Advances in Engineering Software最新文献

{"title":"Structural topology optimization considering material anisotropy induced by additive manufacturing processes","authors":"Chao Wang ,&nbsp;Di Lou ,&nbsp;Zunyi Duan ,&nbsp;Wenfeng Du ,&nbsp;Jianhua Rong ,&nbsp;Bin Xu","doi":"10.1016/j.advengsoft.2025.104021","DOIUrl":"10.1016/j.advengsoft.2025.104021","url":null,"abstract":"<div><div>This work proposes a structural topology optimization method to consider material anisotropy induced by additive manufacturing processes. To quantify the relationship between manufacturing processes and mechanical properties of formed materials, the building direction angle is introduced into a transversely isotropic material model as a design variable. An anisotropic material model related to the building direction is thus established. A parallel optimization framework for structural topology and building direction is proposed by extending the classical compliance minimization formulation. And, to be applicable to gradient-based optimization algorithms, sensitivities related to density and angle variables are derived separately. Especially, to overcome the convergence difficulties caused by the periodic angle variables, an adaptive reduction strategy for the feasible region of angle variables is proposed. Typical numerical examples verify the rationality of the proposed method. The results show that the building direction related process-induced anisotropy significantly affects the optimized structural properties. The fluctuation of the trigonometric functions related to the angle variables would lead to obvious iteration oscillation in the optimization process, which makes the optimization difficult to converge. The proposed adaptive reduction strategy is proven effective in addressing this challenge. Besides, typical numerical properties of the co-optimization of structural topology and building direction are also revealed.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"211 ","pages":"Article 104021"},"PeriodicalIF":5.7,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on coal wall parameter calibration and high precision model construction based on discrete element method","authors":"Xin Jin ,&nbsp;Dongpo Han ,&nbsp;Guochao Zhao ,&nbsp;Lijuan Zhao","doi":"10.1016/j.advengsoft.2025.104032","DOIUrl":"10.1016/j.advengsoft.2025.104032","url":null,"abstract":"<div><div>The accuracy of discrete element coal wall model significantly influences the characterization of coal-rock breaking mechanisms and equipment dynamic response in virtual prototype simulation. Based on coal-rock samples from Ordos Wenyu Mine of Yanzhou Coal Mining, key Tavares UFRJ parameters affecting particle compressive strength were identified through Plackett-Burman test and steepest ascent experiment. Breakage parameters were calibrated using optimal latin hypercube sampling (OLHS) and gaussian process regression (GPR). Hertz-Mindlin with Bonding parameters were then calibrated via uniaxial compression simulation. Model accuracy was verified through discrete element method-multi flexible body dynamics (DEM-MFBD) coupling simulation. Results indicate that D0, E Infinity, and Phi are the most significant parameters with influence rates of 38.5 %, 30.5 %, and 18.6 % respectively. The relative error between simulated and experimental particle compressive strength is below 4.56 %, while uniaxial compression simulation shows maximum relative error below 9.80 %. Comparing tri-axial load curves during shearer drum cutting, the maximum relative error of mean values between experimental and simulation data is 3.72 %, with maximum root mean square error (RMSE) of 4.60 %, outperforming traditional models and validating the model's accuracy and reliability for dynamic cutting process simulation.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"211 ","pages":"Article 104032"},"PeriodicalIF":5.7,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 3D vehicle-bridge interaction framework integrating energy-conserving Hamilton’s principle and stabilized Newmark-β method","authors":"Xinfeng Yin ,&nbsp;Yang Quan ,&nbsp;Linsong Wu ,&nbsp;Tuerdi Kaiersaer ,&nbsp;Zhou Huang","doi":"10.1016/j.advengsoft.2025.104022","DOIUrl":"10.1016/j.advengsoft.2025.104022","url":null,"abstract":"<div><div>This study proposes a novel 3D (Three-dimensional) VBI (Vehicle-bridge interaction) system modeling framework based on Hamilton's principle, coupled with an improved Newmark-<em>β</em> method for solving dynamic responses. By considering the kinetic and potential energies of the system, Hamilton's principle accurately describes the coupled vibrations between vehicles and bridges. The dynamic equations of the VBI system are derived by constructing a Euler-Bernoulli beam theory models and vehicle a spring-damped system models, incorporating 3D road surface irregularities and random traffic loads. The coupled dynamic equations ensure energy conservation under complex traffic loads. An improved Newmark-<em>β</em> method is employed to solve the nonlinear dynamic responses, ensuring numerical stability and accuracy. Theoretical validation demonstrates the model's superior accuracy in describing bridge mid-span displacement and vehicle vertical displacement. Numerical simulations and case comparisons further highlight the advantages of Hamilton's principle. For example, at a vehicle speed of 40 km/h, the maximum deviation of the simulated mid-span displacement from the measured value is only 0.42 mm, with a coefficient of determination (R²) reaching 0.92 and the mean absolute error (MAE) significantly reduced to 0.24.