Efficient Topology Research Articles

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.

Problem-independent machine learning-enhanced structural topology optimization of complex design domains based on isoparametric elements

Topology optimization requires dozens or even hundreds of iterations, each requiring a complete finite element analysis (FEA). Significant computation cost limits the application of topology optimization in engineering, especially for high-resolution problems containing complex design domains. To address the issue, a Problem-Independent Machine Learning (PIML) model based on isoparametric elements is proposed. Effectively reducing the computational time of FEA, the proposed model enables efficient topology optimization and extends the solvable problem range to complex design domains. The essential idea is leveraging the substructure method and establishing a mapping from element shapes and material distribution within the substructure to its numerical shape functions through machine learning models. Both sample generation and model training are conducted offline, allowing the trained machine learning model to be directly employed during the topology optimization process. Since the shape function of the substructure is problem-independent, it requires no sample regeneration or modification of the proposed machine learning model when changing the geometry or boundary conditions of the optimization problem. Numerical examples demonstrate that the proposed machine learning model boosts the efficiency of topology optimization by one order of magnitude without parallel techniques.

Adaptive topology optimization for enhancing resistance to brittle fracture using the phase field model

Computational cost is one of the challenges in the field of fracture resistance topology optimization. An efficient topology optimization approach is proposed for enhancing resistance to structural fracture based on the adaptive isogeometric–meshfree method. The mesh can be adaptively refined in the computational and design domains simultaneously to capture the fracture and structural boundary delicately, which significantly reduces the degree of freedom and improves the efficiency of the topology optimization problems related to crack propagation. The proposed approach naturally inherits the advantages of both the computer-aided design and continuity of the higher-order basis functions, where the geometry and the analysis models in the process of topology optimization are integrated. The problem is formulated to maximize the absorbed energy throughout the crack process using the solid isotropic material with penalization method under volume constraints. The phase field method is used for modeling crack propagation, thereby eliminating the need of an explicit representation of the crack surface and complex tracking procedures, while a delicate mesh is still required. With this approach, the external work required during the fracture process is maximized, and the crack growth is delayed remarkably. Several representative examples are demonstrated to verify the effectiveness of the present approach in fracture-resistance-enhanced topology optimization, and the computational cost shows remarkable improvement.

Soft charging and fewer switches based single‐source double‐boost multilevel DC‐AC converter for low/medium voltage applications

AbstractThis paper presents a novel generalized step‐up switched‐capacitor multilevel inverter (SCMLI) appropriate for low/medium voltage input applications. To reduce the overall number of power switches, the suggested topology makes use of reduction switch count (SC) technology. The complementary working mechanism of each pair of switches simplifies the modulation method. The complete inductive‐load capability is taken into account in the proposed inverter. Furthermore, because capacitor voltages are automatically balanced, complex voltage control circuitry is not necessary. Due to a reduction in current spikes on the devices, the topology also has the ability to soft‐charge the capacitors in a quasi‐resonant manner. Performance is also enhanced by extending the lifetime and dependability of the inverters. The construction, operation, modulation method, values of the capacitors, and circuit losses are explained to demonstrate how the suggested topology should function. One of the inverter's primary differentiators from the others is its double‐boosting capability based on reduced components. Based on loss calculations, a thorough analysis of the suggested topology's efficiency of 98.31% is presented. Next, the benefits of the suggested inverter are compared to those of other inverters that have been recently suggested. The steady‐state and dynamic performance of the suggested topology is then confirmed by modeling the system on the Simulink platform and performing experiments using a DSP‐based prototype experimental setup.

Biomimetic Mechanical Robust Cement-Resin Composites with Machine Learning-Assisted Gradient Hierarchical Structures.

Biological materials relying on hierarchically ordered architectures inspire the emergence of advanced composites with mutually exclusive mechanical properties, but the efficient topology optimization and large-scale manufacturing remain challenging. Herein, this work proposes a scalable bottom-up approach to fabricate a novel nacre-like cement-resin composite with gradient brick-and-mortar (BM) structure, and demonstrates a machine learning-assisted method to optimize the gradient structure. The fabricated gradient composite exhibits an extraordinary combination of high flexural strength, toughness, and impact resistance. Particularly, the toughness and impact resistance of such composite attractively surpass the cement counterparts by factors of approximately 700 and 600 times, and even outperform natural rocks, fiber-reinforced cement-based materials and even some alloys. The strengthening and toughening mechanisms are clarified as the regional-matrix densifying and crack-tip shielding effects caused by the gradient BM structure. The developed gradient composite not only endows a promising structural material for protective applications in harsh scenarios, but also paves a new way for biomimetic metamaterials designing.

