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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Improving Estimation of Betweenness Centrality for Scale-Free Graphs

Betweenness centrality is a graph statistic used to nd vertices that are participants in a large number of shortest paths in a graph. This centrality measure is commonly used in path and network interdiction problems and its complete form requires the calculation of all-pairs shortest paths for each vertex. This leads to a time complexity of O(jV jjEj), which is impractical for large graphs. Estimation of betweenness centrality has focused on performing shortest-path calculations on a subset of randomly- selected vertices. This reduces the complexity of the centrality estimation to O(jSjjEj); jSj < jV j, which can be scaled appropriately based on the computing resources available. An estimation strategy that uses random selection of vertices for seed selection is fast and simple to implement, but may not provide optimal estimation of betweenness centrality when the number of samples is constrained. Our experimentation has identi ed a number of alternate seed-selection strategies that provide lower error than random selection in common scale-free graphs. These strategies are discussed and experimental results are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Position Papers for the ASCR Workshop on Cybersecurity and Privacy for Scientific Computing Ecosystems

At the request of the Department of Energy's (DOE) Office of Advanced Scientific Computing Research (ASCR), this program committee has been tasked with organizing a workshop to identify basic research needs in cybersecurity and privacy to better support DOE's science and energy mission. As part of the process, the program committee is soliciting community input in the form of position papers to help identify significant use cases, facility issues, and other barriers to enabling verifiably trustworthy computational science while preserving data confidentiality as appropriate for scientific workflows of interest to DOE. The program committee will review these position papers and based on the fit of their area of expertise and interest, selected contributors will have the opportunity to participate in the workshop currently planned as a virtual event November 3-5th, 2021. The thrust areas that will be explored by this workshop are the following: (1) Algorithms for secure, scalable, privacy-enhancing technologies and frameworks, including: Federated AI/ML, Differential privacy, Randomized algorithms, Adversarial modeling & simulation, Graph algorithms, and Formal methods; (2) Platforms to support the entire scientific-computing ecosystem, including edge computing for large-scale experiments, focusing on heterogeneous systems and distributed systems, including: Heterogeneous computing systems, Distributed computing systems, and Secure data architectures; and (3) Data workflows to allow agile use of data while preserving integrity and privacy, making the important properties verifiable either at runtime or post-computation, including: Integrity and provenance and Data management infrastructure. Topics that are out-of-scope for the workshop include discussing specific proposed solutions or areas that are clearly out of DOE's fundamental and applied-sciences mission scope, e.g., cryptography, enterprise security, and general-operations technology.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Recurrence Rate and Magma Effusion Rate for the Latest Volcanism on Arsia Mons, Mars

Magmatism and volcanism have evolved the Martian lithosphere, surface, and climate throughout the history of Mars. Constraining the rates of magma generation and timing of volcanism on the surface clarifies the ways in which magma and volcanic activity have shaped these Martian systems. The ages of lava flows on other planets are often estimated using impact crater counts, assuming that the number and size-distribution of impact craters per unit area reflect the time the lava flow has been on the surface and exposed to potential impacts. Here we show that impact crater age model uncertainty is reduced by adding stratigraphic information observed at locations where neighboring lavas abut each other, and demonstrate the significance of this reduction in age uncertainty for understanding the history of a volcanic field comprising 29 vents in the 110-kilometer-diameter caldera of Arsia Mons, Mars. Each vent within this caldera produced lava flows several to tens of kilometers in length; these vents are likely among the youngest on Mars, since no impact craters in their lava flows are larger than 1 kilometer in diameter. First, we modeled the age of each vent with impact crater counts performed on their corresponding lava flows and found very large age uncertainties for the ages of individual vents, often spanning the estimated age for the entire volcanic field. The age model derived from impact crater counts alone is broad and unimodal, with estimated peak activity in the field around 130Ma (megaannum, 1 million years). Next we applied our volcano event age model (VEAM), which uses a directed graph of stratigraphic relationships and random sampling of the impact crater age determinations to create alternative age models. Monte Carlo simulation was used to create 10,000 possible vent age sets. The recurrence rate of volcanism is calculated for each possible age set, and these rates are combined to calculate the median recurrence rate of all simulations. Applying this approach to the 29 volcanic vents, volcanism likely began around 200-300Ma then first peaked around 150Ma, with an average production rate of 0.4 vents per Myr (million years). The recurrence rate estimated including stratigraphic data is distinctly bimodal, with a second, lower peak in activity around 100Ma. Volcanism then waned until the final vents were produced 10-90Ma. Based on this model, volume flux is also bimodal, reached a peak rate of 1-8 cubic kilometers per million years by 150Ma and remained above half this rate until about 90Ma, after which the volume flux diminished greatly. The onset of effusive volcanism from 200-150Ma might be due to a transition of volcanic style away from explosive volcanism that emplaced tephra on the western flank of Arsia Mons, while the waning of volcanism after the 150Ma peak might represent a larger-scale diminishing of volcanic activity at Arsia Mons related to the emplacement of flank apron lavas.

