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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 OSTI2022

Augmenting Graph Convolution with Distance Preserving Embedding for Improved Learning

Graph convolution incorporates topological information of a graph into learning. Message passing corresponds to traversal of a local neighborhood in classical graph algorithms. We show that incorporating additional global structures, such as shortest paths, through distance preserving embedding can improve performance. Our approach, Gavotte, significantly improves the performance of a range of popular graph neu-ral networks such as GCN, GA T,Graph SAGE, and GCNII for transductive learning. Gavotte also improves the performance of graph neural networks for full-supervised tasks, albeit to a smaller degree. As high-quality embeddings are generated by Gavotte as a by-product, we leverage clustering algorithms on these embed dings to augment the training set and introduce Gavotte+. Our results of Gavotte+ on datasets with very few labels demonstrate the advantage of augmenting graph convolution with distance preserving embedding.

Cong, Guojing↗
DOE OSTI2014

Visualizations of coherent center domains in local Polyakov loops

Quantum Chromodynamics exhibits a hadronic confined phase at low to moderate temperatures and, at a critical temperature T{sub C}, undergoes a transition to a deconfined phase known as the quark–gluon plasma. The nature of this deconfinement phase transition is probed through visualizations of the Polyakov loop, a gauge independent order parameter. We produce visualizations that provide novel insights into the structure and evolution of center clusters. Using the HMC algorithm the percolation during the deconfinement transition is observed. Using 3D rendering of the phase and magnitude of the Polyakov loop, the fractal structure and correlations are examined. The evolution of the center clusters as the gauge fields thermalize from below the critical temperature to above it are also exposed. We observe deconfinement proceeding through a competition for the dominance of a particular center phase. We use stout-link smearing to remove small-scale noise in order to observe the large-scale evolution of the center clusters. A correlation between the magnitude of the Polyakov loop and the proximity of its phase to one of the center phases of SU(3) is evident in the visualizations. - Highlights: • We produce visualizations of center clusters in Polyakov loops. • The evolution of center clusters with HMC simulation time is examined. • Visualizations provide novel insights into the percolation of center clusters. • The magnitude and phase of the Polyakov loop are studied. • A correlation between the magnitude and center phase proximity is evident.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Accurate Consensus-based Distributed Averaging with Variable Time Delay in Support of Distributed Secondary Control Algorithms

We report that distributed secondary control has been widely used in hierarchical control structures, where multiple distributed generators (DGs) need to coordinate to regulate system voltage and frequency. In these systems, consensus algorithms determine the average of a group of dynamic states (e.g. voltages measured by a group of DGs). To be useful, consensus algorithms must be computationally efficient, stable and accurate. In practice, numerous practical implementation challenges significantly affect the consensus equilibrium. In this paper, we quantify the accuracy deviations of the distributed average observer algorithms proposed in the literature to demonstrate the problems with the state-of-the-art distributed averaging techniques. A novel approach is proposed that achieves accurate average tracking in the presence of time-varying communication delays among agents. In our implementation, time synchronization of all distributed controllers is enabled by a novel software platform, called Resilient Information Architecture Platform for the Smart Grid (RIAPS). The proposed distributed average observer is implemented on hardware controllers and its effectiveness is validated in a controller hardware-in-the-loop testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Non-Adiabatic Excited State Molecular Dynamics Methodologies: comparison and convergence

Direct atomistic simulation of nonadiabatic molecular dynamics is a challenging goal that allows important insights into fundamental physical phenomena. A variety of frameworks, ranging from fully quantum treatment of nuclei to semiclassical and mixed quantum–classical approaches, were developed. These algorithms are then coupled to specific electronic structure techniques. Such diversity and lack of standardized implementation make it difficult to compare the performance of different methodologies when treating realistic systems. Here, we compare three popular methods for large chromophores: Ehrenfest, surface hopping, and multiconfigurational Ehrenfest with ab initio multiple cloning (MCE-AIMC). These approaches are implemented in the NEXMD software, which features a common computational chemistry model. The resulting comparisons reveal the method performance for population relaxation and coherent vibronic dynamics. Finally, we study the numerical convergence of MCE-AIMC algorithms by considering the number of trajectories, cloning thresholds, and Gaussian wavepacket width. Our results provide helpful reference data for selecting an optimal methodology for simulating excited-state molecular dynamics.

