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At least 1,243 records · Page 69

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DOE OSTI2018

Logarithmic accuracy of parton showers: a fixed-order study

We formulate some first fundamental elements of an approach for assessing the logarithmic accuracy of parton-shower algorithms based on two broad criteria: their ability to reproduce the singularity structure of multi-parton matrix elements, and their ability to reproduce logarithmic resummation results. We illustrate our approach by considering properties of two transverse-momentum ordered final-state showers, examining features up to second order in the strong coupling. In particular we identify regions where they fail to reproduce the known singular limits of matrix elements. Here, the characteristics of the shower that are responsible for this also affect the logarithmic resummation accuracies of the shower, both in terms of leading (double) logarithms at subleading N C and next-to-leading (single) logarithms at leading N C .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2014

A cellular automaton method to simulate the microstructure and evolution of low-enriched uranium (LEU) U–Mo/Al dispersion type fuel plates

Low-enriched uranium (LEU) fuel plates for high power materials test reactors (MTR) are composed of nominally spherical uranium-molybdenum (U-Mo) particles within an aluminum matrix. Fresh U-Mo particles typically range between 10 and 100 mu m in diameter, with particle volume fractions up to 50%. As the fuel ages, reaction-diffusion processes cause the formation and growth of interaction layers that surround the fuel particles. The growth rate depends upon the temperature and radiation environment. The cellular automaton algorithm described in this paper can synthesize realistic random fuel-particle structures and simulate the growth of the intermetallic interaction layers. Examples in the present paper pack approximately 1000 particles into three-dimensional rectangular fuel structures that are approximately 1 mm on each side. The computational approach is designed to yield synthetic microstructures consistent with images from actual fuel plates and is validated by comparison with empirical data on actual fuel plates. (C) 2014 Elsevier B.V. All rights reserved.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation

Sediment accumulation at culverts involves large-scale and interlinked environmental processes that are difficult to address with experimental or physical modeling methods. This article presents an alternative data-driven investigation for shedding insights into these processes. Accordingly, a web-based geovisual analytics application, the IowaDOT platform, was developed, which allows users to explore the complex processes associated with the sediment deposition at culverts. The platform provides systematic procedures for (1) collecting and integrating analytical variables into a single dataset, (2) quantifying the degree of culvert sedimentation using time series of aerial images, (3) identifying drivers that contribute to culvert sedimentation processes from a variety of culvert structural and upstream landscape characteristics using a tree-based feature selection algorithm, and (4) facilitating the understanding of complex spatial and relational patterns of culvert sedimentation processes using multivariate geovisualizations supported by a self-organizing map (SOM). As the outcomes of this study, these patterns identify culvert sedimentation-prone regions in Iowa and quantify empirical relationships between the drivers and culvert sedimentation degrees. Finally, a simple evaluation of the platform was performed to assess the usefulness and user satisfaction of the tool by professional users, and positive feedbacks are received.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

3-D Geological Modeling for Numerical Flow Simulation Studies of Gas Hydrate Reservoirs at the Kuparuk State 7-11-12 Pad in the Prudhoe Bay Unit on the Alaska North Slope

Accurate reservoir evaluation requires reliable three-dimensional (3-D) geological models. Here, this study conducted 3-D geological modeling for numerical flow simulation of the B1 sand gas hydrate reservoir at the Kuparuk State 7-11-12 pad, Prudhoe Bay Unit, Alaska North Slope. The model integrates well logs, core, and seismic data to address spatial heterogeneity in geological structures and reservoir properties. Two modeling types were performed: structural framework modeling and petrophysical property modeling. For structural framework modeling, seismic data and well log markers were used to reproduce subsurface structures characterized by a normal fault system. A volume-based modeling algorithm and stair-stepping grid were applied. The resulting 3-D model comprised 2,640,000 grid cells across 264 layers, including seven fault grids. For petrophysical property modeling, total porosity was initially modeled using sequential Gaussian simulation with collocated cokriging. To reproduce the upward coarsening of the B1 sand, upscaled log-derived total porosity and a three-dimensional (3-D) trend depicting total porosity variation were used as primary and secondary data, respectively. Gas hydrate saturation distribution was modeled similarly, with secondary data from estimated porosity distribution and seismic-derived acoustic impedance map enhancing accuracy. Results indicate higher gas hydrate saturation in the upper part of the B1 sand and areas with higher acoustic impedance. Intrinsic permeability was modeled from the total porosity and clay-bound water volume, and effective permeability was derived from the gas hydrate saturation and intrinsic permeability distributions based on the “Tokyo model”. Effective permeability distributions were influenced by the total porosity, gas hydrate saturation, and intrinsic permeability. Within the same layer, higher gas hydrate saturation leads to decreased effective permeability. In total, 100 sets of multiple scenarios were prepared, providing input data for dynamic flow simulations to evaluate the effects of lateral heterogeneity in reservoir properties and the hydraulic characteristics of faults on production behavior for preassessment before the long-term production test.

