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The ability to predict the effective material property of composites with periodic micro-structures based on homogenization theory has been an effective method to analyze structures with complex heterogeneities. Homogenization codes have been made available for educational purposes including the homogenization code for the prediction of effective elasticity and thermal material properties in MATLAB. The aim of this educational paper is to present a Python version of the existing homogenization code and provide detailed diagrams of its key modules extending its ability to conduct analysis and design studies possibly via integration into commercial FEM software. Python has become a popular programming language due to its wide applicability to several disciplines, its portability, its flexibility by means of programming paradigms, its open-source nature, its well-documented libraries, and its easy-to-learn syntax. To increase the applicability and community reach of the homogenization algorithm presented, we provide a Python translation of the well-known MATLAB implementation. By doing so, we aim to increase the integration potential and adaptability of the homogenization approach to other computing packages and target adoption by a wider audience by leveraging the advantages of basing the solution on a free and open-source platform. • This paper presents a Python code for 3D homogenization of elasticity and thermal conductivity: HomPy. • Detailed flowcharts and key modules for numerical homogenization code are given. • The code is accessible as a free and open-source platform. • Homogenization capability is presented for unit-cell lattices with multi-material constituents. [ABSTRACT FROM AUTHOR]

• This paper proposed a comprehensive framework for parametric semi-automatic BIM and bridge member segmentation. • The parametric algorithm accounted for diverse types of bridge members and produced BIM bridge models using shape information automatically extracted per parameter. • The parametrically generated bridge BIM model was converted into PCD, serving as deep learning train data for automatic segmentation of bridge members. • The trained deep learning model showed high accuracy for segmenting superstructures, indicating promise for automated segmentation of bridge members. • The segmentation accuracy increased when accounting for the point density of members corresponding to the different sizes of bridge members. This paper proposes a comprehensive framework for parametric semi-automatic BIM and bridge member segmentation. For this, a parametric algorithm was developed to accommodate diverse superstructures, piers, abutments, and bearings. Each structural member in a bridge was classified to designate its shape information as parameters. The range of each parameter was defined to account for various dimensions of the shape of the bridge member. Therefore, libraries for each member were established to produce BIM bridge models using shape information automatically extracted per parameter. Then, the framework includes an algorithm that is developed to convert bridge BIM models generated by the parametric algorithm into PCD data. The virtually generated PCD is used as deep learning train data for automatic member segmentation based on the Point-Net algorithm. The trained algorithm using the virtual PCD was applied to a real bridge PCD. The segmentation of the bridge members showed high accuracy for the superstructure but low accuracy for the bearing. The segmentation accuracy of the algorithm including the bearing member could increase by modifying the density of PCD to account for the different sizes of bridge members. Furthermore, the proposed framework was applied to a UK bridge PCD and showed its applicability for use in bridges in different countries. [ABSTRACT FROM AUTHOR]

• The methods to transform 3D gradation to 2D gradation are various, and there lacks a reliable method to verify different methods. • Semi whole scale cross section (SWSCS) method proposed in this paper shows that the phase separation method is the most reliable to transform 3D gradation to 2D gradation. • Geometry shape data library (GSDL) of aggregates and the assembling method developed in this paper is convenient to generate the designed two-dimensional virtual asphalt mixtures. • The FE simulation results are in accordance with literature data and the two-step Mori-Tanaka's analytical method in the study of dynamic modulus of the asphalt mixture with voids. This paper aims at finding a robust method to generate a two-dimensional virtual asphalt mixture and to verify the suitability of the generated virtual mixture. First, three methods namely stereological method, Walraven formula and phase separation method are adopted to convert the actual three-dimensional gradation curves into two-dimensional ones. Then, an approach called semi whole scale cross section (SWSCS) method is developed to verify the results obtained and to determine the most robust procedure to follow. Third, the numerical two-dimensional geometry shape data library (GSDL) of different aggregates and voids and a robust method to assemble representative volume elements (RVEs) are developed using Matlab. Finally, a gradation curve from the literature is considered to generate a virtual asphalt mixture specimen, which is then imported in the finite element (FE) software Abaqus to simulate the dynamic modulus test. The virtual test results are further verified using available literature and another homogenization numerical prediction method based on the Mori-Tanaka's approach. The results show that the phase separation method is effective to convert the three-dimensional gradation curve to a two-dimensional one. The simulation results of the virtual asphalt mixture developed in this study turn out to be in good agreement with both previously presented literature data and Mori-Tanaka's theory. [ABSTRACT FROM AUTHOR]