Topological derivatives for shell structure optimization considering crash loadcases with material nonlinearities and large deformations

The topological derivatives for shell structures in dynamic loadcases, which are often used in lightweight designs, are developed. These sensitivities consider the highly nonlinear material behavior and large deformations of the structure. The aim of the investigation is the support of an efficient topology optimization for crashloaded structures. The considered functionals can in general be described by displacements, velocities and accelerations. In particular, the semi-analytical sensitivity calculations for the internal deformation energy and for single point displacements in arbitrary directions in the shell structure are presented. These functionals cover the most important functions for the development of crash structures. Using the material derivative in combination with the adjoint method, a terminal value problem has to be solved. Depending on whether first differentiation and then discretization in the time domain is performed or first discretization and then differentiation, a separate solution scheme for the adjoint is derived. Both schemes are presented in a comparable description. For the numerical evaluation of the topological derivatives, the basic equations for large deformed shell elements are included as well as a phenomenological material interpolation, which represents the tangential material behavior resulting from plastic strain and isotropic hardening. All assumptions made for the derivation are described in detail and their influences are discussed. The elaborated equations and the procedure for the calculation of the topological derivatives are applied, discussed and checked for plausibility on two clearly defined loading conditions for the internal deformation energy and single point displacements on different locations.

A thermal model for topology optimization in additive manufacturing: Design of support structures and geometry orientation

In this study, we present an efficient Topology Optimization (TO) approach designed to optimize support structures in metal Additive Manufacturing (AM), with a particular focus on Powder Bed Fusion (PBF) technology. The developed framework uses a purely thermal formulation to identify regions within the design that are susceptible to high heat concentrations. In the proposed modeling of the AM process, we postulate that new material layers are included in a partially built design that has already cooled to a controlled temperature. This aspect provides a layer-by-layer model of AM entirely local, enabling the building process parallelization and resulting in an algorithm with superior computational efficiency. Numerical results show the robustness of the proposed strategy, with the successful incorporation of support structures beneath overhanging surfaces and their effectiveness across a wide range of geometries and orientations. Furthermore, this framework is also applied to optimize the geometry orientation within the build chamber, further enhancing its applicability in the AM context.

Efficient topology reconfiguration for NoC-based multiprocessors: A greedy-memetic algorithm

In multi-core processor systems, the Network-on-Chip (NoC) serves as a vital communication infrastructure. To ensure chip reliability during potential failures, this paper proposes a two-level topology reconfiguration algorithm with core-level redundancy technology. Initially, a heuristic topology reconfiguration method utilizing a greedy strategy is proposed to perform local replacement of faulty processing elements (PEs) and generate an initial logical topology with shorter interconnection paths between PEs. Then, an intelligent optimization method based on memetic algorithm is introduced to optimize the generated initial topology for better communication performance. The experimental results demonstrate that compared to the current state-of-the-art algorithm, the proposed algorithm achieves an average improvement of 13.92% and 30.83% on various size topologies in terms of distance factor (DF) and congestion factor (CF), which represent communication delay and traffic balance respectively. The proposed algorithm significantly enhances the communication performance of the target topology, mitigating communication latency and potential congestion problems.

Multipoint Relay Path for Efficient Topology Maintenance Algorithm in Optimized Link State Routing-Based for VANET

The Optimal Link State Routing (OLSR) protocol employs multipoint relay (MPR) nodes to disseminate topology control (TC) messages, enabling network topology discovery and maintenance. However, this approach increases control overhead and leads to wasted network bandwidth in stable topology scenarios due to fixed flooding periods. To address these challenges, this paper presents an Efficient Topology Maintenance Algorithm (ETM-OLSR) for Enhanced Link-State Routing Protocols. By reducing the number of MPR nodes, TC message generation and forwarding frequency are minimized. Furthermore, the algorithm selects a smaller subset of TC messages based on the changes in the MPR selection set from the previous cycle, adapting to stable and fluctuating network conditions. Additionally, the sending cycle of TC messages is dynamically adjusted in response to network topology changes. Simulation results demonstrate that the ETM-OLSR algorithm effectively reduces network control overhead, minimizes end-to-end delay, and improves network throughput compared to traditional OLSR and HTR-OLSR algorithms.