58 GEOSCIENCES↗

DOE OSTI2013

Imaging regenerating bone tissue based on neural networks applied to micro-diffraction measurements

We monitored bone regeneration in a tissue engineering approach. To visualize and understand the structural evolution, the samples have been measured by X-ray micro-diffraction. We find that bone tissue regeneration proceeds through a multi-step mechanism, each step providing a specific diffraction signal. The large amount of data have been classified according to their structure and associated to the process they came from combining Neural Networks algorithms with least square pattern analysis. In this way, we obtain spatial maps of the different components of the tissues visualizing the complex kinetic at the base of the bone regeneration.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

Hopkins-Skellam index and origin of spatial regularity in InAs quantum dot formation on GaAs(001)

We investigate the origin of the spatial regularity of arrays of InAs quantum dots (QDs) grown on GaAs(001). The Hopkins-Skellam index (HSI) is used with a newly developed calculation algorithm to quantify the spatial regularity both of QDs and of nm-sized surface reconstruction territories (SRTs) present in the In{sub x}Ga{sub 1−x}As wetting layer prior to QD nucleation. The SRT is the minimum extent of a surface reconstruction region needed for one QD to nucleate. By computing the evolving HSI of SRTs from sequences of in situ scanning tunnelling microscopy images during growth, we find that the spatial regularity of QDs is traced back to that of the (n × 3) SRTs as early as 0.6 monolayers of InAs coverage. This regularity is disturbed by the (n × 4) SRTs which appear at higher coverage. The SRT approach is discussed in comparison to conventional capture zone theories of surface growth.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US

Incomplete representations of physical processes often lead to structural errors in process-based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow modeling errors but do not enforce physical consistency. As a result, ML algorithms may be unreliable if used to provide future hydroclimate projections where climates and land use patterns are outside the range of training data. Here we test hybrid models built by integrating PB model outputs with a ML algorithm known as Long Short-Term Memory (LSTM) network on their ability to simulate streamflow in 531 catchments representing diverse conditions across the Conterminous United States. Model performance of hybrid models as measured by Nash-Sutcliffe efficiency (NSE) improved relative to standalone PB and LSTM models. More importantly, hybrid models provide highest improvement in catchments where PB models fail completely (i.e., NSE < 0). However, all models performed poorly in catchments with extended low flow periods, suggesting need for additional research.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

SPIDERS: the spectroscopic follow-up of X-ray-selected clusters of galaxies in SDSS-IV

SPIDERS (The SPectroscopic IDentification of ERosita Sources) is a programme dedicated to the homogeneous and complete spectroscopic follow-up of X-ray active galactic nuclei and galaxy clusters over a large area (~7500 deg 2 ) of the extragalactic sky. SPIDERS is part of the Sloan Digital Sky Survey (SDSS)-IV project, together with the Extended Baryon Oscillation Spectroscopic Survey and the Time-Domain Spectroscopic Survey. This study describes the largest project within SPIDERS before the launch of eROSITA: an optical spectroscopic survey of X-ray-selected, massive (~10 14 –10 15 M⊙) galaxy clusters discovered in ROSAT and XMM–Newton imaging. The immediate aim is to determine precise (Δz ~ 0.001) redshifts for 4000–5000 of these systems out to z ~ 0.6. The scientific goal of the program is precision cosmology, using clusters as probes of large-scale structure in the expanding Universe. We present the cluster samples, target selection algorithms and observation strategies. We demonstrate the efficiency of selecting targets using a combination of SDSS imaging data, a robust red-sequence finder and a dedicated prioritization scheme. We describe a set of algorithms and work-flow developed to collate spectra and assign cluster membership, and to deliver catalogues of spectroscopically confirmed clusters. We discuss the relevance of line-of-sight velocity dispersion estimators for the richer systems. We illustrate our techniques by constructing a catalogue of 230 spectroscopically validated clusters (0.031 < z < 0.658), found in pilot observations. Finally, we discuss two potential science applications of the SPIDERS sample: the study of the X-ray luminosity-velocity dispersion (LX–σ) relation and the building of stacked phase-space diagrams.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Structural motifs and bonding in two families of boron structures predicted at megabar pressures

