Final Topology Research Articles

On the influence of soil flexibility in structural topology optimization

AbstractThis paper investigates for the first time the effect of soil flexibility in structural topology optimization. The case of a structure resting on an underlying soil is explored. The response of the soil is modeled using the Indirect Boundary Element Method (IBEM). The structure is modeled using Finite Element Method (FEM) and the soil domain is coupled by imposing continuity and equilibrium conditions at the soil‐structure interface. Original soil stress influence functions are derived to model voids in the soil. The optimization is carried out by using the Topology Optimization of Binary Structures (TOBS), a recently developed method that employs sequential integer linear programming and the branch‐and‐cut algorithm. Structural compliance minimization subject to a volume constraint is solved. Selected optimization problems are solved for various soil‐to‐structure flexibility ratios. Results show that the soil flexibility may have a significant impact in both the optimized topology and in the achievable compliance of the structure. Results considering voids on the soil, which increases the soil flexibility, affected the final topology of the structures as well as the optimized values.

Multiple outgroups can cause random rooting in phylogenomics

Outgroup selection has been a major challenge since the rise of phylogenetics, and it has remained so in the phylogenomic era. Our goal here is to use large phylogenomic animal datasets to examine the impact of outgroup selection on the final topology. The results of our analyses further solidify the fact that distant outgroups can cause random rooting, and that this holds for concatenated and coalescent-based methods. The results also indicate that the standard practice of using multiple outgroups often causes random rooting. Most researchers go out of their way to get multiple outgroups, as this has been standard practice for decades. Based on our findings, this practice should stop. Instead, our results suggest that a single (most closely) related relative should be selected as the outgroup, unless all outgroups are roughly equally closely related to the ingroup.

Interactive Structural Topology Optimization with Subjective Scoring and Drawing Systems

Topology optimization techniques can create efficient and innovative structural designs by redistributing underutilized materials to the most-needed locations. These techniques are typically performed based purely on structural performance without considering factors like aesthetics and other design requirements. Hence, the obtained structural designs may not be suitable for specific practical applications. This study presents a new topology optimization method, SP-BESO, by considering the subjective preferences (SP) of the designer. Here, subjective scoring and drawing systems are introduced into the popular bi-directional evolutionary structural optimization (BESO) technique. The proposed SP-BESO method allows users to iteratively and interactively create topologically different and structurally efficient solutions by explicitly scoring and drawing their subjective preferences. Hence, users do not need to passively accept the optimization results. A user-friendly digital design tool, iBESO, is developed, which contains four optimizers to simultaneously perform the proposed SP-BESO method to assist in the design exploration task. A variety of 2D examples are tested using the iBESO software to demonstrate the effectiveness of the proposed SP-BESO method. It is found that the combination of parameters used in the scoring and drawing systems controls the formation of final structural topologies toward performance-driven or preference-driven designs. The utilization of the proposed SP-BESO method in potential practical applications is also demonstrated.

A Quantitative Air-Gap Construction Method to Maximize Torque of Vernier PM Machines

The vernier permanent magnet machines (VPMMs) have attracted extensive research interest due to inherent high torque density, which is brought by the “magnetic gearing effect” based on slot-toothed air gap. Regularly, the air-gap topology of VPMM is initially determined and produces the certain composition of magnetic field harmonics, which decides the torque capability. Output torque is improved in a limited way via repeatedly optimizing the air-gap structure parameters. Herein, a quantitative air-gap construction method is proposed to construct the air-gap topology that could produce the magnetic field harmonics of which the total torque generation is maximum. With air-gap permeance as the bridge between torque and magnetic field harmonics, the proposed method quantitatively designs the optimal amplitude of individual radial air-gap permeance unit. Then, the length of the corresponding air-gap unit is obtained, which are assembled to form the final air-gap topology. The proposed method is applied on a surface-mounted VPMM (SVPMM) to illustrate. In theory, the torque density of the proposed machine could achieve 32 Nm/L under natural cooling when electric loading is 250 A/cm. Compared to the open-slot counterpart under the same conditions, the rated torque of the proposed machine is theoretically 68% larger, and is still 32% larger despite the saturation in steel. Finally, a prototype is fabricated, and the test results verify the feasibility of the proposed method.

