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DOE OSTI2017

Efficient Scalable Parallel Higher Order Direct MoM-SIE Method With Hierarchically Semiseparable Structures for 3-D Scattering

A novel fast scalable parallel algorithm is proposed for the solution of large 3-D scattering problems based on: 1) the double (geometrical and current-approximation) higher order (DHO) method of moments (MoM) in the surface integral equation (SIE) formulation and 2) a direct solver for dense linear systems utilizing hierarchically semiseparable (HSS) structures. Namely, an HSS matrix representation is used for compression, factorization, and solution of the system matrix. In addition, a rank-revealing QR decomposition for memory compression is used, with a stopping criterion in terms of the relative rank tolerance value. One method for geometrical preprocessing of the scatterers based on the cobblestone distance sorting technique is employed in order to enhance the HSS algorithm accuracy and parallelization. Numerical examples show how the accuracy of the DHO HSS-MoM-SIE method is easily controllable by using the relative tolerance for the matrix compression. Furthermore, the examples demonstrate low memory consumption, as well as much faster simulation time, when compared to the direct LU decomposition. The method enables dramatically faster monostatic scattering computations than iterative solvers and reduced number of unknowns when compared to low-order discretizations. Finally, great scalability of the algorithm is demonstrated on more than one thousand processes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Downscaling-Based Segmentation for Unresolved Images of Highly Heterogeneous Granular Porous Samples

Numerical simulations of pore-scale flow and transport in natural sediments require the knowledge of pore-space topology. Limited resolution of X-ray tomography is often insufficient to fully characterize pore-space structure within fine-grained regions. Single and multilevel threshold-based segmentation approaches are customarily employed to identify solid, pore and porous-solid regions by means of grey intensity thresholds. While the choice of cutoff thresholds is often arbitrary, it dramatically affects the effective properties and the dynamical response of the reconstructed porous structure. As such, we propose an algorithm of downscaling, i.e., the process of increasing image resolution, followed by segmentation, i.e., the identification of different phases, to reconstruct the unresolved pore-space from XCT images of natural geological porous media. The method, applicable to moderately unresolved, chemically homogeneous granular media, is based on a map between local pixel porosity and pore size that does not rely on the definition of arbitrary thresholds and it allows to generate a high-resolution binary image of the porous medium from poorly resolved grey-scale images. First, we validate the method on synthetic unresolved images and compare their known pore-space distribution with the extracted one. Then, we consider a synthetic porous medium and compare the pore size distribution, conductivity, and tortuosity between the original and the reconstructed structures. Finally, we apply the method to extract the pore-space distribution from unresolved XCT images of two natural sediment columns and use it (i) to parametrize a capillary-bundle model and (ii) to estimate the hydraulic conductivity by matching breakthrough behavior of passive solute transport.

58 GEOSCIENCES↗

DOE OSTI2016

A novel approach to background subtraction in contrast-enhanced dual-energy digital mammography with commercially available mammography devices: Noise minimization

Purpose: Dual-energy image subtraction represents a useful tool to improve the detectability of small lesions, especially in dense breasts. A feature it shares with all x-ray imaging techniques is the appearance of fluctuations in the texture of the background, which can obscure the visibility of interesting details. The aim of the work is to investigate the main noise sources, in order to create a better performing subtraction mechanism. In particular, the structural noise cancellation was achieved by means of a suitable extension of the dual-energy algorithm. Methods: The effect of the cancellation procedure was tested on an analytical simulation of a target with varying structural composition. Subsequently, the subtraction algorithm was also applied to a set of actual radiographs of a breast phantom exhibiting a nonuniform background pattern. The background power spectra of the outcomes were computed and compared to the ones obtained from a standard subtraction algorithm. Results: The comparison between the standard and the proposed cancellations showed an overall suppression of the magnitudes of the spectra, as well as a flattening of the frequency dependence of the structural component of the noise. Conclusions: The proposed subtraction procedure provides an effective cancellation of the residual background fluctuations. When combined with the polychromatic correction already described in a companion publication, it results in a high performing dual-energy subtraction scheme for commercial mammography units.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Time Series Discord Detection in Medical Data using a Parallel Relational Database [PowerPoint]

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithms on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.

