{"title":"Numerical Study of Flow and Thermal Characteristics of Pulsed Impinging Jet on a Dimpled Surface","authors":"Amin Bagheri, Kazem Esmailpour, Morteza Heydari","doi":"10.1016/j.taml.2024.100501","DOIUrl":"https://doi.org/10.1016/j.taml.2024.100501","url":null,"abstract":"","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139815664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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":null,"pages":null},"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}
{"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":null,"pages":null},"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}
{"title":"A grouping strategy for reinforcement learning-based collective yaw control of wind farms","authors":"Chao Li, Luoqin Liu, Xiyun Lu","doi":"10.1016/j.taml.2024.100491","DOIUrl":"10.1016/j.taml.2024.100491","url":null,"abstract":"<div><p>Reinforcement learning (RL) algorithms are expected to become the next generation of wind farm control methods. However, as wind farms continue to grow in size, the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces, limiting its potential in practical applications. In this Letter, we employ a RL-based wind farm control approach with multi-agent deep deterministic policy gradient to optimize the yaw manoeuvre of grouped wind turbines in wind farms. To reduce the computational complexity, the turbines in the wind farm are grouped according to the strength of the wake interaction. Meanwhile, to improve the control efficiency, each subgroup is treated as a whole and controlled by a single agent. Optimized results show that the proposed method can not only increase the power production of the wind farm but also significantly improve the control efficiency.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034924000023/pdfft?md5=a559abb669f00422961e7eae65d71fc1&pid=1-s2.0-S2095034924000023-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139457362","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}
{"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":null,"pages":null},"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}
{"title":"Assessment of scale interactions associated with wake meandering using bispectral analysis methodologies","authors":"Dinesh Kumar Kinjangi, Daniel Foti","doi":"10.1016/j.taml.2024.100497","DOIUrl":"https://doi.org/10.1016/j.taml.2024.100497","url":null,"abstract":"","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139635296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 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":null,"pages":null},"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}
Yupei Zhang , Jiawei Zhong , Zhengcai Zhao , Ruiyu Bai , Yanqi Yin , Yang Yu , Bo Li
{"title":"Mechanical Janus lattice with plug-switch orientation","authors":"Yupei Zhang , Jiawei Zhong , Zhengcai Zhao , Ruiyu Bai , Yanqi Yin , Yang Yu , Bo Li","doi":"10.1016/j.taml.2024.100493","DOIUrl":"10.1016/j.taml.2024.100493","url":null,"abstract":"<div><p>In recent years, materials with asymmetric mechanical response properties (mechanical Janus materials) have been found possess numerous potential applications, i.e. shock absorption and vibration isolation. In this study, we propose a novel mechanical Janus lattice whose asymmetric mechanical response can be switched in orientation by a plug. Through finite element analysis (FEA) and experimental verification, this lattice exhibits asymmetric displacement responses to symmetric forces. Furthermore, with such a plug structure inside, individual lattices can switch the orientation of asymmetry and thus achieve reprogrammable design of a mechanical structure with chained lattices. The reprogrammable asymmetry of this material will offer multiple functions in design of mechanical metamaterials</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034924000047/pdfft?md5=c8c4a05054e1941d4538ab59e565222f&pid=1-s2.0-S2095034924000047-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456584","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}
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":null,"pages":null},"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}
{"title":"Multi-Scale-Matching neural networks for thin plate bending problem","authors":"Lei Zhang , Guowei He","doi":"10.1016/j.taml.2024.100494","DOIUrl":"10.1016/j.taml.2024.100494","url":null,"abstract":"<div><p>Physics-informed neural networks (PINN) are a useful machine learning method for solving differential equations, but encounter challenges in effectively learning thin boundary layers within singular perturbation problems. To resolve this issue, Multi-Scale-Matching Neural Networks (MSM-NN) are proposed to solve the singular perturbation problems. Inspired by matched asymptotic expansions, the solution is decomposed into inner solutions for small scales and outer solutions for large scales, corresponding to boundary layers and outer regions, respectively. Moreover, to conform neural networks, we introduce exponential stretched variables in the boundary layers to avoid semi-infinite region problems. Numerical results for the thin plate problem validate the proposed method.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034924000059/pdfft?md5=f559d1a7f02735ab74546cec5d8df8f9&pid=1-s2.0-S2095034924000059-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139537377","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}