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"211 ","pages":"Article 104022"},"PeriodicalIF":5.7,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization design method of C30/C40 fly ash concrete based on machine learning and elite retention genetic algorithm","authors":"Mingyue Hao ,&nbsp;Yue Li ,&nbsp;Xiwang Chen ,&nbsp;Kun Ni ,&nbsp;Wei Li","doi":"10.1016/j.advengsoft.2025.104019","DOIUrl":"10.1016/j.advengsoft.2025.104019","url":null,"abstract":"<div><div>This paper establishes an intelligent optimization design method for fly ash (FA) concrete considering 28-day compressive strength, slump, and carbon emissions based on machine learning (ML) and elite retention genetic algorithm (EGA). The results demonstrate that the Extreme Gradient Boosting (XGB) model achieves high accuracy in predicting compressive strength, while Gradient Boosting (GB) shows higher accuracy and generalization ability in predicting slump. The water-to-binder ratio and cement content have a significant impact on the compressive strength of FA concrete. Reducing the water-to-binder ratio or increasing cement content helps improve compressive strength. The dosage of superplasticizer and the water content are key factors in controlling the slump. Properly increasing the dosage of superplasticizer and water content can effectively improve the slump of concrete. The FA concrete intelligent design system developed based on the XGB model, GB model, and EGA algorithm can efficiently obtain the optimal preparation parameters and accurately predict the corresponding performance. Furthermore, the carbon emissions of the optimized C30 and C40 FA concrete decrease by 12.72 % and 17.44 % respectively compared to the baseline concrete. Finally, the experimental results verify the prediction accuracy and generalization ability of the XGB and GB models, with the relative prediction error of C30 and C40 FA concrete both being less than 8 %.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104019"},"PeriodicalIF":5.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the effectiveness of multigrid preconditioned iterative methods for large-scale frequency response topology optimization problems","authors":"Yongxin Qu ,&nbsp;Niels Aage ,&nbsp;Quhao Li","doi":"10.1016/j.advengsoft.2025.104017","DOIUrl":"10.1016/j.advengsoft.2025.104017","url":null,"abstract":"<div><div>Large-scale static topology optimization of mechanical structures has been successfully realized for linear problems, including giga-voxel resolution aircraft wings and suspension bridges. Wherein the multigrid preconditioned conjugate gradient method (MG-CG) plays an important role in the repetitive solution of the state equations. However, research on large-scale dynamic topology optimization, e.g., frequency response problems, is still limited. Since the coefficient matrix of the dynamic equation is no longer a positive definite symmetric matrix, yet an indefinite, non-Hermitian and complex matrix, the conjugate gradient method (CG) is no longer applicable and the standard weapon-of-choice, the geometric multigrid preconditioner is no longer guaranteed to work. It is therefore of interest to investigate which iterative methods, if any, posses excellent generality and low computational-cost. In this paper, the effectiveness of several typical preconditioned iterative methods is studied, including conjugate gradient method, biconjugate gradient stabilized method (BICGSTAB), induced dimensionality reduction (IDR), generalized minimum residual method (GMRES). A detailed comparison and analysis of iterative methods' convergence, mesh dependence, and sensitivity to stiffness distribution in dealing with indefinite problems is given first. Then, despite its known disabilities, the geometric multigrid method is applied as a preconditioner for GMRES, BICGSTAB and IDR, i.e., MG-GMRES, MG-BICGSTAB and MG-IDR, to facilitate the efficient solution of large-scale frequency response analysis. In addition, the influence of several smoothers, including damped Jacobian iteration, successive over relaxation, symmetric SOR, and incomplete LU factorization, on the convergence of geometric multigrid iterative methods is also discussed. Numerical examples show that MG-BICGSTAB deals with low-frequency problems well, but for the whole frequency range, MG-GMRES with ILU smoother converges quickly and steadily, even if the model is extremely large. Furthermore, the effectiveness of the proposed procedure is further verified in dynamic topology optimization with up to 2.8 million degrees of freedom using a standard desktop computer.