Theoretical and Applied Mechanics Letters最新文献_第3页

Physics-data coupling-driven method to predict the penetration depth into concrete targets 预测混凝土目标穿透深度的物理-数据耦合驱动法

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2024-01-01 DOI: 10.1016/j.taml.2024.100495

Qin Shuai, Liu Hao, Jianhui Wang, Zhao Qiang, Zhang Lei

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":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/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":null,"pages":null},"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

Generative optimization of bistable plates with deep learning 利用深度学习对双稳态板进行生成优化

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2023-11-22 DOI: 10.1016/j.taml.2023.100483

Hong Li, Qingfeng Wang

引用次数: 0

Feature identification in complex fluid flows by convolutional neural networks 基于卷积神经网络的复杂流体流动特征识别

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2023-11-01 DOI: 10.1016/j.taml.2023.100482

Shizheng Wen , Michael W. Lee , Kai M. Kruger Bastos , Ian K. Eldridge-Allegra , Earl H. Dowell

{"title":"Feature identification in complex fluid flows by convolutional neural networks","authors":"Shizheng Wen , Michael W. Lee , Kai M. Kruger Bastos , Ian K. Eldridge-Allegra , Earl H. Dowell","doi":"10.1016/j.taml.2023.100482","DOIUrl":"10.1016/j.taml.2023.100482","url":null,"abstract":"<div><p>Recent advancements have established machine learning’s utility in predicting nonlinear fluid dynamics, with predictive accuracy being a central motivation for employing neural networks. However, the pattern recognition central to the networks function is equally valuable for enhancing our dynamical insight into the complex fluid dynamics. In this paper, a single-layer convolutional neural network (CNN) was trained to recognize three qualitatively different subsonic buffet flows (periodic, quasi-periodic and chaotic) over a high-incidence airfoil, and a near-perfect accuracy was obtained with only a small training dataset. The convolutional kernels and corresponding feature maps, developed by the model with no temporal information provided, identified large-scale coherent structures in agreement with those known to be associated with buffet flows. Sensitivity to hyperparameters including network architecture and convolutional kernel size was also explored. The coherent structures identified by these models enhance our dynamical understanding of subsonic buffet over high-incidence airfoils over a wide range of Reynolds numbers.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034923000533/pdfft?md5=0751e990d15598c516321ddf159bf672&pid=1-s2.0-S2095034923000533-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764169","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

Reinforcement learning for wind-farm flow control: Current state and future actions 风电场流量控制的强化学习:当前状态和未来行为

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2023-11-01 DOI: 10.1016/j.taml.2023.100475

Mahdi Abkar , Navid Zehtabiyan-Rezaie , Alexandros Iosifidis

{"title":"Reinforcement learning for wind-farm flow control: Current state and future actions","authors":"Mahdi Abkar , Navid Zehtabiyan-Rezaie , Alexandros Iosifidis","doi":"10.1016/j.taml.2023.100475","DOIUrl":"https://doi.org/10.1016/j.taml.2023.100475","url":null,"abstract":"<div><p>Wind-farm flow control stands at the forefront of grand challenges in wind-energy science. The central issue is that current algorithms are based on simplified models and, thus, fall short of capturing the complex physics of wind farms associated with the high-dimensional nature of turbulence and multiscale wind-farm-atmosphere interactions. Reinforcement learning (RL), as a subset of machine learning, has demonstrated its effectiveness in solving high-dimensional problems in various domains, and the studies performed in the last decade prove that it can be exploited in the development of the next generation of algorithms for wind-farm flow control. This review has two main objectives. Firstly, it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL methods. By examining the latest research in this area, the review seeks to offer a comprehensive understanding of the advancements made in wind-farm flow control through the application of RL techniques. Secondly, it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on RL. By highlighting these challenges, the review aims to identify areas requiring further exploration and potential opportunities for future research.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034923000466/pdfft?md5=dccf754ba92cf45e1307aa03bb92f0b4&pid=1-s2.0-S2095034923000466-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91977641","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 method of convolutional neural network based on frequency segmentation for monitoring the state of wind turbine blades 基于频率分割的卷积神经网络风电叶片状态监测方法

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2023-11-01 DOI: 10.1016/j.taml.2023.100479

