Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
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    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
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    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
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    - THEORY OF COMPUTING (10 journals)

COMPUTER SCIENCE (1305 journals)

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Structural and Multidisciplinary Optimization
Journal Prestige (SJR): 1.458
Citation Impact (citeScore): 3
Number of Followers: 12  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1615-1488 - ISSN (Online) 1615-147X
Published by Springer-Verlag Homepage  [2468 journals]
  • Adaptive member adding for truss topology optimization: application to
           elastic design

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      Abstract: Abstract This paper provides a rigorous and computationally efficient means of identifying compliance-based optimal truss topologies from high-resolution ground structures—problems involving over 30 million potential elements can be solved in under 1 h on a typical laptop. The adaptive ‘member adding’ approach is shown to provide significant savings in computational time and memory usage compared to directly solving the full optimization problem, whilst obtaining the same optimal solution; this paper presents the first application of this powerful principle to the well-known linear-elastic design problem. The computational advantages are particularly notable for multiple load-case problems, as the numerical structure of these cannot be effectively exploited without understanding of the physical nature of the problem. For such cases, the member adding process reduces the computational time required from approximately \(O(m^2)\) to O(m), where m is the number of potential elements; here, the time required is reduced by a factor of up to 60 for the relatively small problems that could be solved with both approaches. By using the member adding approach, compliance-optimized structures are obtained at a significantly higher resolution than has previously been possible. The findings of this paper have the potential to deliver a step change in the size of problems that can be solved in compliance-based truss topology optimization, and an accompanying Python code is provided to facilitate this.
      PubDate: 2024-07-10
       
  • Active learning for adaptive surrogate model improvement in
           high-dimensional problems

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      Abstract: Abstract This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. In this approach, the principal components and corresponding features of the high-dimensional output are first identified. For each feature, the active subspace technique is used to identify a corresponding low-dimensional subspace of the input domain; then a surrogate model is built for each feature in its corresponding active subspace. A low-dimensional adaptive learning strategy is proposed to identify training samples to improve the surrogate model. In contrast to existing adaptive learning methods that focus on a scalar output or a small number of outputs, this paper addresses adaptive learning with high-dimensional input and output, with a novel learning function that balances exploration and exploitation, i.e., considering unexplored regions and high-error regions, respectively. The adaptive learning is in terms of the active variables in the low-dimensional space, and the newly added training samples can be easily mapped back to the original space for running the expensive physics model. The proposed method is demonstrated for the numerical simulation of an additive manufacturing part, with a high-dimensional field output quantity of interest (residual stress) in the component that has spatial variability due to the stochastic nature of multiple input variables (including process variables and material properties). Various factors in the adaptive learning process are investigated, including the number of training samples, range and distribution of the adaptive training samples, contributions of various errors, and the importance of exploration versus exploitation in the learning function.
      PubDate: 2024-07-10
       
  • Multi-objective structure optimization for interior permanent magnet
           synchronous motors under complex operating conditions

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      Abstract: Abstract In order to improve the dynamic performance of permanent magnet synchronous motors (PMSMs) under complex operating conditions and reduce energy consumption, this paper establishes a finite element model of the PMSM and proposes a method of representing the operating characteristics of the PMSM on the speed-torque plane using equivalent points. Based on the equivalent points, a multi-objective optimization method for the structure of PMSMs under complex operating conditions is proposed. Firstly, the method establishes an approximate model of the finite element model to study the influence of the permanent magnet structure and position on the dynamic performance of the PMSM. Subsequently, an improved particle swarm optimization algorithm is utilized to obtain the Pareto front solutions for the optimization objectives. Finally, a fuzzy membership degree algorithm is employed to extract the optimal compromise solution. The results show that the proposed optimization method can reduce the PMSM’s torque ripple, improve the average torque, decrease the energy consumption, and improve vehicle comfort.
      PubDate: 2024-07-09
       
  • TOMAS: topology optimization of multiscale fluid flow devices using
           variational auto-encoders and super-shapes

