Topological Configurations Research Articles

Retracted: An Efficient Resource and Topological Attribute-Based Configuration Algorithm of Virtual Networks

Retracted: An Efficient Resource and Topological Attribute-Based Configuration Algorithm of Virtual Networks

Designing CO2 reduction microreactor systems under fluctuating renewable H2 supply by multi-objective stochastic optimization

Reverse water-gas shift (RWGS) microreactor systems offer the flexibility to efficiently utilize CO2 and renewable H2 for syngas production. However, the inherent fluctuation in the green H2 supply presents a substantial obstacle for cost-effective production, particularly at larger scales. In this work, the design and operation of RWGS microreactor systems with fluctuating H2 feed are addressed by multi-objective stochastic optimization. The optimization objectives are the reaction performance, the annual total cost, and the carbon footprint of the microreactor system, while the decision variables include the channel geometry, channel topology, and inter-stack flow controller configuration. The results demonstrate that a well-designed hierarchical microreactor system achieves excellent reaction performance even without inter-stack flow controllers under both deterministic and uncertain conditions. Small plate-level distribution channels and moderate-sized higher-level distribution channels allow good and stable reaction performance. To balance all the design objectives, it is suggested to first increase the number of stacks at smaller production scales and then increase the number of substrates within each stack at larger production scales. Our study provides new design insights for distributed production using modular devices under high penetration of renewable energy.

Revealing the Untapped Potential of Photocatalytic Overall Water Splitting in Metal Organic Frameworks

AbstractThe past decade witnessed substantial attention toward metal‐organic frameworks (MOFs) for photocatalytic water splitting owing to their versatile structural and optoelectronic characteristics. However, MOFs capable of efficient photocatalytic overall water splitting (OWS) remain notably scarce. Although MOF‐based photocatalysts with OWS potential are highly promising due to their diverse building blocks and topological configurations, the vast number of possible MOFs renders traditional trial‐and‐error materials discovery approaches impractical. Herein, a data‐driven methodology that integrates machine learning with high‐throughput first‐principles computations to identify MOFs with OWS capability is presented. By systematically assessing factors including water stability, band gap, band positions, charge carrier transport, and optical absorption properties, 14 MOFs from the Quantum‐MOF (QMOF) database containing over 20,000 MOFs as promising candidates for visible‐light‐driven OWS are identified. Notably, five of them exhibit exceptional electronic and optical properties, outperforming previously reported MOF OWS photocatalysts, such as UIO66(Zr)‐NH2, MIL125(Ti)‐NH2, and MIL53(Al)‐NH2 is established. This work represents a large‐scale, data‐driven exploration of MOF‐based photocatalysts for water splitting, shedding light on the untapped potential of photocatalysis in MOFs.

Reliability evaluation of phased-mission systems exposed to random shocks

The paper aims to examine the reliability analysis problem of phased-mission systems that execute multiple consecutive non-overlapping mission phases in a random shock environment. Each phase is carried out by several subsystems which consist of non-repairable components connected in different topology configurations. The random shocks, modeled by a Poisson process, significantly affect the failure rate of components within the dynamic subsystems. The system configuration, success criteria, and component failure behavior may differ from phase to phase. The paper presents technical methodologies to evaluate the mission success probabilities of subsystems in various configurations and proposes a modularization method to compute the reliability of phased-mission systems by combining the mission success probabilities of all subsystems. Finally, a numerical example is provided to illustrate the proposed model and method.

Pair correlation function based on Voronoi topology.

The pair correlation function (PCF) has proven an effective tool for analyzing many physical systems due to its simplicity and its applicability for simulated and experimental data. However, as an averaged quantity, the PCF can fail to capture subtle structural differences in particle arrangements, even when those differences can have a major impact on system properties. Here, we use Voronoi topology to introduce a discrete version of the PCF that highlights local interparticle topological configurations. The advantages of the Voronoi PCF are demonstrated in several examples including crystalline, hyperuniform, and active systems showing clustering and giant number fluctuations.

