Advances in Engineering Software最新文献

{"title":"Intermediately discretized extended α-level-optimization – An advanced fuzzy analysis approach","authors":"Bertram Richter,&nbsp;F. Niklas Schietzold,&nbsp;Wolfgang Graf,&nbsp;Michael Kaliske","doi":"10.1016/j.advengsoft.2025.103865","DOIUrl":"10.1016/j.advengsoft.2025.103865","url":null,"abstract":"<div><div>Appropriate uncertainty models are required for realistic representations of quantities in real world engineering tasks. Uncertainty quantification is applied to estimate the uncertainty of system responses, with respect to uncertain input quantities. In contrast to aleatoric uncertainty, which is based on natural variability, epistemic uncertainty is caused by lack of knowledge, incertitudes or inaccuracy. In this contribution, epistemic uncertainties are modeled by fuzzy quantities and corresponding uncertainty quantification approaches are investigated. The propagation of fuzzy quantities is based on the extension principle. For numerical analyses, a discretization of the extension principle is required, which can be reformulated as an optimization problem. Two different approaches are state-of-the-art for formulating the optimization problem of the extension principle, which are referred to as <span><math><mi>α</mi></math></span>-level optimization and sampling-based approach (SBA). A comparison of these two approaches is presented, highlighting their advantages and deficits with respect to efficiency and accuracy of the fuzzy analyses. Based on the advantages of both <span><math><mi>α</mi></math></span>-level optimization and SBA, a novel approach, the intermediately discretized extended <span><math><mi>α</mi></math></span>-level optimization (IDEALO), is developed. In IDEALO, advantages of <span><math><mi>α</mi></math></span>-level optimization and SBA are combined to a hybrid approach. The superiority of IDEALO over the other two approaches is demonstrated in benchmark examples.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"202 ","pages":"Article 103865"},"PeriodicalIF":4.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HMSimNet: A hexahedral mesh simplification network model for preserving analysis accuracy","authors":"Rui Wang ,&nbsp;Songyuan Liu ,&nbsp;Junhe Yu ,&nbsp;Hongfei Zhan","doi":"10.1016/j.advengsoft.2025.103874","DOIUrl":"10.1016/j.advengsoft.2025.103874","url":null,"abstract":"<div><div>The structure of a hexahedral mesh can be simplified after the mesh has been generated to meet specific requirements, enhancing the analysis precision and efficiency while conserving the storage space. However, the existing methods for mesh simplification do not conduct the physical analysis, so simplified meshes cannot meet the accuracy requirement. To address this shortcoming, this paper presents a network model named HMSimNet for simplifying hexahedral meshes, which can reduce the number of mesh cells while maintaining analysis accuracy. First, the sheets and columns are described using four types of features: geometric, topological, quality, and physical features. Then, a dataset is constructed and used to train the HMSimNet model to learn the relationship between the features and the analysis accuracy. Finally, the probabilities of deleting the sheets and columns are determined by the HMSimNet model, and the sheets and columns with a minor impact on the analysis accuracy are removed iteratively. The experimental results demonstrate that, compared to the input hexahedral meshes, the presented method can significantly reduce the number of hexahedra by approximately 30% while having only a slight impact on the maximum relative error of the analysis.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"202 ","pages":"Article 103874"},"PeriodicalIF":4.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179371","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":"Approximate analytical/numerical solutions for the seismic response of rigid walls retaining a transversely isotropic poroelastic soil","authors":"Qingyuan Gong ,&nbsp;Edmond V. Muho ,&nbsp;Niki D. Beskou ,&nbsp;Ying Zhou","doi":"10.1016/j.advengsoft.2025.103876","DOIUrl":"10.1016/j.advengsoft.2025.103876","url":null,"abstract":"<div><div>Three approximate analytical solutions for the problem of the seismic response of two rigid cantilever walls retaining a transversely isotropic poroelastic soil layer over bedrock are presented under conditions of plane strain and time harmonic ground motion. These approximate solutions come as a result of various reasonable simplifications concerning various response quantities of the problem, which reduce the complexity of the governing equations of motion. The method of solution in all the cases is the same with that used for obtaining the exact solution of the problem, i.e., expansion of response quantities in the frequency domain in terms of sine and cosine Fourier series along the horizontal direction and solution of the resulting system of ordinary differential equations with respect to the vertical coordinate in conjunction with the boundary conditions. The first approximate solution is obtained on the assumption of neglecting all the terms of the equations of motion associated with the fluid acceleration. The second approximate solution is obtained on the assumption that the fluid displacements are equal to the corresponding solid displacements. The third approximate solution is obtained as the sum of the second approximate solution for the whole domain plus a correction inside a boundary layer at the free soil. All three approximate solutions are compared with respect to their accuracy against the exact solution and useful conclusions pertaining the approximate range of the various parameters, like porosity, permeability and anisotropy indices, for minimization of the approximation error are drawn.