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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Comparison and validation of the QuEChERSER mega-method for determination of per- and polyfluoroalkyl substances in foods by liquid chromatography with high-resolution and triple quadrupole mass spectrometry

Instances of food contamination with per- and polyfluoroalkyl substances (PFAS) continue to occur globally, but sample preparation and analytical methods are quite limited and often monitor for a small percentage of known PFAS. This study aimed to evaluate, validate, and compare performance of two instruments with the recently developed “quick, easy, cheap, effective, rugged, safe, efficient, and robust” (QuEChERSER) sample preparation mega-method – a method developed to monitor chemicals over a broad range of physicochemical properties. Initial evaluation of the QuEChERSER mega-method for determination of PFAS in food demonstrated recoveries, matrix interferences, and co-extractive removal comparable to (or better than) US Food and Drug Administration (FDA) and USDA Food Safety and Inspection Service (FSIS) methods. Subsequent validation of QuEChERSER in beef, catfish, chicken, pork, liquid eggs, and powdered eggs on a high-resolution mass spectrometer achieved acceptable recoveries (70–120%) and precision (RSDs ≤20%) for all 33 target analytes at the 1 and 5 ng g –1 levels and 67–88% of analytes at the 0.1 ng g –1 level, depending on the matrix. Additional validation was performed by tandem mass spectrometry on a triple quadrupole instrument. This approach provided no non-detects and better recoveries at the 0.1 ng g –1 level than the HRMS method but exhibited more variability at 1 and 5 ng g –1 spiking levels. Analysis of NIST SRMs 1946 and 1947 gave accuracies of 70–117%. Furthermore, these results demonstrate the capability of combining PFAS analysis with a mega-method previously validated for 350 analytes, while collecting non-target data for future retrospective analysis of emerging alternatives with a high-resolution mass spectrometry method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques

A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post-processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Evidence against naive truncations of the OPE from e + e − → hadrons below charm

The operator product expansion (OPE), truncated in dimension, is employed in many contexts. An example is the extraction of the strong coupling, $\alpha_s$, from hadronic $\tau$-decay data, using a variety of analysis methods based on finite-energy sum rules. Here, we reconsider a long-used method, which parametrizes non-perturbative contributions to the $I=1$ vector and axial vacuum polarizations with the OPE, setting several higher-dimension coefficients to zero in order to implement the method in practice. The assumption that doing this has a negligible effect on the value of $\alpha_s$ is tantamount to the assumption that the low-dimension part of the OPE converges rapidly with increasing dimension near the $\tau$ mass. Were this assumption valid, it would certainly have to be valid at energies above the $\tau$ mass as well. It follows that the method can be tested using data obtained from $e^+e^-\to\mbox{hadrons}$, as they are not limited by the kinematic constraints of $\tau$ decays. We carry out such an investigation using a recent high-precision compilation for the $R$ ratio, arguing that it provides insights into the validity of the strategy, even if it probes a different, though related channel. We find that $e^+e^-$-based tests call into question the implied assumption of rapid convergence of the low-dimension part of the OPE around the $\tau$ mass, and thus underscore the need to restrict finite-energy sum-rule analyses to observables which receive only contributions from lower-order terms in the OPE.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Two-dimensional wave-number spectral analysis techniques for phase contrast imaging turbulence imaging data on large helical device

An analysis method for unfolding the spatially resolved wave-number spectrum and phase velocity from the 2D CO{sub 2} laser phase contrast imaging system on the large helical device is described. This is based on the magnetic shear technique which identifies propagation direction from 2D spatial Fourier analysis of images detected by a 6 × 8 detector array. Because the strongest modes have wave-number at the lower end of the instrumental k range, high resolution spectral techniques are necessary to clearly resolve the propagation direction and hence the spatial distribution of fluctuations along the probing laser beam. Multiple-spatial point cross-correlation averaging is applied before calculating the spatial power spectrum. Different methods are compared, and it is found that the maximum entropy method (MEM) gives best results. The possible generation of artifacts from the over-narrowing of spectra are investigated and found not to be a significant problem. The spatial resolution Δρ (normalized radius) around the peak wave-number, for conventional Fourier analysis, is ∼0.5, making physical interpretation difficult, while for MEM, Δρ ∼ 0.1.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Measurement of the sound speed in dense fluid deuterium along the cryogenic liquid Hugoniot