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      Abstract: Abstract In this paper, we present a framework for multiscale topology optimization of fluid flow devices. The objective is to minimize dissipated power, subject to a desired contact area. The proposed strategy is to design optimal microstructures in individual finite element cells, while simultaneously optimizing the overall fluid flow. In particular, parameterized super-shapes are chosen here to represent microstructures since they exhibit a wide range of permeability and contact area. To avoid repeated homogenization, a finite set of these super-shapes are analyzed a priori and a variational auto-encoder (VAE) is trained on their fluid constitutive properties (permeability), contact area, and shape parameters. The resulting differentiable latent space is integrated with a coordinate neural network to carry out a global multiscale fluid flow optimization. The latent space enables the use of new microstructures that were not present in the original dataset. The proposed method is illustrated using numerous examples in 2D.
      PubDate: 2024-07-08
       
  • Additive manufacturing-driven topological design considering overhang and
           connectivity constraints induced by closed cavity

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      Abstract: Abstract Closed cavity is a common design feature in topology optimization, but quite unfavorable for fabrication, even for highly flexible powder-based additive manufacturing (AM) technology. This is due to the fact that the temporary support material and unmelted powder inside the closed cavity are impossible to remove without damaging the optimized structure, yet would degrade design performance if left in the structure. Thus, this paper presents an AM-driven topological design method to solve the fabrication issues caused by closed cavities, while reducing the effect of manufacturing constraints on design freedom. Specifically, a sequential strategy integrated with self-support topology and connectivity design is developed to tackle the unprintable overhang features and trapped powder problem, rather than directly restricting the generation of closed cavities. Firstly, the closed cavities in the optimized structure are identified by introducing a connected component labeling algorithm, and then the overhang features and connectivity can be evaluated. The self-support topology is achieved by eliminating the overhang elements based on the proposed hybrid modification scheme. On the other hand, the connectivity design is formulated as finding the optimal paths connecting the closed cavities to the structural outside, in which the elements on the paths are deleted as the channels for removing residual powder. To illustrate the effectiveness of the proposed method, multiple 3D numerical examples and manufacturing experiments are conducted. The outcomes consistently demonstrate the advantage of the sequential strategy in achieving printable structures while minimizing any potential performance degradation.Please check and confirm the author names and initials are correct. Also, kindly confirm the details in the metadata are correct.No problem.
      PubDate: 2024-07-06
       
  • Steel optimization for reinforced concrete using an equilibrium-based
           formulation

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      Abstract: Abstract This article introduces an innovative approach to optimize steel reinforcement in reinforced concrete structures using the equilibrium-based formulation described in Ferradi et al. (Comput Struct 286:107095, 2023). The objective is to enhance structural performance and cost-effectiveness by efficiently distributing steel reinforcement within an arbitrary volume representing the concrete, while ensuring external loading equilibrium. To achieve this goal, two approaches are proposed: the first approach focuses on optimizing the cross section of 1D rebars that are already defined in the concrete volume as curves, while the second approach optimizes the steel bulk densities throughout the entire volume, with or without assuming predetermined principal directions for the reinforcement, represented by a steel stress tensor. The latter approach does not rely on any prior knowledge of the geometric distribution of rebars and can be seen as an automated strut and tie method for three-dimensional problems , where the struts are derived from the compression flow, and the ties originate from the tensile flow. Both approaches are formulated as optimization problems, enabling the utilization of the interior point method for effective problem-solving. Numerical examples and comparisons with existing results vividly illustrate the flexibility and potential efficiency gains achievable through the proposed approaches.
      PubDate: 2024-07-04
       
  • Concurrent level set topology and fiber orientation optimization of
           fiber-reinforced composite structures