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"202 ","pages":"Article 103876"},"PeriodicalIF":4.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179360","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":"An application of machine learning for geometric optimization of a dual-throat bent nozzle","authors":"Homin Kim,&nbsp;Dong-Hun Han,&nbsp;Tae Hee Lee,&nbsp;Jung-Wuk Hong","doi":"10.1016/j.advengsoft.2025.103869","DOIUrl":"10.1016/j.advengsoft.2025.103869","url":null,"abstract":"<div><div>The optimal thrust shape for a dual-throat bent nozzle (DTBN), designed as a hybrid thrust vectoring nozzle, is derived through machine learning. A compressible, steady-state numerical analysis using the <span><math><mrow><mi>k</mi><mo>−</mo><mi>ω</mi></mrow></math></span> SST model is employed for model construction. The main geometric parameters that determine the shape of the DTBN are selected as the convergence angle <span><math><msub><mrow><mi>θ</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>, divergence angle <span><math><msub><mrow><mi>θ</mi></mrow><mrow><mi>d</mi></mrow></msub></math></span>, and cavity length <span><math><msub><mrow><mi>l</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>. By varying these parameters, DTBN models with a total of 600 different geometries are generated, and the axial and normal forces at the nozzle exit are observed to derive the thrust magnitude and thrust vectoring angle. A model that can accurately predict the correlation between input and output parameters is built by comparing various machine learning algorithms. The model using the random forest regression algorithm shows the best performance. Based on this developed machine learning model, optimized shapes of the DTBN are presented. The optimally designed DTBNs are expected to contribute to the development of a new system with more convenient thrust control.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"202 ","pages":"Article 103869"},"PeriodicalIF":4.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178990","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 and implementation of an online platform for efficient and accurate ship hull design","authors":"Yi-zheng Yang,&nbsp;Yue-hui Shu,&nbsp;Guang-nian Li,&nbsp;Lin Du,&nbsp;Hai-peng Guo","doi":"10.1016/j.advengsoft.2025.103870","DOIUrl":"10.1016/j.advengsoft.2025.103870","url":null,"abstract":"<div><h3>Motivation</h3><div>With the development of computer graphics in front-end technology and internet applications, there are many universal online modeling tools like Spline and Vectary. However, ship hull design requires specific constraints like displacement volume and standard deformation algorithms such as Lackenby and Free-Form deformation (FFD).</div></div><div><h3>Solution</h3><div>This study developed an online platform of ship hull parametric modeling and design called Ship Hull Iterative Parametrization Online (SHIP Online). Firstly, three deformation methods—linear,interactive FFD, and combined deformation—were implemented using JavaScript and Python. Fast Quadric Mesh Simplification (FQMS) and Nelder-Mead optimization were introduced for combined deformation. Secondly, the user interface was built with HTML5, JavaScript, and CSS. Thirdly, the synchronization and data transmission issues between front and back-end were handled by Django and $. ajax. Finally, the platform would be deployed on a cloud server for multi-system access.</div></div><div><h3>Result</h3><div>The DTMB-5415 standard model was used to evaluate the accuracy, efficiency, and functions of calculations and deformations in SHIP Online. The errors of hydrostatic parameters were nearly 0.0 %. With a 90 % optimal simplification rate and parameter errors under 0.001, combined deformation efficiency improved by 87.78 % and 55.15 % compared to deformation without FQMS and other software. Functional and user testing shows that the functions can be stably and normally executed.</div></div><div><h3>Conclusion</h3><div>By integrating ship deformation algorithms and design constraints with the front-end technology, SHIP Online provides a Web-based rapid hull design platform instead of traditional 3D modeling client software.