Evolution Of Topology Research Articles

Investigation of the flow topology evolution between two tandem cylinders using a discontinuous Galerkin method

Investigation of the flow topology evolution between two tandem cylinders using a discontinuous Galerkin method

Turbulence topology evolution in weakly turbulent two-phase flows

In turbulent two-phase flows, not only the isotropy of velocity fluctuations can be altered by the presence of liquid blobs, ligaments, or pockets, the structure of the turbulent flow field and associated flow topology may also be significantly modified. In this respect, the small-scale wrinkling and curvature of the liquid–gas interface, which is associated with sharp and large density variations, impact turbulence in the gas phase. In the present study, the joint probability density function of the second and third invariants of the velocity gradient tensor (VGT)—and especially its traceless counterpart—is considered so as to scrutinize how the turbulent flow topology in the gas may be altered. These quantities are analyzed on the basis of direct numerical simulation databases of two-phase flows evolution in homogeneous isotropic turbulence. Statistics of the invariants of the VGT are gathered at various distances to the interface so that the gas phase is divided into three sub-regions according to the values of the level-set G, i.e., the signed distance to the liquid/gas interface. The analysis of the VGT invariants shows that turbulent small-scale structures and associated flow topology are modified in the vicinity of the liquid–gas interface. Lagrangian evolution equations of the VGT and its invariants are thus studied to get a refined view of the corresponding changes. To the best of the authors' knowledge, this is first time that these budgets are scrutinized in such conditions. Special emphasis is placed on conditional mean rates of change of the VGT invariants in the plane of the second and third invariants. In the corresponding set of coordinates, the obtained trajectories are found to be mainly driven by (i) mutual interactions between invariants and (ii) pressure Hessian contributions, thus confirming the importance of the latter for the dynamics and structure of turbulent two-phase flows.

Investigation of the flow topology evolution between two tandem cylinders using a discontinuous Galerkin method

Investigation of the flow topology evolution between two tandem cylinders using a discontinuous Galerkin method

TEAM: A Layered-Cooperation Topology Evolution Algorithm for Multi-Sink Internet of Things

Numerous sensor nodes deployed in the Internet of Things (IoT) can form a large heterogeneous network. The increased energy consumption of sensor nodes and the unbalanced communication load on multiple sink nodes reduce the energy efficiency of the network. Moreover, frequent network attacks also pose severe challenges to topology robustness. Optimizing the network topology to achieve the balance between energy efficiency and robustness is a complex problem. Multi-objective heuristic algorithms based on genetic evolution are commonly used to solve joint optimization problems. However, due to the lack of global search ability caused by the loss of genetic diversity, genetic operations are prone to premature convergence during multi-objective evolution. Therefore, this paper introduces multi-population cooperation into the multi-objective evolution process and proposes a novel layered-cooperation Topology Evolution Algorithm for Multi-sink IoT (TEAM). In TEAM, information entropy is used to measure the effectiveness of load balancing on multiple sink nodes. The crossover and mutation probabilities of different populations are dynamically adjusted to ensure genetic diversity. A layered-cooperation mechanism is designed to avoid premature convergence. Extensive experiments confirm that TEAM can effectively improve the energy efficiency and robustness of network topology while balancing the communication load on multi-sink nodes.

Exploration of Rotor Topology Evolution Mechanism in the Stator-PM Machines

Exploration of Rotor Topology Evolution Mechanism in the Stator-PM Machines

Pressure-induced structural evolution to a nearly perfect kagome lattice in β-Mn2(OH)3Cl nanosheets

The frustrated magnets exhibiting kagome geometries and the investigation of their structure-property relationships have garnered substantial attention as potential candidates for quantum spin liquid (QSL). In this study, β-Mn2(OH)3Cl nanosheets with distorted kagome geometries were synthesized via a solid-state reaction, and low-temperature magnetic measurements revealed a spin-glass-like magnetic phase transition around 40 K in the frustrated β-Mn2(OH)3Cl. A pressure-induced structural phase transition was further realized by employing in situ synchrotron X-ray diffraction, leading to a hexagonal crystal structure with a nearly perfect kagome lattice of magnetic Mn2+ ions. High-pressure Raman and UV–vis absorption measurements indicates that the topological evolution can be attributed to the synergistic effects of pressure driven internal hydrogen bonds and the intrinsic Jahn-Teller effects. This study provides a strategy for synthesizing candidate compounds of QSL and sheds light on the mechanism underlying the topological evolution of kagome lattices under high pressure.

TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers

Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender systems, anomaly detection, and graph generation) in various applications. Such temporal graphs exhibit heterogeneous transient dynamics, varying time intervals, and highly evolving node features throughout their evolution. Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics. In this paper, we develop a graph embedding model with uncertainty quantification, TransformerG2G, by exploiting the advanced transformer encoder to first learn intermediate node representations from its current state (t) and previous context (over timestamps [t−1,t−l], l is the length of context). Moreover, we employ two projection layers to generate lower-dimensional multivariate Gaussian distributions as each node’s latent embedding at timestamp t. We consider diverse benchmarks with varying levels of “novelty” as measured by the TEA (Temporal Edge Appearance) plots. Our experiments demonstrate that the proposed TransformerG2G model outperforms conventional multi-step methods and our prior work (DynG2G) in terms of both link prediction accuracy and computational efficiency, especially for high degree of novelty. Furthermore, the learned time-dependent attention weights across multiple graph snapshots reveal the development of an automatic adaptive time stepping enabled by the transformer. Importantly, by examining the attention weights, we can uncover temporal dependencies, identify influential elements, and gain insights into the complex interactions within the graph structure. For example, we identified a strong correlation between attention weights and node degree at the various stages of the graph topology evolution.

Azimuthal orientation guided topological defect evolution across the nematic-smectic phase transition

Azimuthal orientation guided topological defect evolution across the nematic-smectic phase transition

Research on the generation mechanism and characteristics of an Energy Internet backbone network

The Energy Internet adopts the mechanism of “regional coordination and hierarchical control” to realize the clean power compatibility and reliability in power operation. In the network topology, the traditional tree network is transformed to the hierarchical partition network. First, this paper analyzes the topological features of “hierarchical control, intra-layer partition, interregional interconnection, and regional autonomy” of the Energy Internet. On this basis, the hierarchical ring network autonomy (HRNA) topological generation and evolution mechanism of the Energy Internet is proposed, and the different levels of a Beijing power grid framework are taken as an example to expand and evolve to the Energy Internet. Based on the comparison and analysis of the network characteristics constructed in this paper with relevant literature studies, this mechanism generates a network that is close to the Internet in terms of average degree, network diameter, and aggregation coefficient. However, there is no centrality node with a higher degree of nodes in the Internet topology, which better reflects the equivalence concept of the Energy Internet.

Adaptive Neuroevolution With Genetic Operator Control and Two-Way Complexity Variation

Topology and weight evolving artificial neural network (TWEANN) algorithms optimize the structure and weights of artificial neural networks (ANNs) simultaneously. The resulting networks are typically used as policy models for solving control and reinforcement learning (RL) type problems. This paper presents a neuroevolution algorithm that aims to address the typical stagnation and sluggish convergence issues present in other neuroevolution algorithms. These issues are often caused by inadequacies in population diversity preservation, exploration/exploitation balance, and search flexibility. This new algorithm, called the Adaptive Genomic Evolution of Neural-Network Topologies (AGENT), builds on the neuroevolution of augmenting topologies (NEAT) concept. Novel mechanisms for adapting the selection and mutation operations are proposed to favorably control population diversity and exploration/exploitation balance. The former is founded on a fundamentally new way of quantifying diversity by taking a graph-theoretic perspective of the population of genomes and inter-genomic differences. Further advancements to the NEAT paradigm occur through the incorporation of variable neuronal properties and new mutation operations that uniquely allow both the growth and pruning of ANN topologies during evolution. Numerical experiments with benchmark control problems adopted from the OpenAI Gym illustrate the competitive performance of AGENT against standard RL methods and adaptive HyperNEAT, and superiority over the original NEAT algorithm. Further parametric analysis provides key insights into the impact of the new features in AGENT. This is followed by evaluation on an unmanned aerial vehicle collision avoidance problem where maneuver planning models are learnt by AGENT with 33% reward improvement over 15 generations.