• A novel scheme, termed mixed cell compressed sparse row (mCCSR) is proposed to address the sparsity, block structure, and partial sub-matrix symmetry in the matrix. • Time domain boundary element method (TDBEM) in elastodynamics. • The compression performance of mCCSR scheme is much better than other compression methods in TDBEM, such as adaptive cross approximation (ACA) method. In this paper, a novel mixed cell compressed sparse row (mCCSR) scheme is proposed to address the sparsity, block structure, and partial sub-matrix symmetry inherent in the coefficient matrix of the time domain boundary element method (TDBEM) in elastodynamics. The impulse function used in the fundamental solution of TDBEM introduces a time-lag phenomenon in the responses originating from the excitation point, resulting in sparsity within the coefficient matrix involved in convolutional integration at each time step. Furthermore, the fundamental solution of the displacement in the boundary integral equation exhibits symmetry between the component indices, leading to symmetry in the displacement coefficient sub-matrix. In contrast, the traction coefficient sub-matrix lacks such symmetry. The mCCSR scheme is proposed to efficiently store these two types of coefficient matrices in TDBEM, along with a detailed explanation of the corresponding matrix operations and the GMRES iteration solution method. The innovative scheme significantly reduces the number of indicators compared to the general compressed sparse row (CSR) scheme and is suitable for loop unrolling to accelerate computation. Examples are presented to analyze the factors affecting the average proportion of non-zero entries. Additionally, this paper also suggests a scale indicator for assessing computational capability, facilitating a performance comparison with the conventional method, adaptive cross approximation method (ACA), CSR scheme, and mCCSR scheme. The results demonstrate that mCCSR scheme achieves the computational capacity scale 5–10 times and computing speed 15–20 times faster than the conventional method, which significantly outperforms both the ACA scheme and the CSR scheme. [ABSTRACT FROM AUTHOR]

• GSOEM-SV was first proposed to predict slope stability. • GSOEM-SV uses a soft voting method to integrate five mesh search hyperparameter optimized models. • GSOEM-SV outperforms 15 improved models and 2 integrated models. • A practical slope stability prediction application was developed based on uni-app. Slope stability evaluation is a complex and uncertain system problem, and carrying out slope stability prediction is the prerequisite and foundation for slope disaster prevention. In order to achieve fast and accurate prediction of slope stability, this paper considers height, total slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio as input features and proposes an intelligent slope stability prediction method based on grid search optimization ensemble learning model by soft voting (GSOEM-SV). First, 390 sets of on-site data were collected to form a dataset, and analyses including correlation coefficients, density estimates, and box lines were carried out. Then, the grid search optimization algorithm is used to optimize the hyperparameters of five algorithms—Gradient Boosting Decision Trees, Light Gradient Boosting Machine, Categorical Boosting, Support Vector Machine, and Random Forest, and integrates them through soft voting. Furthermore, this paper optimizes the hyperparameters of the above five algorithms based on grid search, particle swarm and simulated annealing algorithms, builds 15 improved models and 2 ensemble models and conducts comparison. The results reveal that the GSOEM-SV has the highest slope stability prediction accuracy, up to 91 %, the area under the curve (AUC) is 0.950, and its F 1 score 0.917, which are better than the 15 improved and 2 integrated models. In addition, a set of slope stability prediction app based on uni-app is developed in the paper. It provides a technical foundation and an open and shareable information service platform for slope hazard prediction in geotechnical engineering. [ABSTRACT FROM AUTHOR]

• A mechanics–thermotics–chemistry coupling response model for energetic materials was developed based on the MPM3D. • The continuous expansion and thickening of jet due to high-temperature and high-pressure products leads to the decrease of penetration depth and the increase of crater diameter. • The simulation results of reactive materials jet against steel targets agree well with the experimental results. With its unique kinetic penetration/chemical implosion combined damage ability, reactive damage element (RDE) can greatly or even leap the damage power of ammunition warhead, and has a promising application prospect in the field of ammunition. However, due to the high transient and nonlinear characteristics of shock release process, there is still a lack of real-time quantitative research methods to investigate its energy release process under impact loads, which makes it difficult to conduct in-depth analysis of its efficient damage mechanism. In this paper, the Grüneissen equation of state in the P-V-T form is derived to solve the problem of the mechanics–thermotics–chemistry coupling response (MTCCR) of reactive materials (RMS) under impact load, and the calculation model of RMS temperature under impact is established. Based on the pressure equilibrium criterion, a two-phase theoretical model of the reaction flow field between the RMS reactant and the reaction product was proposed. Combined with the impact temperature rise theory, heat conduction theory and the Arrhenius chemical reaction kinetic model, the MTCCR model of RMS was established. The method of material point-impact induced chemical reaction (MPM-SICR) is presented and the energy release behavior of RMS is quantitatively researched in real-time. This method is used to numerically simulate the formation and impact induced energy release behavior of reactive materials jet against steel target. The simulation results are in good agreement with the experimental results, indicating that the model and method proposed in this paper can well characterize the dynamic MTCCR behavior of the RDE under the impact. [ABSTRACT FROM AUTHOR]