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

Structures, phase transitions, and magnetic properties of C o 3 Si from first-principles calculations

Co 3 Si was recently reported to exhibit remarkable magnetic properties in the nanoparticle form [Appl. Phys. Lett. 108, 152406 (2016)], yet better understanding of this material is to be promoted. Here we report a study on the crystal structures of Co 3 Si using adaptive genetic algorithm, and discuss its electronic and magnetic properties from first-principles calculations. Several competing phases of Co 3 Si have been revealed from our calculations. We show that the hexagonal Co 3 Si structure reported in experiments has lower energy in non-magnetic state than ferromagnetic state at zero temperature. The ferromagnetic state of the hexagonal structure is dynamically unstable with imaginary phonon modes and transforms to a new orthorhombic structure, which is confirmed by our structure searches to have the lowest energy for both Co 3 Si and Co 3 Ge. Magnetic properties of the experimental hexagonal structure and the lowest-energy structures obtained from our structure searches are investigated in detail.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Quantum Machine-Learning for Eigenstate Filtration in Two-Dimensional Materials

Quantum machine-learning algorithms have emerged to be a promising alternative to their classical counterparts as they leverage the power of quantum computers. Such algorithms have been developed to solve problems like electronic structure calculations of molecular systems and spin models in magnetic systems. However, the discussion in all these recipes focuses specifically on targeting the ground state. Herein we demonstrate a quantum algorithm that can filter any energy eigenstate of the system based on either symmetry properties or a predefined choice of the user. The workhorse of our technique is a shallow neural network encoding the desired state of the system with the amplitude computed by sampling the Gibbs–Boltzmann distribution using a quantum circuit and the phase information obtained classically from the nonlinear activation of a separate set of neurons. We show that the resource requirements of our algorithm are strictly quadratic. To demonstrate its efficacy, we use state filtration in monolayer transition metal dichalcogenides which are hitherto unexplored in any flavor of quantum simulations. We implement our algorithm not only on quantum simulators but also on actual IBM-Q quantum devices and show good agreement with the results procured from conventional electronic structure calculations. We thus expect our protocol to provide a new alternative in exploring the band structures of exquisite materials to usual electronic structure methods or machine-learning techniques that are implementable solely on a classical computer.

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

Determination of the stretch tensor for structural transformations

Structural transformations in crystalline solids are increasingly the basis of the functional behavior of materials. Both low hysteresis and reversibility of phase transformations have been linked to the satisfaction of the nongeneric conditions of compatibility between phases in diverse alloy systems. According to the Cauchy–Born rule, these conditions are expressed as properties of transformation stretch tensor. The transformation stretch tensor is difficult to measure directly due to the lack of knowledge about the exact transforming pathway during the structural change, and the complicating effects of microstructure. In this paper we give a rigorous algorithmic approach for determining the transformation stretch tensor from X-ray measurements of structure and lattice parameters. For some traditional and emerging phase transformations, the results given by the algorithm suggest unexpected transformation mechanisms.

42 ENGINEERING↗
DOE OSTI2017

An Information Theoretic Framework and Self-organizing Agent- based Sensor Network Architecture for Power Plant Condition Monitoring

A central goal of the work was to enable both the extraction of all relevant information from sensor data, and the application of information gained from appropriate processing and fusion at the system level to operational control and decision-making at various levels of the control hierarchy through: 1. Exploiting the deep connection between information theory and the thermodynamic formalism, 2. Deployment using distributed intelligent agents with testing and validation in a hardware-in-the loop simulation environment. Enterprise architectures are the organizing logic for key business processes and IT infrastructure and, while the generality of current definitions provides sufficient flexibility, the current architecture frameworks do not inherently provide the appropriate structure. Of particular concern is that existing architecture frameworks often do not make a distinction between ``data'' and ``information.'' This work defines an enterprise architecture for health and condition monitoring of power plant equipment and further provides the appropriate foundation for addressing shortcomings in current architecture definition frameworks through the discovery of the information connectivity between the elements of a power generation plant. That is, to identify the correlative structure between available observations streams using informational measures. The principle focus here is on the implementation and testing of an emergent, agent-based, algorithm based on the foraging behavior of ants for eliciting this structure and on measures for characterizing differences between communication topologies. The elicitation algorithms are applied to data streams produced by a detailed numerical simulation of Alstom’s 1000 MW ultra-super-critical boiler and steam plant. The elicitation algorithm and topology characterization can be based on different informational metrics for detecting connectivity, e.g. mutual information and linear correlation.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