Topology optimization using super-resolution image reconstruction methods

This paper proposes a new topology optimization method to obtain super-resolution images without increasing mesh refinement by using various methods. For traditional process, low-resolution (LR) images are fed into the Solid Isotropic Material with Penalization (SIMP) and Optimality Criteria (OC) methods. Here, the trained super-resolution images are added to the inner loops to reconstruct the topology and used to obtain high-resolution (HR) images from the LR images at the end of each iteration. After finishing the reconstruction process, the main topology optimization method recovers the original size images from the HR images for the next iteration. Several examples are presented to demonstrate the effectiveness of the proposed method. The final topologies provide noticeably improvement over those of typical SIMP method and create a much sharper and higher contrast images. Moreover, the proposed strategy using the super-resolution image reconstruction methods can give valuable innovation for conventional topology optimization process.

Self‐Assembly of Magnetic Nanochains in an Intrinsic Magnetic Dipole Force‐Dominated Regime

Magnetic nanoparticle chains offer the anisotropic magnetic properties that are often desirable for micro- and nanoscale systems; however, to date, large-scale fabrication of these nanochains is limited by the need for an external magnetic field during the synthesis. In this work, the unique self-assembly of nanoparticles into chains as a result of their intrinsic dipolar interactions only is examined. In particular, it is shown that in a high concentration reaction regime, the dipole-dipole coupling between two neighboring magnetic iron cobalt (FeCo) nanocubes, wassignificantly strengthened due to small separation between particles and their high magnetic moments. This dipole-dipole interaction enables the independent alignment and synthesis of magnetic FeCo nanochains without the assistance of any templates, surfactants, or even external magnetic field. Furthermore, the precursor concentration ([M] = 0.016, 0.021, 0.032, 0.048, 0.064, and 0.096 m) that dictates the degree of dipole interaction is examined-a property dependent on particle size and inter-particle distance. By varying the spinner speed, it is demonstrated that the balance between magnetic dipole coupling and fluid dynamics can be used to understand the self-assembly process and control the final structural topology from that of dimers to linear chains (with aspect ratio >10:1) and even to branched networks. Simulations unveil the magnetic and fluid force landscapes that determine the individual nanoparticle interactions and provide a general insight into predicting the resulting nanochain morphology. This work uncovers the enormous potential of an intrinsic magnetic dipole-induced assembly, which is expected to open new doors for efficient fabrication of 1D magnetic materials, and the potential for more complex assemblies with furtherstudies.

Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths.

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ιwill focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI(rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18months (84 scans in total), and the rs-fMRIdataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.

Filtering artifactual signal increases support for Xenacoelomorpha and Ambulacraria sister relationship in the animal tree of life

Conflicting studies place a group of bilaterian invertebrates containing xenoturbellids and acoelomorphs, the Xenacoelomorpha, as either the primary emerging bilaterian phylum1,2,3,4,5,6 or within Deuterostomia, sister to Ambulacraria.7,8,9,10,11 Although their placement as sister to the rest of Bilateria supports relatively simple morphology in the ancestral bilaterian, their alternative placement within Deuterostomia suggests a morphologically complex ancestral bilaterian along with extensive loss of major phenotypic traits in the Xenacoelomorpha. Recent studies have questioned whether Deuterostomia should be considered monophyletic at all.10,12,13 Hidden paralogy and poor phylogenetic signal present a major challenge for reconstructing species phylogenies.14,15,16,17,18 Here, we assess whether these issues have contributed to the conflict over the placement of Xenacoelomorpha. We reanalyzed published datasets, enriching for orthogroups whose gene trees support well-resolved clans elsewhere in the animal tree.16 We find that most genes in previously published datasets violate incontestable clans, suggesting that hidden paralogy and low phylogenetic signal affect the ability to reconstruct branching patterns at deep nodes in the animal tree. We demonstrate that removing orthogroups that cannot recapitulate incontestable relationships alters the final topology that is inferred, while simultaneously improving the fit of the model to the data. We discover increased, but ultimately not conclusive, support for the existence of Xenambulacraria in our set of filtered orthogroups. At a time when we are progressing toward sequencing all life on the planet, we argue that long-standing contentious issues in the tree of life will be resolved using smaller amounts of better quality data that can be modeled adequately.19.