• Numerical simulation modeling is complex and computationally intensive. • Digital twin deployment requires lightweight and responsive systems. • Process sensor data drives deep learning algorithms to implement digital twins. • Generative adversarial networks enable monitoring of heat distribution changes. • Results contribute to weld quality assurance and blade structure optimization. In this paper, a novel method of using deep learning to construct a digital twin of the thermos-compression bonding blade's temperature field is presented. Usually, a heat transfer model based on numerical analysis can calculate the heat distribution in the welding process, but its complex modeling process and heavy computational effort are not appropriate for the deployment of the digital twin concept. The approach proposed in this paper is distinctive because it directly converts the collected process sensor data into the bonding blade's temperature field images and the bonding surface's temperature distribution curves for monitoring the welding process. This is achieved by training the generator and discriminator of the conditional depth convolutional generative adversarial network separately. The network is then capable of predicting the bonding blade's heat distribution within a certain range of welding parameters. Compared to methods based on numerical simulation, this approach is far more effective. Training and testing datasets were created to assess the suggested method's efficacy. Multi-scale structural similarity index, structural similarity index, peak signal-to-noise ratio, difference hash and mean absolute error were used to evaluate the simulations' quality. The results show that the simulation's results and the actual data agree quite well. The temperature changes that occur during the welding process may be quickly and accurately simulated using the proposed technology. [ABSTRACT FROM AUTHOR]

• A high precision T-mesh blade modeling method based on 2D and 3D knot adaptive refinement is proposed. • LSPIA method combined with curvature-constrained refinement is employed to reconstruct blade cross-sectional profiles. • The concept of periodic curves/surfaces is introduced to the blade modeling process since the early 2D stage. • A local T-spline surface skinning method based on Mid-Closest Refinement for blade surface modeling is proposed. • A synthetic curvature extracting method by bidirectional curves is proposed during the surface adjusting process. The aerodynamic shape of blades in turbo machinery is a key factor that affects blade efficiency, pressure ratio and other performance indicators. To address the lack of global continuity and difficulty in ensuring fairing quality in blade modeling, this paper proposes a novel blade modeling method that uses curvature constraints and a Mid-Closest Criterion to achieve adaptive knot refinement in 2D profile reconstructing and 3D T-mesh establishing process respectively. To begin with, the proposed Curvature-Constrained Periodic Reconstruction (CCPR) is used to reconstruct discrete sampling points of the section profile into a globally fairing periodic curve, and meets minimal error requirement. Additionally, the distribution of control points matches with curvature variation of the curve itself. Next, the Double Curves-guided Mid-Closest T-spline Surface skinning (DCM-TS) is used to construct a T-mesh from the stacked cross-sectional profile curves automatically, and the control vertices of the T-mesh are in a controllable scale and reasonably distributed. Further, the modeling precision is allocated with the curvature information of the ideal blade contour, and based on that, a T-spline skinning surface is obtained through LSPIA method, which updates the model and realizes blade modeling that considers both the design efficiency and accuracy. Eventually the proposed method is verified and compared with several traditional surface skinning methods in practical modeling cases. The result unveils that the presented method is compatible with traditional aerofoil parameterization methods, generates surface with global continuity and high fairing quality, and has sharply reduced and evenly distributed control vertices. [ABSTRACT FROM AUTHOR]

• Industrial robot's servomotor bearing fault detection is presented. • Simple motion and welding motion of industrial robots is considered. • Currents signals of servomotor are converted to scalogram images for analysis. • Transfer learning expunged model training from scratch and streamlined operation. In consequence of their superior performance and durability, industrial robots have enjoyed widespread adoption across a variety of industries. However, despite their sturdy build, they are susceptible to malfunction. The servomotor is a fundamental component of industrial robots, and to ensure smooth and uninterrupted functioning, it is essential to detect any defects it may develop. Although research has addressed methods for detecting bearing failure, diagnosis of a servomotor bearing failure in the industrial robot remains difficult and requires intensive research. In this paper, a novel method for detecting servomotor bearing defects in the industrial robot is provided by integrating knowledge transfer via transfer learning. Initially, current signals of the servomotor are transformed to scalogram images. This processed data is utilized to build the model for fault detection. Applying transfer learning eliminates model training from scratch and streamlined operations. The purported approach shows an average accuracy of more than 99 %. [ABSTRACT FROM AUTHOR]