A Novel Separated Hybrid-Magnetic-Circuit Variable Flux Memory Machine

Aiming at the problems of the existing hybrid-magnetic-circuit variable flux memory machine (HMC-VFMM), such as weak flux regulation (FR) capability and low efficiency in high-speed region, etc., this article proposes a novel separated HMC-VFMM (SHMC-VFMM), which innovatively arranges a series magnetic circuit on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis and a parallel magnetic circuit on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -axis. The SHMC design can simultaneously reduce the PMs fluxes through low-coercive-force PMs configured on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -axis, which will improve the FR capability. The magnetic saturation is alleviated by reducing the usage of high-coercive-force PMs, which further broadens the FR range. The proposed design also simplifies the rotor mechanical structure, thereby improving the rotor design flexibility. Firstly, the topological evolution process and the working principle of the proposed machine are interpreted in in succession. Then, the mechanism of further expanding the FR range of the proposed machine is revealed by comparing its equivalent magnetic circuit model with that of the existing machine. Subsequently, the electromagnetic characteristics of the two compared machines are analyzed by using the finite element method, which validates the performance advantages of the proposed machine. Finally, an SHMC-VFMM prototype is manufactured and measured to verify the superiority of the proposed SHMC design.

A mechanistic study on the effectiveness of star-like and comb-like polycarboxylate superplasticizers in cement pastes

In this paper, star-like polycarboxylate superplasticizer (SPCE) and comb-like polycarboxylate superplasticizer (CPCE) were prepared via atom transfer radical polymerization (ATRP) to study the mechanism of the effectiveness enhancement resulting from this transformation of topological structure. It was found that SPCE molecules acted higher adsorption amount and more Ca2+ complexes, and formed a lower and more compact adsorbed layer and larger surface coverage with a flat conformation on cement surface, depending on more anchored points per unit volume. Through this mechanism, cement particles adsorbed by SPCE showed stronger dispersible capability and colloidal interaction forces (the steric force was dominant), resulting in lower yield stress and plastic viscosity of the cement paste containing SPCE which behaved as an ideal Bingham fluid. The aim of this study is to clarify the mechanistic correlation between topological structure evolution and effectiveness promotion for PCE.

Enhancing electromagnetic wave absorption performance through co-regulation of microstructure and spatial orientation of RE-Ni MOF

The enhancement of electromagnetic wave absorption performance hinges on the intricate evolution of morphology and topology within MOF-derived materials—an issue of paramount significance in the electromagnetic wave absorbing arena. Here, by skillfully modulating multi-dimensional micro-nano structures and orchestrating the asynchronous orientation of nano-sheets across micro–macro dimensions, a more intricate microstructure and magnetoelectric loss mechanism for MOF-based absorbers are cultivated. In tandem, advanced high-frequency simulation software is harnessed to dissect the material's magnetoelectric loss mechanism and substantiate its tangible Far-field microwave absorption capacity. Our study not only pioneers a novel exploration space within the realm of microwave-absorbing materials but also introduces an innovative paradigm, where multi-scaled orientation and microstructural co-regulation converge to enhance electromagnetic wave absorbing properties.

Fast and adaptive dynamics-on-graphs to dynamics-of-graphs translation.