The complex crystal chemistry of elemental boron has led to numerous proposed structures with distinctive motifs as well as contradictory findings. Herein, evolutionary structure searches performed at 100 GPa have uncovered a series of metastable phases of boron, and bonding analyses were carried out to elucidate their electronic structure. These polymorphs, dynamically stable at 100 GPa, were grouped into two families. Here, the first was derived from the thermodynamic minimum at these conditions, α-Ga, whereas channels comprised the second. Two additional intergrowth structures were uncovered, and it was shown they could be constructed by stacking layers of α-Ga-like and channel-like allotropes on top of each other. A detailed bonding analysis revealed networks of four-center σ-bonding functions linked by two-center B-B bonds in the α-Ga based structures, and networks that were largely composed of three-center σ-bonding functions in the channel-based structures. Seven of these high-pressure phases were found to be metastable at atmospheric conditions, and their Vickers hardnesses were estimated to ≈ 36 GPa.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Converting Clinical Pathways to BPM+ Standards: A Case Study in Stable Ischemic Heart Disease

Clinical pathways (CPs) are structured healthcare plans designed to implement evidence-based clinical guidelines, medical algorithms, and protocols. In recent years, a community called BPM+ Health has worked to establish a shareable and computer-consumable representation of CP, leveraging standard notations. These notations, collectively referred to as BPM+, include the Business Process Management and Notation (BPMN), Case Management Model and Notation (CMMN), and Decision Model and Notation (DMN), which aim to support clinical management and standardized communication between different stakeholders. However, the adaptation of these notations for the existing guidelines has largely been left unexplored. This paper introduces procedural steps and criteria considerations to apply components of BPM+ notations to reconstruct a guideline for Stable Ischemic Heart Disease. This paper describes how each of the three different notations is mapped to a medical guideline and discusses the advantages and limitations of representing CPs with BPM+ as compared with paper-based medical guidelines.

Chae, Junghoon↗

DOE OSTI2020

A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch considering Wind Power Penetration

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To estimate the uncertainty in this stochastic economic dispatch problem for forecasting purposes, the conventional Monte-Carlo method is prohibitively time-consuming for practical applications. To overcome this problem, here we propose a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling as the computer experimental design, a Gaussian-process emulator is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated stochastic economic dispatch model. This reduced-order representative allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus test system reveal the impressive performance of the proposed method.

17 WIND ENERGY↗

DOE OSTI2015

Hardware and software for ground tests of onboard charged particle spectrometers

The article presents a hardware and software complex for ground tests of onboard charged particle spectrometers that are designed at the National Research Nuclear University MEPhI for monitoring of nuclear-physical factors of space weather and can be installed in a wide class of satellites. The structural scheme and operating principles of component parts are discussed. The main algorithm and software features are presented. The technique of ground spectrometer tests and calibrations in various measurement modes at atmospheric cosmic particle flows, both in autonomous laboratories and in interface tests as part of a satellite, is also described.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Tensor Toolbox for MATLAB v. 3.0

Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to network analysis. The Tensor Toolbox provides classes for manipulating dense, sparse, and structured tensors using MATLAB's object-oriented features. It also provides algorithms for tensor decomposition and factorization, algorithms for computing tensor eigenvalues, and methods for visualization of results.

Kolda, Tamara↗

DOE OSTI2019

Survey of Current State of the Art Entity-Relation Extraction Tools

In the realm of information extraction from text data, there exists a number of tools with the capability of extracting entities and their relationships with one another. Such information has endless uses in a number of domains, however, the solutions to getting this information is still in early stages and has room for improvement. The topic has been explored from a research perspective by academic institutions, as well as formal tool creation from corporations. Overall, entity extraction is common among these tools, though with varying degrees of accuracy, while relationship extraction is more difficult to find. In this report, we take a look at the top state of the art tools currently available and identify their capabilities, strengths, and weaknesses. We explore the common algorithms in the successful approaches and their ability to efficiently handle both structured and unstructured text data. Finally, we highlight some of the common issues among these tools and summarize the current status in the area of entity-relationship extraction.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Examination of Haines Jump in Microfluidic Experiments via Evolution Graphs and Interface Tracking

