Engineering with Computers最新文献_第3页

Adaptive Kriging-based method with learning function allocation scheme and hybrid convergence criterion for efficient structural reliability analysis 基于学习函数分配方案和混合收敛标准的自适应克里金方法，用于高效结构可靠性分析

IF 8.7 2区工程技术

Engineering with Computers Pub Date : 2024-09-15 DOI: 10.1007/s00366-024-02044-5

Jiaguo Zhou, Guoji Xu, Zexing Jiang, Yongle Li, Jinsheng Wang

{"title":"Adaptive Kriging-based method with learning function allocation scheme and hybrid convergence criterion for efficient structural reliability analysis","authors":"Jiaguo Zhou, Guoji Xu, Zexing Jiang, Yongle Li, Jinsheng Wang","doi":"10.1007/s00366-024-02044-5","DOIUrl":"https://doi.org/10.1007/s00366-024-02044-5","url":null,"abstract":"Structural reliability analysis poses significant challenges in engineering practices, leading to the development of various state-of-the-art approximation methods. Active learning methods, known for their superior performance, have been extensively investigated to estimate the failure probability. This paper aims to develop an efficient and accurate adaptive Kriging-based method for structural reliability analysis by proposing a novel learning function allocation scheme and a hybrid convergence criterion. Specifically, the novel learning function allocation scheme is introduced to address the challenge of no single learning function universally outperforms others across various engineering contexts. Six learning functions, including EFF, H, REIF, LIF, FNEIF, and KO, constitute a portfolio of alternatives in the learning function allocation scheme. The hybrid convergence criterion, combining the error-based stopping criterion with a stabilization convergence criterion, is proposed to terminate the active learning process at an appropriate stage. Moreover, an importance sampling algorithm is leveraged to enable the proposed method with the capability to deal with rare failure events. The efficiency and accuracy of the proposed method are demonstrated through four numerical examples and one engineering case.","PeriodicalId":11696,"journal":{"name":"Engineering with Computers","volume":"23 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258318","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}

引用次数: 0

LatticeGraphNet: a two-scale graph neural operator for simulating lattice structures LatticeGraphNet：用于模拟晶格结构的双尺度图神经算子

IF 8.7 2区工程技术

Engineering with Computers Pub Date : 2024-09-14 DOI: 10.1007/s00366-024-02034-7

Ayush Jain, Ehsan Haghighat, Sai Nelaturi

引用次数: 0

Simulating the aftermath of Northern European Enclosure Dam (NEED) break and flooding of European coast 模拟北欧封闭水坝（NEED）决堤和欧洲海岸洪水泛滥的后果

IF 8.7 2区工程技术

Engineering with Computers Pub Date : 2024-09-12 DOI: 10.1007/s00366-024-02055-2

Paweł Maczuga, Marcin Łoś, Eirik Valseth, Albert Oliver Serra, Leszek Siwik, Elisabede Alberdi Celaya, Anna Paszyńska, Maciej Paszyński

{"title":"Simulating the aftermath of Northern European Enclosure Dam (NEED) break and flooding of European coast","authors":"Paweł Maczuga, Marcin Łoś, Eirik Valseth, Albert Oliver Serra, Leszek Siwik, Elisabede Alberdi Celaya, Anna Paszyńska, Maciej Paszyński","doi":"10.1007/s00366-024-02055-2","DOIUrl":"https://doi.org/10.1007/s00366-024-02055-2","url":null,"abstract":"The Northern European Enclosure Dam (NEED) is a hypothetical project to prevent flooding in European countries following the rising ocean level due to melting arctic glaciers. This project involves the construction of two large dams between Scotland and Norway, as well as England and France. The anticipated cost of this project is 250 to 500 billion euros. In this paper, we present the simulation of the aftermath of flooding on the European coastline caused by a catastrophic break of this hypothetical dam. From our simulation results, we can observe a traveling wave after the accident, with a velocity of approximately 45 kms per hour, raising the sea level permanently inside the dammed region. This observation implies a need to construct additional dams or barriers protecting the Netherlands’ northern coastline and the Baltic Sea’s interior. Our simulations have been obtained using the following building blocks. First, a graph transformation model was applied to generate an adaptive mesh, refined towards the seabed and the seashore topography, approximating the topography of the Earth. We employ the composition graph grammar model to break the mesh’s triangular elements without generating hanging nodes. Second, the wave equation is formulated in a spherical latitude-longitude system of coordinates and solved by a high-order time integration scheme using the generalized (alpha) method. While our paper mainly focuses on the simulation of the NEED dam break, we also provide a stand-alone tool to generate an adaptive mesh of the whole Earth. We can use our software as a stand-alone package in FEniCS or other simulation software.","PeriodicalId":11696,"journal":{"name":"Engineering with Computers","volume":"59 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178560","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}

引用次数: 0

Scalable data-driven micromechanics model trained with pairwise fiber data for composite materials with randomly distributed fibers 针对随机分布纤维的复合材料，利用成对纤维数据训练可扩展的数据驱动微观力学模型

