Search Results (19)

The topology optimization of reinforced-concrete (RC) building frames is relatively underexplored compared to steel structures, partly due to the lack of a systematic approach to generate and select ground structures (GS). Existing methods often use less systematic GS strategies, limiting efficient exploration of the vast and sparse design space shaped by large bay widths and story heights. This work addresses this gap by providing a comprehensive and systematic pipeline tailored for RC frames. The key contributions are: (1) development of a GS generation framework that systematically enumerates all feasible RC frame configurations within user-defined constraints, (2) introduction of a candidate GS selection map, a surrogate-based tool employing graph-based Latin hypercube sampling (LHS) and sparse Gaussian Process (GP) models, which predicts compliance early and strategically guides candidate selection, reducing computational cost by limiting full finite-element evaluations to the order of ${10}^3$ out of up to ${10}^5$ generated frames while serving as a reference for understanding design-parameter influences; and (3) implementation of an integrated topology-optimization pipeline applying particle swarm optimization (PSO) to selected candidates, achieving efficient compliance minimization with reduced computational effort. The complete workflow—which spans GS generation, surrogate-based candidate selection, and iterative optimization—is implemented and validated in two design domains with width-to-height aspect ratios of 1:1 and 1:1.5 and generates 438,984 and 104,032 different frame configurations, respectively. These selected candidates undergo PSO-based optimization, yielding designs with volume fractions below 0.55 and preserving critical floor beams, demonstrating the framework’s ability to identify structurally efficient stiffness-driven RC frame topologies for early-stage screening. The framework is designed as an extensible foundation that can be coupled with more detailed member-level design checks and nonlinear RC analysis in future work, rather than replacing full reinforced-concrete design procedures. Full article

Ship structural failure mode classification still relies heavily on subjective expert judgment, which is time-consuming and may introduce uncertainty in safety assessment. Although deep learning provides a promising avenue for automation, many existing learning approaches rely on 2D image representations and may therefore suffer from geometric occlusion and information loss when projecting complex 3D stiffened structures. To address these challenges, we propose a Physics-Guided Graph Convolutional Network (PGGCN) for failure mode classification. Specifically, our method models finite-element (FE) meshes directly as graphs, preserving the holistic topology and displacement-field fidelity without viewpoint dependency. We further incorporate domain knowledge through a hybrid strategy: a Deep Graph Convolutional Network (DeepGCN) first detects local component buckling states such as plate or web buckling, and a logic matrix derived from classical failure definitions subsequently determines panel-level failure modes. To enable systematic evaluation, we construct a dataset spanning diverse stiffened-panel geometries via Latin Hypercube Sampling. Progressive analysis states from each loading case are organized into task-specific graph samples for supervised learning. Experiments on the test set achieve accuracies of 95.48% and 91.42% for plate- and web-buckling classification, respectively, and 89.56% for panel-level failure mode discrimination. These results demonstrate that the proposed method provides an interpretable framework for automated failure mode classification from FE meshes in ship stiffened panels. Full article

This paper presents a novel dual-module Permanent Magnet Linear Synchronous Motor (PMLSM) featuring an unequal tooth-shoe topology, alongside a highly efficient surrogate-assisted framework to maximize average thrust and minimize thrust ripple. To overcome the computational bottleneck of expensive Finite Element Analysis (FEA), we propose a Constraint-Preserving Maximin Latin Hypercube Design (CP-MmLHD) coupled with an ARD-Kriging model and the Expected Hypervolume Improvement (EHVI) criterion. This closed-loop framework expertly handles strict geometric constraints and anisotropic parameter sensitivities. Within a strict budget of only 150 FEA evaluations, the framework successfully identifies a high-quality Pareto front. Notably, a representative optimal design reduces thrust ripple by over 80% without compromising average thrust. Furthermore, comparative experiments demonstrate superior computational efficiency over conventional algorithms, while multi-run statistical benchmarking and stochastic Monte Carlo analysis rigorously confirm the framework’s algorithmic robustness and manufacturing reliability. Full article