Numerous networks in the real world change with time, producing dynamic graphs such as human mobility networks and brain networks. Typically, the "dynamics on graphs" (e.g., changing node attribute values) are visible, and they may be connected to and suggestive of the "dynamics of graphs" (e.g., evolution of the graph topology). Due to two fundamental obstacles, modeling and mapping between them have not been thoroughly explored: (1) the difficulty of developing a highly adaptable model without solid hypotheses and (2) the ineffectiveness and slowness of processing data with varying granularity. To solve these issues, we offer a novel scalable deep echo-state graph dynamics encoder for networks with significant temporal duration and dimensions. A novel neural architecture search (NAS) technique is then proposed and tailored for the deep echo-state encoder to ensure strong learnability. Extensive experiments on synthetic and actual application data illustrate the proposed method's exceptional effectiveness and efficiency.

On the Contribution of Coronal Mass Ejections to the Heliospheric Magnetic Flux Budget on Different Time Scales

Coronal mass ejections (CMEs) contribute closed magnetic flux to the heliosphere while they are connected at both ends to the Sun and play a key role in adding magnetic flux to the heliosphere. Here, we discuss how the type of magnetic reconnection that opens CME field lines in the inner heliosphere, i.e., interchange (IC) and/or interplanetary (IP) reconnection, determines the length of time CMEs contribute to the heliospheric flux budget. This distinction has not been taken into account in past studies that estimate the CME flux opening timescale. We outline key criteria to aid in distinguishing IC reconnection from IP reconnection based on in situ spacecraft data and highlight these through two example events. Studying the manner in which CMEs reconnect and open in the inner heliosphere yields important insights not only into CMEs’ role in the heliospheric flux budget but also the evolution of CME complexity, connectivity, and topology.

Coronal Magnetic Field Extrapolation and Topological Analysis of Fine-scale Structures during Solar Flare Precursors

Magnetic field plays an important role in various solar eruption phenomena. The formation and evolution of the characteristic magnetic field topology in solar eruptions are critical problems that will ultimately help us understand the origin of these eruptions in the solar source regions. With the development of advanced techniques and instruments, observations with higher resolutions in different wavelengths and fields of view have provided more quantitative information for finer structures. It is therefore essential to improve the method with which we study the magnetic field topology in the solar source regions by taking advantage of high-resolution observations. In this study, we employ a nonlinear force-free field extrapolation method based on a nonuniform grid setting for an M-class flare eruption event (SOL2015-06-22T17:39) with embedded vector magnetograms from the Solar Dynamics Observatory (SDO) and the Goode Solar Telescope (GST). The extrapolation results for which the nonuniform embedded magnetogram for the bottom boundary was employed are obtained by maintaining the native resolutions of the corresponding GST and SDO magnetograms. We compare the field line connectivity with the simultaneous GST/Hα and SDO/Atmospheric Imaging Assembly observations for these fine-scale structures, which are associated with precursor brightenings. Then we perform a topological analysis of the field line connectivity corresponding to fine-scale magnetic field structures based on the extrapolation results. The analysis results indicate that when we combine the high-resolution GST magnetogram with a larger magnetogram from the SDO, the derived magnetic field topology is consistent with a scenario of magnetic reconnection among sheared field lines across the main polarity inversion line during solar flare precursors.

Tuning the band topology of GdSb by epitaxial strain

Rare-earth monopnictide (RE-V) semimetal crystals subjected to hydrostatic pressure have shown interesting trends in magnetoresistance, magnetic ordering, and superconductivity, with theory predicting pressure-induced band inversion. Yet, thus far, there have been no direct experimental reports of interchanged band order in RE-Vs due to strain. This work studies the evolution of band topology in biaxially strained GdSb(001) epitaxial films using angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT). As biaxial strain is tuned from tensile to compressive strain, the gap between the hole and the electron bands dispersed along [001] decreases. The conduction and valence band shifts seen in DFT and ARPES measurements are explained by a tight-binding model that accounts for the orbital symmetry of each band. Finally, we discuss the effect of biaxial strain on carrier compensation and magnetic ordering temperature.

