Topology Optimization Research Articles

Directed interactive topology optimization design for multi-agent affine formation maneuver control

This paper investigates the directed interactive topology optimization design problem for multi-agent affine formation maneuver control. Firstly, considering the optimization indexes such as information interaction cost and information spreading energy consumption, a directed topology optimization model satisfying affine formation maneuver is established, including two sub-models of topology structure construction and weight allocation. Secondly, aiming at the topological structure construction for affine formation maneuver, a directed k-rooted graph detection method is proposed, which can realize the solution of d +1 -rooted constraint for directed information interaction topology, and then an improved NSGA-II topological structure construction optimization algorithm is designed. Finally, a formation of seven agents in twodimensional space is taken as an example for simulation verification. The results show that the improved topology NSGA -II topology construction optimization The algorithm has better optimization effects, can effectively provide a variety of feasible directed interactive topologies for affine formation maneuver control, and the generated interactive topology can meet the requirements of directed d +1 -rooted graph.

Analysis and optimization of the dynamic response characteristics of aircraft cargo rack

In order to effectively achieve the optimization design of the aircraft cargo rack, based on finite element modal simulation, the structure was redesigned and analyzed using two schemes of surrogate model size optimization and topology optimization, ensuring the strength requirements while improving the natural frequency and reducing the weight. According to the modal shape of vibration, additional bridge structures were added at the weak points to improve the dynamic stiffness of the structure. According to the optimization design requirements, the response surface function was constructed, and the particle swarm optimization algorithm was applied in the size optimization and surrogate model solution. A multi-objective optimization model was established for flexibility and low-order natural frequencies, and topology optimization was carried out in HyperWorks. The structural dynamic modification of the topology optimization model was performed using the modal strain energy analysis method. The research results show that both optimization methods can achieve good lightweight design. The static performance of the optimized structure is stable, and the overall modal frequency is improved.

Modal analysis and optimization design of ultra-high acceleration platform rail frame

The ultra-high acceleration macro and micro motion platform has the advantages of high positioning accuracy and small error, and the key mechanism rail frame of the ultra-high acceleration macro and micro motion platform is optimized based on modal analysis to achieve the performance optimization of the platform. SolidWorks software was used to build the rail frame model, and ANSYS Workbench software was used to carry out modal analysis, topology optimization and response surface optimization, etc., so as to reduce the quality of the rail frame as much as possible under the premise of maintaining the stability of the first-order natural frequency. The results show that the optimization of the response surface meets the expected goal, the first-order natural frequency of the guide rail frame increases by 0.7 %, the mass decreases from 6.165 kg to 5.592 kg, and the change rate is 9.2 %, which achieves the purpose of lightweight.

Adaptive isogeometric topology optimization of shell structures based on PHT-splines

This paper proposes an adaptive isogeometric topology optimization framework for shell structures by utilizing a continuous density field represented as Polynomial splines over Hierarchical T-meshes (PHT-splines). This framework ensures an exact representation of shell structures, eliminating the geometric inaccuracies commonly associated with topology optimization. In the meanwhile, the meshes used for design and analysis are refined adaptively and locally along the density boundary to achieve a smooth material layout with reduced degrees of freedom (DOF). The adaptive sensitivity filter is tailored to the characteristics of PHT-splines, where the filter radius is determined automatically and adaptively, without the need to specify parameters in advance. Numerical experiments conducted on various shell structures validate the efficacy of the proposed adaptive framework. In comparison with isogeometric topology optimization based on non-adaptive cases (i.e. B-splines), the proposed adaptive framework demonstrates a notable enhancement in computational efficiency, with a 50%−80% reduction in running time and a 30%−60% reduction in DOF. Additionally, we offer a detailed comparison between PHT-splines and other locally refinable splines in the context of shell topology optimization. Numerical experiments exhibit that the efficient and localized refinement capability of PHT-splines provides advantages in both computational efficiency and structural performance for topology optimization.

Recent Progress on 3D Printing of Lightweight Metal Thin-Walled Structures.

Metal thin-walled structures are ubiquitous in various industrial applications and fabricating them through additive manufacturing (AM) enables intricate thin-wall geometries with no assembly required. However, additively manufactured metal thin walls suffer from increased heat accumulation and reduced structural stability, making it difficult to print geometrically accurate thin walls with minimal distortion and defects. Thin-walled structures also experience different thermal histories and solidification conditions compared to bulk structures, leading to drastic differences in the microstructure and mechanical properties. In this review article, the AM processing of metal thin-walled structures will be introduced. An in-depth discussion on the design strategies of additively manufactured metal thin walls will be presented regarding the restrictions imposed by the AM technology, the integration of thin walls in lightweight structures, and novel design ideation through topology optimization. The effects that the thin wall design has on the geometrical accuracy, microstructure, and mechanical properties will then be elucidated. The utilization of AM for fabricating metal thin walls across various industries will also be summarized. Finally, this review article will close on some perspectives and future work on the AM of metal thin-walled structures.

