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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Bootstrap AMG for spectral clustering

Graph Laplacian is a popular tool for analyzing graphs, particularly in graph partitioning and clustering. Given a notion of similarity (via an adjacency matrix), graph clustering refers to identifying different groups such that vertices in the same group are more similar compared to vertices across different groups. Data clustering can be reformulated in terms of a graph clustering problem when the given set of data is represented as a graph, also known as similarity graph. In this context, eigenvectors of the graph Laplacian are often used to obtain a new geometric representation of the original data set that generally enhances cluster properties and improves cluster detection. Here, we apply a bootstrap algebraic multigrid (AMG) method that constructs a set of vectors associated with the graph Laplacian. These vectors, referred to as algebraically smooth ones, span a low-dimensional Euclidean space, which we use to represent the data, enabling cluster detection both in synthetic and in realistic well-clustered graphs. We show that, in the case of a good quality bootstrap AMG, the computed smooth vectors employed in the construction of the final AMG operator, which by construction is spectrally equivalent to the originally given graph Laplacian, accurately approximate the space in the lower portion of the spectrum of the preconditioned operator. Thus, our approach can be viewed as a spectral clustering technique associated with the generalized spectral problem (Laplace operator versus the final AMG operator), and hence, it can be seen as an extension of the classical spectral clustering that employs a standard eigenvalue problem.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Geoid undulation computations at laser tracking stations

The accuracy of numerical computations of gravimetric undulations using recently proposed modifications of the classical Stokes formula is investigated. The basic formulations of the methods are outlined, and results from trial computations are presented in tables and graphs and compared with actual gravity data and/or GEOS-3/Seasat sea-surface heights. Although all of the methods gave similar results, the method of Sjoberg (1986) is recommended because of its superior theoretical precision. The importance of including terrain-corrected free-air anomalies in the computations is indicated.

Despotakis, Vasilios K.↗
DOE OSTI2021

Multilevel Spectral Coarsening for Graph Laplacian Problems with Application to Reservoir Simulation

We extend previously developed two-level coarsening procedures for graph Laplacian problems written in a mixed saddle point form to the fully recursive multilevel case. The resulting hierarchy of discretizations gives rise to a hierarchy of upscaled models, in the sense that they provide approximation in the natural norms (in the mixed setting). This property enables us to utilize them in three applications: (i) as an accurate reduced model, (ii) as a tool in multilevel Monte Carlo simulations (in application to finite volume discretizations), and (iii) for providing a sequence of nonlinear operators in a full approximation scheme for solving nonlinear pressure equations discretized by the conservative two-point flux approximation. Finally, we illustrate the potential of the proposed multilevel technique in all three applications on a number of popular benchmark problems used in reservoir simulation.

multilevel Monte Carlo↗
DOE OSTI2019

Chokepoints in Mechanical Coupling Associated with Allosteric Proteins: The Pyruvate Kinase Example

Although the critical role of allostery in controlling enzymatic processes is well appreciated, there is a current dearth in our understanding of its underlying mechanisms, including communication between binding sites. One potential key aspect of inter-site communication is the mechanical coupling between residues in a protein. Here, we introduce a graph-based computational approach to investigate the mechanical coupling between distant parts of a protein, highlighting effective pathways via which protein motion can transfer energy between sites. In this method, each residue is treated as a node on a weighted, undirected graph, where the edges are defined by locally correlated motions of those residues and weighted by the strength of the correlation. The method was validated against experimental data on allosteric regulation in the human liver pyruvate kinase as obtained from full-protein alanine-scanning mutagenesis (systematic mutation) studies. The method provides semi-quantitative information on the regulatory importance of specific structural elements. It is shown that these elements are key for the mechanical coupling between distant parts of the protein by providing effective pathway for energy transfer. It is also shown that, while there are a multitude of energy transfer pathways between distant parts of a protein, these pathways share a few common nodes that represent effective “chokepoints.”

Johnson, Lewis E.↗
DOE OSTI2024

Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution

Abstract Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework

The development of deep learning interatomic potentials has enabled efficient and accurate computations in quantum chemistry and materials science, circumventing computationally expensive ab initio calculations. However, the huge number of learnable parameters in deep learning models and their complex architectures hinder physical interpretability and affect the robustness of the derived potential. In this work, we propose graph-EAM, a lightweight graph neural network (GNN) inspired by the empirical embedded atom method to model the interatomic potential of single-element structures. Four material systems: platinum, niobium, silicon, and amorphous-carbon, for which quantum simulation data sets are publicly available, are examined to demonstrate that graph-EAM can achieve high energy and force prediction accuracy-comparable or better than existing state-of-the-art machine learning models-with much fewer parameters. It is also shown that the explicit inclusion of the angular information via three-body atomic density increases the prediction accuracy. In conclusion, the accuracy and efficiency of potentials obtained from graph-EAM can help accelerate the molecular dynamics simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
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 OSTI2020

