Structural and Multidisciplinary Optimization最新文献_第2页

Topology-inclusive aerodynamic shape optimisation using a cellular automata parameterisation. 使用蜂窝自动机参数化技术进行拓扑包容性空气动力学形状优化。

IF 3.6 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2025-01-01 Epub Date: 2025-01-30 DOI: 10.1007/s00158-024-03916-6

M J Wood, T C S Rendall, C B Allen, L J Kedward, N J Taylor, J Fincham, N E Leppard

{"title":"Topology-inclusive aerodynamic shape optimisation using a cellular automata parameterisation.","authors":"M J Wood, T C S Rendall, C B Allen, L J Kedward, N J Taylor, J Fincham, N E Leppard","doi":"10.1007/s00158-024-03916-6","DOIUrl":"https://doi.org/10.1007/s00158-024-03916-6","url":null,"abstract":"A novel geometry parameterisation method constructed from a volume-of-solid driven cellular automata is presented. The method is capable of describing complex geometry of arbitrary topology using a set of volume-of-solid parameters applied to a geometry control mesh. This is done by approximating the smooth geometry of minimum surface area subject to a set of localised constraints on contained volume defined by both the control mesh and volume-of-solid parameters. Localised control mesh refinement is possible through splitting of control mesh cells to provide additional degrees of freedom where necessary. The parameterisation is shown to reconstruct over 98% of a library of aerofoil geometries to within a standard wind tunnel-equivalent geometric tolerance, and to recover known analytical optima in supersonic flow. Using gradient-free optimisation methods, the parameterisation is then shown to construct aerodynamic geometries consisting of multiple objects to package a set of existing geometries. Finally, the parameterisation is used to construct an optimal supersonic multi-body geometry with less than half the drag of the equivalent volume optimal single body.","PeriodicalId":21994,"journal":{"name":"Structural and Multidisciplinary Optimization","volume":"68 2","pages":"23"},"PeriodicalIF":3.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11903545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650881","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}

引用次数: 0

Configurational-force-driven adaptive refinement and coarsening in topology optimization. 拓扑优化中的配置力驱动自适应细化与粗化。

IF 4 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2025-01-01 Epub Date: 2025-08-19 DOI: 10.1007/s00158-025-04096-7

Gabriel Stankiewicz, Chaitanya Dev, Paul Steinmann

{"title":"Configurational-force-driven adaptive refinement and coarsening in topology optimization.","authors":"Gabriel Stankiewicz, Chaitanya Dev, Paul Steinmann","doi":"10.1007/s00158-025-04096-7","DOIUrl":"https://doi.org/10.1007/s00158-025-04096-7","url":null,"abstract":"The iterative nature of topology optimization, especially in combination with nonlinear state problems, often requires the solution of thousands of linear equation systems. Furthermore, due to the pixelated design representation, the use of a fine mesh is essential to obtain geometrically well-defined structures and to accurately compute response quantities such as the von Mises stress. Therefore, the computational cost of solving a fine-mesh topology optimization problem quickly adds up. To address this challenge, we consider a multi-level adaptive refinement and coarsening strategy based on configurational forces. Configurational forces based on the Eshelby stress predict configurational changes such as crack propagation or dislocation motion. Due to a relaxation in the calculation of (Eshelby) stresses with respect to the design variables, discrete configurational forces increase not only in highly stressed regions, but also in gray transition regions (design boundaries). For this reason, they constitute an ideal criterion for mesh adaptivity in topology optimization, especially when avoiding stress failure is a priority. By using configurational forces for refinement, we obtain a high-resolution structure where the refined mesh is present along the design boundaries as well as in stress-critical regions. At the same time, multi-level coarsening using the same criterion drastically minimizes the computational effort.","PeriodicalId":21994,"journal":{"name":"Structural and Multidisciplinary Optimization","volume":"68 8","pages":"152"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144969455","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}

引用次数: 0

Combined truss and continuum topology optimization of structures. 组合桁架与连续体结构拓扑优化。

IF 4 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2025-01-01 Epub Date: 2025-07-29 DOI: 10.1007/s00158-025-04063-2