Approach for multi-valued integer programming in multi-material topology optimization: Random discrete steepest descent (RDSD) algorithm

The present study models the multi-material topology optimization problems as the multi-valued integer programming (MVIP) or named as combinatorial optimization. By extending classical convex analysis and convex programming to discrete point-set functions, the discrete convex analysis and discrete steepest descent (DSD) algorithm are introduced. To overcome combinatorial complexity of the DSD algorithm, we employ the sequential approximate integer programming (SAIP) to explicitly and linearly approximate the implicit objective and constraint functions. Considering the multiple potential changed directions for multi-valued design variables, the random discrete steepest descent (RDSD) algorithm is proposed, where a random strategy is implemented to select a definitive direction of change. To analytically calculate multi-material discrete variable sensitivities, topological derivatives with material contrast is applied. In all, the MVIP is finally transferred as the linear 0–1 programming that can be efficiently solved by the canonical relaxation algorithm (CRA). Explicit nonlinear examples demonstrate that the RDSD algorithm owns nearly three orders of magnitude improvement compared with the commercial software (GUROBI). The proposed approach, without using any continuous variable relaxation and interpolation penalization schemes, successfully solves the minimum compliance problem, strength-related problem, and frequency-related optimization problems. Given the algorithm efficiency, mathematical generality and merits over other algorithms, the proposed RDSD algorithm is meaningful for other structural and topology optimization problems involving multi-valued discrete design variables.

A review of computational optimization of bone scaffold architecture: Methods, challenges, and perspectives

Abstract The design and optimization of bone scaffolds are critical for the success of bone tissue engineering (BTE) applications. This review paper provides a comprehensive analysis of computational optimization methods for bone scaffold architecture, focusing on the balance between mechanical stability, biological compatibility, and manufacturability. Finite Element Method (FEM), Computational Fluid Dynamics (CFD), and various optimization algorithms have been discussed for their roles in simulating and refining scaffold designs. The integration of multiobjective optimization and topology optimization has been highlighted for developing scaffolds that meet the multifaceted requirements of BTE. Challenges such as the need for consideration of manufacturing constraints, and the incorporation of degradation and bone regeneration models into the optimization process have been identified. The review underscores the potential of advanced computational tools and additive manufacturing techniques in evolving the field of BTE, aiming to improve patient outcomes in bone tissue regeneration. The reliability of current optimization methods is examined, with suggestions for incorporating non-deterministic approaches and in vivo validations to enhance the practical application of optimized scaffolds. The review concludes with a call for further research into artificial intelligence-based methodologies to drive the next generation of scaffold design and optimization.

Design improvement of steering knuckle through topology optimization for additive manufacturing

Design improvement of steering knuckle through topology optimization for additive manufacturing

Structural topology optimization based on deep learning

In the mechanical design of structures, traditional topology optimization methods involve numerous finite element iterative analyses, leading to a significant expenditure of computational resources. Therefore, the improved multi-scale gradient generative adversarial networks topology optimization technique is proposed. The topology optimization condition parameters are compressed into a low-dimensional latent space feature representation using the encoder, allowing the model to better extract features from these parameters. To speed up model training, the generator and discriminator networks use lightweight residual convolutional blocks. The hybrid attention mechanism extracts prominent region features from the topology optimization structure map. The model training process is guided by a multi-dimensional fusion loss function to enhance the quality of generated model samples. Finally, transferring the parameters of the low-resolution topology optimization model to the high-resolution model enables complete training on a limited amount of high-resolution topology optimization datasets. The experimental data on the low- and high-resolution topology optimization datasets demonstrate that, when compared to alternative methods, this method produces better-quality topology optimization structure maps. Additionally, it can generate high-resolution topology optimization structure maps in minimal time, enabling real-time topology optimization.