Richardson, Jacob A.↗
DOE OSTI2025

Accelerating computational fluid dynamics simulation of post-combustion carbon capture modeling with MeshGraphNets

Packed columns are commonly used in post-combustion processes to capture CO 2 emissions by providing enhanced contact area between a CO 2 -laden gas and CO 2 -absorbing solvent. To study and optimize solvent-based post-combustion carbon capture systems (CCSs), computational fluid dynamics (CFD) can be used to model the liquid–gas countercurrent flow hydrodynamics in these columns and derive key determinants of CO 2 -capture efficiency. However, the large design space of these systems hinders the application of CFD for design optimization due to its high computational cost. In contrast, data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. We build our surrogates using MeshGraphNets (MGN), a graph neural network framework that efficiently learns and produces mesh-based simulations. We apply MGN to a random packed column modeled with over 160K graph nodes and a design space consisting of three key input parameters: solvent surface tension, inlet velocity, and contact angle. Our models can adapt to a wide range of these parameters and accurately predict the complex interactions within the system at rates over 1700 times faster than CFD, affirming its practicality in downstream design optimization tasks. This underscores the robustness and versatility of MGN in modeling complex fluid dynamics for large-scale CCS analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Distributed Direction-Optimizing Label Propagation for Community Detection

Designing a scalable algorithm for community detec-tion is challenging due to the tradeoff between the performance and the quality of the result. We propose a new distributed algorithm for community detection —Direction-Optimizing Label Propagation Algorithm, based on a novel Label Propagation Algorithm in community detection. The algorithm is inspired by the direction optimization technique for graph traversal algorithms and relies on the use of frontiers and alternates between label push and label pull operations. This organization creates ?exibility and affords opportunities for balancing the performance and the quality of the solution. We implement our algorithm in distributed memory with an active-message based asynchronous many-task runtime, AM++. We experiment with two seeding strategies for initial seeding stage, namely, random seeding and degree seeding. With the Graph Challenge datasets, our distributed implementation, in conjunction with the runtime support, achieves very good performance and reasonable quality of solution.

Liu, Xu↗
DOE OSTI2016

A Novel Centrality Measure for Network-wide Cyber Vulnerability Assessment

In this work we propose a novel formulation that models the attack and compromise on a cyber network as a combination of two parts - direct compromise of a host and the compromise occurring through the spread of the attack on the network from a compromised host. The model parameters for the nodes are a concise representation of the host profiles that can include the risky behaviors of the associated human users while the model parameters for the edges are based on the existence of vulnerabilities between each pair of connected hosts. The edge models relate to the summary representations of the corresponding attack-graphs. This results in a formulation based on Random Walk with Restart (RWR) and the resulting centrality metric can be solved for in an efficient manner through the use of sparse linear solvers. Thus the formulation goes beyond mere topological considerations in centrality computations by summarizing the host profiles and the attack graphs into the model parameters. The computational efficiency of the method also allows us to also quantify the uncertainty in the centrality measure through Monte Carlo analysis.

Centrality↗
DOE OSTI2016

From stochastic processes to numerical methods: A new scheme for solving reaction subdiffusion fractional partial differential equations

We have introduced a new explicit numerical method, based on a discrete stochastic process, for solving a class of fractional partial differential equations that model reaction subdiffusion. The scheme is derived from the master equations for the evolution of the probability density of a sum of discrete time random walks. We show that the diffusion limit of the master equations recovers the fractional partial differential equation of interest. This limiting procedure guarantees the consistency of the numerical scheme. The positivity of the solution and stability results are simply obtained, provided that the underlying process is well posed. We also show that the method can be applied to standard reaction–diffusion equations. This work highlights the broader applicability of using discrete stochastic processes to provide numerical schemes for partial differential equations, including fractional partial differential equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

History dependent quantum random walks as quantum lattice gas automata

Quantum Random Walks (QRW) were first defined as one-particle sectors of Quantum Lattice Gas Automata (QLGA). Recently, they have been generalized to include history dependence, either on previous coin (internal, i.e., spin or velocity) states or on previous position states. These models have the goal of studying the transition to classicality, or more generally, changes in the performance of quantum walks in algorithmic applications. We show that several history dependent QRW can be identified as one-particle sectors of QLGA. This provides a unifying conceptual framework for these models in which the extra degrees of freedom required to store the history information arise naturally as geometrical degrees of freedom on the lattice.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

How random is a random vector?