Aspects of halloysite nanotubes integration in epoxy nanocomposites: Curing kinetics and deformation mechanism

AbstractThe curing kinetics of diglycidyl ether of bisphenol A epoxy resin using amine‐based hardener in the presence of halloysite nanotubes (HNTs) are investigated by performing isothermal and dynamic differential scanning calorimetry measurements, followed by a detailed analysis of the experimental data via the kinetic free method and three common model‐based analysis. Kamal and Sourour model gave the best fit for the experimental data and thus, was further modified by Williams–Landel–Ferry diffusion control equations to calculate the diffusion constants near the glass transition temperature (Tg). The optimized model is used to find the curing kinetics parameters of epoxy/HNTs nanocomposites, and those finding are further validated and explained by performing rheometric measurements. Noncatalytic and autocatalytic curing reactions and diffusion constants are all functions of the amount of HNTs. HNTs shift the curing reaction slightly toward higher temperatures and promote etherification reactions, leading to an increase in the curing enthalpy, as extra bonds will be formed between HNTs and epoxy resin during the polymerization. The combination of acceleration and inhibition mechanisms dictates the complex kinetics of the reactions at high concentrations of HNTs, while the produced local topology of the polymeric network depends highly on HNTs weight ratio. Furthermore, to investigate the mechanical performance of the prepared nanocomposites under flexural loads, three points bending tests are performed in both modes, dynamic and static, followed by a fractography analysis of the fractured surfaces. The mechanical properties have improved by the addition of pristine HNTs without compromising the Tg of the polymer. This study provides a set of observational relationships between various chemical interactions during polymerization and the final topology and performance of the polymer, which in turn aims to bridge the gap in the literature between the curing kinetics of epoxy/HNTs nanocomposites and the performance of these nanocomposites under various thermal and mechanical stresses.

Topology optimization of nonlinear flexoelectric structures

Recent advances in nanotechnology have allowed for the manufacturing of nanostructures and nanodevices with optimized topologies that outperforms their traditional counterparts based on simple geometry in terms of efficiency and function. In this work, a novel nonlinear topology optimization procedure is developed to design optimal layouts of flexoelectric structures undergoing large displacement. The optimal material distribution is determined by optimizing the energy conversion efficiency. Two material properties such as the elasticity coefficient and the permittivity coefficient are interpolated through the solid isotropic material with a penalization approach via an energy interpolation scheme to overcome the numerical instability. Obtained results have revealed that accounting for the geometric nonlinearity leads to a different final optimal topology with a better energy converting efficiency as compared to the linear model. We also found the significant influences of size effects on the optimized structure emphasizing the importance of nonlocal elastic behavior in characterizing and designing micro-structures and flexoelectricity.

A photopaper‐based low‐cost, wideband wearable antenna for wireless body area network applications

This study presents a low-cost, compact, flexible, and wideband wearable antenna for different wireless body area network (WBAN) band applications, covering medical body area network band of 2.4 GHz, industrial, scientific, and medical band of 2.45 GHz, WiMAX band of 3.5 GHz, and wireless local area network band of 5.2 GHz. The final antenna topology is obtained by hexagonal microstrip radiator patch fabricated on commercially available low-cost photo paper substrate and defected ground plane below the feed line acting as a partial ground to achieve a conformable structure wideband operation. The overall size of the fabricated antenna is 30 × 40 mm2 and yields a wide-bandwidth of 3 GHz (2.30–5.30 GHz), radiation efficiency of 84.35%, and the highest gain of 3.48 dBi at the centre frequency of 5.2 GHz, and minimum gain of 1.91 dBi at 2.45 GHz. Furthermore, our detailed numerical and experimental investigations involving specific absorption rate performance assessment and bending analysis revealed the proposed design's excellent robustness to both human body loading and structural deformation scenarios. Therefore, simulated and measured results strongly advocate that the proposed design has profound implications for flexible and body-worn devices in WBAN applications.

