Theoretical and Applied Mechanics Letters最新文献

Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions 基于匹配渐近展开的多尺度物理信息神经网络求解高雷诺数边界层流动

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2024-01-18 DOI: 10.1016/j.taml.2024.100496

Jianlin Huang , Rundi Qiu , Jingzhu Wang , Yiwei Wang

{"title":"Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions","authors":"Jianlin Huang , Rundi Qiu , Jingzhu Wang , Yiwei Wang","doi":"10.1016/j.taml.2024.100496","DOIUrl":"https://doi.org/10.1016/j.taml.2024.100496","url":null,"abstract":"<div><p>Multi-scale system remains a classical scientific problem in fluid dynamics, biology, etc. In the present study, a scheme of multi-scale Physics-informed neural networks (msPINNs) is proposed to solve the boundary layer flow at high Reynolds numbers without any data. The flow is divided into several regions with different scales based on Prandtl’s boundary theory. Different regions are solved with governing equations in different scales. The method of matched asymptotic expansions is used to make the flow field continuously. A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale. The results are compared with the reference numerical solutions, which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows. This scheme can be developed for more multi-scale problems in the future.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":"14 2","pages":"Article 100496"},"PeriodicalIF":3.4,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034924000072/pdfft?md5=3c2bcd1109464bcab55e4a3bad910e96&pid=1-s2.0-S2095034924000072-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Towards data-efficient mechanical design of bicontinuous composites using generative AI 利用生成式人工智能实现数据高效的双连续复合材料机械设计

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2024-01-01 DOI: 10.1016/j.taml.2024.100492

Milad Masrouri , Zhao Qin

{"title":"Towards data-efficient mechanical design of bicontinuous composites using generative AI","authors":"Milad Masrouri , Zhao Qin","doi":"10.1016/j.taml.2024.100492","DOIUrl":"10.1016/j.taml.2024.100492","url":null,"abstract":"<div><p>The distribution of material phases is crucial to determine the composite's mechanical property. While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases, this relationship is difficult to be revealed for complex irregular distributions, preventing design of such material structures to meet certain mechanical requirements. The noticeable developments of artificial intelligence (AI) algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures. It is intriguing how these tools can assist composite design. Here, we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading. We find that generative AI, enabled through fine-tuned Low Rank Adaptation models, can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution. The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness, fracture and robustness of the material with one model, and such has to be done by several different experimental or simulation tests. This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":"14 1","pages":"Article 100492"},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034924000035/pdfft?md5=33fc5a8eba7d7bf17165eca971a0d917&pid=1-s2.0-S2095034924000035-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139454437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A grouping strategy for reinforcement learning-based collective yaw control of wind farms 基于强化学习的风电场集体偏航控制分组策略

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2024-01-01 DOI: 10.1016/j.taml.2024.100491

Chao Li, Luoqin Liu, Xiyun Lu

引用次数: 0

In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning 通过综合有限元建模和深度学习对介电弹性体复合材料的致动性能进行硅优化

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2024-01-01 DOI: 10.1016/j.taml.2024.100490

Jiaxuan Ma , Sheng Sun

{"title":"In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning","authors":"Jiaxuan Ma , Sheng Sun","doi":"10.1016/j.taml.2024.100490","DOIUrl":"10.1016/j.taml.2024.100490","url":null,"abstract":"<div><p>Dielectric elastomers (DEs) require balanced electric actuation performance and mechanical integrity under applied voltages. Incorporating high dielectric particles as fillers provides extensive design space to optimize concentration, morphology, and distribution for improved actuation performance and material modulus. This study presents an integrated framework combining finite element modeling (FEM) and deep learning to optimize the microstructure of DE composites. FEM first calculates actuation performance and the effective modulus across varied filler combinations, with these data used to train a convolutional neural network (CNN). Integrating the CNN into a multi-objective genetic algorithm (NSGA-II) generates designs with enhanced actuation performance and material modulus compared to the conventional FEM-NSGA-II approach within the same time. This framework harnesses artificial intelligence to navigate vast design possibilities, enabling optimized microstructures for high-performance DE composites.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":"14 1","pages":"Article 100490"},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034924000011/pdfft?md5=9b87a8b33810108d81283bd8b6fdbb70&pid=1-s2.0-S2095034924000011-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139454839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An adaptive machine learning-based optimization method in the aerodynamic analysis of a finite wing under various cruise conditions 基于自适应机器学习的优化方法在各种巡航条件下对有限机翼进行气动分析

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2024-01-01 DOI: 10.1016/j.taml.2023.100489

Zilan Zhang , Yu Ao , Shaofan Li , Grace X. Gu

{"title":"An adaptive machine learning-based optimization method in the aerodynamic analysis of a finite wing under various cruise conditions","authors":"Zilan Zhang , Yu Ao , Shaofan Li , Grace X. Gu","doi":"10.1016/j.taml.2023.100489","DOIUrl":"10.1016/j.taml.2023.100489","url":null,"abstract":"<div><p>Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions. Plenty of existing literature has considered two-dimensional infinite airfoil optimization, while three-dimensional finite wing optimizations are subject to limited study because of high computational costs. Here we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms, which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions. This methodology unfolds in three stages: radial basis function (RBF) interpolated wing generation, collection of inputs from computational fluid dynamics (CFD) simulations, and deep neural network that constructs the surrogate model for the optimal wing configuration. It has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical simulations. It also has the potential to optimize various aerial vehicles undergoing different mission environments, loading conditions, and safety requirements.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":"14 1","pages":"Article 100489"},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034923000600/pdfft?md5=7d3e1bf0de44190fab245db3bb82ceda&pid=1-s2.0-S2095034923000600-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139016441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Inverse design of mechanical metamaterial achieving a prescribed constitutive curve 实现规定构成曲线的机械超材料反设计