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104017"},"PeriodicalIF":5.7,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction and analysis of temperature recovery after arch closure grouting of Baihetan Arch Dam","authors":"Jingyu Yan ,&nbsp;Feng Wang ,&nbsp;Yuhang Li ,&nbsp;Chunbo Ma ,&nbsp;Junchi Zhou ,&nbsp;Hu Yu","doi":"10.1016/j.advengsoft.2025.104012","DOIUrl":"10.1016/j.advengsoft.2025.104012","url":null,"abstract":"<div><div>Arch closure grouting is an essential procedure for attaining structural completion during the construction of a concrete arch dam. After the closure, due to the continuous heat emission from cement hydration, the internal temperature of concrete rises rapidly, which affects stress distribution and structural stability. In order to accurately predict the temperature evolution of concrete pouring blocks after arch closure, this paper conducted a comparative study using neural networks and finite element methods. First, a hybrid model, CNN-BiLSTM, was constructed. This model integrates a Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM). The Weighted Mean of Vectors algorithm (INFO) was then introduced to optimize the model parameters. The temperature variation trend of concrete pouring blocks after arch closure was predicted using this approach. Simultaneously, considering the factors such as external temperature, cooling water, adiabatic temperature rise and concrete age, a three-dimensional finite element model of concrete pouring blocks was established to simulate the temperature field distribution of concrete. The comparison results indicate that both methods can achieve the prediction accuracy required by the project (with an error of less than 2 °C). Among them, the finite element simulation performs better in terms of stability (with a difference of less than 1 °C from the measured value). At the same time, the INFO-CNN-BiLSTM model exhibits significant temperature fluctuations during certain periods and demonstrates insufficient generalization ability. However, it offers the advantage of high computational efficiency.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104012"},"PeriodicalIF":5.7,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning-based inverse analysis procedure for concrete softening law prediction using non-experimental datasets","authors":"H. Rodrigo Amezcua ,&nbsp;A. Gustavo Ayala ,&nbsp;Carlos E. González","doi":"10.1016/j.advengsoft.2025.104016","DOIUrl":"10.1016/j.advengsoft.2025.104016","url":null,"abstract":"<div><div>This paper studies the mechanical behaviour of concrete as one of the most widely used quasi-brittle construction materials emphasizing on the importance of knowing its mechanical parameters and their evolution during the inelastic stage, <em>i.e.</em>, the softening law. The softening curve, which describes the response of the material under damage or cracking, is critical for predicting the behaviour of concrete structures subjected to extreme loads. Experimental tests are commonly employed to obtain this information either directly or indirectly. Some of the indirect methods are based on inverse analysis and/or artificial intelligence techniques, both of which capable of predicting the mechanical parameters of concrete from the experimental results of one test, <em>e.g.</em>, a notched beam subjected to vertical loads. However, an important drawback of these procedures is that they require a large dataset constructed from data gathered in multiple experiments in order to be developed. Consequently, most existing methods are tailored to specific types of experiments and even limited to certain specimen dimensions. Additionally, these procedures primarily focus on predicting mechanical parameters rather than determining the softening law. To address these limitations, this paper proposes a machine learning-based algorithm for the inverse analysis of an experimental test capable of predicting both the softening law and the mechanical parameters of concrete. By generating a non-experimental dataset through the Sequentially Linear Analysis (SLA) procedure, the proposed algorithm can be applied to other experimental setups suitable for analysis with SLA. The results of the application example demonstrate the effectiveness of the proposed approach.