a workflow is established for orienting and mapping nanocrystals on a substrate of a different material using scanning Laue diffraction microscopy. The capabilities of the developed algorithms and procedures with both synthetic and experimental data are demonstrated. The robustness is verified by comparing experimental texture maps obtained with Laue diffraction microscopy at the beamline with maps obtained from electron back-scattering diffraction measurements on the same patch of gold nanocrystals. Such tools provide reliable indexing for both isolated and densely distributed nanocrystals, which are challenging to image in three dimensions with other techniques.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Constraining the Multiscale Structure of Geophysical Fields in Machine Learning: The Case of Precipitation

The use of deep-learning algorithms for estimating the value of geophysical variables from remotely sensed information is rapidly expanding. The typical objective function minimized in such algorithms is the mean square error (MSE), which is known to lead to smooth estimates with compressed dynamical range as compared to the true distribution of the variable of interest. Here, we introduce and evaluate alternative objective functions, focusing on the retrieval of precipitation rates from satellite passive microwave radiometric measurements using a deep convolutional neural network. For this testbed application, the results show that explicitly imposing the preservation of the statistical distribution and spatial wavelet power spectrum of the target variable allows to accurately reproduce extreme values and sharp gradients across multiple scales in this study.

58 GEOSCIENCES↗
DOE OSTI2017

Progress Implementing a Model-Based Iterative Reconstruction Algorithm for Ultrasound Imaging of Thick Concrete

All commercial nuclear power plants (NPPs) in the United States contain concrete structures. These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and the degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Concrete structures in NPPs are often inaccessible and contain large volumes of massively thick concrete. While acoustic imaging using the synthetic aperture focusing technique (SAFT) works adequately well for thin specimens of concrete such as concrete transportation structures, enhancements are needed for heavily reinforced, thick concrete. We argue that image reconstruction quality for acoustic imaging in thick concrete could be improved with Model-Based Iterative Reconstruction (MBIR) techniques. MBIR works by designing a probabilistic model for the measurements (forward model) and a probabilistic model for the object (prior model). Both models are used to formulate an objective function (cost function). The final step in MBIR is to optimize the cost function. Previously, we have demonstrated a first implementation of MBIR for an ultrasonic transducer array system. The original forward model has been upgraded to account for direct arrival signal. Updates to the forward model will be documented and the new algorithm will be assessed with synthetic and empirical samples.

concrete NDE↗
DOE OSTI2023

Studying the hadron structure with PANDA and CLAS using machine learning techniques

The hadron spectroscopy and structure are currently very active fields of research to study the non-perturbative regime of quantum chronodynamics. The first one studies the complex structure of excited hadrons by looking at their decay products, while the latter uses lepton scattering on nucleons. Both methods require reconstruction algorithms with great efficiency and good particle identification and background rejection rates. This work aims to provide these by either improving the existing methods or developing new ones. The first part of this document presents a feasibility study of a predicted hybrid charmonium state for the PANDA experiment. Lattice QCD calculations predict the ground state hybrid charmonium to be a spin exotic with quantum numbers of JP C = 1?+ at a mass of around 4.3 GeV with a width to be around 20 MeV. A machine learning based data analysis scheme is proposed to further improve the signal efficiency and the background reduction, alongside with improvements of the analysis software (PandaRoot), that are vital for this study. These improvements include a reworked clustering algorithm for the electromagnetic calorimeter (EMC) and an optimized monte carlo matching for neutral particles. The second part of this document is about studying the proton structure. A multidimensional study of the structure function ratio Fsin(?)LU /FUU has been performed for K±, based on the measurement of beam-spin asymmetries. It uses the high statistics data recorded with the CLAS12 spectrometer at Jefferson Laboratory. Fsin(?)LU is a twist-3 quantity that provides information about the quark gluon correlations in the proton. This document will present for the first time a simultaneous analysis of two kaon channels over a large kinematic range of z, xB , PT and Q2 with virtualities Q2 ranging from 1 GeV2 up to 8 GeV2 using machine learning techniques for improved particle identification.