IF 8.7 2区工程技术

Engineering with Computers Pub Date : 2024-09-11 DOI: 10.1007/s00366-024-02059-y

Chaeyoung Hong, Wooseok Ji

{"title":"Scalable data-driven micromechanics model trained with pairwise fiber data for composite materials with randomly distributed fibers","authors":"Chaeyoung Hong, Wooseok Ji","doi":"10.1007/s00366-024-02059-y","DOIUrl":"https://doi.org/10.1007/s00366-024-02059-y","url":null,"abstract":"A machine learning (ML) model can provide a precise prediction very quickly, if it is well trained with a massive amount of reliable training data. A finite element method (FEM) is often employed to generate substantial training data. However, such a training process can be computationally burdensome especially for a geometrically complex structure. More critically, a specific size and/or configuration of a training model may confine the applicability of the trained model to the same kind only. In this study, we present a scalable ML approach with an efficient training strategy for micromechanical analysis of fiber-reinforced composite materials. Here, a scalable data-driven micromechanics model (SDMM) is proposed for predicting stresses in unidirectional composites with random fiber arrays. The training data for SDMM is defined in the unit of a fiber pair. A single dataset is composed of a stress value between a fiber pair and an image highlighting the pair with nearby fibers affecting the stress. Therefore, the training microstructures can be considerably small, but the pairwise ML model can be applied to every pair of adjacent two fibers inside a much larger microstructure. The scalability of SDMM is demonstrated by predicting the maximum principal stress values acting between every fiber pair in a super-sized representative volume element. The accuracy of the prediction results is evaluated by finite element analysis results. It is shown that a certain number of nearby fibers is required in the training datasets for accurate prediction.","PeriodicalId":11696,"journal":{"name":"Engineering with Computers","volume":"4 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178574","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}

引用次数: 0

3d fluid–structure interaction simulation with an Arbitrary–Lagrangian–Eulerian approach with applications to flying objects 采用任意-拉格朗日-欧勒方法进行三维流固耦合模拟，并将其应用于飞行物体

IF 8.7 2区工程技术

Engineering with Computers Pub Date : 2024-09-11 DOI: 10.1007/s00366-024-02043-6

Daniele Di Cristofaro, Attilio Frangi, Massimiliano Cremonesi

{"title":"3d fluid–structure interaction simulation with an Arbitrary–Lagrangian–Eulerian approach with applications to flying objects","authors":"Daniele Di Cristofaro, Attilio Frangi, Massimiliano Cremonesi","doi":"10.1007/s00366-024-02043-6","DOIUrl":"https://doi.org/10.1007/s00366-024-02043-6","url":null,"abstract":"Air-structure interaction is a key aspect to account for during the design of Micro Air Vehicles. In this context, modelisation and numerical simulations represent a powerful tool to analyse aerodynamic performances. This work proposes an advanced fluid–structure interaction numerical technique for the simulation of dragonfly wings, considered one of the most interesting model due to their complex flapping kinematic. The fluid subproblem, described by incompressible Navier–Stokes equations, is solved in a Finite Element Arbitrary-Lagrangian-Eulerian framework, while the solid subproblem is addressed using structural Finite Element, such as membranes and beams. Moreover, a novel remeshing algorithm based on connectivity manipulation and refinement procedure has been implemented to reduce element distortion in fluid mesh, thus increasing the accuracy of the fluid solution. Firstly, the deformation of a single hindwing has been studied. Secondly, the dragonfly model is enriched by incorporating the forewing and a simplified thorax geometry. Preliminary results highlight the complex dynamic of the fluid around the body as well as the efficiency of the proposed mesh generation algorithm.","PeriodicalId":11696,"journal":{"name":"Engineering with Computers","volume":"2 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178562","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}

引用次数: 0

Stress-based topology optimization using maximum entropy basis functions-based meshless method 使用基于最大熵基函数的无网格法进行基于应力的拓扑优化

IF 8.7 2区工程技术

Engineering with Computers Pub Date : 2024-09-09 DOI: 10.1007/s00366-024-02047-2

Imran Khan, Zahur Ullah, Baseer Ullah, Siraj-ul-Islam, Wajid Khan

{"title":"Stress-based topology optimization using maximum entropy basis functions-based meshless method","authors":"Imran Khan, Zahur Ullah, Baseer Ullah, Siraj-ul-Islam, Wajid Khan","doi":"10.1007/s00366-024-02047-2","DOIUrl":"https://doi.org/10.1007/s00366-024-02047-2","url":null,"abstract":"This paper presents volume-constrained stress minimization-based, topology optimization. The maximum entropy (maxent) basis functions-based meshless method for two-dimensional linear elastic structures is explored. This work focuses to test the effectiveness of the meshless method in handling the stress singularities during the topology optimization process. The commonly used moving least square basis functions are replaced with maximum entropy basis functions, as the latter possess weak Kronecker delta property which leads to the finite element method (FEM) like displacement boundary conditions imposition. The maxent basis functions are calculated once at the beginning of the simulation and then used in optimization at every iteration. Young’s modulus for each background cell is interpolated using the modified solid isotropic material with penalization approach. An open source pre-processor CUBIT is used. A comparison of the proposed approach with the FEM is carried out using a diverse set of problems with simple and complex geometries of structured and unstructured discretization, to establish that maxent-based meshless methods perform better in tackling the stress singularities due to its smooth stress field.","PeriodicalId":11696,"journal":{"name":"Engineering with Computers","volume":"3 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178564","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}