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      Abstract: Abstract By altering the structural shape and fiber orientation, this research aims to optimize the design of Fiber-Reinforced Composite (FRC) structures. The structural geometry is represented by a level set function approximated by quadratic B-spline functions. The fiber orientation field is parameterized with quadratic/cubic B-splines on hierarchically refined meshes. Different levels for B-spline mesh refinement for the level set and fiber orientation fields are studied to resolve geometric features and to obtain a smooth fiber layout. To facilitate FRC manufacturing, the parallel alignment and smoothness of fiber paths are enforced by introducing penalty terms referred to as "misalignment penalty" and "curvature penalty". A geometric interpretation of these penalties is provided. The material behavior of the FRCs is modeled by the Mori–Tanaka homogenization scheme and the macroscopic structure response is predicted by linear elasticity under static multiloading conditions. The governing equations are discretized by a Heaviside-enriched eXtended IsoGeometric Analysis (XIGA) to avoid the need to generate conformal meshes. Instabilities in XIGA are mitigated by the face-oriented ghost stabilization technique. This work considers mass and strain energy in the formulation of the optimization objective, along with misalignment and curvature penalties and additional regularization terms. Constraints are imposed on the volume of the structure. The resulting optimization problems are solved by a gradient-based algorithm. The design sensitivities are computed by the adjoint method. Numerical examples demonstrate with two-dimensional and three-dimensional configurations that the proposed method is efficient in simultaneously optimizing the macroscopic shape and the fiber layout while improving manufacturability by promoting parallel and smooth fiber paths.
      PubDate: 2024-07-04
       
  • Product family design optimization considering manufacturing and assembly
           process costs

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      Abstract: Abstract Simultaneous optimization of a family of related products can yield significant benefits compared to individual product optimization. Standardization of parts across different products and streamlining assembly and manufacturing processes can lead to reduced overall costs for the product family. In this study, we introduce a methodology for optimizing the design of a product family while considering detailed CAD models of individual products. The motivation arises from the challenge of achieving optimal product standardization without compromising individual product performance. Existing methods often impose predefined levels of commonality and seldom account for detailed CAD models and computationally expensive metrics of interest. To address this gap, we propose a novel approach that utilizes surrogate models to mitigate computational complexity and optimize the product family configuration based on total production cost, ensuring economically viable configurations while meeting performance requirements. The objective function incorporates the total production cost of the product family, including manufacturing and assembly process costs and volume discounts. Assembly process complexity and associated costs are quantified using Design for Assembly rules, while individual product performance, evaluated through finite element models, serves as a constraint. The results of the single-objective optimization, applied to a case study involving a product family comprising multiple gearboxes, demonstrate that by considering the product family, a better design in terms of the total production cost can be found compared to optimizing each product individually. Results indicate that the suggested method can efficiently identify optimal product family configurations, balancing standardization benefits with individual product performance. Overall, this study contributes to advancing methodologies for product family optimization and underscores the importance of considering detailed product designs in the optimization process.
      PubDate: 2024-07-02
       
  • Topological derivatives for shell structure optimization considering crash
           loadcases with material nonlinearities and large deformations

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      Abstract: Abstract 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.
      PubDate: 2024-06-29
       
  • Assessing decision boundaries under uncertainty

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      Abstract: Abstract In order to make design decisions, engineers may seek to identify regions of the design domain that are acceptable in a computationally efficient manner. A design is typically considered acceptable if its reliability with respect to parametric uncertainty exceeds the designer’s desired level of confidence. Despite major advancements in reliability estimation and in design classification via decision boundary estimation, the current literature still lacks a design classification strategy that incorporates parametric uncertainty and desired design confidence. To address this gap, this works offers a novel interpretation of the acceptance region by defining the decision boundary as the hypersurface which isolates the designs that exceed a user-defined level of confidence given parametric uncertainty. This work addresses the construction of this novel decision boundary using computationally efficient algorithms that were developed for reliability analysis and decision boundary estimation. The proposed approach is verified on two physical examples from structural and thermal analysis using Support Vector Machines and Efficient Global Optimization-based contour estimation.
      PubDate: 2024-06-29
       
  • Guided optimization: a fast model-based nested cost optimization technique
           for existing product family designs