Kripko, Aron↗
DOE OSTI2014

Characterizing system dynamics with a weighted and directed network constructed from time series data

In this work, we propose a novel method to transform a time series into a weighted and directed network. For a given time series, we first generate a set of segments via a sliding window, and then use a doubly symbolic scheme to characterize every windowed segment by combining absolute amplitude information with an ordinal pattern characterization. Based on this construction, a network can be directly constructed from the given time series: segments corresponding to different symbol-pairs are mapped to network nodes and the temporal succession between nodes is represented by directed links. With this conversion, dynamics underlying the time series has been encoded into the network structure. We illustrate the potential of our networks with a well-studied dynamical model as a benchmark example. Results show that network measures for characterizing global properties can detect the dynamical transitions in the underlying system. Moreover, we employ a random walk algorithm to sample loops in our networks, and find that time series with different dynamics exhibits distinct cycle structure. That is, the relative prevalence of loops with different lengths can be used to identify the underlying dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

The Application of the OPTICS Algorithm to Cluster Analysis in Atom Probe Tomography Data

Atom probe tomography (APT) is a powerful technique to characterize buried 3D nanostructures in a variety of materials. Accurate characterization of those nanometer-scale clusters and precipitates is of great scientific significance to understand the structure-property relationships and the microstructural evolution. The current widely used cluster analysis method, a variant of the DBSCAN algorithm, can only accurately extract clusters of the same atomic density, neglecting several experimental realities, such as density variations within and between clusters and the non-uniformity of the atomic density in the APT reconstruction itself (e.g. crystallographic poles and other field evaporation artifacts). The clustering results of DBSCAN-like method rely heavily on multiple input parameters, but ideal selection of those parameters is challenging and oftentimes ambiguous. In this study, we present a new cluster analysis method consisting of a well-known algorithm, called OPTICS, and a cluster extraction algorithm we developed to enable accurate detection of clusters of varying atomic density in APT data. The new method requires only one free parameter, and other inputs can be estimated based on physical parameters of the material. The effectiveness of this method is demonstrated by application to several small-scale model datasets and a real APT dataset obtained from an oxide-dispersion strengthened ferritic alloy specimen. The method’s sensitivity with respect to parameter selection is discussed and compared with the current widely used methods.

atom probe tomography, cluster analysis, density-b↗
DOE OSTI2020

NWChem: Past, present, and future

Specialized computational chemistry packages have permanently reshaped the landscape of chemical sciences by providing tools to support and guide the experimental effort and for prediction of chemical and materials properties. In this regard, a special role has been played by electronic structure packages where complex chemical and materials processes can be modeled using first-principle-driven methodologies. Over the last few decades, the rapid development of computing technologies and a tremendous increase in computational power has offered a unique chance to study complex chemical transformations using sophisticated and predictive many-body techniques to describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, a critical role has been played by novel parallel algorithms capable of taking advantage of computational resources to address polynomial scaling of electronic structure methods. NWChem was among the first electronic structure codes that focused on delivering scalable performance for electronic structure simulations. Herein, we briefly review the NWChem suite of computational codes including its history, design principles, parallel tools, current capabilities, outreach and outlook.

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

GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging

Tomography has made a radical impact on diverse fields ranging from the study of 3D atomic arrangements in matter to the study of human health in medicine. Despite its very diverse applications, the core of tomography remains the same, that is, a mathematical method must be implemented to reconstruct the 3D structure of an object from a number of 2D projections. Here, we present the mathematical implementation of a tomographic algorithm, termed GENeralized Fourier Iterative REconstruction (GENFIRE), for high-resolution 3D reconstruction from a limited number of 2D projections. GENFIRE first assembles a 3D Fourier grid with oversampling and then iterates between real and reciprocal space to search for a global solution that is concurrently consistent with the measured data and general physical constraints. The algorithm requires minimal human intervention and also incorporates angular refinement to reduce the tilt angle error. We demonstrate that GENFIRE can produce superior results relative to several other popular tomographic reconstruction techniques through numerical simulations and by experimentally reconstructing the 3D structure of a porous material and a frozen-hydrated marine cyanobacterium. As a result, equipped with a graphical user interface, GENFIRE is freely available from our website and is expected to find broad applications across different disciplines.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗
DOE OSTI2025

Extracting scattering amplitudes for arbitrary two-particle systems with one-particle left-hand cuts via lattice QCD

We derive a general formalism that relates the spectrum of two-particle systems in a finite volume to physical scattering amplitudes, taking into account the presence of any left-hand branch cuts due to single-particle exchanges. The method first relates the finite-volume spectrum to an infinite-volume short-range quantity, denoted ${\mathcal{M}}_0$, and then relates the latter to the physical scattering amplitudes via known integral equations. The derivation of both relations is performed using all-orders perturbation theory and is exact up to neglected exponentially suppressed volume dependence. The relations hold for arbitrary two-particle systems with any number of coupled channels, non-identical and non-degenerate particles, and any intrinsic spin.

algorithms↗