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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Benefits and Limits of Phasing Alleles for Network Inference of Allopolyploid Complexes

Abstract Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared with haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where the depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American Dryopteris fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATÉ: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared with using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical nonidentifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes.

Evolutionary Biology↗
DOE OSTI2021

Donald J. Trump’s Presidency in Cyberspace: A Case Study of Social Perception and Social Influence in Digital Oligarchy Era

In the past few years, with the rapid growth of digital technologies, Facebook, Twitter, and other social media platforms have become the digital oligarchies, which have the enormous capabilities to potentially control what is discussed in cyberspace. In the digital oligarchy era, social perception and social influence in different complex social systems have evolved quickly. In this article, we conducted large-scale empirical studies on social perception and social influence regarding the Trump phenomenon from personal perception, media, and public attention perspectives. We found that there exist obvious correlations between the posting behavior of Trump and the attention of news media. By constructing public attention networks using complex networks based on Google search information, we further reveal that digital platforms could affect social perception and social influence significantly. Especially, we obtained that the public attention can always be influenced by the political moments.

digital oligarchy↗
DOE OSTI2024

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗
DOE OSTI2020

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials

Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. Finally, the ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Neural Networks for Prediction of Complex Chemistry in Water Treatment Process Optimization

Water chemistry plays a critical role in the design and operation of water treatment processes. Detailed chemistry modeling tools use a combination of advanced thermodynamic models and extensive databases to predict phase equilibria and reaction phenomena. The complexity and formulation of these models preclude their direct integration in equation-oriented modeling platforms, making it difficult to use their capabilities for rigorous water treatment process optimization. Neural networks (NN) can provide a pathway for integrating the predictive capability of chemistry software into equation-oriented models and enable optimization of complex water treatment processes across a broad range of conditions and process designs. Herein, we assess how NN architecture and training data impact their accuracy and use in equation-oriented water treatment models. We generate training data using PhreeqC software and determine how data generation and sample size impact the accuracy of trained NNs. The effect of NN architecture on optimization is evaluated by optimizing hypothetical black-box desalination processes using a range of feed compositions from USGS brackish water data set, tracking the number of successful optimizations, and testing the impact of initial guess on the final solution. Our results clearly demonstrate that data generation and architecture impact NN accuracy and viability for use in equation-oriented optimization problems.

Dudchenko, Alexander V↗
DOE OSTI2025

FlbB forms a distinctive ring essential for periplasmic flagellar assembly and motility in Borrelia burgdorferi

Spirochetes are a widespread group of bacteria with a distinct morphology. Some spirochetes are important human pathogens that utilize periplasmic flagella to achieve motility and host infection. The motors that drive the rotation of periplasmic flagella have a unique spirochete-specific feature, termed the collar, crucial for the flat-wave morphology and motility of the Lyme disease spirochete Borrelia burgdorferi. Here, we deploy cryo-electron tomography and subtomogram averaging to determine high-resolution in-situ structures of the B. burgdorferi flagellar motor. Comparative analysis and molecular modeling of in-situ flagellar motor structures from B. burgdorferi mutants lacking each of the known collar proteins (FlcA, FlcB, FlcC, FlbB, and Bb0236/FlcD) uncover a complex protein network at the base of the collar. Importantly, our data suggest that FlbB forms a novel periplasmic ring around the rotor but also acts as a scaffold supporting collar assembly and subsequent recruitment of stator complexes. The complex protein network based on the FlbB ring effectively bridges the rotor and 16 torque-generating stator complexes in each flagellar motor, thus contributing to the specialized motility and lifestyle of spirochetes in complex environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

rNets: a standalone package to visualize reaction networks

In the study of chemical processes, visualizing reaction networks is pivotal for identifying crucial compounds and transformations. Traditional methods, such as network schematics and reaction path linear plots, often struggle to effectively represent complex reaction networks due to their size and intricate connectivity. Alternatives capable of leading with complexity include graph methods, but they are not user-friendly, lacking simplicity and modularity, which hinders their integration with widely-used research software. This work introduces rNets an innovative tool designed for the efficient visualization of reaction networks with a user-friendly interface, modularity, and seamless integration with existing software packages. The effectiveness of rNets is demonstrated through its application in analyzing three catalytic reactions, showcasing its potential to significantly enhance research both in homogeneous and heterogeneous catalysis fields. This tool not only simplifies the visualization process but also opens new avenues for exploring complex reaction networks in diverse research contexts.