Discovery of Ultrasmall Polar Merons and Rich Topological Phase Transitions: Defects Make 2D Lead Chalcogenides Flexible Topological Materials

AbstractAtomic‐scale polar topological configurations, such as skyrmions and merons, have garnered enormous interest due to their rich emergent physical phenomena and promising applications in next‐generation electronics. Despite recent progress in the exploration of 2D ferroelectrics, isolated polar topological structures in 2D lattices have not yet been explored. Here, an original design principle is proposed to remove the point group limit for polar structures while achieving atomic‐scale polar topological structures in non‐ferroelectric monolayers caused by defects in 2D materials. The first‐principles calculations show that an isolated polar meron with a diameter < 3.0 nm is generated in the deficient lead chalcogenide monolayer, and its formation is attributed to the synergic effects of vacancy‐induced radial atomic displacements and symmetry reduction in 2D materials. The emergent polar meron can transform to rich topological configurations under external stimuli or by manipulation of the defect concentrations. Furthermore, this strategy of atomic‐scale symmetry breaking via point defect engineering can be applied to a wide variety of 2D materials to induce polar topological structures. This work generalizes the polar topology from perovskite oxides to 2D materials, facilitating exciting opportunities to create high‐density topological configurations that enable the exploration of meron/skyrmion‐based functional nanodevices.

Removal and fluorescence detection of antibiotics from wastewater by layered double oxides/metal-organic frameworks with different topological configurations

Owing to the serious potential side-effects on the environment and human health, the rapid detection and removal of antibiotics have become an important research focus. In this work, four zinc-based metal-organic frameworks (MOFs) with different functional groups, i.e., Zn-MOF, Zn-MOF-CH3, Zn-MOF-NO2, Zn-MOF-COOH, were utilized for the construction of LDO/MOF composite materials with a nickel-iron-cobalt-based layered double oxide, NiFeCo-LDO. The results showed that the LDO/MOF composites not only had high sensitivity in detecting sulfonamide and quinolone antibiotics, but also had an appreciable ability to adsorb them from wastewater. The maximum adsorption capacities of all the four types of LDO@Zn-MOFs to all antibiotics can at least reach 150 mg/g, and the limits of detection in relation to all four antibiotics were at least as low as 100 µg/L. Our work suggested the dual-function extraction performance can be attributed to the synergistic effects between the LDO and the MOFs. Moreover, the strong ferromagnetism derived from the LDO provided great convenience for the separation and regeneration of the LDO/MOF composites.

Stable chimera states: A geometric singular perturbation approach.

Over the past decades, chimera states have attracted considerable attention given their unexpected symmetry-breaking spatiotemporal nature and simultaneously exhibiting synchronous and incoherent behaviors under specific conditions. Despite relevant precursory results of such unforeseen states for diverse physical and topological configurations, there remain structures and mechanisms yet to be unveiled. In this work, using mean-field techniques, we analyze a multilayer network composed of two populations of heterogeneous Kuramoto phase oscillators with coevolutive coupling strengths. Moreover, we employ the geometric singular perturbation theory through the inclusion of a time-scale separation between the dynamics of the network elements and the adaptive coupling strength connecting them, gaining a better insight into the behavior of the system from a fast-slow dynamics perspective. Consequently, we derive the necessary and sufficient condition to produce stable chimera states when considering a coevolutionary intercoupling strength. Additionally, under the aforementioned constraint and with a suitable adaptive law election, it is possible to generate intriguing patterns, such as persistent breathing chimera states. Thereafter, we analyze the geometric properties of the mean-field system with a coevolutionary intracoupling strength and demonstrate the production of stable chimera states. Next, we give arguments for the presence of such patterns in the associated network under specific conditions. Finally, relaxation oscillations and canard cycles, seemingly related to breathing chimeras, are numerically produced under identified conditions due to the geometry of our system.

Completely switchable multi-mode narrowband terahertz absorber: Monolayer graphene, coupled topological interface states, and Rabi splitting

In the present study, we have explored the absorption properties of a monolayer graphene-based photonic heterostructure, which consists of topological photonic configurations, graphene, and a Bragg mirror. The optical attributes were computed by using the 4×4 transfer matrix method. The results indicate that the creation of topological interface states and strong coupling between these resonant modes result in mode splitting, leading to the formation of hybrid modes known as coupled topological interface states with unity transmittance. The number of absorption modes can be modulated with an appropriate selection of cascaded photonic structures. Our proposed design with 1, 3, 5, and 7 cascaded topological photonic structures provides 1, 5, 9, and 13 absorption modes with greater than 90% absorption. The findings also reveal that absorption peak strength is greatly influenced by Fermi-level and magnetic fields; switching of the modes from absorption to reflection and vice versa is achieved by suitable electrical and magnetic biasing. Our proposed design offers various applications, such as switchable filters, absorbers, and modulators.

