Advances in Engineering Software最新文献

The efficiency of solving sparse linear equations in isogeometric topology optimization (ITO) can be improved by the multigrid algorithm due to its excellent convergence rate. However, its convergence rate heavily relies on the smoother's parameters. To address this problem, a new h-refinement multigrid conjugate gradient method with adaptive damped Jacobi (ADJ-hMGCG) has been developed. By analyzing the eigenvalues of the stiffness matrix, the damping coefficient of the smoother that achieves the fastest convergence rate has been determined. Due to the significant computational resources required to compute eigenvalues in the stiffness matrix, this paper also presents a preconditioned power method based on ITO and geometric multigrid characteristics to improve the efficiency of adaptive damping solutions. The results of 2D and 3D numerical examples show that the ADJ-hMGCG method successfully improves the solution speed and robustness while meeting the accuracy requirements of topology optimization, and the total computational cost can be reduced by up to 59 % compared to traditional solvers for large-scale problems.

An adaptive dimension-reduction Chebyshev metamodel (ADC) is proposed to balance the accuracy and efficiency of dimension-reduction Chebyshev metamodels. A univariate dimension-reduction Chebyshev metamodel (UDC) is constructed by the dimension-reduction method and the Chebyshev metamodel. Based on the UDC, the bivariate terms largely impacting the metamodel are selected using an adaptive selection method, and are combined with the UDC to construct the ADC. The ADC has higher accuracy than the UDC because more calculated sample points are added. Compared with the bivariate dimension-reduction Chebyshev metamodel, the ADC needs fewer sample points and has higher efficiency. The result of numerical examples illustrate that ADC has higher accuracy compared with other commonly-used metamodels and is more suitable for approximating high-dimensional complex models.

Parallel computing is essential for enhancing computational efficiency and advancing computational mechanics. To reduce the computational cost, peridynamics, a nonlocal numerical method, has been coupled with the finite element method (FEM). However, the accurate modeling of plastic deformation within the coupling framework of the FEM and non-ordinary state-based peridynamics (NOSB-PD) requires further investigation and might add to the computational expense. In this study, the open multi-processing application interface (OpenMP) is implemented for the plastic coupling of the FEM and stabilized NOSB-PD. The framework for the plastic coupling model using OpenMP is described in detail. The implemented code is used to investigate the coupling boundary effect on plastic deformation depending on the size of the coupling zone. After verifying the plastic coupling, the parallelization performance of the coupling model is examined. The efficient coupling model is applied to simulate plastic deformation on a plate with a circular hole, and the displacement results show good agreement with the reference solution. The proposed coupling model can be applied to efficiently solve the plastic deformation and fracture in future studies.

Products created by additive manufacturing often have surface imperfections that require post-processing operations to remove extraneous material in order to meet design specifications. The usage of computational fluid dynamics (CFD) simulations to predict material removal rates of components, allows practitioners to optimize the setup and usage of post-processing equipment. However, those without in-depth knowledge of CFD or the related specialized software, require an easy-to-use and cost-effective application to manage the computational workflow. The two specific surface finishing applications investigated here, are, abrasive flow machining (AFM) and robotic stream finishing (RSF). In order to satisfy user requirements, a modular, threaded, fault-tolerant and object-oriented project management application, written with the Python programming language and PyQt6 framework, has been developed to conduct surface finishing-related CFD simulations using OpenFOAM®. The advantages of the proposed software are: 1) the modern PyQt6 framework is used to develop a cross-platform and user-friendly application which employs the model-view class architectural paradigm for data management and its display, 2) step-by-step interactive project workflows have been tailored specifically for AFM and RSF simulations, 3) the developed steady-state viscoelastic flow solver for AFM and continuum-based steady-state dense granular flow solver for RSF, offer advantages over those provided by OpenFOAM® and 4) simulation results have been corroborated by experimental data to assess the improved accuracy of material removal prediction of the current software when compared to other commercial applications.

Mantis shrimps employ their telson, or tail plate, to mitigate the impact with hard surfaces, thanks to its unique double-sine shaped microstructures that absorb energy through deformation. Inspired by this natural impact-resistant design, similar lightweight energy absorbers have been developed for applications in transportation systems and personal protective equipment. This study presents a data-driven approach to analyze and optimize these structures subjected to crushing loads. The structure's geometry is defined by three simple parameters based on a sine wave shape function and fabricated using ABS-M30 polymer through 3D printing. Material tests and compression tests under uniaxial loading conditions are conducted to characterize the material properties and structural behavior. Finite element models are created to simulate these tests, and Machine Learning techniques are applied to study the structure's behavior. A total of 100 Design of Computer Experiments are generated by manipulating the design variables, and the Decision Tree method categorizes deformation modes. Intrinsic and response parameters are predicted as functions of the geometric parameters. Using these relationships, a multi-objective optimal design is achieved, enhancing specific energy absorption while reducing peak crush force. The Pareto Front, representing optimal designs for these objectives, is obtained through genetic algorithms. A multi-criteria decision-making algorithm factors in designer preferences to narrow down the optimal design dataset. This study highlights the potential of bio-inspired structures and design methodologies for innovative lightweight protective equipment in transportation systems and human wearables.

