Topology Design Research Articles

Anomalous scaling law of strength and toughness in polymers with strong interfacial secondary bonds

In materials design, the increase of toughness usually leads to the decrease of strength, which is a long-standing challenge. Polymeric materials with strong interfacial secondary bonds have recently emerged as promising solutions due to their remarkable capabilities to increase toughness and strength simultaneously. Nevertheless, a quantitative understanding on how toughness increases with strength is unavailable, posing challenges for deeper understanding and on-demand design for stronger and tougher advanced materials. In this work, we reveal a quadratic scaling law of simultaneously increased strength and toughness that unifies multiscale evidences from theories, computations and experiments across multiple material systems, where the deformation mechanisms are dominated by breaking and reforming of interfacial secondary bonds. Our results highlight the significance of interfacial secondary bonding combined with topology design to enable a wide range of strong and tough advanced materials.

Effect of the lattice structure on the interface zone and the final properties of novel PrintCast Ti64-AlSi9Cu3 interpenetrating phase composites

It is an ongoing challenge, especially in metals, to simultaneously improve the strength and energy absorption capacities while remaining lightweight, since these properties have frequently conflicted with each other. Here, we present a topology design approach to address this issue by developing novel metal-metal interpenetrating phase composite systems (IPCs) by infiltrating an AlSi9Cu3 melt into three-dimensionally additively manufactured Ti64 strut-based lattice structures (Cubic and BCCZ). Our key interest lies in forming an interface zone between the lattice structure and the matrix, which can play a critical role in determining the final properties of the IPCs. The results demonstrated that both the AM-Ti64 lattices and AlSi9Cu3 matrix have continuously bonded by the formation of an interface zone to form solid metal-metal IPC systems. Changing the design of the lattice structure from Cubic to BCCZ reduced the Al matrix-casting pores and defects due to the increased surface area fraction. Furthermore, both IPC systems exhibit superior mechanical performance compared with the rule-of-mixtures prediction and compared with each material separately, most likely due to the continuous interface zone that firmly bound the materials. Through a comparative analysis of our IPCs with published literature on metal-metal IPCs, we demonstrated the superior combination of compressive strength and lightweight characteristics exhibited by our IPC systems. This study sheds light on how the lattice structure architecture can tailor the final properties and the interface zone composition of the Ti64-AlSi9Cu3 IPC systems. Our results establish important groundwork for widespread research efforts on IPC systems with unprecedented mechanical and physical properties.

Optimization Strategies of Covalent Organic Frameworks and Their Derivatives for Electrocatalytic Applications

AbstractCovalent organic frameworks (COFs) are crystalline organic porous polymers that can be precisely integrated by building blocks to achieve pre‐designed composition, components, and functions, making them a powerful platform for the development of molecular devices in the field of electrocatalysis. The precise control of channel/dopant positions and highly ordered network structures of COFs provide an ideal material system for the applications of advanced electrocatalysis. In this paper, the topological structure design and synthesis methods of COFs are reviewed in detail, and their design principles are deeply analyzed. In addition, the applications of COFs and their derivatives in the field of electrocatalysis are systematically summarized and the optimization strategies are proposed. Finally, the application prospects of COFs and their derivatives in electrocatalysis and the challenges that may be encountered in the future are prospected, providing helpful guidance for future research.

Cushioning performance design of meta-sandwich structures inspired by Kirigami

ABSTRACT Novel ultralight meta-sandwich structures inspired by Kirigami (MSS) are proposed in this study. The topological designs of uniform (MSS-U) and non-uniform (MSS-N) sizes are studied based on the inconsistent distribution of the biomaterials. First, MSS-U and MSS-N are manufactured using laser powder bed fusion (LPBF) technology, and energy absorption prediction formulas are successfully derived by extracting the experimental deformation features. It is found that MSS-N exhibits a higher energy absorption level than MSS-U. Compared to the conventional corrugated sandwich structures (CSS), the MSS achieves better crashworthiness indicators and more folding numbers. Moreover, the parameter effect of the MSS is analysed, and the minimum/maximum thickness and folding angle significantly influence the crashworthiness responses. The mechanical properties of MSS-N can be regulated by changing the folding angle while maintaining the same thickness. The MSS proposed in this study can achieve a higher energy absorption efficiency at the same density compared to other existing mechanical structures. Interestingly, the feasibility of applying MSS to automobiles and plant protection drones is verified by changing the materials and macro-configurations. The design concept presented in this study provides a promising method for obtaining sandwich structures with excellent cushioning properties for engineering protective equipment.