Topology optimization for metamaterials with negative thermal expansion coefficients using energy-based homogenization

Since the existing topology designs of negative thermal expansion metamaterials are primarily based on the asymptotic homogenization theory, this paper conducts a topology optimization method of negative thermal expansion metamaterials based on the computationally efficient energy-based homogenization for the first time. In this research, (1) a new effective thermal stress coefficient equation is pioneeringly proposed using energy-based homogenization frame, where its theoretical derivation process is presented as well as its effectiveness and computational efficiency are verified by comparative cases. Additionally, the matlab code is open-sourced for public learning. (2) A topology optimization design of both 2D and 3D metamaterials with negative thermal expansion properties is established innovatively with Discrete Material Optimization (DMO). Its advantages are illustrated compared with the convectional method and its results are validated by Finite Element Method simulations. The new methods have promising applications in the evaluation and optimization of thermal expansion properties of composites.

Topology optimization of extreme mechanical metamaterials considering the anisotropy of additive manufactured parts

Abstract Additive manufacturing (AM) has the advantage of fabricating complex geometries designed by topology optimization. However, the layer-by-layer stacking of AM causes the anisotropic property of the manufactured parts, which is rarely considered in the topology optimization of metamaterials. Hence, this paper presents a new method for the topology optimization of metamaterials with anisotropic properties. First, the elastic moduli in different directions of anisotropic materials are introduced into the solid isotropic material with penalization interpolation function. Then, the effective elastic properties of anisotropic metamaterials are evaluated using the energy-based homogenization method, and the formulation of the topology optimization of anisotropic metamaterials is constructed, and it is iteratively solved by the method of moving asymptotes algorithm. Finally, several two-dimensional and three-dimensional numerical examples and a set of mechanical experiment are conducted to demonstrate the effectiveness of the proposed method.

Compact Ultra-Wideband MIMO Antenna for 5G Communication Systems

Ultra-wideband (UWB) technology is highly respected in modern communication systems because it can enable high-speed, high-data-rate communication over short distances. It is still difficult to achieve optimal multiple-input multiple-output (MIMO) performance in real-world multipath settings, even with breakthroughs in traditional antenna designs. UWB-MIMO antenna solutions have been created and simulated in order to address this problem and fulfill present demands. Nevertheless, maintaining high antenna efficiency and the requirement for antenna designs tailored to a particular bandwidth are two of the difficulties UWB systems must overcome. Recently, a variety of optimization strategies have been employed to obtain high gain and UWB performance by fine-tuning antenna characteristics. The major obstacles are resolved by the optimization process, including the time and computational resource requirements. The difficulty is further compounded by the intricacy of accurately modifying antenna settings to maximize gain. A multi-objective advancement for the design and improvement of the reduced UWB-MIMO test radio wire for 5G was offered as a solution to these problems. In order to attain the UWB feature, we first aim to use a single elliptical-square form antenna that operates from 3.4 to 70 GHz with isolation larger than 29.54 dB. Furthermore, we optimize the impedance bandwidth by tuning the parameters of the structural dimensions using the improved proportional topology optimization (IPTO) technique. Additionally, we present the orthogonal correction using the binarized convolutional neural network (BCNN).

A geometry projection method for designing and optimizing additively manufactured variable-stiffness composite laminates

A method for designing laminates is presented using geometry projection to optimize the layout of additively manufactured variable-stiffness composite laminates. By considering fiberreinforced bars as geometric primitives, the geometry projection methodology is extended to include optimizing regions with intersecting load paths. This is achieved by utilizing a dual representation of bars, which considers the geometric parameters and the element-wise density field representation. The dual representation enables the combining and overlapping of bars, resulting in a localized orthotropic material response at overlapping regions that mitigates the transverse compliant response of fiberreinforced components. The proposed method’s effectiveness is demonstrated through minimizing the compliance of the Messerschmitt-Bölkow-Blohm beam problem, a well-known benchmark problem in topology optimization.

Current status and challenges of lightweight design and manufacturing technology for aerospace structures

Lightweight technology refers to the technology of reducing the structural mass by optimizing materials, structures and manufacturing processes while meeting the requirements of structural performance. It has become one of the key technologies for the development of the new generation of aerospace equipment. This paper first analyzes the development history of lightweight technology. Secondly, from the perspectives of design principles, composition methods and optimization methods, three types of lightweight design methods are introduced: bionic structure design, cellular structure design and efficient topology optimization design. Then, from the perspective of lightweight manufacturing processes and modes, the application of additive manufacturing, collaborative manufacturing and composite material manufacturing in lightweight manufacturing of aerospace structures is explained. Finally, the challenges and future development of lightweight technology are discussed.