47 OTHER INSTRUMENTATION↗

DOE OSTI2015

SDSS-III Baryon Oscillation Spectroscopic Survey data release 12: Galaxy target selection and large-scale structure catalogues

The Baryon Oscillation Spectroscopic Survey (BOSS), part of the Sloan Digital Sky Survey (SDSS) III project, has provided the largest survey of galaxy redshifts available to date, in terms of both the number of galaxy redshifts measured by a single survey, and the effective cosmological volume covered. Key to analysing the clustering of these data to provide cosmological measurements is understanding the detailed properties of this sample. Potential issues include variations in the target catalogue caused by changes either in the targeting algorithm or properties of the data used, the pattern of spectroscopic observations, the spatial distribution of targets for which redshifts were not obtained, and variations in the target sky density due to observational systematics. We document here the target selection algorithms used to create the galaxy samples that comprise BOSS. We also present the algorithms used to create large-scale structure catalogues for the final Data Release (DR12) samples and the associated random catalogues that quantify the survey mask. The algorithms are an evolution of those used by the BOSS team to construct catalogues from earlier data, and have been designed to accurately quantify the galaxy sample. Furthermore, the code used, designated mksample, is released with this paper.

(cosmology:) large-scale structure of Universe↗

DOE OSTI2016

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Detecting divertor damage during steady state operation of Wendelstein 7-X from thermographic measurements

Wendelstein 7-X (W7-X) aims to demonstrate the reactor capability of the stellarator concept, by creating plasmas with pulse lengths of up to 30 min at a heating power of up to 10 MW. The divertor plasma facing components will see convective steady state heat flux densities of up to 10 MW/m 2 . These high heat flux target elements are actively cooled and are covered with carbon fibre reinforced carbon (CFC) as plasma facing material. The CFC is bonded to the CuCrZr cooling structure. Over the life time of the experiment this interface may weaken and cracks can occur, greatly reducing the heat conduction between the CFC tile and the cooling structure. Furthermore, there is not only the need to monitor the divertor to prevent damage by overheating but also the need to detect these fatigue failures of the interface. A method is presented for an early detection of fatigue failures of the interface layer, solely by using the information delivered by the IR-cameras monitoring the divertor. This was developed and validated through experiments made with high heat flux target elements prior to installation in W7-X.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Reinforcement Learning of Structured Stabilizing Control for Linear Systems With Unknown State Matrix

This paper delves into designing feedback control gains for a continuous-time linear quadratic regulator (LQR) problem that is constrained to certain predefined structure with unknown state matrix. We bring forth the ideas from reinforcement learning (RL) in conjunction with sufficient stability and performance guarantees in order to design these structured gains using the trajectory measurements of states and controls. Here we first formulate a model-based framework using dynamic programming (DP) to embed the structural constraint to the LQR gain computation in the continuous-time setting, and then subsequently, formulate a policy iteration RL algorithm that can alleviate the requirement of known state matrix in conjunction with maintaining the feedback gain structure. The design enables a distributed learning control design which is necessary for many large-scale cyber-physical systems. Theoretical guarantees are provided for stability and convergence of the structured reinforcement learning (SRL) algorithm. We validate our theoretical results with numerical simulations on a multi-agent networked linear time-invariant (LTI) dynamic system.

42 ENGINEERING↗

DOE OSTI2015

SU-E-T-397: Evaluation of Planned Dose Distributions by Monte Carlo (0.5%) and Ray Tracing Algorithm for the Spinal Tumors with CyberKnife

Purpose: To analyze and evaluate dose distribution between Ray Tracing (RT) and Monte Carlo (MC) algorithms of 0.5% uncertainty on a critical structure of spinal cord and gross target volume and planning target volume. Methods: Twenty four spinal tumor patients were treated with stereotactic body radiotherapy (SBRT) by CyberKnife in 2013 and 2014. The MC algorithm with 0.5% of uncertainty is used to recalculate the dose distribution for the treatment plan of the patients using the same beams, beam directions, and monitor units (MUs). Results: The prescription doses are uniformly larger for MC plans than RT except one case. Up to a factor of 1.19 for 0.25cc threshold volume and 1.14 for 1.2cc threshold volume of dose differences are observed for the spinal cord. Conclusion: The MC recalculated dose distributions are larger than the original MC calculations for the spinal tumor cases. Based on the accuracy of the MC calculations, more radiation dose might be delivered to the tumor targets and spinal cords with the increase prescription dose.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Differential evolution: Global search problem in LEED-IV surface structural analysis

