Computers & Structures最新文献

In this paper, a novel and efficient neural network-based methodology is proposed to achieve seismic total cost optimization of steel moment-resisting frames in a timely manner. The computational burden of an optimization process based on performing nonlinear time-history analysis is prohibitively high. To address this crucial issue, a new and efficient neural network model is proposed in this paper to accurately predict the nonlinear time history response of steel frames during the optimization process. In the proposed neural network model, an ensemble of parallel neural networks is used to provide excellent prediction accuracy. In addition, a new repairability constraint is proposed to check the seismic damage level of structures during the optimization process with the aid of the proposed neural network model. Moreover, an efficient metaheuristic algorithm is used to achieve the optimization task. Two numerical examples are illustrated to demonstrate the efficiency of the proposed methodology. The results show that the proposed neural network model outperforms the existing standard models in terms of prediction accuracy. Furthermore, it is shown that by using the proposed methodology, the optimal seismic total cost of steel frames increases by less than 2.5%, yet their seismic collapse capacity increases by at least 30%.

Reactive transport within porous reactors is crucial to many diverse applications, and the efficacy of these reactors hinges on their microstructure. Mathematical modeling and optimization play a pivotal role in the exploration of efficient designs, enabling the generation of structures that may not be achievable through random realizations of packings. In this study, we propose a framework for high-resolution topological optimization of porous flow-through reactors based on pore-scale simulations using a non-dominated sorting genetic algorithm II. A pore network model for an advection–diffusion-reaction system is developed to simulate reactor performance. This model is integrated with a mathematical optimization algorithm, incorporating a background grid and Delaunay tessellation. The optimization framework generates enhanced porous structures, simultaneously maximizing conversion rates while minimizing pumping costs. Striking a balance between permeability and reactive surface area, the final designs yield a set of Pareto optimal solutions, encompassing diverse non-dominated designs with varying reaction rates and hydraulic requirements. The results demonstrate that optimal pore configurations lead to a 280% increase in conversion rates and a 6% reduction in pumping costs at one end, while on the opposite end of the Pareto front, a 15.2% increase in reaction rates and an 11.3% reduction in pumping costs are observed.

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.

The coupled motion between bridges and vehicles is known as vehicle–bridge interaction (VBI). It is crucial for bridge design, monitoring, and vehicle safety and comfort. VBI studies typically rely on general-purpose finite element (FE) software. Although precise, they are not optimized for simulating large-scale bridges with numerous vehicles, which can result in long processing times and modeling challenges. This paper presents a self-developed framework in MATLAB™ for large-scale VBI simulation. The framework divides the simulation task into five modules and supports asynchronous seismic excitation (ASE), handles different deck geometries, unifies all road vehicle models and inputs with a vehicle library, supports variable vehicle velocity (VVV) and different traffic scenarios, and handles wheel–deck detachment. All functions have been designed with easily accessible interfaces to facilitate secondary development. The framework was verified using a 2D sprung mass benchmark case compared to a closed-form solution, and a 3D simplified model compared to commercial FE software. It was also validated through a laboratory experiment. Further demonstrations of a large-scale VBI system highlighted new phenomena and emphasized the significance of considering the ASE effect in similar systems. With ongoing improvements, the framework has the potential to become a practical tool for VBI simulation.

This paper aims for developing topology optimization methodology to design the shape of electrodes in Dielectrophoresis (DEP)-based devices. The DEP force is due to a non-uniform electric field induced by applied voltages to the electrodes. Shape of the electrodes has the principal effect on the direction and magnitude of the DEP force. In medical therapy microfluidic devices, DEP force is used for cell sorting and cell separation. While the direction and magnitude of the DEP force are desired to be determined and maximized respectively, the magnitude of the electric field should be minimized to avoid damaging cells. Approaching these goals is counter intuitive where the existing electrode designs are basic. Therefore, a detailed finite element model (FEM) is developed for DEP force and electric field to formulate an optimization problem to maximize the DEP force in a particular direction while there is a constraint on electric field's magnitude. Using the developed FEM, explicit formulations for sensitivity analysis are derived to implement a gradient-based topology optimization. The performance of developed methodology is assessed numerically to determine the direction of the DEP force and constraining the electric field and experimentally in a practical case study of particle trapping in a microfluidic channel.