A fast Fourier transform-based solver for elastic micropolar composites

This work presents a spectral micromechanical formulation for obtaining the full-field and homogenized response of elastic micropolar composites. The algorithm relies on a coupled set of convolution integral equations for the micropolar strains, where periodic Green’s operators associated with a linear homogeneous reference medium are convolved with functions of the Cauchy and couple stress fields that encode the material’s heterogeneity, as well as any potential material nonlinearity. Such convolution integral equations take an algebraic form in the reciprocal Fourier space that can be solved iteratively. In this vein, the fast Fourier transform (FFT) algorithm is leveraged to accelerate the numerical solution, resulting in a mesh-free formulation in which the periodic unit cell representing the heterogeneous material can be discretized by a regular grid of pixels in two dimensions (or voxels in three dimensions). For verification, the numerical solutions obtained with the micropolar FFT solver are compared with analytical solutions for a matrix with a dilute circular inclusion subjected to plane strain loading. The developed computational framework is then used to study length-scale effects and effective (micropolar) moduli of composites with various topological configurations.

Topological Graph Representation and Configuration Synthesis for Power-Split Hybrid Transmissions of Multi-Planetary Gear Trains

Abstract Power-split hybrid transmissions are the core components of power-split hybrid electric vehicles (HEVs), and the quest for a more energy-efficient and higher-performing power-split hybrid transmission has long been the focus of study. In contrast to previously published methodologies, this paper proposes a novel approach for directly synthesizing power-split hybrid transmissions that makes use of the results of previously synthesized planetary gear trains (PGTs) rather than necessitating a resynthesis of their PGTs. A new topological graph that can construct a bridge between the PGTs and power-split hybrid transmission has been developed, reducing the computational complexity in the synthesis process. The new topological graph is obtained by adding topological characteristics of the power-split hybrid transmission to the PGT graph. A standard structure matrix is proposed to further screen out all the isomorphic configurations. The present method can generate various types of multi-PGT hybrid transmissions while avoiding mechanical structural interference. The design process of configurations for power-split hybrid transmission with three-column PGTs (3-PGT) is used as an example to prove the rationality of the method.

Semi-Analytical Solutions for Wave Propagation of Periodically Repetitive Schwarz Primitive Triply Periodic Minimal Surface Based Structure Embedded With Acoustic Black Hole Resonators

The Schwarz Primitive triply periodic minimal surface (P-TPMS) lattice structure is attracting more attention due to its superior mechanical properties and unique topological configuration. In this study, a new periodic configuration is proposed in which the unit cell is formed by embedding the acoustic black hole (ABH) component in the primary P-TPMS component. A semi-analytical method is developed to calculate the wave propagation properties of the proposed structure. The dynamical equilibriums of the primary P-TPMS and ABH components are described by the finite element method and the WKB method respectively. By considering the compatibility at the connections between the components and using the equivalent dynamic stiffness technique, dispersion analysis can be implemented in the framework of the wave and finite element (WFE) method. Various numerical examples with different number, type and connection position of the resonators are investigated. By comparing the calculation results from the present semi-analytical method with that from the finite element method, the effectiveness of the proposed method is validated. The influences of the number, type, and connection position of the resonators on the bandgap properties are investigated.