The computational tractability of Integrated Information Theory (IIT) is fundamentally constrained by the exponential cost of identifying the Minimum Information Partition (MIP), which is required to quantify integrated information (Φ). Existing approaches become impractical beyond ~15–20 variables, limiting IIT analyses on realistic neural and complex systems. We introduce GeoMIP, a geometric–topological framework that recasts the MIP search as a graph-based optimization problem on the n-dimensional hypercube graph: discrete system states are modeled as graph vertices, and Hamming distance adjacency defines edges and shortest-path structures. Building on a tensor-decomposed representation of the transition probabilities, GeoMIP constructs a transition-cost (ground cost) structure by dynamic programming over graph neighborhoods and BFS-like exploration by Hamming levels, exploiting hypercube symmetries to reduce redundant evaluations. We validate GeoMIP against PyPhi, ensuring reliability of MIP identification and Φ computation. Across multiple implementations, GeoMIP achieves 165–326× speedups over PyPhi while maintaining 98–100% agreement in partition identification. Heuristic extensions further enable analyses up to ~25 variables, substantially expanding the practical IIT regime. Overall, by leveraging the hypercube’s explicit graph structure (vertices, edges, shortest paths, and automorphisms), GeoMIP turns an intractable combinatorial search into a scalable graph-based procedure for IIT partitioning. Full article

In traditional scheduling operations, dispatchers mainly rely on SCADA/EMS systems or personal experience. However, with access to a large number of new energy sources, the scale of the distribution network continues to expand, and its topology becomes increasingly complex, leading to potential security risks in scheduling operations. Therefore, it is very important to carry out risk assessments before scheduling operations. In this paper, risk theory is introduced into the field of distribution network scheduling operations, and a new risk assessment method is proposed considering various uncertain factors in the distribution network. In order to comprehensively analyze the influence of uncertainty factors in the operational process of a new distribution network, the output probability models of wind power, photovoltaic power, and load are first constructed in this study. Then, the improved Latin hypercube sampling method is used to extract the operating state of the distribution network system from the probability model, and the node voltage over-limit and line power flow overload are used as indicators to measure the severity of the consequences so as to establish a quantitative scheduling operation risk assessment system and analyze its framework in detail. Finally, simulation analysis is carried out in the improved IEEE-RTS79 test system: taking 15–25 lines from the operation state to the maintenance state as an example, this paper analyzes the influence of different locations and capacities of wind and solar access on the scheduling operation risk of distribution networks. The results can provide a reference for dispatchers to prevent risks before operation. Full article

Analyzing network connectivity is important for evaluating the robustness, efficiency, and overall performance of various architectural designs. By examining the intricate interactions among nodes and their connections, researchers can determine a network’s resilience to failures, its capacity to support efficient information flow, and its adaptability to dynamic conditions. These insights are critical across multiple domains—such as telecommunications, computer science, biology, and social networks—where optimizing connectivity can significantly enhance functionality and reliability. In the literature, there are many variations of connectivity to measure network resilience and fault tolerance. In this survey, we focus on connectivity, tightly super connectivity, and h-extra connectivity within DVcube networks—a compound architecture combining disk-ring and hypercube-like topologies. Additionally, we identify several open problems to encourage further exploration in future research. Full article

To address the shortcomings of the Dung Beetle Optimization (DBO) algorithm in ship power-system fault reconfiguration, such as low population diversity and an imbalance between global exploration and local exploitation, the authors of this paper propose an improved Dung Beetle Optimization (LESSDBO) algorithm. The improvements include optimizing the initial population using Latin hypercube sampling and an elite population strategy, optimizing parameters with an improved sigmoid activation function, introducing the sine–cosine algorithm (SCA) for position update optimization, and performing multi-population mutation operations based on individual quality. The LESSDBO algorithm was applied to simulate the fault reconfiguration of a ship power system, and it was compared with the traditional DBO, Genetic Algorithm (GA), and Modified Particle Swarm Optimization (MSCPSO) methods. The simulation results showed that LESSDBO outperformed the other algorithms in terms of convergence accuracy, convergence speed, and global search capability. Specifically, in the reconfiguration under Fault 1, LESSDBO achieved optimal convergence in seven iterations, reducing convergence iterations by more than 30% compared with the other algorithms. In the reconfiguration under Fault 2, LESSDBO achieved optimal convergence in eight iterations, reducing convergence iterations by more than 23% compared with the other algorithms. Additionally, in the reconfiguration under Fault Condition 1, LESSDBO achieved a minimum of four switch actions, which is 33% fewer than the other algorithms, on average. In the reconfiguration under Fault Condition 2, LESSDBO achieved a minimum of eight switch actions, which is a 5.9% reduction compared with the other algorithms. Furthermore, LESSDBO obtained the optimal reconfiguration solution in all 50 trials for both Faults 1 and 2, demonstrating a 100% optimal convergence probability and significantly enhancing the reliability and stability of the algorithm. The proposed method effectively overcomes the limitations of the traditional DBO in fault reconfiguration, providing an efficient and stable solution for the intelligent topology reconfiguration of ship power systems. Full article