• The MeshLink framework provides a tool that supports high-quality surface structured mesh generation. • A mesh-based feature information framework (MFIF) facilitates automated data linkage between mapping relationship data and mesh data. • The surface structured mesh generation technique based on conformal mapping can reduce the difficulty of generating structured meshes on complex geometric shapes. • By introducing graph neural network into the mesh quality evaluation process, a mesh quality iterative optimization framework based on deep learning is developed. • An example of a detailed generation process for high-quality surface structured meshes is given. Generated meshes for other models are also shown. With the rapid progress in engineering simulation, there is an increasing industrial need for accurate mesh generation methods. Furthermore, the development of data-driven methods require an innovative mesh generation framework that can integrate deep learning models to facilitate automatic data linkage. This paper develops a surface structured mesh generation framework named MeshLink. This framework extends the support for mesh data and associated algorithms through the Mesh-based Feature Information Framework (MFIF). First, in order to map the model to the parametric domain, we discretize the input model using triangular meshes. To handle complex geometric shapes, we develop a structured mesh generation technique based on conformal mapping. Then, we generate the surface structured mesh of the model based on inverse mapping algorithm. The MeshLink framework overcomes the limitations of traditional mesh generation workflows by integrating deep learning models. We adopted a structured mesh evaluation model based on graph neural network, which enhance the efficiency of the framework. Finally, based on the mesh quality evaluation results, we use the corresponding mesh optimization algorithm to generate high-quality surface structure meshes. The MeshLink framework not only provides a tool that supports high-quality surface structured mesh generation, but also facilitates the storage, linking and retrieval of mesh data sources. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper deals with the numerical simulation of the flow induced sound as generated in the human phonation process including vocal folds (VF) contact. The hybrid approach is used allowing to separate the acoustic and the biomechanical description of the considered problem. The two-dimensional fluid–structure interaction (FSI) problem is modelled by a simplified linear elastic model coupled to the incompressible Navier–Stokes equations in the arbitrary Lagrangian–Eulerian form and special attention is paid to the treatment of the contact problem in the FSI context. Following approach of [Sváček and Horáček, 2021] the incompressible flow model is modified by an addition of fictitious porosity term and the penalization inlet boundary condition is used. Further, the Lighthill acoustic analogy is applied for calculation of sound sources and their propagation through a vocal tract model. All partial differential equations, describing the elastic body vibration, the fluid flow and the acoustics, are approximated by finite element method. Numerical results of one-sided vocal fold paralysis are presented. • Numerical simulation of human phonation including contact. • Fluid–structure interaction problem modified to enable closed channel configuration. • Fictitious porous domain and penalization boundary condition provide relevant values. • Sound generation and propagation modelled by Lighthill aeroacoustic analogy. [ABSTRACT FROM AUTHOR]

This paper presents the development and validation of hybridDCFoam, a coupled DSMC/Navier–Stokes–Fourier solver, for simulating steady-state multiscale rarefied gas flows. The developed solver is based on the domain decomposition method, while the state-based information exchange approach is implemented between the particle and continuum solvers. The solver is developed within the OpenFOAM framework and couples the dsmcFoam+ and rhoCentralFoam solvers in order to handle the particle and continuum flow regions. hybridDCFoam can tackle arbitrary 3D geometries and supports parallel simulations using the MPI domain decomposition available in OpenFOAM. The developed solver is validated for flow over a flat plate, flow past a cylinder, pressure-driven flow through a slit, and expansion through a conical nozzle. The presented benchmarks yield an excellent agreement between hybrid and pure DSMC simulations. Moreover, hybridDCFoam is able to accurately recover both numerical and experimental results reported in the literature. Notably, hybridDCFoam achieves significant computational gains compared to pure dsmcFoam+ simulations, rendering it well-suited for addressing multiscale rarefied gas flows of engineering importance. • Development of hybrid steady-state DSMC/NSF solver hybridDCFoam. • Validation of hybridDCFoam in several multiscale rarefied gas flow cases. • Excellent agreement between hybridDCFoam, dsmcFoam+ and literature results. • hybridDCFoam achieves significant speedup over pure DSMC. [ABSTRACT FROM AUTHOR]