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2024-01-01 DOI: 10.1016/j.taml.2023.100486

Zongliang Du , Tanghuai Bian , Xiaoqiang Ren , Yibo Jia , Shan Tang , Tianchen Cui , Xu Guo

{"title":"Inverse design of mechanical metamaterial achieving a prescribed constitutive curve","authors":"Zongliang Du , Tanghuai Bian , Xiaoqiang Ren , Yibo Jia , Shan Tang , Tianchen Cui , Xu Guo","doi":"10.1016/j.taml.2023.100486","DOIUrl":"10.1016/j.taml.2023.100486","url":null,"abstract":"<div><p>Besides exhibiting excellent capabilities such as energy absorption, phase-transforming metamaterials offer a vast design space for achieving nonlinear constitutive relations. This is facilitated by switching between different patterns under deformation. However, the related inverse design problem is quite challenging, due to the lack of appropriate mathematical formulation and the convergence issue in the post-buckling analysis of intermediate designs. In this work, periodic unit cells are explicitly described by the moving morphable voids method and effectively analyzed by eliminating the degrees of freedom (DOFs) in void regions. Furthermore, by exploring the Pareto frontiers between error and cost, an inverse design formulation is proposed for unit cells. This formulation aims to achieve a prescribed constitutive curve and is validated through numerical examples and experimental results. The design approach presented here can be extended to the inverse design of other types of mechanical metamaterials with prescribed nonlinear effective properties.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":"14 1","pages":"Article 100486"},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034923000570/pdfft?md5=b02e406bfe862cadf35cfeb9025297c5&pid=1-s2.0-S2095034923000570-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138620065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Mechanical Janus lattice with plug-switch orientation 带插头开关方向的机械杰纳斯晶格

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2024-01-01 DOI: 10.1016/j.taml.2024.100493

Yupei Zhang , Jiawei Zhong , Zhengcai Zhao , Ruiyu Bai , Yanqi Yin , Yang Yu , Bo Li

引用次数: 0

Multi-Scale-Matching neural networks for thin plate bending problem 用于薄板弯曲问题的多尺度匹配神经网络

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2024-01-01 DOI: 10.1016/j.taml.2024.100494

Lei Zhang , Guowei He

引用次数: 0

A Call for Enhanced Data-Driven Insights into Wind Energy Flow Physics 呼吁加强数据驱动的风能流体物理洞察力

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2024-01-01 DOI: 10.1016/j.taml.2023.100488

Coleman Moss , Romit Maulik , Giacomo Valerio Iungo

{"title":"A Call for Enhanced Data-Driven Insights into Wind Energy Flow Physics","authors":"Coleman Moss , Romit Maulik , Giacomo Valerio Iungo","doi":"10.1016/j.taml.2023.100488","DOIUrl":"10.1016/j.taml.2023.100488","url":null,"abstract":"<div><p>With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations, machine learning (ML) models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays, the generated wakes and their interactions, and wind energy harvesting. However, the majority of the existing ML models for predicting wind turbine wakes merely recreate CFD-simulated data with analogous accuracy but reduced computational costs, thus providing surrogate models rather than enhanced data-enabled physics insights. Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models, using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue. In this letter, we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations, along with new promising research strategies.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":"14 1","pages":"Article 100488"},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034923000594/pdfft?md5=e872455d061c3f9513aed7fe9e9335ac&pid=1-s2.0-S2095034923000594-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139017282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Micropillar compression using discrete dislocation dynamics and machine learning 利用离散位错动力学和机器学习实现微柱压缩

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2023-11-22 DOI: 10.1016/j.taml.2023.100484

Jin Tao , Dean Wei , Junshi Yu , Qianhua Kan , Guozheng Kang , Xu Zhang

{"title":"Micropillar compression using discrete dislocation dynamics and machine learning","authors":"Jin Tao , Dean Wei , Junshi Yu , Qianhua Kan , Guozheng Kang , Xu Zhang","doi":"10.1016/j.taml.2023.100484","DOIUrl":"https://doi.org/10.1016/j.taml.2023.100484","url":null,"abstract":"<div><p>Discrete dislocation dynamics (DDD) simulations reveal the evolution of dislocation structures and the interaction of dislocations. This study investigated the compression behavior of single-crystal copper micropillars using few-shot machine learning with data provided by DDD simulations. Two types of features are considered: external features comprising specimen size and loading orientation and internal features involving dislocation source length, Schmid factor, the orientation of the most easily activated dislocations and their distance from the free boundary. The yielding stress and stress-strain curves of single-crystal copper micropillar are predicted well by incorporating both external and internal features of the sample as separate or combined inputs. It is found that the Machine learning accuracy predictions for single-crystal micropillar compression can be improved by incorporating easily activated dislocation features with external features. However, the effect of easily activated dislocation on yielding is less important compared to the effects of specimen size and Schmid factor which includes information of orientation but becomes more evident in small-sized micropillars. Overall, incorporating internal features, especially the information of most easily activated dislocations, improves predictive capabilities across diverse sample sizes and orientations.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":"14 1","pages":"Article 100484"},"PeriodicalIF":3.4,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034923000557/pdfft?md5=44595e3450567ba84dff1ea6b9f88acb&pid=1-s2.0-S2095034923000557-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138549352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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