A shakedown oriented topology optimization algorithm utilizing second-order cone programming (SOCP) and its application in spacecraft structure design

A shakedown oriented topology optimization algorithm utilizing second-order cone programming (SOCP) and its application in spacecraft structure design

Topology optimization and numerical validation for heat transfer improvement in a packed-bed reactor with monolithic catalyst

This study focuses on optimizing heat transfer in packed-bed reactors by simplifying the problem to a two-dimensional steady-state heat conduction scenario. The objective is to efficiently arrange a limited volume of high-conductivity material to transport heat from the source to the low-conductivity heat-absorbing materials, representing the reacting fluid phase. The topology optimization problem is tackled using a density-based method that relies on a gradient-based algorithm. The optimized design is extruded and compared to a honeycomb internal structure using high-fidelity simulations for steam methane reforming. Results show a 6.04 % improvement in CH4 conversion for the optimized structure, highlighting the potential of this method to enhance monolithic catalysts, particularly in cases where heat transfer critically influences the reaction.

Optimization of offshore wind farm cluster transmission system topology based on Stackelberg game

Offshore wind energy is pivotal in the global energy transition, with a global installed capacity reaching 64.3 GW by 2022 and an expected annual increase of 60.2 GW over the next decade. This study aims to optimize the topology of transmission systems (TS) for offshore wind farm (OWF) clusters using Stackelberg game theory. The OWF investor (OWFI) acts as the leader, optimizing investment returns while considering wake effects, and the offshore TS operator (OTSO) follows by adjusting transmission strategies to reduce costs. The analysis includes the wake effects within OWF clusters and their impact on power generation efficiency. Simulation results demonstrate that the proposed model can balance stakeholder interests and enhance the economic viability of OWF clusters, showing a potential increase in net present value (NPV) by up to 30 %. This study validates the practical application of the Stackelberg game model in optimizing OWF cluster TS topology, contributing to more efficient and cost-effective renewable energy integration.

Topology optimization for elasticity problem of isotropic and orthotropic materials based on the complex variable element-free Galerkin method

Topology optimization for elasticity problem of isotropic and orthotropic materials based on the complex variable element-free Galerkin method

Aircraft joint structure lightweight design

PurposeThe purpose of this article is to carry out a lightweight design of the joint structure according to the service condition of the joint.Design/methodology/approachThe finite element analysis techniques are used along with the variable density structure topology optimization method, the multi-island genetic algorithm for structural dimension optimization and the fatigue life analysis method.FindingsUtilizing the topology optimization method and size optimization method, the mass of the optimized model for the A100 material model is 9.67 kg. Compared to the pre-optimized model, the mass decreases by 8.23 kg, representing a weight reduction of 46.0% in the optimized model; the fatigue life of the aircraft joint is predicted to be a maximum of 1,460,017 cycles.Originality/valueThe originality of this study is that it provides new design ideas for the lightweight design of aircraft load-bearing structures.

Topology optimization based on the improved high-order RAMP interpolation model and bending properties research for curved square honeycomb sandwich structures

Curved honeycomb sandwich structures with larger surface areas effectively reduce the number of fasteners and connectors, resulting in weight reduction, cost savings, and reliability improvement. Square honeycombs exhibit higher in-plane tensile strength, and are more compatible with mechanical components. Topology optimization can realize the variable-density design of square honeycombs, enhancing the strength and stiffness of curved sandwich structures, but the high-order Rational Approximation of Material Properties (RAMP) interpolation model with a fast convergence rate has the relatively poor clarity and stability in topology boundaries. Hence, a variable-density topology optimization method based on the improved high-order RAMP model is developed for curved square honeycomb sandwich structures. The high-order RAMP interpolation model is improved by incorporating a minimum modulus term into the material interpolation function and employing the Bi-directional Evolutionary Structural Optimization (BESO) to refine the Optimality Criteria (OC) method. A functional relationship between the wall thickness of cross-shaped cells (i.e., simplified models of four adjacent square cells arranged in a cross) and the relative density of topology units is constructed using a density mapping method, and the topology optimization with the relative density of cross-shaped cells as the design variable is performed to minimize the compliance of the core. Three-point bending tests are conducted on 3D printed non-optimized and optimized curved honeycomb sandwich structures with different core material retention rates and panel thicknesses, and the experimental results are compared with those of simulations to explore the bending properties and failure behaviors. The results indicate that the modified high-order RAMP interpolation model enhances the clarity and stability of topology structures, the variable-density topology optimization significantly improves the bending properties of curved square honeycomb sandwich structures, and the experimental and numerical results are largely consistent.