• Developing optimized neural network-based models for trapdoor stability prediction. • New FELA solutions for 3D rectangular trapdoors in anisotropic clays. • Developing hybrid machine learning models based on ANNs with various optimization algorithms. • Examining several performance metrics, regression plots, and rank analysis. The problem of trapdoor stability is a crucial problem in geotechnical engineering. This study is the first to introduce novel neural network-based metaheuristic models for the stability prediction of 3D rectangular trapdoors in anisotropic and nonhomogeneous clays. However, no researcher has considered such trapdoor problems in the past. In this study, the dataset is obtained by using finite element limit analysis (FELA). The proposed hybrid machine learning models based on artificial neural networks (ANNs) and various types of optimization algorithms (OAs), namely, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competition algorithm (ICA), and shuffled complex evolution (SCE), are also proposed in this paper by undergoing rigorous optimization to ensure accuracy and efficiency in capturing the intricate dynamics of the stability investigations from the models. The performance of the proposed ANN-based models is assessed using several performance metrics, regression plots, and rank analysis. The proposed ANN-SCE model outperforms the other proposed models in predicting trapdoor stability, where the ANN-SCE model achieved the highest rank, with a score of 58, followed by the ANN-ALO (47), ANN-ICA (33), and ANN-ACO (22) models. The proposed neural network-based metaheuristic models deliver precise and effective forecasts of trapdoor stability to make informed decisions concerning road design and mitigation tactics, ultimately improving the robustness of infrastructure facing geotechnical challenges. [ABSTRACT FROM AUTHOR]

Recently, asynchronous coarse-grid correction has been achieved within additive Schwarz-type and primal Schur domain decomposition frameworks. Both additive and multiplicative coarse-grid corrections were discussed, however, the implemented asynchronous Schwarz-type solver with additive correction relies on the specific design of the restricted additive Schwarz (RAS) method, and also requires an overlap between the subsets of unknowns. In this paper, we first highlight a gap between the theoretical analysis from the literature and the associated RAS implementation. It turns out that communications delays would actually need to be bounded in order to fit the theory. This has to be stressed since, despite the asynchronous nature of the solver, the coarse-grid correction requires non-blocking global synchronization, which is subject to communications bottleneck. Second, we propose an implementation approach which applies to a wider class of additive Schwarz-type methods while still coping with the bounded delays requirement. • Generalization of asynchronous coarse grid to various Schwarz type methods. • Reduced gap between theoretical and practical iteration mappings. • Construction of globally coherent right-hand side for the coarse problem. • Higher proportion of two-level iterations instead of only one per coarse solution. [ABSTRACT FROM AUTHOR]

In this paper, we present the parallel computation of physical electromagnetic (EM) solvers, namely physical optics (PO), shooting and bouncing rays (SBR), and the physical theory of diffraction (PTD), which are used for EM scattering/diffraction calculations. The accelerated solvers have been tested on CPU and GPU kernels for significantly faster calculation times. As an application, the developed implementation is employed to recently developed tool of PREDICS that uses combined techniques PO, SBR, and PTD for the calculation of EM scattering/diffraction and radar cross section (RCS) from electrically large, complex objects. First, the CPU parallelization of these three combined techniques on the PREDICS code is explained. The parallelization implementation is specially tailored for the nature of SBR technique such that the CPU iteration loop is accomplished over triangular facets of the CAD file by employing a read/write spin lock task dispatching technique. Next, a lock-free implementation of the PO, SBR, and PTD techniques on the GPU threads is presented. Thanks to availability of many GPU cores, the parallelization procedure is widened such that triangular facets, observation angles of elevation and azimuth are used for a much faster calculation set-up. Numerical examples for the EM/RCS calculation of simple canonical and electrically big, complex objects are shared to demonstrate the effectiveness, accuracy and the computational acceleration based on the multi-CPU and multi-GPU implementations. The key metric of the contribution of the study is the speed-up value of the implementations. The acceleration gained via multi-CPU realization yielded directly in parallel with the number of available CPU cores. On the other hand, the acceleration factor can reach to hundreds when multi-GPUs are used. In fact, the acceleration factor has reached to 4.7 for a machine with 4 Core 8 Thread CPU processor, and 141.3 for a 2560 CUDA GPU Core system. [ABSTRACT FROM AUTHOR]

• Apply python to interface with plaxis software to simulate the embankment settlement for a case study in Australia. • Develope a GEP-based prediction model to estimate the settlement of ground improvement. • Conduct a parametric study to examine the effects of input variables on the settlement of the ground. • Optimize the design of CMC diameters and CMC spacings for specific ground conditions. This paper is going to present the advanced feature in Plaxis using remote scripting with Python wrapper. Python allows engineers to create Plaxis models, calculate construction phases, and plot the results automatically. The settlement estimation of a high embankment, supported by cement mixing columns (CMC), is presented as a case study. Approximately 500 models were created and analyzed within a few hours with different embankment heights, soft soil thickness, CMC spacing, and CMC diameter. This original database was used to develop a regression model using a Gene-expression programming (GEP) algorithm. The proposed GEP-based model with high accuracy could be applied to optimize the CMC design. In detail, the coefficient of correlation (R-value) of all phases is high and fluctuates from 0.967 to 0.976, while the mean absolute error of the model is lower than 0.009 m. The parametric study indicates that increasing the height of the fill embankment, the thickness of the peat layer, or the spacing between CMCs causes high settlement, while increasing the CMC diameter could significantly reduce the settlement of the embankment. The research results demonstrate that using Plaxis remote scripting and the GEP technique could help engineers run the numerical analysis much faster, leading to a more in-depth analysis to create better decision-making. [ABSTRACT FROM AUTHOR]