Topology optimization of hierarchical structures based on floating projection

• A novel hierarchical design strategy is proposed. • Floating projection is employed to globally optimize the topologies. • The mechanical relationship among three design layers is investigated. • This method is with high efficiency, good connectivity and multifunctional. Representative of the advanced engineering designs, hierarchical structures have attracted particular attention in academia and engineering. This study proposed a novel design strategy to obtain hierarchical structures with three layers. The macroscopic layer contained the structures to be optimized, which were periodically fashioned by multi-phase and multi-configuration mesoscopic structures. The configurations of the mesoscopic structures were determined by several classical types of material micro-structures at the microlayer . The micro-structures and mesoscopic structures were independent of optimization, while the equivalent elastic properties of the mesoscopic structures were evaluated using the homogenization method . The floating projection technique was employed to search for the final macro-structural topologies, where the minimum volume was used as the objective function when subjected to one or more displacement constraints. All constraints were added to the objective function through Lagrange multipliers , which were iteratively computed using the Newton-Raphson method. Moreover, a heuristic optimality criterion was established to stably update the design variables. Numerical examples were provided to reveal the effect of material properties and microscopic and mesoscopic structures on the design of hierarchical structures. Graphical Abtract .

A Practical Disturbance Rejection Control Scheme for Permanent Magnet Synchronous Motors

This paper proposes a novel disturbance rejection control scheme for permanent magnet synchronous motor (PMSM) drives. Based on the framework of modified disturbance observer (DOB)-based control, the final topology of the proposed disturbance rejection proportional–integral (DR-PI) controller includes a pre-filter and a controller in a proportional–integral (PI) form. The proposed DR-PI control scheme is practical with a straightforward gain tuning rule. Note that the gain selection method is the main issue of not only conventional PI controllers but also advanced methods such as DOB-based controllers. In addition, by starting from the framework of modified DOB, this paper also proves that the PI controller with an pre-filter possesses a disturbance rejection ability similar to a DOB-based control method. To the best of our knowledge, this is the first time that such a simple and effective PI controller is designed for the speed control of PMSMs as well as theoretically proven to have a perfect disturbance rejection ability. This paper shows the steps of selecting the parameters of the proposed controller in terms of the parameters of a desired plant model, disturbance observer and compensator. Hence, unlike a traditional DOB case, in this approach, one can simultaneously tune the controller and observer at the same time. The appearance of the pre-filter from the modified DOB scheme solves an overshoot problem, thus the general motor operation is significantly improved, which is validated by experiments. The experimental evidence under two scenarios of load torque change and speed change prove the effectiveness of the proposed method compared to conventional PI and DOB control (DOBC) schemes. All the experiments were implemented on a 300 W PMSM of a setup manufactured by Lucas-Nuelle GmbH with a digital signal processor.

Defect-Driven topology optimization for fatigue design of additive manufacturing structures: Application on a real industrial aerospace component

In this paper, a generalized formulation of defect-driven topology optimization (TO) for fatigue design, named TopFat, is proposed, where the first principal stress, that causes the crack propagation, is limited in accordance with the defect size distribution of the additive manufacturing (AM) process. As the largest defect depends on the final volume of the component, which changes according to the topology, an iterative procedure is adopted to avoid volume constraints in the optimization problem. The procedure is applied to an actual aerospace bracket component and its strength and limitations are discussed in comparison with the TO formulations presented in the literature. Results show that considering the defect population significantly affects the final topology, while leading to a feasible optimum. Further, even though the computational time increases, an additional 15% of mass saving is achieved by adopting the proposed iterative procedure with respect to a volume constraint-based formulation of TopFat.