In this study, a Physics-Informed Neural Network (PINN) framework is extended and applied to predict the geometrically nonlinear responses of pretensioned cable net structures without utilizing any incremental-iterative algorithms as well as Finite Element Analyses (FEAs). Instead of solving nonlinear equations as in existing numerical models, the core idea behind this approach is to employ a Neural Network (NN) that minimizes a loss function. This loss function is designed to guide the learning process of the network based on Total Potential Energy (TPE), pretension forces, and Boundary Conditions (BCs). The NN itself models the displacements given the corresponding coordinates of joints as input data, with trainable parameters including weights and biases that are regarded as design variables. Within this computational framework, these parameters are automatically adjusted through the training process to get the minimum loss function. Once the learning is complete, the nonlinear responses of cable net structures can be easily and quickly obtained. A series of numerical examples is investigated to demonstrate the effectiveness and applicability of the PINN for the geometrically nonlinear analysis of cable net structures. The obtained results indicate that the PINN framework is remarkably simple to use, robust, and yields higher accuracy.

To ensure continuous monitoring of reinforced soil-retaining walls (RSWs) even under low-illuminance conditions, such as during the night, it is imperative to evaluate the performance of deep learning-based detection. In this study, we constructed a laboratory RSW model and generated a dataset with varying illuminance levels to assess the impact of image enhancement and block detection. Various image enhancement methods were applied to improve image quality and evaluate their effect on deep learning. RGB optimization (RO) was proposed to optimize RGB intensity and compared with gamma correction, histogram equalization, and low-light image enhancement with illumination map estimation. RO demonstrated outstanding image enhancement performance, as evidenced by lightness order error, peak signal-to-noise ratio, and structural similarity index measure, ensuring high image quality. The trained RO model using Mask R-CNN exhibited excellent accuracy, recall, and F1 score, delivering remarkable detection performance under low illuminance conditions, resulting in a 7.44 % improvement in the F1 score. Image enhancement techniques that maintain similarity, such as lightness order error and structural similarity, across varying illuminance conditions contribute to enhancing the block detection performance of Mask R-CNNs.

To address the challenge of establishing and solving mathematical models for engineering structural optimization, a new topological optimization method that integrates load-transfer path theory with the engulfment algorithm is presented in this paper. The presented method applies the load-transfer path theory to identify the main load-bearing areas of the structure and utilizes the principle of concentrating more materials in relatively high-stress regions and fewer materials in relatively low-stress regions. An engulfment algorithm is introduced to optimize the material distribution. A comparative analysis between the presented and variable-density methods revealed that the path-engulfment method enhances the structural stiffness and strength while reducing its mass, confirming its precision and efficacy in structural optimization. The path-engulfment method was implemented on a truck crane frame, resulting in an optimized structure with increased stiffness and strength and reduced mass compared to the original design. Furthermore, this method eliminates the need for establishing and solving complex mathematical models while addressing issues related to checkerboards and gray-scale elements. A smooth boundary approach was introduced by leveraging the engulfment algorithm, enabling the direct application of the optimized structure for manufacturing purposes, particularly in engineering applications.

The deformation behavior of shrink energy-absorbing structures is influenced by numerous factors, and improper matching of parameters in the design process can easily lead to buckling instability, or even failure to absorb energy. Existing research methods can only obtain descriptive laws on how structural parameters affect deformation modes, but cannot determine the parameter domain for stable shrink mode, leading to poor prediction and optimization effects. For this purpose, a crashworthiness prediction framework based on deformation image generation and classification network (DIGCNet) was proposed to accurately predict the mean crushing force (MCF) and specific energy absorption (SEA) in the shrink mode domain. An image generator and a classification network were used to establish mapping relationships from structural parameters to deformation modes. The effects of the DIGCNet hyperparameters on prediction accuracy were analyzed. Subsequently, the shrink energy-absorbing structure was optimized under deformation constraint, and compared to the unconstrainted solution. The results show that the DIGCNet can eliminate abnormal deformations and achieve the structural optimization under the parameter domain of the shrink mode.

The traditional research methods for the temperature field of bridge under solar radiation suffer from issues such as high workload and high costs. The temperature field of steel-concrete composite beam (SCCB) is studied in this paper using the ANSYS finite element software and MATLAB software. Firstly, a finite element temperature field model of SCCB is established based on measured meteorological data. Furthermore, the accuracy of the finite element temperature field model of SCCB is validated by collecting a small amount of temperature measurement data. The temperature sample database of SCCB was expanded based on this. Finally, a large amount of historical meteorological data was collected. The ANSYS software and Genetic Algorithm Back Propagation (GA-BP) hybrid model were used for calculation, and the representative temperature differences T_d1 and T_d2 of SCCB were obtained separately. The measured values are in good agreement with the finite element analysis results, showing consistent trends over time with a maximum difference not exceeding 1.6 °C. The GA-BP hybrid model proposed in this study, characterized by ‘structural features, temporal features, environmental features—node temperatures’, exhibits a high degree of nonlinear mapping capability. It has been demonstrated that the GA-BP hybrid model also possesses a high level of accuracy through verification. The SCCBs’ maximum vertical positive temperature differences (T_v), computed using ANSYS software and the GA-BP hybrid model, follow Generalized Extreme Value (GEV) distributions with parameters (-0.2722, 12.8715, 1.4105) and (-0.2855, 12.813, 1.3714), respectively. The representative values (T_d) of the maximum vertical positive temperature differences of SCCB, calculated by ANSYS software and the GA-BP hybrid model, are 17.613 °C (T_d1) and 17.2 °C (T_d2), respectively. The proposed temperature field calculation model for SCCB is based on meteorological parameters and the GA-BP hybrid model. It can accurately calculate the temperature field of SCCB in Guangdong region and improve computational efficiency.