This work examines a type of rapid pore-filling event in multiphase flow through permeable media that is better known as Haines Jump. While existing microfluidic experiments on Haines Jump mostly seek to maintain quasi-steady states through very low bulk flow rates over long periods of time, this work explores the combined use of a highly structured microscale transport network, high-speed fluorescent microscopy, displacement front segmentation algorithms, and a tracking algorithm to build evolution graphs that track displacement fronts as they evolve through high-speed video recording. The resulting evolution graph allows the segmentation of a high-speed recording in both space and time, potentially facilitating topology-cognitive computation on the transport network. Occurrences of Haines Jump are identified in the microfluidic displacement experiments and their significance in bulk flow rates is qualitatively analyzed. The bulk flow rate has little effect on the significance of Haines Jump during merging and splitting, but large bulk flow rates may obscure small bursts at the narrowest part of the throat.

permeable media↗

DOE OSTI2014

Nanoscale Cluster Detection in Massive Atom Probe Tomography Data

Recent technological advances in atom probe tomography (APT) have led to unprecedented data acquisition capabilities that routinely generate data sets containing hundreds of millions of atoms. Detecting nanoscale clusters of different atom types present in these enormous amounts of data and analyzing their spatial correlations with one another are fundamental to understanding the structural properties of the material from which the data is derived. Extant algorithms for nanoscale cluster detection do not scale to large data sets. Here, a scalable, CUDA-based implementation of an autocorrelation algorithm is presented. It isolates spatial correlations amongst atomic clusters present in massive APT data sets in linear time using a linear amount of storage. Correctness of the algorithm is demonstrated using large synthetically generated data with known spatial distributions. Benefits and limitations of using GPU-acceleration for autocorrelation-based APT data analyses are presented with supporting performance results on data sets with up to billions of atoms. To our knowledge, this is the first nanoscale cluster detection algorithm that scales to massive APT data sets and executes on commodity hardware.

Seal, Sudip K↗

DOE OSTI2015

SU-E-J-107: The Impact of the Tumor Location to Deformable Image Registration

Purpose: For four-dimensional planning and adaptive radiotherapy, the accuracy of deformable image registration (DIR) is essential. We evaluated the accuracy of an in-house program with the free-downloadable DIR software library package (NiftyReg) and two commercially available DIR software programs (MIM Maestro and Velocity AI) in lung SBRT cancer patients. In addition to it, the relationship between the tumor location and the accuracy of the DIRs was investigated. Methods: The free-form deformation was implemented in the in-house program and the MIM. The Velocity was based on the B-spline algorithm. The accuracy of the three programs was evaluated in comparison for the structures on 4DCT image datasets between at the peak-inhale and at the peak-exhale. The dice similarity coefficient (DSC) and normalized DSC (NDSC) were measured for the gross tumor volumes from 19 lung SBRT patients. Results: The DSC measurement showed the median values of the DSC were 0.885, 0.872 and 0.798 for the In-house program, the MIM and the Velocity, respectively. The Velocity showed significant difference compared to the others. The median NDSC values were 1.027, 1.005 and 0.946 for the In-house, the MIM and the Velocity, respectively. This indicated that the spatial overlap agreement between the reference and the deformed structure for the in-house and MIM was comparable with the accuracy within 1mm uncertainty. There was larger discrepancy within 1–2mm uncertainty for the Velocity. The In-house and the MIM showed the higher NDSC values than the median values when the GTV was not attached to the chest wall and diaphragm(p < 0.05). However, there is no relationship between the accuracy and the tumor location in the Velocity. Conclusion: The difference of the DIR program would affect different accuracy and the accuracy may be reduced when the tumor is located or attached to chest wall or diaphragm.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs

In recent years, a new kind of accelerated hardware has gained popularity in the artificial intelligence (AI) community which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI-hardware, to develop a mixed precision approach for computing a dense matrix factorization of the inverse overlap matrix in electronic structure theory, S –1 . This factorization of S –1 , written as ZZT = S –1 , is used to transform the general matrix eigenvalue problem into a standard matrix eigenvalue problem. Here we present a mixed precision iterative refinement algorithm where Z is given recursively using matrix–matrix multiplications and can be computed with high performance on Tensor cores. To understand the performance and accuracy of Tensor cores, comparisons are made to GPU-only implementations in single and double precision. Additionally, we propose a nonparametric stopping criteria which is robust in the face of lower precision floating point operations. The algorithm is particularly useful when we have a good initial guess to Z, for example, from previous time steps in quantum-mechanical molecular dynamics simulations or from a previous iteration in a geometry optimization.

36 MATERIALS SCIENCE↗