Weijun Zhu, Yunan Wu, Zhenye Sun, Wenzhong Shen, Guangxing Guo, Jianwei Lin

{"title":"A method of convolutional neural network based on frequency segmentation for monitoring the state of wind turbine blades","authors":"Weijun Zhu, Yunan Wu, Zhenye Sun, Wenzhong Shen, Guangxing Guo, Jianwei Lin","doi":"10.1016/j.taml.2023.100479","DOIUrl":"10.1016/j.taml.2023.100479","url":null,"abstract":"<div><p>Wind turbine blades are prone to failure due to high tip speed, rain, dust and so on. A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed. On the experimental measurement data, variational mode decomposition filtering and Mel spectrogram drawing are conducted first. The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network. Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients, considering the complexity of the real environment. The surfaces of Wind turbine blades are classified into four types: standard, attachments, polishing, and serrated trailing edge. The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%. In addition to support the differentiation of trained models, utilizing proper score coefficients also permit the screening of unknown types.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034923000508/pdfft?md5=49f235d2825c62d403a3e3aa9917f8b2&pid=1-s2.0-S2095034923000508-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764036","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

Machine learning potential for Ab Initio phase transitions of zirconia 氧化锆从头算相变的机器学习潜力

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2023-11-01 DOI: 10.1016/j.taml.2023.100481

Yuanpeng Deng, Chong Wang, Xiang Xu, Hui Li

{"title":"Machine learning potential for Ab Initio phase transitions of zirconia","authors":"Yuanpeng Deng, Chong Wang, Xiang Xu, Hui Li","doi":"10.1016/j.taml.2023.100481","DOIUrl":"10.1016/j.taml.2023.100481","url":null,"abstract":"<div><p>Zirconia has been extensively used in aerospace, military, biomedical and industrial fields due to its unusual combination of high mechanical, electrical and thermal properties. However, the fundamental and critical phase transition process of zirconia has not been well studied because of its difficult first-order phase transition with formidable energy barrier. Here, we generated a machine learning interatomic potential with <em>ab initio</em> accuracy to discover the mechanism behind all kinds of phase transition of zirconia at ambient pressure. The machine learning potential precisely characterized atomic interactions among all zirconia allotropes and liquid zirconia in a wide temperature range. We realized the challenging reversible first-order monoclinic-tetragonal and cubic-liquid phase transition processes with enhanced sampling techniques. From the thermodynamic information, we gave a better understanding of the thermal hysteresis phenomenon in martensitic monoclinic-tetragonal transition. The phase diagram of zirconia from our machine learning potential based molecular dynamics simulations corresponded well with experimental results.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034923000521/pdfft?md5=f70ba7ea49139490effb6fe082db679a&pid=1-s2.0-S2095034923000521-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764299","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

Large eddy simulation of supersonic flow in ducts with complex cross-sections 复杂截面管道中超声速流动的大涡模拟

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2023-11-01 DOI: 10.1016/j.taml.2023.100469

Huifeng Chen , Mingbo Sun , Dapeng Xiong , Yixin Yang , Taiyu Wang , Hongbo Wang

引用次数: 0

Investigation and simulation of parabolic trough collector with the presence of hybrid nanofluid in the finned receiver tube 翅片管中存在混合纳米流体时抛物面槽集热器的研究与仿真

IF 3.4 3区工程技术

Theoretical and Applied Mechanics Letters Pub Date : 2023-11-01 DOI: 10.1016/j.taml.2023.100465

M. Javidan, M. Gorji-Bandpy, A. Al-Araji

{"title":"Investigation and simulation of parabolic trough collector with the presence of hybrid nanofluid in the finned receiver tube","authors":"M. Javidan, M. Gorji-Bandpy, A. Al-Araji","doi":"10.1016/j.taml.2023.100465","DOIUrl":"https://doi.org/10.1016/j.taml.2023.100465","url":null,"abstract":"<div><p>The present study discusses the thermal performance of the receiver tube, which contains a wall with various fin shapes in the parabolic trough collector. Inserted fins and bulge surfaces of the inner wall of the receiver tube increase the turbulent fluid flow. In pursuance of uniform distribution of heat transfer, various fin shapes such as square-shape, circle-shape, triangle-shape, and combined square-circle shapes were inserted, examined, and compared. A study of the temperature differences and fluid flow is meaningful for this project therefore Finite Volume Method was used to investigate heat transfer. Also, hybrid Nano-Fluid AL<sub>2</sub>O<sub>3</sub><sub><img></sub>CuO, TiO<sub>2</sub><sub><img></sub>Cu, and Ag-MgO were applied to increase thermal diffusivity. When the combined square-circle-shaped fin was inserted, the thermal peak of fluid flow in the receiver tube was lower than the other studied fin shapes by almost 1%. Besides, the hybrid nano-fluid Ag-MgO Syltherm-oil-800 has lower thermal waste in comparison to others by more than 3%.</p></div>","PeriodicalId":46902,"journal":{"name":"Theoretical and Applied Mechanics Letters","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095034923000363/pdfft?md5=e868955c17fce6480c1e1f4efe44f709&pid=1-s2.0-S2095034923000363-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91998792","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