Over 80 years ago Samuel Wilks proposed that the “generalized variance” of a random vector is the determinant of its covariance matrix. To date, the notion and use of the generalized variance is confined only to very specific niches in statistics. In this paper we establish that the “Wilks standard deviation” –the square root of the generalized variance–is indeed the standard deviation of a random vector. We further establish that the “uncorrelation index” –a derivative of the Wilks standard deviation–is a measure of the overall correlation between the components of a random vector. Both the Wilks standard deviation and the uncorrelation index are, respectively, special cases of two general notions that we introduce: “randomness measures” and “independence indices” of random vectors. In turn, these general notions give rise to “randomness diagrams”—tangible planar visualizations that answer the question: How random is a random vector? The notion of “independence indices” yields a novel measure of correlation for Lévy laws. In general, the concepts and results presented in this paper are applicable to any field of science and engineering with random-vectors empirical data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Constructing compact and effective graphs for recommender systems via node and edge aggregations

Exploiting graphs for recommender systems has great potential to flexibly incorporate heterogeneous information for producing better recommendation results. As our baseline approach, we first introduce a naive graph-based recommendation method, which operates with a heterogeneous log-metadata graph constructed from user log and content metadata databases. Although the na ve graph-based recommendation method is simple, it allows us to take advantages of heterogeneous information and shows promising flexibility and recommendation accuracy. However, it often leads to extensive processing time due to the sheer size of the graphs constructed from entire user log and content metadata databases. In this paper, we propose node and edge aggregation approaches to constructing compact and e ective graphs called Factor-Item bipartite graphs by aggregating nodes and edges of a log-metadata graph. Furthermore, experimental results using real world datasets indicate that our approach can significantly reduce the size of graphs exploited for recommender systems without sacrificing the recommendation quality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Predicting Large‐Scale Systematic Missing Pipe Attributes in Water Distribution Networks

Water distribution network (WDN) models are an essential tool used by water utilities for hydraulic analysis. Unfortunately, missing data and insufficient resources often make creating and maintaining these models unfeasible. Existing methods to address missing pipe properties, like sequential imputation for missing values and reconstruction using graph metrics, are designed to accommodate random patterns of missing information and require a significant percentage of the system's attributes to be known. However, these data completeness assumptions do not always align with real‐world scenarios where large sections of the WDN model have missing data. To address this challenge, this study proposes a data‐driven approach for estimating pipe diameter when considering different spatial patterns and degrees of data completeness (i.e., 0%–90%). Using data from 16 WDNs in Kentucky, this study compares the use of machine learning (ML) using topological and geospatial features against an existing deterministic approach. Results demonstrate that WDN models with pipe diameters predicted by the proposed ML method had comparable hydraulic performance to the ground truth models. Moreover, results showed that ML method performance varies between WDNs of differing topological classification. Insights from this study help advance the ability to leverage partial data to create and maintain WDN models amid uncertainty and inadequate resources.

Poff, Jason W. [Oregon State Univ., Corvallis, OR ↗
DOE OSTI2021

Sparse Approximate Multifrontal Factorization with Butterfly Compression for High-Frequency Wave Equations

In this work, we present a fast and approximate multifrontal solver for large-scale sparse linear systems arising from finite-difference, finite-volume or finite-element discretization of high-frequency wave equations. The proposed solver leverages the butterfly algorithm and its hierarchical matrix extension for compressing and factorizing large frontal matrices via graph-distance guided entry evaluation or randomized matrix-vector multiplication-based schemes. Complexity analysis and numerical experiments demonstrate $\mathcal{O}(N\log^2 N)$ computation and $\mathcal{O}(N)$ memory complexity when applied to an $N\times N$ sparse system arising from 3D high-frequency Helmholtz and Maxwell problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Holographic tensor networks with bulk gauge symmetries

Abstract Tensor networks are useful toy models for understanding the structure of entanglement in holographic states and reconstruction of bulk operators within the entanglement wedge. They are, however, constrained to only prepare so-called “fixed-area states” with flat entanglement spectra, limiting their utility in understanding general features of holographic entanglement. Here, we overcome this limitation by constructing a variant of random tensor networks that enjoys bulk gauge symmetries. Our model includes a gauge theory on a general graph, whose gauge-invariant states are fed into a random tensor network. We show that the model satisfies the quantum-corrected Ryu-Takayanagi formula with a nontrivial area operator living in the center of a gauge-invariant algebra. We also demonstrate nontrivial,n-dependent contributions to the Rényi entropy and Rényi mutual information from this area operator, a feature shared by general holographic states.