The search process associated with the quantitative theory–experiment comparison in Low Energy Electron Diffraction surface structural analysis can be very time consuming, especially in the case of complex materials with many atoms in the unit cell. Global search algorithms need to be employed to locate the global minimum of the reliability factor in the multi-dimensional structural parameter space. In this study we investigate the use of the Differential Evolution algorithm in Low Energy Electron Diffraction structural analysis. Despite the simplicity of its mechanism the Differential Evolution algorithm presents an impressive performance when applied to ultra-thin films of BaTiO{sub 3}(001) in a theory–theory comparison. A scaling relation of N{sup (1.47} {sup ±} {sup 0.08)} was obtained, where N is the total number of parameters to be optimized. - Highlights: • We investigated the use of the Differential Evolution algorithm (DE) for the LEED search problem. • The DE method was applied to the optimization of the surface structure of the BaTiO{sub 3}(001) ultra-thin films. • A very favorable scaling relation of N{sup 1.47} was obtained, where N is the total number of parameters to be optimized.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Revealing the Structure and Dynamics of Self-Generated Electric and Magnetic Fields Near Plasma Stagnation in Laser-Driven Hohlraums

By coupling newly developed triparticle charged particle radiography with radiography reconstruction algorithms and novel reconstruction postprocessing techniques, the spatial structure and time evolution of self-generated electric and magnetic fields in laser-driven vacuum hohlraums have been quantitatively revealed. Through high-fidelity data from a series of experiments, it is shown that late in the hohlraum evolution (after the end of laser drive) these fields are strongly correlated in both space and time, providing evidence that their evolution is primarily dominated by advection with the plasma flow. At these late times, plasma flow velocities inferred from both gross radiography analysis and field reconstructions (and corroborated with Thomson scattering measurements) indicate that the plasma is approaching stagnation near the hohlraum axis. Finally, these experiments provide not only new physical insight into spontaneously generated hohlraum fields, but also provide important spatially and temporally resolved information for future benchmarking of numerical codes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Optimized Quantum Phase Estimation for Simulating Electronic States in Various Energy Regimes

While quantum algorithms for simulation exhibit better asymptotic scaling than their classical counterparts, they currently cannot be implemented on real-world devices. Instead, chemists and computer scientists rely on costly classical simulations of these quantum algorithms. In particular, the quantum phase estimation (QPE) algorithm is among several approaches that have attracted much attention in recent years for its genuine quantum character. However, it is memory-intensive to simulate and intractable for moderate system sizes. Herein, this paper discusses the performance and applicability of QPESIM, a new simulation of the QPE algorithm designed to take advantage of modest computational resources. In particular, we demonstrate the versatility of QPESIM in simulating various electronic states by examining the ground and core-level states of H 2 O. For these states, we also discuss the effect of the active-space size on the quality of the calculated energies. For the high-energy core-level states, we demonstrate that new QPE simulations for active spaces defined by 15 active orbitals significantly reduce the errors in core-level excitation energies compared to earlier QPE simulations using smaller active spaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

A fingerprint based metric for measuring similarities of crystalline structures

Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method can be a useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms, and high-throughput screenings.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

A differentiable approach to the maximum independent set problem using dataless neural networks

The success of machine learning solutions for reasoning about discrete structures has brought attention to its adoption within combinatorial optimization algorithms. Such approaches generally rely on supervised learning by leveraging datasets of the combinatorial structures of interest drawn from some distribution of problem instances. Reinforcement learning has also been employed to find such structures. Here, in this paper, we propose a different approach in that no data is required for training the neural networks that produce the solution. In this sense, what we present is not a machine learning solution, but rather one that is dependent on neural networks and where backpropagation is applied to a loss function defined by the structure of the neural network architecture as opposed to a training dataset. In particular, we reduce the popular combinatorial optimization problem of finding a maximum independent set to a neural network and employ a dataless training scheme to refine the parameters of the network such that those parameters yield the structure of interest. Additionally, we propose a universal graph reduction procedure to handle large-scale graphs. The reduction exploits community detection for graph partitioning and is applicable to any graph type and/or density. Experimental results on both real and synthetic graphs demonstrate that our proposed method performs on par or outperforms state-of-the-art learning-based methods in terms of the size of the found set without requiring any training data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Microstructure Reconstruction of Sheet Molding Composite Using a Random Chips Packing Algorithm