Multilevel graph embedding

The goal of the present paper is the design of embeddings of a general sparse graph into a set of points in $\mathbb{R}^d$ for appropriate d ≥ 2. The embeddings that we are looking at here aim to keep vertices that are grouped in communities together and keep the rest apart. To achieve this property, we utilize coarsening that respects possible community structures of the given graph. We employ a hierarchical multilevel coarsening approach that identifies communities (strongly connected groups of vertices) at every level. The multilevel strategy allows any given (presumably expensive) graph embedding algorithm to be made into a more scalable (and faster) algorithm. We demonstrate the presented approach on a number of given embedding algorithms and large-scale graphs and achieve speed-up over the methods in a recent paper.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Monte Carlo Study of Four-Dimensional Self-avoiding Walks of up to One Billion Steps

We study self-avoiding walks on the four-dimensional hypercubic lattice via Monte Carlo simulations of walks with up to one billion steps. We study the expected logarithmic corrections to scaling, and find convincing evidence in support the scaling form predicted by the renormalization group, with an estimate for the power of the logarithmic factor of 0.2516(14), which is consistent with the predicted value of 1/4. We also characterize the behaviour of the pivot algorithm for sampling four dimensional self-avoiding walks, and conjecture that the probability of a pivot move being successful for an N-step walk is O([log N]{sup −1/4}).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Lattice Monte Carlo simulation of Galilei variant anomalous diffusion

The observation of an increasing number of anomalous diffusion phenomena motivates the study to reveal the actual reason for such stochastic processes. When it is difficult to get analytical solutions or necessary to track the trajectory of particles, lattice Monte Carlo (LMC) simulation has been shown to be particularly useful. To develop such an LMC simulation algorithm for the Galilei variant anomalous diffusion, we derive explicit solutions for the conditional and unconditional first passage time (FPT) distributions with double absorbing barriers. According to the theory of random walks on lattices and the FPT distributions, we propose an LMC simulation algorithm and prove that such LMC simulation can reproduce both the mean and the mean square displacement exactly in the long-time limit. However, the error introduced in the second moment of the displacement diverges according to a power law as the simulation time progresses. We give an explicit criterion for choosing a small enough lattice step to limit the error within the specified tolerance. We further validate the LMC simulation algorithm and confirm the theoretical error analysis through numerical simulations. The numerical results agree with our theoretical predictions very well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Inventory Uncertainty Quantification using TENDL Covariance Data in Fispact-II

The new inventory code Fispact-II provides predictions of inventory, radiological quantities and their uncertainties using nuclear data covariance information. Central to the method is a novel fast pathways search algorithm using directed graphs. The pathways output provides (1) an aid to identifying important reactions, (2) fast estimates of uncertainties, (3) reduced models that retain important nuclides and reactions for use in the code's Monte Carlo sensitivity analysis module. Described are the methods that are being implemented for improving uncertainty predictions, quantification and propagation using the covariance data that the recent nuclear data libraries contain. In the TENDL library, above the upper energy of the resolved resonance range, a Monte Carlo method in which the covariance data come from uncertainties of the nuclear model calculations is used. The nuclear data files are read directly by FISPACT-II without any further intermediate processing. Variance and covariance data are processed and used by FISPACT-II to compute uncertainties in collapsed cross sections, and these are in turn used to predict uncertainties in inventories and all derived radiological data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Reducing computation in an i-vector speaker recognition system using a tree-structured universal background model

The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of the identified systems, the posterior probabilities and sufficient statistics calculations represent a computational bottleneck in both enrollment and testing. We propose a multi-layered hash system, employing a tree-structured GMM–UBM which uses Runnalls’ Gaussian mixture reduction technique, in order to reduce the number of these calculations. Moreover, with this tree-structured hash, we can trade-off reduction in computation with a corresponding degradation of equal error rate (EER). As an example, we also reduce this computation by a factor of 15× while incurring less than 10% relative degradation of EER (or 0.3% absolute EER) when evaluated with NIST 2010 speaker recognition evaluation (SRE) telephone data.

42 ENGINEERING↗
DOE OSTI2020

Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks

Materials representation plays a key role in machine learning-based prediction of materials properties and new materials discovery. Currently both graph and three-dimensional (3D) voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD-based 3D convolutional neural networks (CNNs) for predicting the elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2170 $Fm\bar3m$ face-centered-cubic materials show that our ECD-based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Materials-Agnostic Platform for Informatics and Exploration features and ECD descriptors achieved the best fivefold cross-validation performance. More importantly, we showed that our ECD-based CNN models can achieve significantly better extrapolation performance when evaluated over nonredundant data sets, where there are few neighbor-training samples around test samples. As an additional validation, we evaluated the predictive performance of our models on 329 materials of space group $Fm\bar3m$ by comparing to density functional theory calculated values, which shows a better prediction power of our model for bulk modulus than shear modulus. Because of the unified representation power of ECD, it is expected that our ECD-based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