Many experimental studies, spanning several decades of research and using various dynamic compression schemes, have been conducted to investigate cryogenic liquid deuterium under strong shock compression. The consensus emerging from these studies is that of a progressive dissociation of the D 2 molecules into an electrically conducting, atomic plasma, when subjected to shock pressures exceeding ~50 GPa. While state-of-the-art numerical simulations based on density-functional-theory or quantum Monte-Carlo techniques capture this behavior quite well, subtle differences subsist between these simulations and the available experimental data regarding the pressure-density compressibility. Here, leveraging a recently developed analysis method for high-resolution Doppler interferometric velocity data, we present Eulerian sound speed measurements in compressed deuterium to shock pressures between 50 and 200 GPa. These results, extracted from laser-driven shockwave experiments, are found to agree with several of the most accurate equation of state models for deuterium at those conditions up to ~150 GPa. However, the data indicate that these models fail to reproduce the experimentally observed sound speed at higher pressures, approaching 200 GPa. In particular, we unveil a discrepancy between the experimental results and the equation of state model that is most commonly used in inertial confinement fusion at the National Ignition Facility.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Stochastic Richardson Extrapolation Based Numerical Error Estimation for Kinetic Plasma Simulations

We present a numerical error estimation technique specifically formulated to deal with stochastic code output with multiple discretization parameters. This method is based on multiple fits to an error model with arbitrary convergence rates and cross-coupling terms, performed using nonlinear optimization. The fitting approach varies by the type of residual norm which influences the importance of outliers, and weights which influences the relative importance of data points in the coarse and refined regions of discretization parameter space. To account for the influence of stochastic noise, these fits are performed on multiple bootstrap values based on the underlying response data set. Using an automated discretization domain selection scheme, the fits are performed on a series of reduced sets of discretization levels in order to find an optimal fully-converged result estimate in the minimum variance sense; this automated approach enables straightforward analysis of multiple quantities of interest and/or time and spatially-dependent response data. The overall numerical error analysis method is useful for verification and validation problems for stochastic simulation methods and forms a key component in the overall uncertainty quantification process. The method was demonstrated for steady and unsteady electron diode problems simulated using a particle-in-cell kinetic plasma code, demonstrating excellent results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

How reliable is distribution of relaxation times (DRT) analysis? A dual regression-classification perspective on DRT estimation, interpretation, and accuracy

The distribution of relaxation times (DRT) has gained increasing attention and adoption in recent years as a versatile method for analyzing electrochemical impedance spectroscopy (EIS) data obtained from complex devices like fuel cells, electrolyzers, and batteries. The DRT deconvolutes the impedance without a priori specification of a generative model, which is especially useful for interpretation and model selection when the governing principles of the system under study are not fully understood. However, DRT estimation is an ill-posed inversion problem that must be addressed with a subjective choice of regularization and tuning, which leaves substantial risk of misleading interpretations of EIS data. In this work, we suggest a new classification view of the DRT inversion to clarify DRT estimation and interpretation. We introduce a dual regression-classification framework that unifies the classification and regression views of the DRT inversion with wide-reaching implications for DRT analysis. The dual framework is employed to demonstrate a new kind of DRT inversion algorithm and develop novel evaluation metrics that capture previously ignored aspects of DRT accuracy. These approaches are applied to both synthetic data and experimental spectra collected from a protonic ceramic fuel cell and a lithium-ion battery to illustrate their broad utility. The dual inversion algorithm shows promising performance for accurate DRT estimation and autonomous model identification, while the dual evaluation approach produces metrics that meaningfully assess the strengths and risks of DRT algorithms. Here this work provides valuable insight for both practical application of the DRT to experimental data and further development of EIS analysis methods.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Study of movement of the western and central belts of Peninsular Malaysia using GPS data analysis

Since the large earthquakes in Sumatera and Nias, there were some tremors incidents at Bukit Tinggi. Therefore, a study on the earth’s crust movement and the effects of the earthquake in Indonesia on the tectonic blocks of Peninsular Malaysia have been carried out using GPS data analysis. GPS data from five MyRTKnet stations within Peninsular Malaysia have been analyzed to monitor the movement of two major tectonic blocks of Peninsular Malaysia which are the western belt represented by the Behrang (BEHR) and UPM Serdang (UPMS) stations and the central belt represented by Bentong (BENT), Jerantut (JRNT) and Temerloh (TLOH) stations. GPS data recorded from 2005 to 2010 were analysed based on horizontal and vertical displacements of the respective stations by using Trimble Business Centre (TBC) software. Based on the results of accumulated displacements of recorded GPS data from January 2006 to December 2013, it shows that the western belt which represented by UPMS has shifted 0.096m towards northwest with changes of ellipsoidal height of +0.030m while the central belt which represented by TLOH has shifted 0.080m towards northwest with changes of ellipsoidal height of −0.015m. Meanwhile, BENT station which is located on the Bentong-Raub suture zone turns to its original position as well as JRNT station. However, BEHR station which are located in western belt do not show any movements. All of these movements may be due to the influence of reactive faults in the stations area stimulated by several large earthquakes that occurred in 2005 to 2010. Study on using the GPS data analysis and combine with integrated geophysical methods are necessary to understand in detail about the tectonic evolution of Peninsular Malaysia.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Machine-Learned Manifold-Based Models for Large Eddy Simulation of Turbulent Combustion