Pablo-García, Sergio↗
DOE OSTI2026

Stochastic fracture generation and thermo-hydro-mechanical modeling in an equivalent continuum framework for enhanced geothermal systems

Enhanced geothermal systems (EGS) involve fracturing low permeability material to establish well connectivity and then injecting and circulating fluid into the fractured subsurface for geothermal power production. Changes in fracture aperture from contraction of the cooling matrix rock may alter network connectivity and risk thermal short-circuiting. Thermo-hydro-mechanical (THM) models are a useful tool to study these processes. However, as fracture networks are complex, and data may be limited, fracture networks in THM models are often stochastically generated. Given reliance on stochastic fracture networks and THM modeling to represent the subsurface and assess productivity of EGS, increased understanding of the influence of such statistically derived fracture networks on flow and heat transport in THM models is needed. Here, a new fracture process model is developed in the reactive transport code PFLOTRAN to stochastically generate fracture families and simulate changes in fracture aperture over time due to temperature changes of the rock matrix. Sixty-four different fracture networks ranging from well to poorly-connected, are modeled in PFLOTRAN with and without mechanical processes (THM vs TH). Results indicate that for well-connected fracture networks, thermal short-circuiting is less of a concern due to the abundance of available alternative flowpaths. For poorly-connected fracture networks, inclusion of mechanical processes showed steep thermal drawdown coincident with increase in fracture aperture along developing colder flowpaths, demonstrating the risk of thermal short-circuiting. Simulations with additional, larger fractures engineered to establish connectivity in a poorly-fractured subsurface, indicate that while stochastic variation of fracture orientation of the background network had limited influence, such variation in the engineered fractures significantly affected flow and heat transport.

Discrete fracture networks (DFN)↗
DOE OSTI2020

Localized High Concentration Electrolytes for High Voltage Lithium-Metal Batteries: Correlation between the Electrolyte Composition and Its Reductive/Oxidative Stability

We demonstrate a first-principles screening methodology as an effective tool to explore electrolyte formulations for the new generation of high energy density rechargeable batteries. We study the liquid structure and electronic properties in dilute electrolytes, high concentration electrolytes (HCE), and localized high concentration electrolytes (LHCE), with focus on electrolyte formulations based on lithium bis(fluorosulfonyl)imide (LiFSI), dimethyl carbonate (DMC), and bis(2,2,2-trifluoroethyl) ether (BTFE) as a diluent. We describe the solvation complexes in the dilute electrolyte and explore structural changes triggered by the increase in lithium salt concentration for HCEs and the diluent effects in LHCEs. In HCE formulations, there is a 4-fold coordination environment of lithium-ions as in the dilute electrolyte, but the number of lithium-ion interactions with O atoms from FSI- anions dominates. In these solutions, the ability of the FSI- anions to interact with multiple lithium-ions allows complex 3D network formation and influences the reductive/oxidative behavior of the electrolyte. Interestingly, in LHCEs, the BTFE diluent molecules do not change the 3D solution structure when diluting the HCE formulation from 5.49 to 3.83 M. However, there is a composition threshold where the structural and electronic behavior may change. We show that diluting the HCE electrolyte with BTFE down to 1.77 M breaks the three-dimensional solution structure into an island-like solvation complex. We relate these structural changes to the electronic properties of the electrolytes finding a causal relationship between the reductive/oxidative behavior and the lithium-oxygen interaction mechanisms in the solvated complexes. The coordination with lithium-ions lowers the electrolyte LUMO and HOMO levels: the higher is the number of interactions with lithium-ions, the more likely the solvent molecule, FSI- anion, or diluent molecule is to be reduced and the less likely it is to become oxidized. The evolution of the solvated ion structure in HCE and LHCE suggests a close connection to a corresponding change in the lithium-ion transport mechanisms for these electrolytes.