Mean wake evolution behind low aspect-ratio wall-mounted finite prisms

The evolution in the mean wake of wall-mounted prisms with varying depth-ratio (length-to-width) between 0.016 and 4 is examined numerically over a range of Reynolds numbers (Re = 50 - 500). The aspect-ratio (height-to-width) of the prism is limited to 1. This study aims to ascertain the mechanism of the evolution of mean wake topology due to changing depth-ratio. The mean wake topology is classified into Dipole, Multipole, and Quadrupole types as functions of Reynolds number and depth-ratio. The threshold depth-ratio for the wake evolution changes with Reynolds number. The Multipole-type wake appears as an evolutionary or intermediate wake pattern between Dipole and Quadrupole-type wakes. Increasing the depth-ratio leads to the enhancement of the downwash flow, resulting in the mean wake evolving from Quadrupole to Dipole wake. This suggests that the downwash flow dominates in dictating the wake topology downstream of the prism. Enhancing the downwash flow (with increase in depth-ratio) results in the reattachment of the flow on the prism surfaces, which further results in the suppression of velocity gradients and streamwise momentum transfer downstream of the prism trailing edge. This suppression of gradients and momentum leads to the deterioration of the base vortex, which evolves the wake from Quadrupole (at a small depth-ratio) to Dipole-type (at a large depth-ratio).

Tandem-biocatalysis reactors constructed by topological evolution of CaCO3 particles into hollow metal hydroxide spheres

Despite remarkable advances in the design and synthesis of hollow inorganic spheres (HISs), the harsh synthetic conditions have precluded the applications of HISs to biochemical and biological fields. Herein we report a biocompatible strategy for synthesizing metal hydroxide HISs (MH-HISs) by simply mixing CaCO3 particles with metal ions in water. The ion-exchange reaction between Ca2+ and metal ions leads to the structural and chemical evolution from solid CaCO3 particles to hollow MH-HISs via core-shell and yolk-shell structures, while enabling the encapsulation of enzymes to the shells without loss of catalytic activities. The biocompatible protocol makes multienzymatic cascade reactions achievable, with great recyclability due to mechanical durability of MH-HISs.

Diffuse interface method for solid composite propellant ignition and regression

Solid Composite Propellants (SCPs) are extensively used in the field of propulsion for their chemical and mechanical stability in long-term storage, and for having simple production and operation processes. Computational modeling enables cost reduction, increased efficiency, and greater coverage of the configuration space in the SCP design process. However, accurate and efficient SCP modeling presents a number of numerical challenges. A primary obstacle in modeling these systems is capturing the complex evolving interface. Recently, it has been shown that the phase-field method has a strong ability to model the combustion behavior of SCPs, implicitly capturing the topological evolution at a relatively low computational cost. Initial phase-field methods show promise in their ability to do predictive regression modeling but require a number of approximations and heuristic modeling methods. This work presents a new formulation for the phase field regression model that combines a thermal solver with an Arrhenius rate law to model interface regression using a comprehensive and physics-based approach. This improves the capability of previous methods by increasing the number of kinematic forces that are accounted for, and allowing the study of thermal diffusivity in the system. It also enables the integration of a fully coupled solid-fluid interface. To demonstrate the efficacy of the model, it is applied and calibrated to a homogeneous monopropellant (ammonium perchlorate). The model is validated for a range of temperatures, with a reasonable quantitative match to experimental data. It was also demonstrated that the model recovered the relationship between ignition time and heat flux, with no fitting required. Overall, the method showed great capability of reproducing experimental data by matching temperature, burn rate, and thermal diffusivity profiles. Statement of SignificanceA new diffuse interface method is developed for calculating ignition and regression rates in SCPs. While this builds on previous work in diffuse interface modeling of SCPs, it is novel in its coupling to thermal transport, as well as its development of (and coupling to) a approximation for gas phase heat flux. The model is shown to reproduce experimental regression rate data with reasonable accuracy. Moreover, it quantitatively captures ignition behavior with no additional modifications to the model. The model is implemented in a high performance computational framework that is able to resolve large, three-dimensional mesostuctures.