Hongjia Lu, Helen E Fairclough, Linwei He, Matthew Gilbert

{"title":"Combined truss and continuum topology optimization of structures.","authors":"Hongjia Lu, Helen E Fairclough, Linwei He, Matthew Gilbert","doi":"10.1007/s00158-025-04063-2","DOIUrl":"10.1007/s00158-025-04063-2","url":null,"abstract":"Truss layout optimization and continuum topology optimization are both well-established methods, with each having a wide range of applications. Whereas truss layout optimization is best suited for low volume fraction problems (i.e. where the optimal structure occupies a low proportion of the original design domain), continuum topology optimization is best suited for medium and high volume fraction problems. However, real-world design problems often include both high and low volume fraction regions. To address this, a two-step hybrid optimization approach is proposed. First, low and high volume fraction regions are identified within a problem. These are then populated with truss and continuum elements respectively, which are connected via suitable interfaces. The combined optimization formulation is conic, and can be efficiently solved using interior point solvers. Numerical examples are presented to demonstrate the efficacy of the proposed approach. The results show that the approach is capable of identifying structures which contain a mixture of length scales, incorporating both bulk continuum regions and fine truss elements.","PeriodicalId":21994,"journal":{"name":"Structural and Multidisciplinary Optimization","volume":"68 7","pages":"142"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12307569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144761299","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}

引用次数: 0

Flexible feature mapping topology optimization using NURBS-based component projection. 基于nurbs的构件投影的柔性特征映射拓扑优化。

IF 3.6 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2025-01-01 Epub Date: 2025-07-09 DOI: 10.1007/s00158-025-04047-2

Reinier Giele, Can Ayas, Matthijs Langelaar

引用次数: 0

Topology optimization of multi-material active structures to reduce energy consumption and carbon footprint 拓扑优化多材料活性结构，降低能耗和碳足迹

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2024-01-01 DOI: 10.1007/s00158-023-03698-3

Yafeng Wang, Ole Sigmund

引用次数: 0

Active learning for adaptive surrogate model improvement in high-dimensional problems. 在高维问题中改进自适应代用模型的主动学习。

IF 3.6 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2024-01-01 Epub Date: 2024-07-10 DOI: 10.1007/s00158-024-03816-9

Yulin Guo, Paromita Nath, Sankaran Mahadevan, Paul Witherell

{"title":"Active learning for adaptive surrogate model improvement in high-dimensional problems.","authors":"Yulin Guo, Paromita Nath, Sankaran Mahadevan, Paul Witherell","doi":"10.1007/s00158-024-03816-9","DOIUrl":"10.1007/s00158-024-03816-9","url":null,"abstract":"This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. In this approach, the principal components and corresponding features of the high-dimensional output are first identified. For each feature, the active subspace technique is used to identify a corresponding low-dimensional subspace of the input domain; then a surrogate model is built for each feature in its corresponding active subspace. A low-dimensional adaptive learning strategy is proposed to identify training samples to improve the surrogate model. In contrast to existing adaptive learning methods that focus on a scalar output or a small number of outputs, this paper addresses adaptive learning with high-dimensional input and output, with a novel learning function that balances exploration and exploitation, i.e., considering unexplored regions and high-error regions, respectively. The adaptive learning is in terms of the active variables in the low-dimensional space, and the newly added training samples can be easily mapped back to the original space for running the expensive physics model. The proposed method is demonstrated for the numerical simulation of an additive manufacturing part, with a high-dimensional field output quantity of interest (residual stress) in the component that has spatial variability due to the stochastic nature of multiple input variables (including process variables and material properties). Various factors in the adaptive learning process are investigated, including the number of training samples, range and distribution of the adaptive training samples, contributions of various errors, and the importance of exploration versus exploitation in the learning function.","PeriodicalId":21994,"journal":{"name":"Structural and Multidisciplinary Optimization","volume":"67 7","pages":"122"},"PeriodicalIF":3.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11236939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617153","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}

引用次数: 0

Concurrent optimization of actuator/sensor layout and control parameter on piezoelectric curved shells with active vibration control for minimizing transient noise 同时优化具有主动振动控制功能的压电曲面壳上的致动器/传感器布局和控制参数，以最大限度地降低瞬态噪声

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-21 DOI: 10.1007/s00158-023-03707-5

Hao Zheng, Guozhong Zhao, Wen-Xi Han, Yang Yu, Weizhen Chen

引用次数: 0

Aerodynamic shape optimization of gas turbines: a deep learning surrogate model approach 燃气轮机气动外形优化：深度学习代用模型方法

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-21 DOI: 10.1007/s00158-023-03703-9

Vahid Esfahanian, Mohammad Javad Izadi, Hosein Bashi, Mehran Ansari, Alireza Tavakoli, Mohammad Kordi

引用次数: 0

Stability constraints for geometrically nonlinear topology optimization 几何非线性拓扑优化的稳定性约束

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-01 DOI: 10.1007/s00158-023-03712-8

Peter D. Dunning

引用次数: 0

Multi-material and thickness optimization of laminated composite structures subject to high-cycle fatigue 承受高循环疲劳的层压复合材料结构的多材料和厚度优化

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-01 DOI: 10.1007/s00158-023-03708-4

S. Hermansen, Erik Lund

引用次数: 0