• A database on compressive strength of recycled aggregate concrete is built up. • The compressive strength prediction models with high accuracy are constructed. • The multi-objective optimization design of recycled aggregate concrete mixture proportions based on machine learning and NSGA-II algorithm is carried out. • The influence factors on compressive strength of recycled aggregate concrete are analyzed. This paper employs Support Vector Regression (SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) algorithms to establish the compressive strength prediction models for Recycled Aggregate Concrete (RAC) and analyze the influence of ten inputs on RAC compressive strength. Combined with the best prediction model, the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-II) is applied for multi-objective optimization of mixture proportions in RAC addressing cost, carbon emissions, and compressive strength as key objectives. The results demonstrate that the RAC compressive strength prediction model using the GB algorithm exhibits the highest accuracy, with the R2 value of 0.99 for the training set and 0.85 for the testing set. Feature importance and SHAP analysis reveal that cement and water contents are the primary factors affecting the compressive strength of RAC. Among the three mineral admixtures including Silica Fume (SF), Fly Ash (FA), and Ground Granulated Blast Furnace Slag (GGBFS), SF exhibits superior improvement in the compressive strength of RAC compared to FA and GGBFS. Partial dependency plot analysis indicates that concrete strength remains unaffected by recycled coarse aggregate content within the range of 0 to 300 kg/m3. Pareto fronts of a tri-objective mixture optimization problem for RAC are successfully obtained through the GB model combined with the NSGA-II algorithm, which can guide the optimization of RAC preparation. [ABSTRACT FROM AUTHOR]

• The mechanics of the vein domains are revealed to be correlated with morphology. • The layout of wing veins is explained by multi-objective optimization. • Structural biology is evolved based on the gradient optimization. The intricate network of veins on dragonfly wings has captured the attention of many researchers due to its fascinating configuration and excellent mechanical properties. In this paper, based on the observation of the layout of the vein domains on the hind wing of dragonfly, the types of domains were characterized from the aspects of area and shape. The numerical analysis revealed that different shaped domains enhance either stiffness or vibration stability of the wing. According to this feature, a multi-objective gradient optimization algorithm based on stiffness, mass transport efficiency and first order frequency was proposed. Associated by the size constraints, the optimal layout of the venation was obtained and subsequently used to generate the Voronoi patterns, whose positions and shapes could be further optimized by an evolutionary algorithm to match the mechanical analysis results. The effectiveness of this multi-cellular configuration was verified by testing the bending and vibration stability of the additive manufactured model. The aerodynamic simulation analysis of this flapping wing structure was also investigated. This study has important implications for understanding the evolutionary mechanisms of venation on biological wings and designing reinforcement for sheets. [ABSTRACT FROM AUTHOR]

This paper, a multi-material topology optimization method (MMTO) is presented for two-dimensional structures using modified ordered solid isotropic material with penalization (SIMP) under multiple constraints such as mass, stiffness, frequency, and stress. The element density is obtained by a density filtering and the Heaviside approximation, which are combined to avoid gray elements and numerical issues of stress. Effective improvement of multiphase material topology optimization was performed including the criteria for stiffness, stress, and free vibration. P -norm stress aggregation is employed to calculate the maximum von Mises stress and sensitivity of von Mises stress is formulated by the adjoint method. The natural frequency and stress constraints are solved simultaneously to enhance the stability and reduce the stress concentration of the optimized structures, which are not much covered in the literature. The multi-mode phenomenon in the eigenfrequency problem is treated by the simply proposed technique. Results indicate that the multi-material designs with multi-constraints can be illustrated and optimized effectively. The optimized topologies, for stress minimization, indicate that the maximum stress can be significantly reduced compared with compliance minimization. The maximum von Mises stress of the topological results considering the maximum stiffness with natural frequency and mass constraints is higher than that maximum stiffness with only mass constraints, which indicates that the optimized structure created considering the eigenvalues is safer. The proposed approach can obtain a reasonable result that effectively controls the stress level and reduces the stress concentration at the high stress regions of multi-phase material structures with multi-constraints. Benchmark numerical examples are presented to confirm the effectiveness of the presented method. • MMTO under multiple constraints is solved simultaneously via the ordered SIMP method. • Multi-objective MMTO is performed to enhance the results of single-objective MMTO. • Maximizing the fundamental frequency with mass constraints is established. • Lighter and cheaper materials are prioritized. • The influence of various constraints is investigated. [ABSTRACT FROM AUTHOR]