Modeling Gd3+ Complexes for Molecular Dynamics Simulations: Toward a Rational Optimization of MRI Contrast Agents.

The correct parametrization of lanthanide complexes is of the utmost importance for their characterization using computational tools such as molecular dynamics simulations. This allows the optimization of their properties for a wide range of applications, including medical imaging. Here we present a systematic study to establish the best strategies for the correct parametrization of lanthanide complexes using [Gd(DOTA)]- as a reference, which is used as a contrast agent in MRI. We chose the bonded model to parametrize the lanthanide complexes, which is especially important when considering the study of the complex as a whole (e.g., for the study of the dynamics of its interaction with proteins or membranes). We followed two strategies: a so-called heuristic approach employing strategies already published by other authors and another based on the more recent MCPB.py tool. Adjustment of the Lennard-Jones parameters of the metal was required. The final topologies obtained with both strategies were able to reproduce the experimental ion to oxygen distance, vibrational frequencies, and other structural properties. We report a new strategy to adjust the Lennard-Jones parameters of the metal ion in order to capture dynamic properties such as the residence time of the capping water (τm). For the first time, the correct assessment of the τm value for Gd-based complexes was possible by recording the dissociative events over up to 10 μs all-atom simulations. The MCPB.py tool allowed the accurate parametrization of [Gd(DOTA)]- in a simpler procedure, and in this case, the dynamics of the water molecules in the outer hydration sphere was also characterized. This sphere was divided into the first hydration layer, an intermediate region, and an outer hydration layer, with a residence time of 18, 10 and 19 ps, respectively, independent of the nonbonded parameters chosen for Gd3+. The Lennard-Jones parameters of Gd3+ obtained here for [Gd(DOTA)]- may be used with similarly structured gadolinium MRI contrast agents. This allows the use of molecular dynamics simulations to characterize and optimize the contrast agent properties. The characterization of their interaction with membranes and proteins will permit the design of new targeted contrast agents with improved pharmacokinetics.

A Neural Network Model with Connectivity-Based Topology for Production Prediction in Complex Subsurface Flow Systems

Summary This paper presents a neural network architecture for prediction of production performance under different operating conditions by integration of domain insight and simulated production response data. The neural network topology in the developed approach is derived from interwell communication and connectivity between a producer and its surrounding supporting injection wells. Instead of a fully connected neural network that represents a global (field-scale) model that allows any injector to be connected to a given producer, and hence too many unrealistic and irrelevant connections, a local view is taken in building the proxy model. In this case, each producer is assumed to be supported by very few surrounding injection wells and is likely to have weak or no communication with distant wells. However, interwell connectivity in complex large-scale reservoirs is not just a function of distance and rather difficult to determine. Therefore, multiple randomly sized regions around each producer are considered to include different numbers of injectors in each local network for any given producer. The variability in the neighborhood size reflects the prior uncertainty about the potential connectivity between a producer and its nearby injection wells at different distances. This approach results in many local neural networks (several local networks per each producer) that can be aggregated into a single large neural network model with a predefined topological structure to represent possible connections. Training with simulated data is then used to estimate the weights in the resulting neural network architecture. Once the training process is completed, for each producer, the local model with the best prediction performance on the test data is selected and used to construct the final topology of the neural network model for the entire field. The method is applied to predict interwell connectivity and oil production in a large-scale mature field that undergoes waterflooding. The results demonstrate that even a simple domain insight, such as distance-based elimination of wells in a large field, can significantly reduce the amount of training data need and lead to noticeable improvement in the prediction performance of the resulting neural network model.