Physics↗
DOE OSTI2017

Occupation Time of a Randomly Accelerated Particle on the Positive Half Axis: Results for the First Five Moments

In the random acceleration process a point particle is accelerated by Gaussian white noise with zero mean. Although several fundamental statistical properties of the motion have been analyzed in detail, the statistics of occupation times is still not well understood. We consider the occupation or residence time T{sub +} on the positive x axis of a particle which is randomly accelerated on the unbounded x axis for a time t. The first two moments of T{sub +} were recently derived by Ouandji Boutcheng et al. (J Stat Mech 053213:1–10, 2016). With an alternate approach utilizing basis functions which have proved useful in other studies of randomly accelerated motion, results for the first five moments are obtained in this paper.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A Solid of Revolution Time Study using COG11.1 and MCNP6.1

The performance of two continuous energy Monte Carlo transport codes, COG11.1 and MCNP6.1, has been compared for the {sup 239}Pu Jezebel Benchmark using cross sections based on the ENDF/B-VII.1 evaluated nuclear data library. The purpose of this study is to determine the length of time it takes each code to complete a series of criticality calculations, using two different methods to model a solid of revolution. A solid of revolution is essential in many fields of engineering and mathematics, and has been a standard feature in computer-aided design and manufacturing since the 1970's. In the field of criticality safety, a solid of revolution can be used to reproduce and simplify complex engineering drawings and systems that would otherwise require excessive use of geometric primitives and computational resources to achieve a comparable level of fidelity. There are two basic methods to model a solid of revolution: Method 1 (COG) specifies a set of x-y pairs or polar coordinates and rotate the curve about the x-axis. Method 2 (COG and MCNP) creates a set of end-to-end conical frustums and/or cylinders. Method 1 is only available in COG and can be implemented using one surface and sector specification. Method 2 can be implemented in both COG and MCNP. This time study was initially conceived as a result of criticality safety analysis performed for a complex system with a high degree of rotational symmetry. Each COG input deck typically took 1-2 days to run in serial mode. COG input decks were then converted to MCNP format and run on the same computer cluster. Instead of expected 1-2 day runtimes, it took MCNP6.1 more than one week to complete each run. Since MCNP typically runs faster than many codes, these lengthy runtimes were considered unusual. This study attempts to investigate the runtime discrepancy between the two codes. The methods and benchmark in this time study were selected to provide a consistent, generalized approach to modeling an arbitrary solid of revolution. While there are many different ways to model the {sup 239}Pu Jezebel Benchmark, the focus of this study is on investigating the practical difference between the two basic methods to model a surface of revolution. Throughout this study, the only code that maintains reasonably low runtimes is COG11.1 (Method 1). Based on the observed performance of each code and method, it is likely that the divergent runtimes are the result of tracking particles across planes that lie between conical frustums, as opposed to defining the object boundary as a single surface. Slight fluctuations in graphed results were also observed, due to random sampling and CPU loading. This was confirmed by re-running input decks and comparing average runtimes to a preliminary curve fit. From the perspective of a criticality safety analyst, the results of this study are expected to contribute towards understanding one of the practical trade-offs between codes and methods as they apply to real-world analysis. Ultimately, it's about choosing the right tool for the job.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Cyclotomic Shuffles

Analogues of 1-shuffle elements for complex reflection groups of type G(m,1,n) are introduced. A geometric interpretation for G(m,1,n) in terms of rotational permutations of polygonal cards is given. We compute the eigenvalues, and their multiplicities, of the 1-shuffle element in the algebra of the group G(m,1,n). Considering shuffling as a random walk on the group G(m,1,n), we estimate the rate of convergence to randomness of the corresponding Markov chain. We report on the spectrum of the 1-shuffle analogue in the cyclotomic Hecke algebra H(m,1,n) for m=2 and small n.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Some functional limit theorems for compound Cox processes

An improved version of the functional limit theorem is proved establishing weak convergence of random walks generated by compound doubly stochastic Poisson processes (compound Cox processes) to Lévy processes in the Skorokhod space under more realistic moment conditions. As corollaries, theorems are proved on convergence of random walks with jumps having finite variances to Lévy processes with variance-mean mixed normal distributions, in particular, to stable Lévy processes.

97 MATHEMATICS AND COMPUTING↗