Deep-learning accelerating topology optimization of three-dimensional coolant channels for flow and heat transfer in a proton exchange membrane fuel cell

To quickly optimize the cooling performance of three-dimensional coolant channels in a proton exchange membrane fuel (PEMFC) cell, a generative adversarial network (GAN) model that adopts small-scale sample data to predict the coolant channel topology structure is constructed. The coordinate information of the topological pseudo-density field is directionally input into the neural network to improve prediction accuracy. The results show that more microtopologies in the coolant channels can be predicted with the GAN model than with traditional thermal–fluid–structural topology optimization. The topological configuration with a maximum temperature of 0.15 K, lower than the training dataset, is achieved. The well-trained GAN model can generate topological structures for the optimization objective, that is, thermal performance superior to the training set. The proposed model can also generate topological structures with better fluid and structural performance, which is 20.44% and 9.25% superior to that of the training dataset, respectively. The computation time required by the GAN model is <1 s, while the computation time for the same case is 30.2 h using the traditional thermal–fluid–structural topology optimization. These findings can aid in designing coolant channels in PEMFCs.

A Stream Function Smoothing Method for the Design of MRI Gradient Coils on Non-Developable Surfaces.

Insert gradient coils with similar imaging body shapes typically have smaller dimensions and higher spatial efficiency. This often allows the gradient coils the achievement of stronger and faster gradient fields. Thus, improving existing methods to make them applicable to the design of MRI gradient coils on complex surfaces has also become a challenge. This article proposes an algorithm that smooths the implicitly expressed stream function based on the intrinsic surface Laplace-Beltrami operator. This algorithm can be used to simplify the design procedure of MRI gradient coils on non-developable surfaces. The following steps are performed by the proposed algorithm: an initial design of the stream function configuration, extraction of the surface mesh, discretization of the surface smoothing operator, and a smoothing of the contour lines. To evaluate the quality of the smoothed streamline configuration, several technical parameter metrics-including magnetic field accuracy, coil power consumption, theoretical minimum wire spacing, and the maximum curvature of the contour lines-were evaluated. The proposed method was successfully validated in a design gradient coil on both developable and non-developable surfaces. All examples evolved from an initial value with a locally non-smooth and complex topological configuration to a smooth result while maintaining high magnetic field accuracy.

A dynamic analytical model on electrical circuit response of magnetorheological clutch

Recently, a considerable amount of research has been conducted on MRF-based clutches or actuators due to the significant role such device plays in torque coupling and control applications. It is desired to find an accurate yet quick dynamic analytical model to design the electrical circuit of MR clutch. The previous researches on the dynamic response of MR devices have focused on the magnetic response, while less on the current response, mostly on experimental test aspects. Aim at this issue, considering the combination of driving electronics and excitation coil, this study proposes the analytical model from command voltage to excitation current for electrical circuit of MR clutch for the first time. The analytical model develops first principle and is capable of accounting for driving electronics topology and coil configuration. The feasibility of the model was verified by experimental results. Moreover, the model has been implemented to investigate the effect of specific parameters on the electrical circuit dynamic of MR device. As mentioned above, the proposed analytical model provides comprehensive details for optimal design and improvement of circuit performance of MR devices.

Butterfly lattice materials for controllable multi-stage energy absorption

Energy absorption properties of cellular materials have gained increasing interest due to their superior performance and immense design space. This paper introduces a novel lattice material with a Butterfly configuration that has controllable multi-stage crushing behavior and high specific energy absorption capacity. A theoretical model is developed to predict the plateau stress, elastic modulus and critical strain of each stage, in order to obtain the multi-stage stress-strain curve. Compression experiments and the finite element modelling are also carried out to validate the theoretical model. Theoretical and experimental results show that the Butterfly lattice material has more than two crushing stages and a specific energy absorption property comparable to the traditional stretching-dominated lattice material. Finally, effects of topology configuration, cell number, and geometrical parameter on the energy absorption behavior are analyzed. It is found that the length of the vertical beam dramatically influences the plateau load and deformation behavior of each stage.