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"202 ","pages":"Article 103870"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179362","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":"Thermomechanical free vibration buckling of FG graphene-reinforced doubly-curved sandwich shells","authors":"Mustafa Eroğlu ,&nbsp;Mehmet Akif Koç ,&nbsp;İsmail Esen","doi":"10.1016/j.advengsoft.2025.103875","DOIUrl":"10.1016/j.advengsoft.2025.103875","url":null,"abstract":"<div><div>This study explores the thermomechanical free vibration and buckling behavior of doubly-curved sandwich shell structures with a core of FG graphene-reinforced foam and metal or ceramic face layers. Using Hamilton's principle and a Navier-type solution method, the governing equations and boundary conditions for simply supported, functionally graded doubly-curved shells are derived. The numerical results are validated through comparison with existing literature. The analysis focuses on the effects of the material grading index, foam void ratio, graphene volume fraction, side-to-thickness ratio, and temperature variations. Notably, it was observed that as <em>R₁</em> increases from α to 10α, the buckling temperature significantly decreases, highlighting a reduction in thermal stability with increasing radii or inverse correlation between <em>R₁</em> and <em>R₂</em>. These findings reveal unexpected thermal instability trends that could influence future design considerations for high-temperature applications. This study provides novel insights into the thermomechanical behavior of advanced sandwich structures, offering valuable contributions to the field in light of evolving technological needs.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"202 ","pages":"Article 103875"},"PeriodicalIF":4.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179361","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 composite particle swarm optimization algorithm with future information inspired by non-equidistant grey predictive evolution for global optimization problems and engineering problems","authors":"Rui Hao ,&nbsp;Zhongbo Hu ,&nbsp;WenTao Xiong ,&nbsp;Shaojie Jiang","doi":"10.1016/j.advengsoft.2025.103868","DOIUrl":"10.1016/j.advengsoft.2025.103868","url":null,"abstract":"<div><div>Particle swarm optimization (PSO) and its numerous performance-enhancing variants are a kind of stochastic optimization technique based on collaborative sharing of swarm information. Many variants took current particles and historical particles as current and historical information to improve their performance. If future information after each current swarm can be mined to participate in collaborative search, the algorithmic performance could benefit from the comprehensiveness of the information including historical, current and future information. This paper proposes a composite particle swarm optimization algorithm with future information inspired by non-equidistant grey predictive evolution, namely NeGPPSO. The proposed algorithm firstly employs non-equidistant grey predictive evolution algorithm to predict a future particle as future information for each particle of a current swarm. Secondly, four particles including prediction particle, particle best and swarm best of the current swarm, and a history memory particle are used as guide particles to generate four candidate positions. Finally, the best one in the four positions is greedily selected as an offspring particle. Numerical experiments are conducted on 42 benchmark functions given by the Congress on Evolutionary Computation 2014/2022 and 3 engineering problems. The experimental results demonstrate the overall advantages of the proposed NeGPPSO over several state-of-art algorithms.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"202 ","pages":"Article 103868"},"PeriodicalIF":4.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179369","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":"Sub-population evolutionary particle swarm optimization with dynamic fitness-distance balance and elite reverse learning for engineering design problems","authors":"Gang Hu ,&nbsp;Keke Song ,&nbsp;Mahmoud Abdel-salam","doi":"10.1016/j.advengsoft.2025.103866","DOIUrl":"10.1016/j.advengsoft.2025.103866","url":null,"abstract":"<div><div>The main idea of particle swarm optimization (PSO) is to imitate the social behavior of birds, and the individuals in the algorithm can quickly converge to the optimal region of the solution space, to effectively find the optimal solution to the problem. When faced with high and complex optimization problems, it is easy to fall into local optimal solutions. To this end, this paper improved and optimized the original particle swarm optimization algorithm by introducing four stages, namely, the introduction of elite reverse learning strategy, dynamic fitment-distance balance (dFDB) strategy, subpopulation evolution strategy, factor adjustment and random position update strategy, and named the improved algorithm dFDBMPSO. Firstly, the diversity of the population is enhanced by introducing the elite reverse learning strategy in the initial stage. Secondly, in order to prevent the algorithm from identifying the optimal solution more effectively, dFDB policies are added to the algorithm to provide better guidance for the birds during the search process. In addition, subpopulation evolution strategy and random location update are introduced into the algorithm as new individual update methods, which makes the search method of birds in the algorithm more flexible. Finally, the formula factor of bird location update in the algorithm is adjusted to better balance the ability of global search and local search. In this paper, to test the performance of the proposed algorithm, the proposed algorithm is compared with a variety of comparison algorithms in different dimensions of the CEC2020 test set, four engineering design problems, two other truss topology optimization problems, and parameter optimization problems based on VCCS-FOPID vehicle control system. The experimental results show that the newly developed algorithm is superior to the previous algorithm. In addition, a new grey prediction model RC_TDGM (1,1,<em>r,ξ, Csz</em>) based on triangular residual correction and dFDBMPSO is proposed and applied to the forecast of global biofuel production. The predicted results are closer to the trend of the original data, which further verifies the competitiveness of the algorithm.