SOME PROPERTIES OF T0 FUZZY SOFT TOPOLOGICAL SPACES IN QUASI-COINCIDENCE SENSE

In this paper, we have introduced and studied some new notions of T0 separation axiom in fuzzy soft topological spaces using quasi-coincident relation for fuzzy soft points. We have shown a relationship between ours and other counterparts and observed that all these notions satisfy good extension, hereditary, productive, and projective properties. Moreover, we have also shown that these notions are preserved under one-one, onto, and fuzzy soft continuous mappings. Finally, initial and final soft topologies are studied also.

Synthesis and Numerical Analysis of Compliant Devices: A Topology Optimization Approach for Mechanisms and Robotic Systems

The topology optimization design invariably shall be used in various applications such as four bar mechanisms, robotics designs, aircraft engineering designs, and many other mechanical innovative systems for improving the efficiency in the system. This research paper emphasizes more on general topology optimization design for a rectangular domain in which numerically analyzed with defined boundary conditions. Furthermore, the same setting geometry has been taken for sensitivity analysis to find the objective stress and nonstress zones. Then, the geometry is topology optimized to analyze stress, safety factor, output deflection, and mass reduction. Also this research work focuses more on topology optimization, design synthesis, and objective function comparison in different materials. Hence, the results are suitable volume and mass reduction in various robotic devices. Validation and comparison of compliance base materials will further support the paper to extend the work for experimental analysis. This final topology optimized and validated device can be manufactured and forced to experimental fatigue endurance test cycle test condition in static and dynamic state. The results outshoot the final destination of this optimization design.

Large-Scale Emulation Network Topology Partition Based on Community Detection With the Weight of Vertex Similarity

Abstract Due to the limitations of physical resources, if a large-scale emulation network environment composed of millions of vertices and edges is constructed by virtualization technology, the whole network topology should be partitioned into a set of subnets. The topology partition is a work of graph partition. The existing topology partition methods have shortcomings, such as low efficiency and poor practicability, especially for large-scale network topology. The emulation network is a kind of complex network and has the characteristics of community structure. Therefore, we proposed LENTP (large-scale emulation network topology partition) based on the community detection with the weight of the vertex similarity for large-scale topology partition. In the first stage, the tree-structured area compression reduces the topology scales significantly to improve partition efficiency. And then, the improved Louvain algorithm is used to topology partitioning and obtain an initial set of subnets with the minimum number of subnets and remote links. Finally, after repartitioning and merging for the initial subnets, the result of subnets is the final topology partition that reaches the optimization objectives with the conditions of the virtual resources. In the experiment, the method is tested in five groups of network topology with different scales. The results demonstrate that LENTP can partition the network topology over 1 000 000 nodes and significantly improve the running-time efficiency of the network topology partition.

Performance evaluation of neural networks in modeling exhaust gas aftertreatment reactors

A comprehensive evaluation of neural network (NN) methods to model exhaust gas aftertreatment reactors is presented in this study. A combination of non-linear autoregressive model with exogenous inputs (NARX) network and static NN is utilized to capture the dynamic behavior of the state and outlet variables, such as coverage fractions, temperatures, and conversions, of monolith reactors used in the abatement of automobile emissions. The efficacy of this methodology is illustrated by modeling the diesel oxidation catalyst (DOC) and selective catalytic reduction (SCR) reactors of a diesel engine exhaust gas aftertreatment system. The final NN topology is designed by carefully comparing the model performances in the training and testing datasets. Model parsimony quantified by Akaike’s Information Criterion is employed for choosing the number of input and feedback delays in the NARX network. The novelty of this work lies in generating quality training and validation data using a physics-based system level model comprising of the engine and aftertreatment reactor components. The accuracy and generalizability of the NN models are demonstrated by comparing the model predictions with physical model simulation results in five disparate test datasets used in vehicle certification protocols. Overall, the NN models are found to reproduce all the qualitative trends in the various state and output variables while needing minimum computing resources resulting in better than real-time performance. Therefore, with appropriate training, these models can be embedded in the electronic control unit (ECU) and utilized for model-based predictive control systems.