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      Abstract: Abstract Cutting production costs is a crucial instrument for companies to increase profitability and remain competitive. However, companies aim to cut costs without compromising performance requirements of the products. Commonality between components reduces the costs, while diversity between them differentiates the key attributes of the products in a product family. This commonality-diversity trade-off is the essence of the product family design optimization. Current model-based methodologies consider optimizing the design for commonality rather than cost. This article proves that higher commonality does not always amount to minimal cost and, therefore, a simple commonality index cannot replace a cost model. A tunable cost model that includes simplified formulas for standardization benefits is introduced to be used by cost optimization techniques. Current product family optimization methods are often combinatorial and perform inefficiently due to searching large design space. These methods also optimize the product family design from scratch. This limits the applicability of the current methods in industrial settings that are typically complex and brownfield. This article proposes a model-based cost optimization methodology that accelerates the cost optimization by starting from an existing design. The proposed methodology is a two-stage nested optimization algorithm, in which the commonality matrix is optimized with sensitivity analysis on component types in the outer loop and the associated design variables are optimized in the inner loop. The methodology is numerically demonstrated on an industrial example and benchmarked against state-of-the-art cost optimization methods. The proposed methodology ensures a gradual improvement in cost reduction with a significant acceleration in performance.
      PubDate: 2024-06-26
       
  • Topology optimization of truss structure considering kinematic stability
           based on mixed-integer programming approach

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      Abstract: Abstract Kinematic instability due to unstable nodes is an often neglected but critical aspect of mathematical optimization models in truss topology optimization problems. On the one hand, kinematically unstable structures cannot be used in the actual structural design. On the other hand, unstable nodes within continuous parallel bars can make the calculation of bar length wrong and affect the optimization effect. To avoid kinematic instability, a computationally efficient nominal disturbing force (NDF) approach for truss topology optimization is presented in this paper. Using the NDF approach, the most favorable structure for the optimization goal can be selected in three schemes: (1) adding bracings at unstable nodes, (2) removing unstable nodes and replacing short bars with long ones, or (3) selecting a new topology form to avoid containing unstable nodes. Compared with the widely used nominal lateral force (NLF) approach in the literature, the NDF approach can not only improve the optimization efficiency but also obtain lighter optimization results. Moreover, using the NDF approach, a mixed-integer linear optimization model for minimizing the weight of truss with discrete cross-sectional areas subject to constraints on kinematic stability, bar buckling, allowable stress, nodal displacement, bar crossing, and overlapping is proposed in this study. Because the objective and constraint functions are linear expressions in terms of variables, the globally optimal structures can be obtained by using the proposed model. In addition, two necessary conditions for kinematic stability are proposed to speed up the computational efficiency and delete unnecessary nodes within consecutive tension bars. Finally, the effectiveness of the proposed NDF method and the necessary conditions for kinematic stability are studied on four truss topology optimization problems in two and three dimensions.
      PubDate: 2024-06-26
       
  • A truly meshless approach to structural topology optimization based on the
           Direct Meshless Local Petrov–Galerkin (DMLPG) method

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      Abstract: Abstract With a wide applicability in several types of engineering problems, topology optimization is one of the most interesting fields of structural optimization. Many meshless methods have been developed, however, they were less explored in topology optimization compared to other methods, as the Finite Element Method (FEM). The Direct Meshless Local Petrov–Galerkin (DMLPG) is characterized as a truly meshless method since it does not use a mesh at any stage of its development. It has been applied to solve many boundary value problems, achieving results with good precision and computational efficiency. Instead of performing the numerical integral of complicated shape functions, the DMLPG considers low-degree polynomials. The new topology optimization approach proposed in this work couples the DMLPG method with a Bi-Directional Evolutionary Structural Optimization (BESO) method. DMLPG is used to obtain smooth nodal displacements, strains, and stresses, and BESO updates the structural geometry based on design sensitivity values. Numerical examples performed were compared with the results obtained with FEM and with other works in the literature, showing the applicability and validity of the technique.
      PubDate: 2024-06-22
       
  • Buckling optimization of variable stiffness composite wing boxes with
           manufacturing defects