Permanent magnet-assisted synchronous reluctance motors (PMA-SynRMs) are widely used in various industries as a relatively inexpensive and high-performance energy conversion device. The model proposed in this article relies on a magnetic pole-biased permanent magnet synchronous reluctance motor with a magnetic focusing effect. Two types of models with Halbach array and magnetic focusing effect have been proposed, which increase excitation and make the internal magnetic circuit of the rotor more saturated, thereby achieving higher electromagnetic torque. Through finite element simulation analysis and verification, the motor characteristics of the basic and proposed permanent magnet-assisted synchronous reluctance motor were calculated, including the air gap flux density and back electromotive force (EMF) in no-load analysis, as well as the average torque, torque ripple, and efficiency in load analysis. In addition, multi-objective optimization was also conducted on the rotor topology structure of proposed model two, using the uniform Latin hypercube sampling method to uniformly sample the data samples and the Pearson correlation coefficients to perform a sensitivity analysis on the data. The pilOPT multi-objective autonomous optimization algorithm was used to perform multi-objective autonomous optimization on parameters with high correlation, and the best-found solution based on the Pareto front was selected. Compared with proposed model two, the average torque of the optimized model increased by 18.14%, the efficiency increased by 1.05% and the torque ripple decreased by 5.22%. Finally, the anti-demagnetization performance of the optimized model’s permanent magnet was analyzed. Full article

The seal and weight of the Type IV hydrogen storage vessel are the key problems restricting the safety and driving range of fuel cell vehicles. The boss, as a metal medium connecting the inner liner of the Type IV hydrogen storage vessel with the external pipeline, affects the sealing performance of the Type IV hydrogen storage vessel, and there is no academic research on the weight of the boss. Therefore, according to the force characteristics of the boss, this paper divides the upper and lower areas (valve column and plate). The valve column with seal optimization and light weight is manufactured with a 3D printing additive, while the plate bearing and transferring the internal pressure load is manufactured by forging. Firstly, a two-dimensional axisymmetric simulation model of the sealing ring was established, and the effects of different compression rates on its seal performance were analyzed. Then, the size and position of the sealing groove were sampled, simulated, and optimized based on the Latin Hypercube method, and the reliability of the optimal seal structure was verified by experiments. Finally, the Solid Isotropic Material with Penalization (SIMP) topology method was used to optimize the weight of the boss with optimal sealing structure, and the reconstructed model was checked and analyzed. The results show that the weight of the optimized boss is reduced by 9.6%. Full article

This research proposes an efficient technique for reliability-based topology optimization (RBTO), which deals with uncertainty and employs proportional topology optimization (PTO) to achieve the optimal reliability structure. The recent technique, called proportional topology optimization for reliability (PTOr), uses Latin hypercube sampling (LHS) for uncertainty quantification. The difficulty of the double-loop nested problem in uncertainty quantification (UQ) with LHS can be alleviated by the power of PTO, enabling RBTO to be performed easily. The rigorous advantage of PTOr is its ability to accomplish topology optimization (TO) without gradient information, making it faster than TO with evolutionary algorithms. Particularly, for reliability-based topology design, evolutionary techniques often fail to achieve satisfactory results compared to gradient-based techniques. Unlike recent PTOr advancement, which enhances the RBTO performance, this achievement was previously unattainable. Test problems, including an aircraft pylon, reveal its performances. Furthermore, the proposed efficient framework facilitates easy integration with other uncertainty quantification techniques, increasing its performance in uncertainty quantification. Lastly, this research provides computer programs for the newcomer studying cutting-edge knowledge in engineering design, including UQ, TO, and RBTO, in a simple manner. Full article

All-to-all broadcasting means to distribute the exclusive message of each node in the network to all other nodes. It can be handled by rings, and a Hamiltonian cycle is a ring that visits each vertex exactly once. Multiple edge-disjoint Hamiltonian cycles, abbreviated as EDHCs, have two application advantages: (1) parallel data broadcast and (2) edge fault-tolerance in network communications. There are three edge-disjoint Hamiltonian cycles on n-dimensional locally twisted cubes and n-dimensional crossed cubes while n ≥ 6, respectively. Locally twisted cubes, crossed cubes, folded locally twisted cubes (denoted as FLTQ_n), and folded crossed cubes (denoted as FCQ_n) are among the hypercube-variant network. The topology of hypercube-variant network has more wealth than normal hypercubes in network properties. Then, the following results are presented in this paper: (1) Using the technique of edge exchange, three EDHCs are constructed in FLTQ₅ and FCQ₅, respectively. (2) According to the recursive structure of FLTQ_n and FCQ_n, there are three EDHCs in FLTQ_n and FCQ_n while n ≥ 6. (3) Considering that multiple faulty edges will occur randomly, the data broadcast performance of three EDHCs in FLTQ_n and FCQ_n is evaluated by simulation when 5 ≤ n ≤ 9. Full article