Fiber-reinforced polymer composites are strong candidates for structural materials to replace steel and light alloys in lightweight vehicle design because of their low density and relatively high strength. In the integrated computational materials engineering (ICME) development of carbon fiber composites, microstructure reconstruction algorithms are needed to generate material microstructure representative volume element (RVE) based on the material processing information. The microstructure RVE reconstruction enables the material property prediction by finite element analysis (FEA)This paper presents an algorithm to reconstruct the microstructure of a chopped carbon fiber/epoxy laminate material system produced by compression molding, normally known as sheet molding compounds (SMC). The algorithm takes the result from material’s manufacturing process as inputs, such as the orientation tensor of fibers, the chopped fiber sheet geometry, and the fiber volume fraction. The chopped fiber sheets are treated as deformable rectangle chips and a random packing algorithm is developed to pack these chips into a square plate. The RVE is built in a layer-by-layer fashion until the desired number of lamina is reached, then a fine tuning process is applied to finalize the reconstruction. Compared to the previous methods, this new approach has the ability to model bended fibers by allowing limited amount of overlaps of rectangle chips. Furthermore, the method does not need SMC microstructure images, for which the image-based characterization techniques have not been mature enough, as inputs. Case studies are performed and the results show that the statistics of the reconstructed microstructures generated by the algorithm matches well with the target input parameters from processing.

Huang, Tianyu↗

DOE OSTI2025

Predicting interface structure using the minima hopping method

Here, we adapt the minima hopping method (MHM) to the problem of interfacial structure prediction and apply it to study a canonical problem, the tilt grain boundaries in SrTiO 3 . Our method employs a hybrid approach by first exploring the potential energy surface (PES) of different grain boundary samplings with an empirical force field, among which the fifteen candidates with lower energies are then refined using ab initio density functional theory (DFT) calculations. During the exploratory stage, we bias the search using a local order parameter to primarily sample various reconstructions in the vicinity of the interface, while preserving the crystallinity of the bulk regions. We further enhance the search by incorporating initial structures with rigid body displacements to account for translational variations between bulk phases, enabling the MHM to effectively generate both stoichiometric and nonstoichiometric SrTiO 3 Σ⁢3(111)[110] and Σ⁢3(112)[110] grain boundaries. From an algorithmic standpoint, MHM outperforms earlier studies based on genetic algorithms (GA) by identifying more stable interfacial structures of several SrTiO 3 grain boundaries. The performance of the present implementation of the MHM approach is primarily limited by exploring an approximate description of the PES with a rather simple Buckingham potential. This limitation leads to variations in performance when compared to approaches utilizing more advanced surrogate PES models, such as direct DFT-PES sampling or GA with the embedded atom method (EAM). Despite the present limitations, the MHM approach is able to yield interfacial structures with comparable or lower interfacial energies in specific cases, such as Σ⁢3(111)[110] Γ=1, ±0.5 and Σ⁢3(112)[110] Γ= ±1, −2, underscoring the robustness of the MHM approach even with a simple approximation of the DFT PES. The MHM interfacial structure prediction method thus offers an efficient approach to understanding the grain boundaries and heterointerfaces at the atomic scale, providing an important prerequisite for effective materials design.

density functional theory↗

DOE OSTI2015

Integrated IO Services for Trilinos Data Structures

The Trilinos Project is an effort to develop algorithms and enabling technologies within an object- oriented software framework for the solution of large-scale, complex multi-physics engineering and scientific problems. A unique design feature of Trilinos is its focus on packages. While the abstractions make it easy to incorporate advanced processing and data manipulation tools, it is not always obvious how to take advantage of these features. The trios package incorporated two years ago offers general data management services, but has yet to offer integrated support for core Trilinos data structures, such as those offered in the Tpetra package. An initial attempt to incorporate native Trilinos data structure support into trios services revealed the complexity, from a non-mathematician's perspective, of using Trilinos. This project sought to understand the complexities and potential barriers not just for non-mathematicians that want to contribute to or use Trilinos, but potentially for new mathematically-inclined users as well that may want to offer services to support users. This report documents the challenges for trios to offer some simple data manipulation required as a precursor to any direct data services integration and makes recommendations for clarifying the performance implications and general approach to use.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

NMR_DIFFSIM v. 1.0

The software generates and analyzes nuclear magnetic resonance spin diffusion simulations for the purpose of measuring domain sizes or spin diffusion constants in a heterogenous sample. Its primary capabilities are: 1. It solves the spin magnetization diffusion equation to simulate the results of a spin diffusion experiment using any structural input geometry, including structure models generated by molecular dynamics or other computational or experimental methods. 2. It compares the results of simulations on an input structure to experimental data, providing a data fitting algorithm to extract physical information from the experimental data via the simulation. 3. It can provide statistical measures to compare correlations between structural models, to determine domain clustering and connectedness, and to provide goodness-of-fit measures including chord length distributions, chi-squared values, Pearson coefficients and pair correlation functions. 4. It provides output reports, data files, and movies to visualize simulation results.

Sorte, Eric↗