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      Abstract: Abstract Composite structures can achieve greater performance through variable stiffness design. In consideration of inevitable manufacturing defects introduced by the Automatic Fiber Placement machine, modified models are established to calculate equivalent properties for materials with gaps or overlaps. A modeling method for variable stiffness structures with defects is proposed based on the modified models. This approach significantly reduces the dependence on mesh size for analysis accuracy, thereby improving modeling and calculation efficiency. Upon validation of the proposed modeling method, a Hierarchical Kriging surrogate modeling is employed to optimize the buckling performance of a variable stiffness wing box with defects. The impact of different manufacturing strategies on optimization results is also investigated. The findings demonstrate that the variable stiffness design improves the wing box buckling performance under a combined torsion-bending condition. Finally, the potential of utilizing overlaps without cut-restart is analyzed for weight reduction in the design of variable stiffness wing boxes.
      PubDate: 2024-06-22
       
  • Multi-material and thickness optimization of a wind turbine blade root
           section

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      Abstract: Abstract Structural optimization has been shown to be an invaluable tool for solving large-scale challenging design problems, and this work concerns such optimization of a state-of-the-art laminated composite wind turbine blade root section. For laminated composites structures, the key design parameters are material choice, fiber orientation, stacking sequence, and layer thickness, however a framework for treating these simultaneously in optimization, on the current wind turbine blade scale, has not been demonstrated. Thus, the motivation and novelty of the present work is providing and demonstrating a general gradient-based approach applicable to wind turbine blades, where the key design parameters and structural criteria, i.e., buckling, static strength, and fatigue damage, are considered for multiple design load cases. The optimization framework is based on a variation of the Discrete Material and Thickness Optimization approach, where the thickness is directly parametrized, allowing for appropriately treating the sandwich parts of the blade. It is demonstrated how optimization leads to a design consisting of complex variable-thickness laminates, a good overall distribution of the structural criteria in the model, and a significant reduction in mass compared to the initial design.
      PubDate: 2024-06-21
       
  • Optimization approach for the core structure of a wind turbine blade

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      Abstract: Abstract The core of the modern lightweight wind turbine blade may be a truss structure based on carbon-fiber rods connected to stiff ribs. A specialized robot manufactures these structures by spatially winding impregnated carbon fibers around pre-positioned ribs with cutouts for securing the fiber bundle. Quantity of the ribs, their positions, and positions of the rods can vary, making this type of blades well-suited for optimization. We present an optimization approach for designing the core of a lightweight wind turbine blade. Our approach involves dividing the task into two stages and utilizing multiple optimization algorithms at each stage. Proposed approach used the FUD, DAJA, and Evolution Strategy algorithms. To test the approach, an existing blade structure with known characteristics was used. Comparison of the resulting design with the original one reveals 53 percent smaller value of objective function. These results demonstrate the effectiveness of applying the proposed approach. New approach allows for the optimization of complex rod with ribs’ structures that cannot be optimized using standard optimization method. This will hopefully result in a significant decrease of wind turbine production cost.
      PubDate: 2024-06-21
       
  • Design optimization for the entire aircraft structure of civil aircraft
           with blended-wing-body layout

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      Abstract: Abstract The Blended-Wing-Body (BWB) layout represents an innovative subsonic transport aircraft design. Drawing inspiration from the Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) proposed by National Aeronautics and Space Administration (NASA), this study focuses on a design optimization for the entire structure of a BWB civil aircraft. A PRSEUS-based finite element model was established and subjected to a static analysis. The results indicate a considerable structural strength margin, suggesting potential for lightweight design advancements. Meanwhile, the structural region division techniques were adopted to analyze the sensitivity of the BWB aircraft structure and to sort the parameters affecting its mass. Subsequently, seven surrogate modeling techniques were employed to train a surrogate model for the BWB aircraft structure to analyze the primary factors affecting its prediction accuracy. Among various modeling approaches, the optimal heuristic computation (ES) method demonstrates superior prediction accuracy and enhances the efficiency of optimal solution searches, resulting in a 18.45% mass reduction in the optimized BWB civil aircraft structure. Based on the optimization results of the ES model, a dual-loop optimization strategy was proposed by considering the vibration effects on the BWB aircraft. This strategy facilitates the optimization of the dimensional parameters of the BWB aircraft structure, resulting in substantial 17.83% increase in the first-order natural frequency of the optimized structure. After two rounds of optimization, the mass of the optimized BWB aircraft structure accounted for only 25% of the maximum takeoff mass. Consequently, the proposed optimization strategies present robust applicability and high efficiency, providing a valuable reference for designers and researchers in related fields.
      PubDate: 2024-06-17
       