Dynamic response-oriented multiscale topology optimization for geometrically asymmetric sandwich structures with graded cellular cores

Compared with conventional symmetric sandwich structures with two identical face-sheets and uniform core, geometrically asymmetric sandwich structures (GASSs) enable better dynamic performance due to the expanded design space provided by two unidentical face-sheets and graded cellular cores (GCCs). This paper proposes a dynamic response-oriented multiscale topology optimization method for the GASSs, which is capable of designing the thicknesses of two solid face-sheets, graded distribution of GCCs and their topological configurations to minimize dynamic compliance. Specifically, at macroscale, a variable thickness sheet method is employed to optimize the thicknesses of two solid face-sheets and then generate an overall free distribution of cellular cores within sandwich layers. At microscale, a parametric level set method combining with a numerical homogenization approach is adopted to progressively optimize multiple representative cellular cores (RCCs), achieving their similar topological configurations. Benefitting from the level set-based topology description of RCCs, a shape interpolation technology is conveniently applied to interpolate the shapes of these RCCs to generate the configurations of GCCs. Moreover, a Kriging metamodel constructed by some cellular cores as sample points is adopted to predict the effective properties of each cellular core within sandwich layers, significantly reducing the computational burden. Several 2D and 3D numerical examples are illustrated to show the validity of the proposed method. The results indicate that the optimized GASSs show superior dynamic performances over the conventional sandwich structures designed by microscale and macroscale topology optimization, as well as filled with the commonly applied lattice and honeycomb cores.

Elastic topological resonator in pseudo-spin Hall edge states

This paper proposes an elastic topological resonator derived from the Kekulé tight-binding model, which enables the quantum spin Hall effect (QSHE). Band inversion in the spin system is realized through fine-tuning the intracellular coupling constants. With full-wave simulations, the topological interface configurations demonstrate the robust spin-locked edge state transport characteristic, featuring excellent fidelity via defects and anti-direct transmission. Then, a hexagonal resonator is located beside the straight waveguide to achieve multiple effective coupling directed by the edge state. The effect of the distance between the straight waveguide and the hexagonal part is further analyzed on the number of the resonant mode. Based on the functionally graded design concept, the topological resonance rainbow is also proposed to spatially separate distinct frequency components in accordance with projected band branches, to realize the progressive propagation of Lamb waves along the resonator route. Our work provides a novel mechanism for manipulating elastic waves with topological edge states, which is anticipated to serve as a foundation for creating programmable sensors and multifunctional testing equipment.

A Footpad Structure with Reusable Energy Absorption Capability for Deep Space Exploration Lander: Design and Analysis

The footpad structure of a deep space exploration lander is a critical system that makes the initial contact with the ground, and thereby plays a crucial role in determining the stability and energy absorption characteristics during the impact process. The conventional footpad is typically designed with an aluminum honeycomb structure that dissipates energy through plastic deformation. Nevertheless, its effectiveness in providing cushioning and energy absorption becomes significantly compromised when the structure is crushed, rendering it unusable for reusable landers in the future. This study presents a methodology for designing and evaluating structural energy absorption systems incorporating recoverable strain constraints of shape memory alloys (SMA). The topological configuration of the energy absorbing structure is derived using an equivalent static load method (ESL), and three lightweight footpad designs featuring honeycomb-like Ni-Ti shape memory alloys structures and having variable stiffness skins are proposed. To verify the accuracy of the numerical modelling, a honeycomb-like structure subjected to compression load is modeled and then compared with experimental results. Moreover, the influence of the configurations and thickness distribution of the proposed structures on their energy absorption performance is comprehensively evaluated using finite element simulations. The results demonstrate that the proposed design approach effectively regulates the strain threshold to maintain the SMA within the constraint of maximum recoverable strain, resulting in a structural energy absorption capacity of 362 J/kg with a crushing force efficiency greater than 63%.

Bifurcation instructed design of multistate machines

We propose a design paradigm for multistate machines where transitions from one state to another are organized by bifurcations of multiple equilibria of the energy landscape describing the collective interactions of the machine components. This design paradigm is attractive since, near bifurcations, small variations in a few control parameters can result in large changes to the system's state providing an emergent lever mechanism. Further, the topological configuration of transitions between states near such bifurcations ensures robust operation, making the machine less sensitive to fabrication errors and noise. To design such machines, we develop and implement a new efficient algorithm that searches for interactions between the machine components that give rise to energy landscapes with these bifurcation structures. We demonstrate a proof of concept for this approach by designing magnetoelastic machines whose motions are primarily guided by their magnetic energy landscapes and show that by operating near bifurcations we can achieve multiple transition pathways between states. This proof of concept demonstration illustrates the power of this approach, which could be especially useful for soft robotics and at the microscale where typical macroscale designs are difficult to implement.