Fail-Safe Topology Optimization Using Damage Scenario Filtering

Within the framework of isotropic materials, this paper introduces an efficient topology optimization method that incorporates fail-safe design considerations using a penalty function approach. Existing methods are either computationally expensive or overlook fail-safe requirements during optimization. This approach not only achieves optimized structures with fail-safe characteristics, but also significantly enhances the computational efficiency of fail-safe topology optimization. In this method, the minimization of worst-case compliance serves as the optimization objective, employing the Kreisselmeier–stein Hauser function to approximate the non-differentiable maximum operator. A sensitivity analysis, derived through the adjoint method, is utilized, and a universal fail-safe optimization criterion is developed to update the design variables. During the optimization process for fail-safe strategies, a density-based filtering method is applied, effectively reducing damage scenarios. Finally, the effectiveness and computational efficiency of this method are validated through several numerical examples.

A Comprehensive and Efficient Topology Representation in Routing Computation for Large-Scale Transmission Networks

A Comprehensive and Efficient Topology Representation in Routing Computation for Large-Scale Transmission Networks

Topology optimization of extruded beams modeled with the XFEM for maximizing their natural frequencies

In this paper, an efficient topology optimization approach is developed for maximizing the fundamental natural frequency of extruded beams. Mass fraction and static compliance bounds are defined using inequality-type constraints in the optimization problem. An XFEM approach, previously proposed by the authors for analyzing beam elements, is extended herein to compute the natural frequencies of the beam. The method allows for 3D modeling of beams with a significant reduction in the number of degrees of freedom and therefore also yields efficient optimization procedure. This reduction is made possible by incorporating global enrichment functions in the longitudinal direction, which enables a significant reduction in the number of elements in that direction without loss of accuracy. A nonlinear optimization problem is formulated using continuous density-based design variables that represent the material distribution in the beam’s cross-section. The optimization problem is then solved using a gradient-based approach with analytical sensitivities. The well-known Solid Isotropic Material with Penalization (SIMP) method is used to acquire discrete solutions. We study the optimal design of short and long beams. It is shown that for short beams, localized vibration modes appear within the cross-section, leading to a significant distortion deformation mode of the cross-section. The optimized design of the long beam shows global deformation modes with an increase of 15% in the fundamental frequency compared with a non-optimized design consisting of a hollow rectangular cross-section with the same mass.

An efficient topology partitioning algorithm for system-level parallel simulation of mega satellite constellation communication networks

Satellite Internet, as an important component of the integrated space-ground information network, is a hot research hotspot nowadays. Many scholars have undertaken research in the areas of constellation networking design, network protocol design, and communication performance assessment, and their main research tool is software simulation. Traditional stand-alone network simulation simulators based on OPNET or NS3 are constrained in the simulation efficiency of mega satellite networks because of the limitations of computer hardware conditions and software performance. Based on the above characteristics, we propose a parallel network simulation architecture based on low correlation between different areas of the global satellite network, and in order to improve the parallel network simulation performance, the network topology needs to be divided effectively. Therefore, firstly we consider CPU and memory resource consumption as a measure of topology partitioning performance indicators, propose a resource assessment algorithm and use the result of this assessment as the topology partitioning optimization objective; secondly, we propose a load balancing based intelligent topology partitioning algorithm (LBTP); thirdly, we propose a time slice algorithm (TSA) for parallel simulation in each time cycle. To demonstrate the algorithm proposed in this paper, we built a simulation platform based on the combination of STK (Satellite Tool Kit), OPNET and Proxmox VE, and experimentally verified that the proposed architecture and algorithm significantly improve the simulation efficiency.

PCB-Based Magnetic Integration and Design Optimization for Three-Phase LLC

The LLC converter is the most efficient topology in server and telecom applications. Furthermore, three interleaved LLC converters have been shown to yield efficiency advantages at power levels of several kilowatts. The magnetic components of a multiphase LLC, on the other hand, are complex, laborious, and difficult to build cost-effectively. To solve these issues, a high-frequency GaN-based three-phase LLC converter is used in this research. With GaN working at 500 kilohertz, all magnetic components, including six inductors and six transformers, can be housed in a single structure, while all magnetic windings are accommodated in a 4-layer PCB with 3 oz. of copper. Furthermore, EMI can be improved by virtue of three-phase by 20 dB and additional two layers is added to gain additional 20-30 dB reduction from a low frequency up to 30 MHz. The proposed construction is simple and economically mass-producible. A 6kW 400V/48V 3-phase prototype is implemented, with a peak efficiency over 99% and a power density of 1000 W/in3 (61 kW/L).

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.