Optimizing structural topology design through consideration of fatigue crack propagation

This paper presents an advanced approach for structural topology optimization by incorporating fatigue crack propagation analysis. The extended finite element method (X-FEM) is employed to model initial crack propagation, while the Paris model serves as the basis for simulating fatigue crack growth. The proposed methodology aims to optimize the structural design by minimizing compliance while considering volume and fatigue constraints. The proposed method employs the developed bi-directional evolutionary structural optimization (BESO) algorithm. The accuracy of the proposed technique is validated through the solution of benchmark problem and is further demonstrated in its effectiveness and robustness by examining several numerical examples. The optimization process considers various crack conditions, including the absence of cracks, horizontal and vertical cracks of different lengths. The optimized topologies obtained through the proposed algorithm clearly demonstrate the impact of crack presence, crack direction, and crack length on the material distribution. Furthermore, the convergence histories of the objective function, represented by mean compliance, highlight the influence of crack length on the stiffness and converged compliance of the structure. The results demonstrate its ability to adapt the material distribution based on fatigue cracks propagation conditions and achieve optimal topologies that balance structural integrity and performance.

Topology Design of 3D Printing Continuous Fiber‐Reinforced Structure Considering Strength and Non‐Equidistant Fiber

The utilization of continuous fiber‐reinforced composites (CFRC) technology in 3D printing opens up new avenues for designing composite products. This paper introduces a novel parallel topology optimization framework tailored for CFRC 3D printing. The framework incorporates strength constraints and uses topology, fiber volume fraction, and fiber orientation as essential design variables. A fiber material interpolation model for multiple design variables is proposed based on the law of mixtures for composites. Bidirectional evolutionary structural optimization and solid isotropic material with penalization are used for optimization of topology and fiber volume fraction, respectively, while the fiber orientation herein is determined using the principal stress method. Strength constraint is considered according to Tsai–Hill criterion. To match manufacturing process demands, a nonequidistant continuous fiber path optimization method based on Hermite interpolation function is introduced. The optimization framework introduced herein is successfully used for optimal design of L‐shaped beam, cantilever beam, and Michell beam. It is concluded that the design space of composite structure can be further broadened using the proposed method. To validate the viability of the proposed optimization method and fiber path design approach, the optimization results are printed using a custom‐developed continuous fiber 3D printer.

Less Is More: Understanding Network Bias in Proof-of-Work Blockchains

Blockchains are becoming increasingly important in today’s Internet, enabling large-scale decentralized applications with strong security and transparency properties. In a blockchain system, participants maintain and update the server-side state of an application by appending data as blocks onto an immutable, distributed ledger through a consensus protocol within a peer-to-peer network. There has been a significant increase in profit in mining blocks. For instance, Bitcoin miners currently receive over USD 200,000 per mined block. An essential determinant of these rewards is the time it takes to disseminate newly mined blocks across the network. This paper addresses the challenge of optimizing mining rewards by exploring topology design in a wide-area blockchain network utilizing a Proof-of-Work consensus protocol. We show that under low block times, the geographical location of a miner critically impacts the number of successful blocks mined by the miner. We also show that a miner may improve its success rate by increasing its connectivity to the network. However, contrary to the general wisdom that a faster network is always better for a miner, we show that increasing network connectivity (e.g., by adding more neighbors) is beneficial to a miner only up to a point after which the miner’s rewards degrade. This is because when a miner improves its connectivity, it inadvertently also aids other miners in increasing their connectivity. We also present a network-level collusion attack in which a miner can increase its block success rate by becoming part of a tightly connected cluster. Here too, we observe that the mining gains obtained increase with cluster size only up to a point, and decrease thereafter. Our findings highlight that the network topology is a key variable affecting miner performance in PoW blockchains that must not be overlooked. We demonstrate our observations via detailed simulations modeled using real-world measurement data.

Topology optimization of controllable porous microstructure with maximum thermal conductivity

Porous structures are lightweight and thus possess tailorable thermophysical properties through topological design. A multi-constraint topology optimization scheme is developed for designing 2D periodic lattice material with controllable porosity and optimal thermal conductivity. The porosity, pore size and specific surface area are modulated by imposing local density constraints, and thus the structure-determined thermal conductivity can be systematically investigated. The isotropic porous structures after optimization show effective thermal conductivity close to the Hashin-Shtrikman theoretical bound. The specific surface area of base cell is enlarged with imposing local density constraint. In addition, microstructure with anisotropic thermal conductivity can be also obtained. The samples of lattice structure are additively manufactured via selective laser melting, and the thermal conductivities are experimentally validated with deviation within 10 %. The proposed porous structures with targeted porosity have potential application in skeleton embedded with phase change materials.