anions, molecules, oxygen, solvents, electrolytes↗
DOE OSTI2021

Transient cycling of nitrogen, organic carbon and oxygen within the free-flowing Columbia River corridor: Linking exposure time dependent biogeochemical reactions to river stage fluctuations (Final project report)

The objective of this project was to develop next-generation techniques for representing the transformations of complex reaction networks like those impacting transient river-corridors, then apply them to develop upscaling tools. The project contributed significantly to the development of Lagrangian “mass transfer particle tracking” (MTPT) tools that are the first numerical framework that explicitly separates mixing and spreading processes; a crucial distinction for accurate representations of reaction rates across scales. The MTPT approach uses an operator splitting scheme where physical transport processes are simulated using classical random walk methods and each particle is treated as a “container” that carries with it any number of chemical species. Mixing between particles is simulated using a colocation-probability based mass transfer kernel and reactions are evaluated on each particle after mixing. The method is stable, accurate, and also allows the explicit modeling of residence time distributions of the individual containers of mass. The MTPT scheme allowed us to explore several research questions related to reactive transport in river corridors and complex reaction networks. We found that it is possible to quantify the exposure time distributions (ETDs) of reactants and that this does suggest some pathways to upscaling. However, comparable approximations can be obtained using simplified (reduced dimensionality) MTPT simulations in less time with similar uncertainty, so development of extensive ETD-based methods was not productive. The major findings are that i) the MTPT schemes are robust and highly accurate across a range of arbitrarily complex reactions, ii) the methods can be efficiently parallelized and the parallel performance characteristics of MTPT are predictable, and iii) the MTPT tools allow simultaneous tracking of residence time. The development of these research tools into efficient software packages continues. The methods are already available to the community because open-source, working examples have been included with all publications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Powers of magnetic graph matrix: Fourier spectrum, walk compression, and applications

Magnetic graphs, originally developed to model quantum systems under magnetic fields, have recently emerged as a powerful framework for analyzing complex directed networks. Existing research has primarily used the spectral properties of the magnetic graph matrix to study global and stationary network features. However, their capacity to model local, nonequilibrium behaviors, often described by matrix powers, remains largely unexplored. We present a combinatorial interpretation of the magnetic graph matrix powers through directed walk profiles—counts of graph walks indexed by the number of edge reversals. Crucially, we establish that walk profiles correspond to a Fourier transform of magnetic matrix powers. The connection allows exact reconstruction of walk profiles from magnetic matrix powers at multiple discrete potentials, and more importantly, an even smaller number of potentials often suffices for accurate approximate reconstruction in real networks. This shows the empirical compressibility of the information captured by the magnetic matrix. This fresh perspective suggests further applications; for example, we illustrate how powers of the magnetic matrix can identify frustrated directed cycles (e.g., feedforward loops) and can be effectively employed for link prediction by encoding local structural details in directed graphs.

complex networks↗
DOE OSTI2024

Simulation Evaluation of a Large-Scale Implementation of Virtual-Phase Link-Based Model Predictive Control

Traffic congestion is a serious problem in the US, and traffic signal control is one of the effective solutions to congestion. Previous research on model predictive control (MPC)-based traffic signal control showed substantial benefits over conventional methods. This study focused on implementing MPC over a large-scale network with complex intersections and the impact of cycle length, network size, and imperfect state estimation on performances. This study implemented a virtual phase link (VPL)-based model predictive control method which used the number of vehicles in each VPL as input state variables and was suitable for National Electrical Manufacturing Association (NEMA) ring-barrier control. To test the impact of network size, the performance of distributed MPC (36 intersections in the network are divided into five subnetworks) was compared with that of MPC over the full network for a set of cycle lengths. To test the impact of imperfect state estimation, we synthetically infused estimation error and developed two scenarios, MPC-error and MPC-error narrow, which had higher and lower estimation errors, respectively. The performance of these MPC methods was compared with that of the existing time-of-day (TOD) method and an offline method that used Webster's method for split and MULTIBAND for cycle length and offset optimization. Trajectory and linkwise signal performance measures were collected from the simulation to evaluate performance. The distributed MPC method with perfect state estimation had the lowest delay and highest energy efficiency of all the methods. The performance of MPC decreased as the prediction inaccuracy increased. MPC-error had 7% and 11% more delay than MPC-error narrow in the morning and evening peaks, respectively. Overall, simulation results suggest that even with imperfect state estimation, MPC methods will outperform offline methods significantly.