cuAlign: Scalable Network Alignment on GPU Accelerators

Given two graphs, the objective of network alignment is to find the best one-to-one mapping of vertices in one graph (??) to vertices in the other (??), such that the number of overlaps is maximized. We say that edges(??, ??) ???and(??', ??') ??? are overlapped if ?? is mapped to ??' and ?? is mapped to??'. Network alignment is an important optimization problem with several applications in bioinformatics, computer vision and ontology matching. Since it is an NP-hard problem, efficient heuristics and scalable implementations are necessary. In this work, we introduce a new framework that combines the concepts of intra-network proximity using vertex embedding,Belief Propagation (BP) and approximate weighted matching, and provides qualitative improvements up to22%over state-of-the-art approaches. We also provide scalable implementations on GPU accelerators, demonstrating up to19×speedup for Belief Propagation and 3× speedup for approximate weighted matching relative to previous multithreaded implementation. A combination of combinatorial and algebraic kernels within the network alignment algorithm poses significant hurdles for parallelization. Load imbalance and irregular DRAM traffic limit achievable performance on GPUs. Our novel approach identifies and exploits unique structural proper-ties of the BP-based algorithm and employs code fusion to reduce data movement between different steps of the algorithm. Using a diverse set of inputs, we demonstrate qualitative improvements of our algorithms, and performance gains of our GPU-accelerated implementation. We believe that our work will enable algorithmic improvements and practical applications of network alignment.

Xiang, Lizhi↗
DOE OSTI2014

CARMA Large Area Star Formation Survey: project overview with analysis of dense gas structure and kinematics in Barnard 1

We present details of the CARMA Large Area Star Formation Survey (CLASSy), while focusing on observations of Barnard 1. CLASSy is a CARMA Key Project that spectrally imaged N{sub 2}H{sup +}, HCO{sup +}, and HCN (J = 1 → 0 transitions) across over 800 square arcminutes of the Perseus and Serpens Molecular Clouds. The observations have angular resolution near 7'' and spectral resolution near 0.16 km s{sup –1}. We imaged ∼150 square arcminutes of Barnard 1, focusing on the main core, and the B1 Ridge and clumps to its southwest. N{sub 2}H{sup +} shows the strongest emission, with morphology similar to cool dust in the region, while HCO{sup +} and HCN trace several molecular outflows from a collection of protostars in the main core. We identify a range of kinematic complexity, with N{sub 2}H{sup +} velocity dispersions ranging from ∼0.05 to 0.50 km s{sup –1} across the field. Simultaneous continuum mapping at 3 mm reveals six compact object detections, three of which are new detections. A new, non-binary dendrogram algorithm is used to analyze dense gas structures in the N{sub 2}H{sup +} position-position-velocity (PPV) cube. The projected sizes of dendrogram-identified structures range from about 0.01 to 0.34 pc. Size-linewidth relations using those structures show that non-thermal line-of-sight velocity dispersion varies weakly with projected size, while rms variation in the centroid velocity rises steeply with projected size. Comparing these relations, we propose that all dense gas structures in Barnard 1 have comparable depths into the sky, around 0.1-0.2 pc; this suggests that overdense, parsec-scale regions within molecular clouds are better described as flattened structures rather than spherical collections of gas. Science-ready PPV cubes for Barnard 1 molecular emission are available for download.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Search for interacting galaxy clusters from SDSS DR-17 employing optimized friends-of-friends algorithm and multimessenger tracers