引用次数: 0

Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies 通过多保真模拟、机器学习和边界细化策略实现高效的反向设计优化

IF 8.7 2区工程技术

Engineering with Computers Pub Date : 2024-09-09 DOI: 10.1007/s00366-024-02053-4

Luka Grbcic, Juliane Müller, Wibe Albert de Jong

{"title":"Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies","authors":"Luka Grbcic, Juliane Müller, Wibe Albert de Jong","doi":"10.1007/s00366-024-02053-4","DOIUrl":"https://doi.org/10.1007/s00366-024-02053-4","url":null,"abstract":"This paper introduces a methodology designed to augment the inverse design optimization process in scenarios constrained by limited compute, through the strategic synergy of multi-fidelity evaluations, machine learning models, and optimization algorithms. The proposed methodology is analyzed on two distinct engineering inverse design problems: airfoil inverse design and the scalar field reconstruction problem. It leverages a machine learning model trained with low-fidelity simulation data, in each optimization cycle, thereby proficiently predicting a target variable and discerning whether a high-fidelity simulation is necessitated, which notably conserves computational resources. Additionally, the machine learning model is strategically deployed prior to optimization to compress the design space boundaries, thereby further accelerating convergence toward the optimal solution. The methodology has been employed to enhance two optimization algorithms, namely Differential Evolution and Particle Swarm Optimization. Comparative analyses illustrate performance improvements across both algorithms. Notably, this method is adaptable across any inverse design application, facilitating a synergy between a representative low-fidelity ML model, and high-fidelity simulation, and can be seamlessly applied across any variety of population-based optimization algorithms.","PeriodicalId":11696,"journal":{"name":"Engineering with Computers","volume":"24 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178567","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}

引用次数: 0

Reducing spatial discretization error on coarse CFD simulations using an openFOAM-embedded deep learning framework 使用嵌入深度学习框架的 openFOAM 减少粗糙 CFD 模拟的空间离散化误差

IF 8.7 2区工程技术

Engineering with Computers Pub Date : 2024-09-09 DOI: 10.1007/s00366-024-02057-0

J. Gonzalez-Sieiro, D. Pardo, V. Nava, V. M. Calo, M. Towara

{"title":"Reducing spatial discretization error on coarse CFD simulations using an openFOAM-embedded deep learning framework","authors":"J. Gonzalez-Sieiro, D. Pardo, V. Nava, V. M. Calo, M. Towara","doi":"10.1007/s00366-024-02057-0","DOIUrl":"https://doi.org/10.1007/s00366-024-02057-0","url":null,"abstract":"We propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with fine-grid data after projecting it to the coarse-grid discretization. We substitute the default differencing scheme for the convection term by a feed-forward neural network that interpolates velocities from cell centers to face values to produce velocities that approximate the down-sampled fine-grid data well. The deep learning framework incorporates the open-source CFD code OpenFOAM, resulting in an end-to-end differentiable model. We automatically differentiate the CFD physics using a discrete adjoint code version. We present a fast communication method between TensorFlow (Python) and OpenFOAM (c++) that accelerates the training process. We applied the model to the flow past a square cylinder problem, reducing the error from 120% to 25% in the velocity for simulations inside the training distribution compared to the traditional solver using an x8 coarser mesh. For simulations outside the training distribution, the error reduction in the velocities was about 50%. The training is affordable in terms of time and data samples since the architecture exploits the local features of the physics.","PeriodicalId":11696,"journal":{"name":"Engineering with Computers","volume":"10 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178565","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}

引用次数: 0

Physics-aware neural network-based parametric model-order reduction of the electromagnetic analysis for a coated component 基于物理感知神经网络的带涂层部件电磁分析参数模型阶次缩减

IF 8.7 2区工程技术

Engineering with Computers Pub Date : 2024-09-07 DOI: 10.1007/s00366-024-02056-1

SiHun Lee, Seung-Hoon Kang, Sangmin Lee, SangJoon Shin

引用次数: 0

Correction to: Generic volume transfer for distributed mesh dynamic repartitioning 更正：分布式网格动态重新划分的通用体积转移

IF 8.7 2区工程技术

Engineering with Computers Pub Date : 2024-09-04 DOI: 10.1007/s00366-024-02052-5

Guillaume Damiand, Fabrice Jaillet, Vincent Vidal

引用次数: 0