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"202 ","pages":"Article 103866"},"PeriodicalIF":4.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179367","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 transformer neural network based framework for steel defect detection under complex scenarios","authors":"Gaoyang Liu ,&nbsp;Yi Chen ,&nbsp;Jun Ye ,&nbsp;Yan Jiang ,&nbsp;Hongchuan Yu ,&nbsp;Jing Tang ,&nbsp;Yang Zhao","doi":"10.1016/j.advengsoft.2025.103872","DOIUrl":"10.1016/j.advengsoft.2025.103872","url":null,"abstract":"<div><div>Steel defect detection is crucial for guaranteeing the long-term quality and safety of steel structures. With the increasing use of steel structures, there is a pressing need to automatically detect defects in complex scenarios. This paper proposes a transformer neural network-based framework for steel defect detection under complex scenarios. Firstly, a comprehensive dataset of steel defects was collected, and a statistical analysis was conducted to categorize different defect types. The images were then manually labeled. The proposed framework enhances the UNet model by incorporating a transformer encoder (TransUNet) to improve the model's ability to extract defect features in complex environments. A quantitative evaluation of different models was performed on existing datasets, demonstrating that the TransUNet model surpassed other models across multiple evaluation metrics, including Intersection over Union (IoU), F1-score, precision, recall, and Dice coefficient. Secondly, simulations of complex environments for steel defect detection were conducted. Under various lighting and fog conditions, the TransUNet model consistently maintained high segmentation accuracy, with a mean IoU (mIoU) ranging from 87.11 % to 98.46 %, showing minimal variation in performance. Finally, in the verification tests of the proposed framework, the TransUNet model showcased its potential and value in detecting and segmenting defects in steel bridges. The TransUNet model consistently delivered stable segmentation results and excellent performance, whether under ideal experimental conditions or in complex real-world scenarios. The proposed method for segmenting steel defects under complex scenarios using TransUNet holds broad application prospects and high practicality for steel structure inspections. This study opens up a new approach for steel defect detection and safety evaluation under complex scenarios, providing a fundamental basis for the digital twin of steel structures.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"202 ","pages":"Article 103872"},"PeriodicalIF":4.0,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep neural network based head injury criterion estimation for more efficient pedestrian protection performance evaluation in bonnet structure design","authors":"Tianci Zhang ,&nbsp;Weiwei Wang ,&nbsp;Bo Li ,&nbsp;Ting Zeng ,&nbsp;Fanliang Meng","doi":"10.1016/j.advengsoft.2025.103867","DOIUrl":"10.1016/j.advengsoft.2025.103867","url":null,"abstract":"<div><div>In the early development stage of a new car model, fast evaluation of pedestrian protection performance is required to accelerate the bonnet structure design process. Current evaluation methods rely on finite element (FE) simulation to calculate the Head Injury Criterion (HIC) values at specified points on the bonnet, before proceeding to the more expensive real-world impact test using headform impactors. However, the FE based approach typically takes several days to complete the meshing, boundary condition setting and HIC calculation to evaluate a single design candidate. To further increase the evaluation efficiency, this paper presents a novel HIC estimation approach based on deep learning. An end-to-end deep neural network model is proposed which can directly generate the HIC value without resorting to FE methods. It uses seven variables pertaining to the panel height, structural difference, thickness and head type as the input based on the definition of HIC. Convolution layers are utilised to aggregate the surrounding structural information for each target point. To demonstrate the effectiveness of the proposed approach, cross validation results are presented based on a dataset of over five thousands target points collected from 28 cars. For the green target regions, the average HIC estimation accuracy is 93.1 %, which outperforms the result of 83 % reported in previous work. A comparison with the traditional support vector regression method demonstrates the advantages of the proposed approach.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"202 ","pages":"Article 103867"},"PeriodicalIF":4.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179368","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}