Reduced-order manifold combustion models are commonly used to lower the cost of reacting Large Eddy Simulations (LES) and may be derived either from physical principles as in flamelet models or from data-driven methods like Principal Component Analysis (PCA). In either case, neural networks are increasingly used as part of these models to provide a nonlinear mapping between a small set of pre-defined variables that parameterize the manifold and outputs of interest, such as reaction rates. In this work, we propose a new manifold-based modeling approach that combines the definition of the manifold-parameterizing variables (linear combinations of species), the nonlinear mapping to the outputs, and closure of filtered quantities for LES into the structure of a single neural network. This allows the process used to train the neural network to simultaneously optimize both the functional form of the model and the identities of the inputs to the model. The new approach can flexibly incorporate thermochemical data from any combustion system; if trained on data from 1D flames it can be interpreted as an optimized flamelet model, but it can also be used to learn models from data from more complex configurations. This work presents a priori evaluations of the new approach in both contexts. Evaluation using data from 1D premixed flames demonstrates the physical interpretability of the manifold variables generated by the new approach. Evaluation using data from direct numerical simulations of turbulent flames shows improved predictions relative to either flamelet or PCA-based models in a more complex configuration.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Fluorescence Correlation Spectroscopy with Photobleaching Correction in Slowly Diffusing Systems

Fluorescence correlation spectroscopy (FCS) is a powerful tool to quantitatively study the diffusion of fluorescently labeled molecules. It allows in principle important questions of macromolecular transport and supramolecular aggregation in living cells to be addressed. However, the crowded environment inside the cells slows diffusion and limits the reservoir of labeled molecules, causing artifacts that arise especially from photobleaching and limit the utility of FCS in these applications. We present a method to compute the time correlation function from weighted photon arrival times, which compensates computationally during the data analysis for the effect of photobleaching. We demonstrate the performance of this method using numerical simulations and experimental data from model solutions. Using this technique, we obtain correlation functions in which the effect of photobleaching has been removed and in turn recover quantitatively accurate mean-square displacements of the fluorophores, especially when deviations from an ideal Gaussian excitation volume are accounted for by using a reference calibration correlation function. This allows quantitative FCS studies of transport processes in challenging environments with substantial photobleaching like in living cells in the future.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2014

Computerized detection of noncalcified plaques in coronary CT angiography: Evaluation of topological soft gradient prescreening method and luminal analysis

Purpose: The buildup of noncalcified plaques (NCPs) that are vulnerable to rupture in coronary arteries is a risk for myocardial infarction. Interpretation of coronary CT angiography (cCTA) to search for NCP is a challenging task for radiologists due to the low CT number of NCP, the large number of coronary arteries, and multiple phase CT acquisition. The authors conducted a preliminary study to develop machine learning method for automated detection of NCPs in cCTA. Methods: With IRB approval, a data set of 83 ECG-gated contrast enhanced cCTA scans with 120 NCPs was collected retrospectively from patient files. A multiscale coronary artery response and rolling balloon region growing (MSCAR-RBG) method was applied to each cCTA volume to extract the coronary arterial trees. Each extracted vessel was reformatted to a straightened volume composed of cCTA slices perpendicular to the vessel centerline. A topological soft-gradient (TSG) detection method was developed to prescreen for NCP candidates by analyzing the 2D topological features of the radial gradient field surface along the vessel wall. The NCP candidates were then characterized by a luminal analysis that used 3D geometric features to quantify the shape information and gray-level features to evaluate the density of the NCP candidates. With machine learning techniques, useful features were identified and combined into an NCP score to differentiate true NCPs from false positives (FPs). To evaluate the effectiveness of the image analysis methods, the authors performed tenfold cross-validation with the available data set. Receiver operating characteristic (ROC) analysis was used to assess the classification performance of individual features and the NCP score. The overall detection performance was estimated by free response ROC (FROC) analysis. Results: With our TSG prescreening method, a prescreening sensitivity of 92.5% (111/120) was achieved with a total of 1181 FPs (14.2 FPs/scan). On average, six features were selected during the tenfold cross-validation training. The average area under the ROC curve (AUC) value for training was 0.87 ± 0.01 and the AUC value for validation was 0.85 ± 0.01. Using the NCP score, FROC analysis of the validation set showed that the FP rates were reduced to 3.16, 1.90, and 1.39 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. Conclusions: The topological soft-gradient prescreening method in combination with the luminal analysis for FP reduction was effective for detection of NCPs in cCTA, including NCPs causing positive or negative vessel remodeling. The accuracy of vessel segmentation, tracking, and centerline identification has a strong impact on NCP detection. Studies are underway to further improve these techniques and reduce the FPs of the CADe system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Data Transferability and Collection Consistency in Marine Renewable Energy