Abstract Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

Sun, Alexander Y.↗
DOE OSTI2020

Guest Editorial: Planning and operation of resilient distribution system for integrated multi-energy

Resilience is the ability of power systems to prepare for and adapt to low-probability, high-impact incidents and withstand and recover rapidly from disruptions. With the ageing of electricity distribution infrastructure and increasing threats of weather-related incidents and natural disasters, the need to effectively enhance the resilience of the electricity distribution system has become urgent and has attracted worldwide attention. Although there are an increasing number of publications related to enhancing resilience strategies, resilience is an emerging concept in power systems. Existing practices are mostly focused on deploying distributed energy resources (DERs) and microgrids, hardening the existing infrastructures and building redundant capacities. However, from a broader perspective, resilience enhancement of power distribution system is a systematic engineering, involving long-term system planning and upgrading (e.g., deployment of smart grid technologies and intelligent switches), short-term proactive scheduling, real-time robust and resilient control of DERs, and post-event restoration and recovery strategies. Based on this point, this Special Issue in IET Energy System Integration focuses on soliciting the most recent and original technologies, scheduling and control strategies for improving the resilience of power distribution system. Eight papers are presented in this Special Issue, covering various aspects related to resilience enhancement of power distribution system, including fault-tolerant frequency measurement, robust scheduling of integrated electricity and district heating systems, robust control of DERs, efficient methods for safety verification as well as novel graph theory-based approach to restore the distribution systems after multiple simultaneous faults. A brief introduction of these 8 papers is provided below.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Monte Carlo random walk simulation of electron transport in confined porous TiO{sub 2} as a promising candidate for photo-electrode of nano-crystalline solar cells

Monte Carlo continuous time random walk simulation is used to study the effects of confinement on electron transport, in porous TiO{sub 2}. In this work, we have introduced a columnar structure instead of the thick layer of porous TiO{sub 2} used as anode in conventional dye solar cells. Our simulation results show that electron diffusion coefficient in the proposed columnar structure is significantly higher than the diffusion coefficient in the conventional structure. It is shown that electron diffusion in the columnar structure depends both on the cross section area of the columns and the porosity of the structure. Also, we demonstrate that such enhanced electron diffusion can be realized in the columnar photo-electrodes with a cross sectional area of ∼1 μm{sup 2} and porosity of 55%, by a simple and low cost fabrication process. Our results open up a promising approach to achieve solar cells with higher efficiencies by engineering the photo-electrode structure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Phase diagram of the triangular-lattice Potts antiferromagnet

Here, we study the phase diagram of the triangular-lattice Q-state Potts model in the real $(Q, v)$ -plane, where $v={\rm e}^J-1$ is the temperature variable. Our first goal is to provide an obviously missing feature of this diagram: the position of the antiferromagnetic critical curve. This curve turns out to possess a bifurcation point with two branches emerging from it, entailing important consequences for the global phase diagram. We have obtained accurate numerical estimates for the position of this curve by combining the transfer-matrix approach for strip graphs with toroidal boundary conditions and the recent method of critical polynomials. The second goal of this work is to study the corresponding $A_{p-1}$ RSOS model on the torus, for integer $p=4, 5, \ldots, 8$ . We clarify its relation to the corresponding Potts model, in particular concerning the role of boundary conditions. For certain values of p, we identify several new critical points and regimes for the RSOS model and we initiate the study of the flows between the corresponding field theories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Pedestrian-Involved Traffic Signal Optimization Using Decentralized Graph-based Multi-Agent Reinforcement Learning

Incorporating pedestrian movements in traffic signal timing optimization is essential for ensuring smooth and safe urban transportation systems. To develop an effective signal timing plan, it is crucial to comprehend how various pedestrian accommodation strategies impact both vehicle and pedestrian flow. This study explores the application of the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) method for signal timing optimization. It considers both pedestrian and vehicle traffic state and assesses the effects of fixed and adaptive pedestrian request response on traffic and signal timing. The evaluation of DGMARL encompasses vehicles Eco_PI which is fuel consumption impact related to vehicle stops and stop delay, and pedestrian waiting time to ensure overall system efficiency. The proposed approach was evaluated using a Digital Twin microscopic traffic simulation model of MLK Smart Corridor in Chattanooga, Tennessee. The evaluation outcomes of vehicles Eco_PI, pedestrian waiting time and serving time are compared with the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall to determine the most suitable approaches for various traffic conditions. The results indicated that, on average, the strategy of Signal timing optimization with adaptive pedestrian request response improved the Eco_PI by 32.43%, and Signal timing optimization with both automated pedestrian traffic demand and adaptive pedestrian request response improved the Eco_PI by 31.62% compared to the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall.

K Kumarasamy, Vijayalakshmi↗