ABSTRACT In the theoretical framework of hierarchical structure formation, galaxy clusters evolve through continuous accretion and mergers of substructures. Cosmological simulations have revealed the best picture of the universe as a 3D filamentary network of dark-matter distribution called the cosmic web. Galaxy clusters are found to form at the nodes of this network and are the regions of high merging activity. Such mergers being highly energetic, contain a wealth of information about the dynamical evolution of structures in the Universe. Observational validation of this scenario needs a colossal effort to identify numerous events from all-sky surveys. Therefore, such efforts are sparse in literature and tend to focus on individual systems. In this work, we present an improved search algorithm for identifying interacting galaxy clusters and have successfully produced a comprehensive list of systems from SDSS DR-17. By proposing a set of physically motivated criteria, we classified these interacting clusters into two broad classes, ‘merging’ and ‘pre-merging/postmerging’ systems. Interestingly, as predicted by simulations, we found that most cases show cluster interaction along the prominent cosmic filaments of galaxy distribution (i.e. the proxy for dark matter filaments), with the most violent ones at their nodes. Moreover, we traced the imprint of interactions through multiband signatures, such as diffuse cluster emissions in radio or X-rays. Although we could not find direct evidence of diffuse emission from connecting filaments and ridges; our catalogue of interacting clusters will ease locating such faintest emissions as data from sensitive telescopes such as eROSITA or SKA, becomes accessible.

Oak, Tejas↗
DOE OSTI2016

A comprehensive simulation framework for imaging single particles and biomolecules at the European X-ray Free-Electron Laser

The advent of newer, brighter, and more coherent X-ray sources, such as X-ray Free-Electron Lasers (XFELs), represents a tremendous growth in the potential to apply coherent X-rays to determine the structure of materials from the micron-scale down to the Angstrom-scale. There is a significant need for a multi-physics simulation framework to perform source-to-detector simulations for a single particle imaging experiment, including (i) the multidimensional simulation of the X-ray source; (ii) simulation of the wave-optics propagation of the coherent XFEL beams; (iii) atomistic modelling of photon-material interactions; (iv) simulation of the time-dependent diffraction process, including incoherent scattering; (v) assembling noisy and incomplete diffraction intensities into a three-dimensional data set using the Expansion-Maximisation-Compression (EMC) algorithm and (vi) phase retrieval to obtain structural information. Furthermore, we demonstrate the framework by simulating a single-particle experiment for a nitrogenase iron protein using parameters of the SPB/SFX instrument of the European XFEL. This exercise demonstrably yields interpretable consequences for structure determination that are crucial yet currently unavailable for experiment design.

42 ENGINEERING↗
DOE OSTI2016

B-Trees and Cache-Oblivious B-Trees with Different-Sized Atomic Keys

Most B-tree articles assume that all $\textit{N}$ keys have the same size $\textit{K}$, that $\textit{f = B/K}$ keys fit in a disk block, and therefore that the search cost is $O(\text{log}_{f+1}N) $ block transfers. When keys have variable size, B-tree operations have no nontrivial performance guarantees, however. This article provides B-tree-like performance guarantees on dictionaries that contain keys of different sizes in a model in which keys must be stored and compared as opaque objects. The resulting atomic-key dictionaries exhibit performance bounds in terms of the average key size and match the bounds when all keys are the same size. Atomic-key dictionaries can be built with minimal modification to the B-tree structure, simply by choosing the pivot keys properly. Further, this article describes both static and dynamic atomic-key dictionaries. In the static case, if there are $\textit{N}$ keys with average size $\textit{K}$, the search cost is $O(\lceil K/B\rceil \text{log}_{1 + \lceil K/B\rceil} N)$ expected transfers. It is not possible to transform these expected bounds into worst-case bounds. The cost to build the tree is $\textit{O(NK)}$ operations and $\textit{O(NK/B)}$ transfers if all keys are presented in sorted order. If not, the cost is the sorting cost. For the dynamic dictionaries, the amortized cost to insert a key $\textit{κ}$ of arbitrary length at an arbitrary rank is dominated by the cost to search for $\textit{κ}$. Specifically, the amortized cost to insert a key $\textit{κ}$ of arbitrary length and random rank is $O(\lceil K/B\rceil log_{1+ \lceil B/K\rceil } N + |κ|/B)$ transfers. A dynamic-programming algorithm is shown for constructing a search tree with minimal expected cost. This article also gives a cache-oblivious static atomic-key B-tree, which achieves the same asymptotic performance as the static B-tree dictionary, mentioned previously. A cache-oblivious data structure or algorithm is not parameterized by the block size $\textit{B}$ or memory size $\textit{M}$ in the memory hierarchy; rather, it is universal, working simultaneously for all possible values of $\textit{B}$ or $\textit{M}$. On a machine with block size $\textit{B}$, if there are $\textit{N}$ keys with average size $\textit{K}$, search operations costs $O(\lceil K/B \rceil log_{1+ \lceil B/K \rceil} N)$ block transfers in expectation. This cache-oblivious layout can be built in $\textit{O(N}\text{log}\textit{(NK))}$ processor operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Robust clustering of the local Milky Way stellar kinematic substructures with Gaia eDR3