  • A novel non-probabilistic reliability-based design optimization method
           using bilevel accelerated microbial genetic algorithm

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      Abstract: Abstract In this study, an efficient algorithm for non-probabilistic reliability-based design optimization (NRBDO) is presented. To improve the convergence rate, the sequential Kriging model is applied to the inner-layer optimization of the double-nested optimization model, maximizing the utility of each sampling point. During the global exploration stage, the algorithm employs an expected improvement criterion and a parallel sampling strategy. In the local exploration stage, a minimum surrogate prediction criterion is utilized to identify new sampling points, resulting in enhanced efficiency and accuracy of Kriging surrogate model. The optimization of each sampling criterion is performed using the differential evolution algorithm. Adaptive switching between global and local exploration is achieved by considering the relationship between new and known sample points, ensuring the identification of the optimal solution. To further enhance optimization efficiency, an Aitken \(\Delta^{2}\) acceleration strategy is applied to improve the current population, while a heuristic pattern-based local search method is employed to enhance the subpopulation, developing of a bilevel accelerated microbial genetic algorithm to solve optimal solution. The efficiency of the proposed method is demonstrated through two numerical cases and an engineering application involving the ram of the TK6932 heavy-duty floor-type milling and boring machine.
      PubDate: 2024-06-17
       
  • Two-stage automatic structural design of steel frames based on parametric
           modeling and multi-objective optimization

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      Abstract: Abstract Traditional structural design involves drawing recognition, repeated modeling, parameter tuning, and numerous mechanical analyses by skilled designers, which is time-consuming and inefficient. To address those problems, a two-stage automatic structural design of the steel frame based on expert experiences is proposed. In the first stage, from the computer-aided design plain drawing, semantic features (walls and openings) and geometrical information of architectural elements are extracted by a layer classification method. The segmentation of rooms is conducted by an enclosed region detection method and the connectivity graph is generated using the connected component analysis method. Based on expert experiences considering both structure and architectural function requirements, the structural member configuration and the floor load distribution are automatically established to obtain the parametric structural model. In the second stage, a modified particle swarm optimization (MPSO) based on expert experiences is proposed for single-objective structural optimization according to the design codes. Then based on MPSO, a hierarchical multi-objective optimization method is adopted to obtain more available solutions with different economic benefit and redundant safety. The results show that the proposed two-stage structural design framework is fully automatic and highly efficient. It integrates parametric modeling and structural optimization, and also enables effective transfer of different data items including architectural plan and structural model. It provides a guideline to automatic structural design of steel frames.
      PubDate: 2024-06-15
       
  • Mesh-based topology, shape and sizing optimization of ribbed plates

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      Abstract: Abstract In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction industry. A coupled plate-beam finite element model and its computational mesh enable simultaneous topology, shape and sizing optimization of ribbed plate systems. Using analytical sensitivity analysis and gradient-based optimization, we achieve significant weight reductions in the range of 24–57%, in comparison to reference designs with regular ribbing patterns. The results strengthen the argument in favor of ribbed plates as a structural system that can serve the environmental goals of the construction industry. While our focus is on ribbed concrete plates in buildings, the proposed mesh-based design parameterization is applicable in the general case of optimizing stiffened shells—with potential contributions also to automotive and aerospace applications. All computer codes used in this study are freely available through a public repository, https://zenodo.org/records/11489996.
      PubDate: 2024-06-13
       
 
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  Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
    - ANIMATION AND SIMULATION (33 journals)
    - ARTIFICIAL INTELLIGENCE (133 journals)
    - AUTOMATION AND ROBOTICS (116 journals)
    - CLOUD COMPUTING AND NETWORKS (75 journals)
    - COMPUTER ARCHITECTURE (11 journals)
    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
    - E-BUSINESS (21 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (42 journals)
    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
    - SOCIAL WEB (61 journals)
    - SOFTWARE (43 journals)
    - THEORY OF COMPUTING (10 journals)

COMPUTER SCIENCE (1305 journals)

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