IGA-based topology optimization in the design of stress-constrained compliant mechanisms

Topology design of compliant mechanisms has gained wide popularity among the scientific community, and their use in the mechanical engineering field is being of upmost importance. In this paper, an isogeometric analysis (IGA) formulation is used to solve the topology optimization problem of compliant mechanisms. Stress constraints are introduced in the problem to guarantee the attainment of realistic solutions. For this purpose, an overweight constraint is considered for the design process, replacing the use of local stress constraints. The material distribution in the domain is modeled with quadratic B-splines and with a uniform relative density within each element of the mesh. These strategies to define the material layout are used to compare the IGA-based formulation with the finite element (FEM) formulation. The IGA formulation provides several advantages with respect to the classical FEM-based approaches that are shown and analyzed with an input-parameters sensitivity analysis. The sensitivity analysis and the assessment of the importance of introducing of stress constraints in the problem are developed by solving two benchmark problems. Regarding the sensitivity analysis of input parameters, the results show that the ratio between the material and the springs stiffnesses is the parameter with the largest influence on the solutions of the problem. Moreover, the advantages of the IGA formulations over FEM formulations are related with the computational time, the smoothness of the structural borders, and the non-appearance of the checkerboard patterns. With respect to the stress constraints, the results show that they have to be considered in order to avoid instability and structural integrity problems.

3D-printed highly stretchable curvy sandwich metamaterials with superior fracture resistance and energy absorption

This paper focuses on the potential of curvy mechanical metamaterials to show how topological design can significantly enhance fracture toughness along the in-plane and out-of-plane (through-depth) directions. The conventional re-entrant unit cell is first reformulated by introducing local curvy ligaments and then additively manufactured by three-dimensional (3D) printing to form a center/edge-notch lattice metamaterial. The new conceptual design provides multi-stiffness unit cells, helping to control stress distribution within a structure under tensile load, specifically in the vicinity of the notches where stress concentrations occur. In other words, curvy unit cells are capable of arresting and blunting the notch under tensile loads and toughening the metamaterials. The crack tip opening displacement (CTOD) method calculates the fracture toughness. Not only can the fracture of lattice metamaterials be controlled along the in-plane direction by replacing unit cells in the sensitive parts of the metamaterials, but a new assembly method is also proposed. This offers that different thin plates of metamaterials with different layouts can be sandwiched to control out-of-plane fracture propagation (through-depth propagation of opening mode fracture) for the first time in fracture mechanics. This novel sandwiching method offers a multi-step fracture and significantly improves the fracture behavior of the lattice metamaterials from brittle to ductile by taking advantage of multiple through-thickness thin plates instead of considering one thick specimen. A detailed analysis of the effects of the ligament curvature value on the fracture behavior is presented. The results reveal that the more curvature, the more extension (ductility) will be realized, but too large curvature design can provide lower energy absorption capacity.

IrisVSA: Infinite-rotation infinite-stiffness Variable Stiffness Actuator towards physical human–robot-interaction

In Human–Robot-Collaboration applications, variable stiffness actuators provide an attractive actuation solution due to their well-established capabilities in ensuring accuracy and safety. Although, several existing VSAs have achieved an infinite-range of stiffness, those are generally relatively bulky, incapable of attaining an infinite-range of rotation, and their stiffness adjustment rate is rather slow. In this paper, we introduce a new infinite-rotation infinite-stiffness Variable Stiffness Actuator (irisVSA). The novelty of this actuator lies in its design topology, where two long-armed torsional springs are mounted on the output link and connected to the input link via a Force Contact Point (FCP). The variation in stiffness is realized by altering the relative distance from the FCP to the joint rotation center (effective arm). This unique topology offers a wide range of stiffness ranging from transparency (0 stiffness) to physically rigid. Moreover, the rate of stiffness adjustment is considerably fast (0.7 sec) through the utilization of a hypocycloid gear train. The working principle, design, prototyping, and characterization process for the irisVSA are detailed here. A Nonlinear Proportional–Integral–Derivative Controller is proposed to ensure the system’s accuracy. The safety of the actuator was evaluated by subjecting it to both impact force criterion experiments as well as Head Injury Criterion.