Concerns about the potential effects of tidal turbines and wave energy devices on the marine environment continue to slow siting and permitting/consenting of single devices and arrays worldwide. While research studies and early results from post-installation monitoring over the past decade have informed interactions between marine renewable energy (MRE) devices, marine animals, and habitats, regulators still demonstrate considerable reluctance to accelerate the permitting/consenting process for devices and arrays. Furthermore, the MRE industry is struggling with the high costs of baseline assessments and post-installation monitoring, as well as long timelines for obtaining permits, which leads to uncertainty and risk related to project financing. Regulators require assessment and monitoring information to allow them to carry out the necessary analyses to describe, permit/consent, and manage the environmental risks associated with new MRE technologies and new uses of ocean space. One way to reduce risks to the industry and the environment and to allow for acceleration of the permitting/consenting process could be to transfer learning, analyses, and data sets from one country to another, among projects, and across jurisdictional boundaries. In addition, data are collected around early-stage MRE devices using many different methods, instruments, and measurement scales. If similar parameters and accessible methods of data collection were used for baseline assessments and post-installation monitoring around all early-stage devices and MRE developments, the results would be more readily comparable. This comparability would lead to a decrease in scientific uncertainty and support a common understanding of the risk of MRE devices to the marine environment. This in turn would facilitate more efficient and shorter permitting/consenting processes, which would decrease the financial risk for MRE project development. As a means of addressing the concept of transferring data (information, learning, analyses, and data sets) among projects and collecting data consistently, Annex IV has developed a data transferability process that has been socialized with the MRE community, which includes regulators, industry, developers, consultants, and researchers. The data transferability process consists of five components: 1. A Data Transferability Framework brings together data sets in an organized fashion, compares the applicability of each data set for use on other projects, and guides the process of data transfer 2. A Data Collection Consistency Table provides preferred measurement methods or processes, reporting units, and the most common methods of analysis or interpretation and use of data 3. A Monitoring Data Sets Discoverability Matrix allows a practitioner to discover data sets based on the approach presented in the Framework 4. Best Management Practices (BMPs) include five BMPs related to data transferability and collection consistency 5. An Implementation Plan presents an approach for implementing and applying the data transferability process. This report documents the background and development of the data transferability process and associated components and summarizes the next steps needed to successfully implement and apply the data transferability process.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

Morpho-Hawk

Morpho-Hawk applies projectional mathematical morphology in a fundamentally new way. Morpho-Hawk extracts object information from digital images by filtering with simple geometrical figures such as rectangles or simple curves. Two core algorithms are used to accomplish this: 1) Object Detection and Feature Extraction from Images: Using projectional morphology, Morpho-Hawk finds features of interest within an image, such as contours, shapes, colors, infrared spectra, and more. Because Morpho-Hawk identifies features based on shape or form, the method can uniquely handle different image or object conditions and directions. MorphoHawk can evaluate all possible images of the analyzed scene using the same transformations that are applied just to one image of that scene. Hence, recognizing a defect within an image or part of an image provides information to assist in recognizing other objects in the image. In addition, known background image information can be morphologically subtracted out in order to focus on the appearance or disappearance of the features of interest. 2) Object Analysis: Upon detection of an object of interest, Morpho-Hawk can analyze the object based learned information from prior images or end-user defined criteria for certain object of special interest by selecting feature (e.g., color, size, shape, apparent volume) and/or symbol (e.g., letter, number). The novel algorithm can analyze targeted objects, even as conditions of such as illumination, shadows, and spectral regions change strongly in following images as compared to the original image. This enables robust recognition, interpolation and prediction. As the analysis shows the presence of these features in the optical signal, the algorithm can make a determination based on user defined probability. The object information can be stored for later analysis using simple morphological data compression methods and/or retention of the original optical images.