In this paper, a new method to address strength-based topology optimisation (TO) problems is proposed. Specifically, failure criteria for anisotropic materials are integrated into a TO algorithm making use of Non-Uniform Rational Basis Spline (NURBS) hyper-surfaces to represent the pseudo-density field describing the topology of the continuum, thus providing solutions that can be easily exported to any computer-aided design software. The notion of failure load factor is introduced to obtain optimised topologies that do not depend on the magnitude of the applied loads. A unified formulation of the main phenomenological criteria for anisotropic materials, i.e., Tsai–Wu, Hoffman and Tsai–Hill criteria, is used and typical issues related to stress-based TO problems, such as local behaviour and singularity of stresses, are handled thanks to: the properties of NURBS blending functions, a special adaptive χ -norm aggregation function that avoids overflow/underflow issues, and a modified p q relaxation approach. The effectiveness of the proposed approach is demonstrated on 2D and 3D problems. A sensitivity analysis of the optimised topologies to the integer parameters involved in the definition of the NURBS entity is performed. Moreover, the influence of the penalisation scheme used for the elasticity and the stress tensors, the failure criterion, and the orientation of the material on the optimal solution is also investigated. • General method for strength-based topology optimisation of anisotropic continua. • Density-based algorithm reformulated in NURBS hyper-surfaces framework. • General problem formulation based on the concept of failure load factor. • The influence of the penalisation scheme on the optimised solution is investigated. • The combined effect of failure criterion type and material orientation is analysed. [ABSTRACT FROM AUTHOR]

The paper introduces a practical method for the construction of large-scale point sets for analysis of computer models. The constructed experimental design is useful for (probabilistic) integration, construction of approximation or a screening. The essence of the presented approach is the stratification of the design domain into an orthogonal grid of substrata and a subsequent tiling with tiles of points. If optimized, such tiles experience a major reduction of the number of degrees of freedom in the optimization process. That way, optimal or near-optimal point patterns can be feasibly identified and are further utilized for construction of larger point sets, thanks to the idea of self-similarity and structured space stratification The space-filling properties of the resulting point sets may be further enhanced by various "scrambling" strategies, which may remove the undesired sample collapsibility achieved via regular tiling. The performance of the constructed point sets is compared to Quasi Monte Carlo (QMC), Randomized Quasi Monte Carlo (RQMC) sequences, which are still today considered by engineers and even scientists as choices for variance reduction of numerical integration Further, the mentioned sampling strategies are compared in the terms of robustness when integrating a multivariate function with a localized feature. It is concluded that the proposed sampling approach reaches a superior performance in numerical integration and identification of function extremes as compared to sampling methods used by practicing researchers and engineers. Additionally, the reader is supplied with the open-access, ready-to-use implementation of the presented algorithm named SampleTiler. • Construction of large-scale point samples by balancing the bias–variance tradeoff. • Stratification of the design domain and subsequent tiling with tiles of points. • Construction of large optimized experimental designs due to self-similarity. • Superior performance of samples as compared to commonly used methods. • Open-access Python code provided on a GitHub repository. [ABSTRACT FROM AUTHOR]

• The manuscript investigates a reliability-based topology optimization with low cycle fatigue (plasticity-based) constraints. • An effective two-level algorithm is introduced, where minimization of the objective function is formulated as a nested optimization problem. • In the proposed nested approach, the outer loop is responsible for minimizing the objective function, while constraints are verified in the inner loop. • Cubic shape functions are used to achieve highly comparable results between numerical simulations and experimental outcomes, particularly in fatigue-resistant design cases. • Additionally, software architecture for the proposed approach is described and implemented in the MATLAB environment. This paper addresses a challenging engineering problem that combines stress-limited topology optimization, reliability analysis, and plasticity-based low-cycle fatigue. Each of these issues represents a complex problem on its own, necessitating significant computational effort. In this study, we propose a novel approach that integrates safety assessment into the topology optimization process while considering the number of cycles for low-cycle fatigue. Our method employs a linear approximation of the performance function for safety control, incorporating the number of failure cycles within a complex, multi-level load program. The methodology is validated through real experiments, using a finite element model with cubic shape functions that yield nearly identical results between numerical and experimental outcomes in the case of fatigue-resistant design for a bi-axially tensioned structural joint. [ABSTRACT FROM AUTHOR]