Technology and design study of 3D physics-based inductor on FDSOI in GHz-range

This study investigates several ways to improve the quality factor and (or) the inductance by evaluating the possibility to add magnetic materials around an inductor integrated into the BEOL of CMOS technology. Performance boost evaluation is done through 3D numerical simulations of an inductor integrated on an SOI substrate made in a 28 nm UTBB FDSOI technology. The choice of materials and their design topologies are mainly the study parameters which leads to the selection of a solution according to the final application: RF for quantum. We demonstrate an ideal improvement of L by a factor 5 and of Q by a factor 3, and a realistic improvement of Q of 2-turn inductors by 25 % with a shield; 46 % with a patterned magnetic material; and a Q improvement of 1-turn inductor by 14 % with an HR substrate.

Design and Analysis of Power Converter Topologies in Fuel Cell Applications.

The global transition from carbon-based fuel to cleaner energy sources has brought an application of hydrogen as fuel in multiple hybrid applications from backup power to onboard power sources, especially in the automobile industry. The application in the fuel cell hybrid system brings the integration of fuel cells with Lithium-Ion batteries and ultracapacitors. The efficient and reliable operation of the vehicle drivetrain highly depends on the power control mechanisms integrated with the system. Power converters play a significant role in converting the DC to DC or AC and AC to DC which maintains the power supply driving the hybrid power transmission system and other vehicle auxiliary loads. Fuel cell vehicles require high efficiency of the converters to optimize fuel consumption in various operating conditions. The paper introduces various topologies of Power electronic converters used in hybrid applications along with their pros and cons in various switching techniques adopted. The different power electronic converters are modeled in MATLAB-SIMULINK and comparative analysis has been performed to improve the efficiency and reliability of the system.

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To imbue traditional plastics with self‐healing attributes akin to living organisms, it is essential to judiciously constitutional dynamic chemistry and adopt precise topological structure designs during the synthesis of repairable plastics. Furthermore, their practical application demands enhancement of their mechanical properties, particularly toughness. Thus, this review encapsulates the existing work and accomplishments in the domain of high‐performance healable plastics by focusing on three main aspects: topological structures, constitutional dynamic chemistry, and strategies for toughening. image

Topological Design Optimization Approach for Winding Oil Flow Paths in Oil-Natural Air-Natural Transformers Based on a Fluidic-Thermal Coupled Model

Power transformers are a critical component in transmission and distribution systems. To limit the temperature rise to extend the life-cycle while operating with enough reliability and controllability of a transformer, the design of the cooling system has attracted extensive research attention. In this paper, a topology optimization (TO) methodology is presented for designing the coolant flow paths in a prototype oil-natural air-natural (ONAN) power transformer to maximize the winding heat dissipation capacity without any compromise on the flow resistance. First, the thermal and fluid flow characteristics in both the winding and the radiator of the prototype transformer are numerically investigated through a fluidic-thermal coupled field approach. The preliminary cooling effectiveness of the oil circulation system is initialized. Second, a material-density-based TO scheme is employed to realize the optimal design of the oil baffle structures. In the TO optimization, artificial Darcy frictional forces are applied to the solidified cells to block their internal flows in the fluidic calculations, while the thermal conduction coefficients in those cells are enhanced to separate the baffles from the flowing oil in thermal analyses. Finally, the fluidic and thermal coupled fields of the prototype transformer winding region with structurally optimized oil baffles are numerically investigated to validate the TO effectiveness.

A proposition to teach engineering projects using Problem-based Learning (PBL) concepts

This article shows the design of a mechanical engineering project using problem-based learning concepts. The work shows the complexity of the teaching-learning process of a complex problem such as developing, simulating, and testing a prototype of a piezoresistive pressure sensor. This type of problem is typical in theoretical-practical classes involving different concepts learned during an undergraduate course. The cognitive structure may or may not be influenced by the presentation of ideas and methods for organizing the content and executing the desired project. Using a pressure sensor project made it possible to develop the concepts of learning models by applying the idea of project-based learning (PBL). The main results obtained were the product design topology, operating concepts of a sensor device, computer-aided design projects, analysis of simulation results and assembly, and mechanical prototyping concepts, involving different materials for machining and product encapsulation.