large-scale simulation↗
DOE OSTI2021

Efficient Sampling of Complex Interdependent and Multiplex Networks

Efficient sampling of interdependent and multiplex infrastructure networks is critical for effectively applying failure and recovery algorithms in real-world settings, as well as to generate property-preserving reduced-order graph-based ensembles that address topological uncertainties. In this paper, we first explore the performance, i.e. the success in preserving graph properties, of graph sampling algorithms for interdependent and multiplex networks with synthetic and real-world graphs. We simulate sampling algorithms under different parameter settings. These settings include probabilistic graph generators, coupling patterns, and various performance metrics. Our results show that while Random Node and Random Walk sampling algorithms perform best for interdependent networks, Random Edge and Forest Fire sampling algorithms perform best for multiplex networks. Second, we propose and implement a novel similarity-based sampling algorithm for multiplex networks that samples only log(N) number of layers of an N-layer multiplex network while yielding computational savings with performance guarantees. Experimental results show that similarity sampling outperforms complete sampling of all layers while decreasing performance costs from a linear scale to a logarithmic one. Our results also indicate that similarity-based sampling outperforms complete sampling and random selection in nearly all scenarios when tested with real-world data.

Subasi, Omer↗
DOE OSTI2023

Social network structure and the spread of complex contagions from a population genetics perspective

Ideas, behaviors, and opinions spread through social networks. If the probability of spreading to a new individual is a non-linear function of the fraction of the individuals’ affected neighbors, such a spreading process becomes a “complex contagion”. This non-linearity does not typically appear with physically spreading infections, but instead can emerge when the concept that is spreading is subject to game theoretical considerations (e.g. for choices of strategy or behavior) or psychological effects such as social reinforcement and other forms of peer influence (e.g. for ideas, preferences, or opinions). Here we study how the stochastic dynamics of such complex contagions are affected by the underlying network structure. Motivated by simulations of complex contagions on real social networks, we present a framework for analyzing the statistics of contagions with arbitrary non-linear adoption probabilities based on the mathematical tools of population genetics. The central idea is to use an effective lower-dimensional diffusion process to approximate the statistics of the contagion. This leads to a tradeoff between the effects of ”selection” (microscopic tendencies for an idea to spread or die out), random drift, and network structure. Our framework illustrates intuitively several key properties of complex contagions: stronger community structure and network sparsity can significantly enhance the spread, while broad degree distributions dampen the effect of selection compared to random drift. Finally, we show that some structural features can exhibit critical values that demarcate regimes where global contagions become possible for networks of arbitrary size. Our results draw parallels between the competition of genes in a population and memes in a world of minds and ideas. Our tools provide insight into the spread of information, behaviors, and ideas via social influence, and highlight the role of macroscopic network structure in determining their fate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The effect of differential mineral shrinkage on crack formation and network geometry

Rock, concrete, and other engineered materials are often composed of several minerals that change volumetrically in response to variations in the moisture content of the local environment. Such differential shrinkage is caused by varying shrinkage rates between mineral compositions during dehydration. Using both 3D X-ray imaging of geo-architected samples and peridynamic (PD) numerical simulations, we show that the spatial distribution of the clay affects the crack network geometry with distributed clay particles yielding the most complex crack networks and percent damage (99.56%), along with a 60% reduction in material strength. We also demonstrate that crack formation, growth, coalescence, and distribution during dehydration, are controlled by the differential shrinkage rates between a highly shrinkable clay and a homogeneous mortar matrix. Sensitivity tests performed with the PD models show a clay shrinkage parameter of 0.4 yields considerable damage, and reductions in the parameter can result in a significant reduction in fracturing and an increase in material strength. Additionally, isolated clay inclusions induced localized fracturing predominantly due to debonding between the clay and matrix. These insights indicate differential shrinkage is a source of potential failure in natural and engineered barriers used to sequester anthropogenic waste.

58 GEOSCIENCES↗