Glazoff, MichaelV.↗
DOE OSTI2016

ARE THERE TWO DISTINCT SOLAR ENERGETIC PARTICLE RELEASES IN THE 2012 MAY 17 GROUND LEVEL ENHANCEMENT EVENT?

We examine ion release times in the solar vicinity for the 2012 May 17 Ground Level Enhancement event using the velocity dispersion analysis method. In situ energetic proton data from Solar and Heliospheric Observatory (SOHO)/Energetic and Relativistic Nuclei and Electron and Geostationary Operational Environmental Satellite are used. We find two distinct releases of Solar Energetic Particles (SEPs) near the Sun, separated by ∼40 minutes. From soft X-ray observations, we find that the first release coincides with the solar flare eruption: the release starts from the flare onset and ends near the peak of the soft X-ray; type-III radio bursts also occur when the release starts. A type II radio burst may also start at the begining of the release. However, the associated Coronal Mass Ejection (CME) only has a height of 0.08R{sub s} from extrapolation of SOHO/LASCO data. At the start of the second release, the CME propagates to more than 8.4R{sub s} in height, and there are signatures of an enhanced type II radio burst. The time-integrated spectra for the two releases differ. The spectrum for the second release shows the common double-power-law feature of gradual SEP events. The spectrum for the first release does not resemble power laws because there is considerable modulation at lower energies. Based on our analysis, we suggest that SEPs of the first release were dominated by particles accelerated at the flare, and those of the second release were dominated by particles accelerated at the associated CME-driven shock. Our study may be important to understand certain extreme SEP events.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

COMBIgor: Data-Analysis Package for Combinatorial Materials Science

Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially-resolved characterization of structure and properties. Here, due to maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment, designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data sets. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data analysis and visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms. Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Langmuir probe analysis in electronegative plasmas

This paper compares two methods to analyze Langmuir probe data obtained in electronegative plasmas. The techniques are developed to allow investigations in plasmas, where the electronegativity α{sub 0} = n{sub –}/n{sub e} (the ratio between the negative ion and electron densities) varies strongly. The first technique uses an analytical model to express the Langmuir probe current-voltage (I-V) characteristic and its second derivative as a function of the electron and ion densities (n{sub e}, n{sub +}, n{sub –}), temperatures (T{sub e}, T{sub +}, T{sub –}), and masses (m{sub e}, m{sub +}, m{sub –}). The analytical curves are fitted to the experimental data by adjusting these variables and parameters. To reduce the number of fitted parameters, the ion masses are assumed constant within the source volume, and quasi-neutrality is assumed everywhere. In this theory, Maxwellian distributions are assumed for all charged species. We show that this data analysis can predict the various plasma parameters within 5–10%, including the ion temperatures when α{sub 0} > 100. However, the method is tedious, time consuming, and requires a precise measurement of the energy distribution function. A second technique is therefore developed for easier access to the electron and ion densities, but does not give access to the ion temperatures. Here, only the measured I-V characteristic is needed. The electron density, temperature, and ion saturation current for positive ions are determined by classical probe techniques. The electronegativity α{sub 0} and the ion densities are deduced via an iterative method since these variables are coupled via the modified Bohm velocity. For both techniques, a Child-Law sheath model for cylindrical probes has been developed and is presented to emphasize the importance of this model for small cylindrical Langmuir probes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

Abstract Background Modern synchrotron light sources and detectors produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used imaging techniques that generates data at tens of gigabytes per second is computed tomography (CT). Although CT experiments result in rapid data generation, the analysis and reconstruction of the collected data may require hours or even days of computation time with a medium-sized workstation, which hinders the scientific progress that relies on the results of analysis. Methods We present Trace, a data-intensive computing engine that we have developed to enable high-performance implementation of iterative tomographic reconstruction algorithms for parallel computers. Trace provides fine-grained reconstruction of tomography datasets using both (thread-level) shared memory and (process-level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations that we apply to the replicated reconstruction objects and evaluate them using tomography datasets collected at the Advanced Photon Source. Results Our experimental evaluations show that our optimizations and parallelization techniques can provide 158× speedup using 32 compute nodes (384 cores) over a single-core configuration and decrease the end-to-end processing time of a large sinogram (with 4501 × 1 × 22,400 dimensions) from 12.5 h to <5 min per iteration. Conclusion The proposed tomographic reconstruction engine can efficiently process large-scale tomographic data using many compute nodes and minimize reconstruction times.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