The hypercube is one of the best models for the network topology of a distributed system. Recently, Padovan cubes and Lucas–Padovan cubes have been introduced as new interconnection topologies. Despite their asymmetric and relatively sparse interconnections, the Padovan and Lucas–Padovan cubes are shown to possess attractive recurrent structures. In this paper, we determine the cube polynomial of Padovan cubes and Lucas–Padovan cubes, as well as the generating functions for the sequences of these cubes. Several explicit formulas for the coefficients of these polynomials are obtained, in particular, they can be expressed with convolved Padovan numbers and Lucas–Padovan numbers. In particular, the coefficients of the cube polynomials represent the number of hypercubes, a symmetry inherent in Padovan and Lucas–Padovan cubes. Therefore, cube polynomials are very important for characterizing these cubes. Full article

The aluminium alloy front subframe of an automobile was developed through multi-operating condition topology optimization and multi-objective optimization methods. By considering the influences of loads on the strength, static stiffness, and modal of the aluminium alloy front subframe under typical operating conditions, the performance parameters of the aluminium alloy front subframe after topology optimization were obtained. After topology optimization was performed, the parametric model of the aluminium alloy front subframe was established. Based on the Isight optimization platform, sample points were generated with the optimal Latin hypercube test method, and the response surface approximate model was constructed. The minimum mass and maximum first-order frequency were taken as the objectives, the stress under typical working conditions did not exceed the set target value, and the maximum displacement of the installation point was taken as the constraint condition. The multi-objective particle swarm optimization algorithm was used to optimize the aluminium alloy front subframe. The error of the free modal and finite element free modal analysis of the aluminium alloy front subframe samples was less than 15%. The optimized aluminium alloy front subframe was 2.4 kg lighter than the original subframe under the premise of satisfying various performance indices, and the lightweight rate was up to 12%. Full article

We obtain a large number of degree and distance-based topological indices, graph and Laplacian spectra and the corresponding polynomials, entropies and matching polynomials of n-dimensional hypercubes through the use of Hadamard symmetry and recursive dynamic computational techniques. Moreover, computations are used to provide independent numerical values for the topological indices of the 11- and 12-cubes. We invoke symmetry-based recursive Hadamard transforms to obtain the graph and Laplacian spectra of nD-hypercubes and the computed numerical results are constructed for up to 23-dimensional hypercubes. The symmetries of these hypercubes constitute the hyperoctahedral wreath product groups which also pave the way for the symmetry-based elegant computations. These results are used to independently validate the exact analytical expressions that we have obtained for the topological indices as well as graph, Laplacian spectra and their polynomials. We invoke a robust dynamic programming technique to handle the computationally intensive generation of matching polynomials of hypercubes and compute all matching polynomials up to the 6-cube. The distance degree sequence vectors have been obtained numerically for up to 108-dimensional cubes and their frequencies are found to be in binomial distributions akin to the spectra of n-cubes. Full article

Aiming at the current problems of low excitation efficiency and poor reliability of single-rotor hybrid excitation generators, the large axial length of dual-rotor structure, and difficulty in magnetic field analysis, a new type of the dual-rotor hybrid excitation generator topology with high power density is proposed, with two rotors side-by-side coaxial, sharing a set of armature windings, and the magnetic fields do not interfere with each other, so the magnetic field analysis and optimization of the two rotors can be carried out separately. The magnetic density distribution of the new permanent magnet (PM) claw pole rotor is analyzed by the joint application of the equivalent magnetic circuit method and the equivalent magnetic network method, which ensures the simplicity of calculation and improves the calculation accuracy. The multi-objective optimization of the key structural parameters is carried out based on the Latin hypercube sampling–Pareto frontier solution method. The subdomain method is improved by segmented equivalence, the unique solution of the salient-pole rotor magnetic field is obtained, and the multi-objective optimization of the salient-pole rotor is used by the particle swarm algorithm. The trial prototype was experimental, and the results showed that the output characteristics of the optimized hybrid excitation generator were significantly improved, and the overall performance of the generator was improved. Full article