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104016"},"PeriodicalIF":5.7,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep reinforcement learning-based control algorithm for flight kinematics of insect-scale flyers","authors":"Seungpyo Hong ,&nbsp;Sejin Kim ,&nbsp;Innyoung Kim ,&nbsp;Donghyun You","doi":"10.1016/j.advengsoft.2025.104014","DOIUrl":"10.1016/j.advengsoft.2025.104014","url":null,"abstract":"<div><div>An autonomous flight control algorithm based on deep reinforcement learning (DRL) is developed for insect-scale flyers with flexible wings in complex flow environments, addressing the challenges posed by highly unsteady and nonlinear aeroelastic dynamics. Unlike conventional model-based approaches, this study employs high-fidelity computational fluid–structural dynamics (CFD-CSD) simulations that fully resolve the governing equations of both the fluid and the flyer, providing physically consistent data for training the DRL agent. To mitigate the computational cost, a novel physics-guided data augmentation strategy is introduced, which synthetically expands the training dataset by replicating CFD-CSD data across diverse virtual flight scenarios while preserving the underlying physics. This approach enables the DRL agent to learn a robust control policy that generalizes across a broad range of aerodynamic conditions, demonstrating strong control performance even in complex and untrained flow environments. This work establishes a scalable framework for the autonomous control of flexible, bio-inspired flyers under realistic aerodynamic conditions, representing a significant step toward fully autonomous insect-scale flight.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104014"},"PeriodicalIF":5.7,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complex profile optimization of marine diesel engine piston pin bore using hybrid GA-BP neural network and NSGA-II algorithm","authors":"Guoxi Jing ,&nbsp;Qiqiang Tong ,&nbsp;Yafei Fu ,&nbsp;Libin Zhao ,&nbsp;Yi Han ,&nbsp;Chao Liu","doi":"10.1016/j.advengsoft.2025.104015","DOIUrl":"10.1016/j.advengsoft.2025.104015","url":null,"abstract":"<div><div>To address deformation mismatch and stress concentration in the pin holes of a steel-topped aluminum-skirted combined piston under service conditions, this study proposes a surface optimization methodology integrating axial and circumferential bore profiles. By constructing a genetic algorithm-optimized backpropagation neural network surrogate model combined with the NSGA-II multi-objective optimization algorithm and CRITIC weighting decision mechanism, this approach achieves multi-parameter collaborative optimization for the pin hole's intricate geometric configuration. Results demonstrate that compared to the original design, the optimized complex surface reduces peak contact pressure by 66.7 %, decreases equivalent stress by 52.0 %, and lowers equivalent stress at bolt counterbores by 44.1 %. Relative to axial profile-only optimization, the contact pressure is further reduced by 12.4 %. The proposed method effectively resolves stress inhomogeneity induced by elliptical deformation, with finite element simulations verifying that axial-circumferential collaborative optimization significantly enhances load distribution uniformity and fatigue resistance. This work provides a systematic algorithmic approach for high-reliability piston design, advancing the application of intelligent optimization techniques in engine component engineering.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104015"},"PeriodicalIF":5.7,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Less than 500 lines self-contained Python finite element implementation of the phase-field method for fracture mechanics","authors":"Nathan Shauer","doi":"10.1016/j.advengsoft.2025.104013","DOIUrl":"10.1016/j.advengsoft.2025.104013","url":null,"abstract":"<div><div>This paper presents a simple self-contained finite element implementation of the phase-field method for fracture mechanics. The implementations are done in Python, and they only use the standard <span>NumPy</span> and <span>SciPy</span> libraries for basic matrix operations and to solve the resulting systems of equations. The AT2 phase-field model is adopted and the additive decomposition of the energy density is employed to prevent fracture propagation under compressive stresses. The alternate minimization algorithm is adopted for solving the nonlinear system of equations. The implementation is verified using three examples: a bar under tension, a notched plate under tension, and a three-point bending test. The results display good agreement with analytical solutions and solutions from other authors. Each example is less than 500 lines long, and they are available on GitHub at <span><span>https://github.com/nathanshauer/phasefield-jr-py</span><svg><path></path></svg></span> and as supplementary data to this article. These Python scripts are intended for educational purposes and to provide a simple starting point for those interested in the phase-field method for fracture mechanics.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104013"},"PeriodicalIF":5.7,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}