Composite materials are essential for many advanced engineering applications, but numerical quantification of strength variability can be computationally expensive. This paper proposes a novel methodology that drastically reduces the number of finite element simulations required to characterise composite material strength variability using the concept of vine copulas. This concept provides a flexible tool for the representation of high-dimensional dependencies. The methodology considers spatial scatter of three material variabilities: fibre volume fraction, fibre misalignment and fibre strength. First, the cross-correlation between stress, fibre volume fraction and fibre misalignment is fitted by a vine copula using results from a limited number of finite element simulations. Next, the vine copula is used to predict new conditional stress realisations when given realisations for the fibre volume fraction and fibre misalignment. This effectively replaces the finite element simulations with a vine copula that is much faster to evaluate. The methodology is verified by predicting the tensile failure load of a unidirectional composite coupon. Predictions are very similar to an exclusively finite element-based approach, while reducing the number of finite element simulations by a factor of 200.

Topology optimization methods are used to design high performance structural components that often have complex geometric layouts. In several industries, components are required to be cleanable, and for this research cleaning by jetting is considered. Thus, being able to ensure jet access on the entire surface of a structure is of interest in topology optimization. In this paper, a jetting filter is proposed, that turns a blueprint design into a jet accessible design. Two methods are considered to find an access field for each jet. These individual jet access fields are then combined into a total access field, to obtain a cleanable design. Consistent sensitivity analysis is used and the additional computational cost of the jetting filter is modest compared to the finite element analysis. The performance of the two methods is demonstrated with 2D and 3D numerical examples for mechanical and thermal topology optimization problems.

In this paper, we propose a novel thermal deformation formulation for elastic bodies' thermomechanical simulation with total Lagrangian smoothed particle hydrodynamics (TLSPH), which employing fixed reference configuration. We utilize implicit corotated TLSPH as a base methodology to address tensile instability and enhance the robustness of simulation. The reference configuration of heat transfer model is updated, but the reference configuration of thermal deformation model is fixed like TLSPH. As particle's temperature is changed, the inter-particle distance in reference configuration is updated by using linear thermal expansion theory. Validation involved two basic cantilever beam loading cases and a thermomechanical analysis with heat-induced thermal deformation on a cantilever beam. The TLSPH simulation results were compared against Finite Element Method simulations and analytical solutions, demonstrating equivalent accuracy across all tests. This study indicates the potential for conducting a wide array of simulations involving thermal deformation using the TLSPH method, which efficiently supports parallel computing on an individual particle basis.

Fracture mechanisms of brittle rocks are usually different from each other, i.e., in mode-I and mode-II. To simulate crack propagation in rock-like materials under compression, the distinctions of mode-I and mode-II failure behavior must be taken into account. Here we present a novel double-nonlocal damage formulation, which is highly suitable for modeling mixed-mode brittle failure in rock and rock-like materials. The damage formulations describe the fracture mechanisms of the two modes through two separate damage evolution equations (corresponding to mode-I and mode-II, respectively) based on our recently developed energy limiter-based gradient-enhanced damage model. The differences in the opening and shear modes of the crack are distinguished by the input data required for the two damage evolution equations including the fracture energy, initial damage thresholds, and crack driving forces. Numerical simulations under the standard FEM framework are performed to reveal the advantages and capacity of the developed double damage model for quasi-static crack growth in rocks. Obtained numerical solutions are then validated to referred experimental data and numerical results from other modeling techniques in the literature to see the accuracy of the developed theory.

This paper describes the shape optimization of hyperelastic structures exhibiting finite deformation and contact. The structure's shapes are parameterized with B-splines and the spline control point coordinates serve as the design variables. This design parametrization provides precise boundary representations and analytical sensitivity computations. To accommodate non-matching grids between the contacting bodies the mortar method is employed and, to hasten computations the accelerated Uzawa scheme is used to solve the displacement and contact pressure update equations. The formulation is used to optimize structures to achieve the desired mechanical response by exploiting both the non-linear hyperelastic material response and the contact phenomena.