This paper presents a novel continuous planar path planning algorithm based on principal stress orientation field to improve structural performance of the products fabricated with additive manufacturing. For a given 3D shape, it is first sliced into planar layers with specified layer thickness, which are discretized into grids for the optimization of tool-paths. Then, the principal stresses and the corresponding orientation field are calculated with the finite element analysis method, which are projected onto grids. Since the principal stress orientations of elements with principal stress values close to zero are arbitrary, a weighted filtering strategy is applied to generate a smooth principal stress orientation field. Finally, the tool-paths are generated with genetic algorithm through minimizing the idle travel distance of the nozzle. Furthermore, the tool-path width is adaptively adjusted according to the tool-path orientation to avoid the overfill issue caused by the discretization. Additionally, a method is introduced to achieve global continuous tool-paths, aiming at eliminating idle travel completely. The optimized tool-paths are simulated and compared with the state-of-the-art methods. Experimental results demonstrate that the objects fabricated with the proposed method have superior structural performance. • A novel continuous planar path planning algorithm was proposed based on the principal stress orientation field to improve structural performance and completely avoid the idle travel during printing. • Several strategies were designed to generate smooth principal stress orientation field, reduce the isolation points, and the short stress lines. • The tool-paths were optimized with GA method, and their width was adaptively adjusted to mitigate over-fill issue according to their printing orientations. [ABSTRACT FROM AUTHOR]

The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. This paper presents SMT 2.0, a major new release of SMT that introduces significant upgrades and new features to the toolbox. This release adds the capability to handle mixed-variable surrogate models and hierarchical variables. These types of variables are becoming increasingly important in several surrogate modeling applications. SMT 2.0 also improves SMT by extending sampling methods, adding new surrogate models, and computing variance and kernel derivatives for Kriging. This release also includes new functions to handle noisy and use multi-fidelity data. To the best of our knowledge, SMT 2.0 is the first open-source surrogate library to propose surrogate models for hierarchical and mixed inputs. This open-source software is distributed under the New BSD license. 2 2 https://github.com/SMTorg/SMT. • For engineering purposes, we propose a Python Open Source Toolbox for Surrogate Modeling named the Surrogate Modeling Toolbox 2.0. • SMT 2.0 is the most efficient Python modeling toolbox. • SMT had been extensively used for Engineering and Research modeling tasks with more than 200 citations already. • This toolbox can easily model black box problems for optimization purposes based on Kriging models (Gaussian Process). • The SMT 2.0 Toolbox provides meta/decreed/continuous mixed hierarchical (dimensional) variables for architectural modeling. • This 2.0 release also includes new functions to handle noisy and use multifidelity data. [ABSTRACT FROM AUTHOR]

• An improved version of Stochastic Subset Optimization (SSO), named iSSO, is presented. • Voronoi tessellation is implemented to partition the design space into non-overlapping subregions. • Double-sort algorithm is implemented to identify the optimal subregions. • For sampling in the design space, a weighted sampling method is proposed. • Several optimization problems are used to demonstrate effectiveness and efficiency. The Stochastic Subset Optimization (SSO) algorithm was proposed for optimal reliability problems that minimizes the probability of system failure over the admissible space for the design parameters. It is based on the simulation of samples of the design parameters from an auxiliary Probability Density Function (PDF) and exploiting the information contained in these samples to identify subregions for the optimal design parameters within the original design space. This paper presents an improved version of SSO, named iSSO to overcome the shortcomings in the SSO. In the improved version, the Voronoi tessellation is implemented to partition the design space into non-overlapping subregions using the pool of samples distributed according to the auxiliary PDF. A double-sort approach is then used to identify the subregions for the optimal design. The iSSO is presented as a generalized design optimization approach primarily tailored for the stochastic structural systems but also adaptable to deterministic systems. Several optimization problems are considered to illustrate the effectiveness and efficiency of the proposed iSSO. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a topology optimization approach aimed at improving the energy absorption of quasi-brittle structures under impact loading. Our method focuses on incorporating dynamic fracture behavior throughout the impact process to maximize the energy acting on the structure. To achieve this, we integrate a dynamic phase field method into density-based topology optimization, enabling us to simulate the initiation and propagation of complex dynamic fractures. The optimization formulation aims to maximize the absorbed energy over a specified period while adhering to material volume and compliance constraints. Sensitivity analysis was originally provided to accelerate computations and optimization. Several numerical examples show that the incorporation of dynamic fracture effects leads to superior energy absorption of the optimized structure compared to static strategies or neglecting the fracture process. Moreover, the proposed scheme facilitates tailored designs for different impact loading rates. • A topology optimization approach is proposed for the energy absorption of quasi-brittle structures subjected to impact loading. • Dynamic fracture is allowed while maximizing the energy acting on the structure during the whole impact process. • Sensitivity analysis is originally provided to accelerate the computations and optimization. • Numerical results illustrate that the present scheme improves energy absorption as compared to conventional methods. [ABSTRACT FROM AUTHOR]