Torque Ripple Suppression for Standalone Brushless Doubly-Fed Induction Generator-DC System Based on Power Winding Harmonic Current Control

The brushless doubly-fed induction generator-dc (BDFIG-DC) system has received more and more attentions with the cost-effective and efficient topology in recent years. However, it suffers from the torque ripple due to the distorted flux and current caused by the diode rectifier, which reduces the mechanical reliability of the system. In order to address this issue, this paper proposes a torque ripple suppression method based on the PW harmonic current control. Compared with the traditional method, the proposed method takes the 6th and 12th harmonic components of the torque generated by the -5th and 7th harmonic fluxes and currents into account, which can maximize the torque ripple suppression effect of all the controllable PW harmonic currents. Besides, it is with the simpler control algorithm and higher robustness. Comprehensive experimental results based on a 5-kVA BDFIG demonstrate the effectiveness of the proposed method.

A Transmission-Reliable Topology Control Framework Based on Deep Reinforcement Learning for UWSNs

This paper focuses on the topology optimization problem to decrease transmission delay and prolong network lifetime on the premise of reliable transmission in Underwater Wireless Sensor Networks (UWSNs). This is extremely challenging owing to dynamic ocean current movement, harsh underwater communication channel and increasingly demanding application requirements. With the development of software defined network architectures, the centralized topology control (TC) strategy with a global perspective in UWSNs is expected to become a more effective way to tackle the above challenges compared with the existing distributed and heuristic TC strategies involving local network state information. Therefore, we first transform the topology optimization problem of UWSNs into an integer nonlinear programming (INLP) model and design a centralized TC framework to solve the INLP model. In this framework a TC center is built to periodically generate the network topology for UWSNs according to the current network state information. Further, an efficient topology generation algorithm based on deep reinforcement learning (TGA-DRL) is proposed in the TC center. In TGA-DRL, to reduce computing overhead and improve operational efficiency, we formulate an action-space narrowed Markov decision process suitable for network topology generation and solve it with the aid of the rainbow algorithm which is a deep reinforcement learning model. Finally, the performance of our centralized TC framework is verified in terms of the node out-degree, algorithm convergence and optimization effect.

Three-dimensional metamaterials exhibiting extreme isotropy and negative Poisson's ratio

This research will develop a new set of mechanical metamaterials with simultaneous ideal elastic isotropy and extreme negative Poisson's ratio through a topology optimization approach. Firstly, we propose a new method to generate a three-dimensional ground structure with geometric cubic symmetry. Secondly, a more efficient topology optimization method is developed by utilizing a small number of both integer and continuous design variables for topological description of the design based on the discrete ground structure. Thirdly, two new geometric constraints are incorporated into the mathematical formulation to ensure creation of designs that are practical. Finally, the genetic algorithm is used to solve this type of mixed-integer programming problems. Several numerical examples have been used to demonstrate the generation of a series of three-dimensional microarchitectures from a single integrated design. The microlattices can also be customized to obtain a broad range of negative Poisson's ratios across different relative densities. The effective material properties and macroscopic deformation behaviors of the designs are numerically validated using commercial software.

Computationally efficient topology optimization for the SynRMs based on the torque curve interpolation

Computationally efficient topology optimization for the SynRMs based on the torque curve interpolation

A 3D Structure Mapping-Based Efficient Topology Optimization Framework

Abstract It is well known that the computational cost of classic topology optimization (TO) methods increases rapidly with the size of the design problem because of the high-dimensional numerical simulation required at each iteration. Recently, the technical route of replacing the TO process with artificial neural network (ANN) models has gained popularity. These ANN models, once trained, can rapidly produce an optimized design solution for a given design specification. However, the complex mapping relationship between design specifications and corresponding optimized structures presents challenges in the construction of neural networks with good generalizability. In this paper, we propose a new design framework that uses deep learning techniques to accelerate the TO process. Specifically, we present an efficient topology optimization (ETO) framework in which structure update at each iteration is conducted on a coarse scale and a structure mapping neural network (SMapNN) is constructed to map the updated coarse structure to its corresponding fine structure. As such, fine-scale numerical simulations are replaced by coarse-scale simulations, thereby greatly reducing the computational cost. In addition, fragmentation and padding strategies are used to improve the trainability and adaptability of SMapNN, leading to a better generalizability. The efficiency and accuracy of the proposed ETO framework are verified using both benchmark and complex design tasks. It has been shown that with the SMapNN, TO designs of millions of elements can be completed within a few minutes on a personal computer.