Understanding local stellar kinematic substructures in the solar neighbourhood helps build a complete picture of the formation of the Milky Way, as well as an empirical phase space distribution of dark matter that would inform detection experiments. We apply the clustering algorithm HDBSCAN on the Gaia early third data release to identify a list of stable clusters in velocity space and action-angle space by taking into account the measurement uncertainties and studying the stability of the clustering results. We find 1405 (497) stars in 23 (6) robust clusters in velocity space (action-angle space) that are consistently not associated with noise. We discuss the kinematic properties of these structures and study whether many of the small clusters belong to a similar larger cluster based on their chemical abundances. They are attributed to the known structures: the Gaia Sausage-Enceladus, the Helmi Stream, and globular cluster NGC 3201 are found in both spaces, while NGC 104 and the thick disc (Sequoia) are identified in velocity space (action-angle space). Although we do not identify any new structures, we find that the HDBSCAN member selection of already known structures is unstable to input kinematics of the stars when resampled within their uncertainties. We therefore present the stable subset of local kinematic structures, which are consistently identified by the clustering algorithm, and emphasize the need to take into account error propagation during both the manual and automated identification of stellar structures, both for existing ones as well as future discoveries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A new wavelet-based EFCM clustering model was introduced toward noise reduction and detail preservation. The proposed method improves the overall US image quality, which in turn could affect the decision-making on whether additional imaging and/or intervention is needed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Landau discriminants

Scattering amplitudes in quantum field theories have intricate analytic properties as functions of the energies and momenta of the scattered particles. In perturbation theory, their singularities are governed by a set of nonlinear polynomial equations, known as Landau equations, for each individual Feynman diagram. The singularity locus of the associated Feynman integral is made precise with the notion of the Landau discriminant, which characterizes when the Landau equations admit a solution. In order to compute this discriminant, we present approaches from classical elimination theory, as well as a numerical algorithm based on homotopy continuation. These methods allow us to compute Landau discriminants of various Feynman diagrams up to 3 loops, which were previously out of reach. For instance, the Landau discriminant of the envelope diagram is a reducible surface of degree 45 in the three-dimensional space of kinematic invariants. We investigate geometric properties of the Landau discriminant, such as irreducibility, dimension and degree. In particular, we find simple examples in which the Landau discriminant has codimension greater than one. Furthermore, we describe a numerical procedure for determining which parts of the Landau discriminant lie in the physical regions. In order to study degenerate limits of Landau equations and bounds on the degree of the Landau discriminant, we introduce Landau polytopes and study their facet structure. Finally, we provide an efficient numerical algorithm for the computation of the number of master integrals based on the connection to algebraic statistics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Optical quantum bit string comparator

Quantum computation has attracted much attention and several quantum algorithms have been proposed in the literature. However, the hardware able to implement such algorithms is still a challenge. In this work, we provide an optical setup for implementation of a quantum bit string comparator, QBSC, for polarization-based qubit, using the non-linear Kerr effect. The QBSC is an important structure for implementation of conditional statements in quantum algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Ab initio many-fermion structure calculations on a quantum computer

To overcome the limitations of existing algorithms for solving self-bound quantum many-body problems—such as those encountered in nuclear and particle physics—that access only a restricted subset of energy levels and provide limited structural information, we introduce and demonstrate a novel quantum-classical approach capable of resolving the complete bound-state spectrum. This method also provides the total angular momentum 𝐽 associated with each eigenstate. Here, our approach is based on expressing the Hamiltonian in second-quantized form within a novel input model combined with a scan scheme, enabling broad applicability to configuration-interaction calculations across diverse fields. We apply this hybrid method to compute, for the first time, the bound-state spectrum together with corresponding 𝐽 values of 20 O using a realistic strong-interaction Hamiltonian. Our approach applies to hadron spectra and 𝐽 values solved in the relativistic basis light-front quantization approach.

Du, Weijie [Chinese Academy of Sciences (CAS), Lan↗