Taming Connectedness in Machine-Learning-Based Topology Optimization with Connectivity Graphs

Despite the remarkable advancements in machine learning (ML) techniques for topology optimization, the predicted solutions often overlook the necessary structural connectivity required to meet the load-carrying demands of the resulting designs. Consequently, these predicted solutions exhibit subpar structural performance because disconnected components are unable to bear loads effectively and significantly compromise the manufacturability of the designs.In this paper, we propose an approach to enhance the topological accuracy of ML-based topology optimization methods by employing a predicted dual connectivity graph. We show that the tangency graph of the Maximal Disjoint Ball Decomposition (MDBD), which accurately captures the topology of the optimal design, can be used in conjunction with a point transformer network to improve the connectivity of the design predicted by Generative Adversarial Networks and Convolutional Neural Networks. Our experiments show that the proposed method can significantly improve the connectivity of the final predicted structures. Specifically, in our experiments the error in the number of disconnected components was reduced by a factor of 4 or more without any loss of accuracy. We demonstrate the flexibility of our approach by presenting examples including various boundary conditions (both seen and unseen), domain resolutions, and initial design domains. Importantly, our method can seamlessly integrate with other existing deep learning-based optimization algorithms, utilize training datasets with models using any valid geometric representations, and naturally extend to three-dimensional applications.

Ultrabroadband (DC–80 GHz) RF MEMS DC/capacitive load shunt switch

AbstractThis study presents the design and fabrication of a novel direct current (DC)/capacitive radio frequency (RF) Micro‐Electro‐Mechanical Systems (MEMS) switch in the ultrabroadband range from DC to 80 GHz. This proposed RF MEMS switch consists of two arms, with one arm providing a DC loading state (), whereas the second arm provides a capacitive loading state () to the ground when pull‐in voltage is applied. Insertion loss less than 1.85 dB and return loss better than 10 dB are achieved in the OFF state () from DC to 80 GHz (no pull‐in voltage is applied). Due to the novel design topology, this switch can act as a DC‐only switch (0–17 GHz) that provides isolation better than 25 dB during the state. A capacitive‐only switch (48–80 GHz) provides isolation better than 17 dB. This proposed structure can act as either DC(S1) or a capacitive () switch (17–48 GHz) and provides isolation better than 10 dB in either state. This proposed switch finds its best application in reconfigurable circuits like phase shifters due to switching between DC and capacitive loading during the On state from 17 to 48 GHz. The proposed switch's measured pull‐in and lift‐off voltages are 3.5 and 2.85 V, respectively.

A topology optimization framework for 3D phononic crystals via the method of successive iteration of analysis and design

Topology optimization has become a useful tool for designing Phononic Crystals (PnCs) to achieve desired elastic wave propagation properties. However, topological design of three-dimensional (3D) PnCs remains a significant challenge due to extremely high computational cost required for the repeated solution of the nested double-loop problem of design optimization and eigenvalue analysis to determine the band structure during the optimization iteration process. This paper presents a more efficient topology optimization method based on the concept of successive iteration of analysis and design. The method employs a sequential approximation of the eigenpairs through an inverse iteration-like procedure to avoid solving the expensive eigenvalue problems in each design iteration. To further improve the efficiency of the design iteration, the eigenmodes of the intermediate PnCs designs are first computed on a relatively coarse finite element mesh. Then they are mapped onto the fine mesh and used as the initial trial eigenmodes in the eigenpair analysis and design sensitivity analysis. The high efficiency of the proposed method, as compared with conventional nested double-loop optimization approaches, is demonstrated by 3D numerical examples with over one million degrees of freedom. It is shown that this approach can achieve simultaneous convergence of the eigenvectors along with the design evolution of the unit cell, therefore substantially reduces the computational burden for high-resolution topological design of 3D PnCs.

From Linear to Nets: Multiconfiguration Polymer Structure Generation with PolyFlin.

Molecular modeling and simulations are essential tools in polymer science and engineering, enabling researchers to predict and understand the properties of macromolecules, including their structure, dynamics, thermodynamics, and overall material characteristics. However, one of the key challenges in polymer simulation and modeling lies in the initial topology design, as existing programs often lack the capability to generate all types of polymer forms. In this study, we present PolyFlin, a powerful Python module that addresses this limitation by allowing the generation of a wide range of polymer structures, from simple homopolymers to complex copolymers, including grafts, cyclic, star, dendrimers, and nets. PolyFlin offers a versatile and efficient tool for exploring and creating diverse polymer architectures, facilitating advancements in various fields that require precise polymer modeling and simulation.