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Theoretical Analysis of Fracture Behavior of Adhesively Bonded Bi-Material Dcb Joints

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SSRN 2024年

作者： Wang, Wandong Zhang, Shijie Zhu, Yangxuan Yao, Xudan Zhao, Tian Ma, Yu'e School of Aeronautics Northwestern Polytechnical University Shaanxi Xi’an710072 China Hubei Key Laboratory of Theory and Application of Advanced Material Mechanics School of Science Wuhan University of Technology Wuhan430070 China Beijing Key Laboratory of Lightweight Multi-functional Composite Materials and Structures Institute of Advanced Structure Technology Beijing Institute of Technology Beijing100081 China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing100081 China

There has been a disputation about how to achieve a pure mode I fracture mode and thus measure its toughness for an adhesively bonded bi-material DCB specimen. This paper therefore develops a theoretical methodology to calculate the tensile and shear stress distributions in the adhesive layer and to perform data reduction and mode partitioning for generic bi-material DCB specimens. The theoretical model is validated using experimental data. The adhesive layer in a generic bi-material DCB joint is loaded both in tensile and shear based on the predicted stress distributions in the adhesive layer, resulting in a mixed mode fracture behavior. The prediction results substantiate that a strain-based design principle eliminates the shear stresses and thus leads to pure mode I fracture in bi-material DCB specimens. This paper justifies that the established data reduction method is applicable for measuring mode I fracture toughness when a bi-material DCB is designed according to the strain-based design criterion. © 2024, The Authors. All rights reserved.

关键词： Stress concentration

Microstructural Evolution of Defects Under Proton Irradiation in Porous Cu

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SSRN 2024年

作者： Li, Honglin Wang, Hao Huang, Zhifeng Wen, Pin Yin, Guanchao Chen, Fei Hubei Longzhong Laboratory Xiangyang441106 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China International School of Materials Science and Engineering Wuhan University of Technology Wuhan430070 China Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of Science Wuhan University of Technology Wuhan430070 China State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical Physics Chinese Academy of Sciences Lanzhou730000 China

关键词： Radiation damage

Tougher metallic glasses through quantum assisted design

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Tougher metallic glasses through quantum assisted design

第十三届全国物理力学学术会议

作者： X.F.Wang T.E.Jones Y.Wu Z.P.Lu S.Halas T.Durakiewicz M.E.Eberhart Key Laboratory of Low Dimensional Materials and Application Technology (inistry of Education) Xiangtan University School of Physics The University of Sydney State Key Laborato Mry for Advanced Metals and Materials University of Science and Technology Beijing Institute of Physics Maria Curie-Sklodowska University Los Alamos National Laboratory Molecular Theory Group Colorado School of Mines

Bulk metallic glasses(BMGs)are characterized by a number of remarkable physical and me-chanical ***,these same materials are often intrinsically brittle,which limits their *** a consequence considerable efforts have been expended searching for correlations between the phenomenologically complex mechanical properties of metallic glasses and more basic and controllable *** such correlation is that between intrinsically brittle behavior in both

关键词： Tougher metallic glasses through quantum assisted design

Detecting the chiral magnetic effect via deep learning

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Physical Review C 2022年第5期106卷 L051901-L051901页

作者： Yuan-Sheng Zhao Lingxiao Wang Kai Zhou Xu-Guang Huang Physics Department and Center for Particle Physics and Field Theory Fudan University Shanghai 200433 China Frankfurt Institute for Advanced Studies Ruth Moufang Strasse 1 D-60438 Frankfurt am Main Germany Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Fudan University Shanghai 200433 China

The search for chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attention. Multiple observables are proposed, but all suffer large background contaminations. In this study, we construct an end-to-end CME-meter based on a deep convolutional neural network. After being trained over a dataset generated by a multiphase transport model, the CME-meter shows high accuracy in recognizing the CME-featured charge separation from the final-state pion spectra. It also exhibits remarkable robustness to diverse conditions including different collision energies, centralities, and elliptic flow backgrounds. In extrapolation tests, the CME-meter is validated in isobaric collisions and different model simulation, showing robust generalization performance. Moreover, based on variational approaches, we utilize the DeepDream method to derive the most responsive CME-spectrum that demonstrates the physical contents the machine learned.

关键词： Quantum chromodynamics Relativistic heavy-ion collisions Artificial neural networks Chiral symmetry

Mode I Fracture Analysis of Adhesively Bonded Bi-Material Dcb Joints

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SSRN 2023年

作者： Wang, Wandong Zhang, Shijie Chen, Guiyong Zhu, Yangxuan Zhao, Tian School of Civil Aviation Northwestern Polytechnical University Shaanxi Xi’an710072 China Co. Ltd 610000 China Hubei Key Laboratory of Theory and Application of Advanced Material Mechanics School of Science Wuhan University of Technology Wuhan430070 China Beijing Key Laboratory of Lightweight Multi-functional Composite Materials and Structures Institute of Advanced Structure Technology Beijing Institute of Technology Beijing100081 China

There has been a disputation about how to design bi-material double cantilever beam (DCB) joints to measure pure mode I fracture toughness. This paper develops a theoretical frame to calculate the stresses in the adhesive layer and perform the data reduction and mode partitioning for generic bi-material DCB specimens. The theoretical model was validated using experimental data and conventional finite element method. The prediction results substantiate that the strain-based design strategy leads to a pure mode I fracture of the bi-material DCB joints. The proposed theoretical method can promote the application of strain-based criterion for designing the adhesively bonded bi-material DCB joints to appropriately obtain the pure mode I critical fracture toughness. © 2023, The Authors. All rights reserved.

关键词： Adhesives

A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction

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arXiv

arXiv 2022年

作者： Dang, Yuchen Chen, Ziqi Li, Heng Shu, Hai Center for Data Science New York University New YorkNY United States Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Province Shenzhen China Department of Biostatistics School of Global Public Health New York University New YorkNY United States

Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. In particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models. Our XGBoost-DL achieves the best forecasting performance (RMSE = 25.70 and MAE = 19.82) in the comparison, outperforming the best non-deep learning model SARIMA (RMSE = 54.11 and MAE = 45.51), the best deep learning model Informer (RMSE = 29.90 and MAE = 22.35) and the NASA's forecast (RMSE = 48.38 and MAE = 38.45). Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 for Solar Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to but later than the NASA's at 137.7 in October 2024 and 161.2 in December 2034. An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at https://***/yd1008/ts_ensemble_sunspot. Copyright © 2022, The Authors. All rights reserved.

关键词： Solar energy

Bioinspired Dual-Phase Composite Metamaterial for Customized Deformation Behavior and Performance Characteristic

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SSRN 2023年

作者： Yang, Haoming Zhang, Yao Wang, Zhuangzhuang Cao, Xiaofei Li, Ying School of Aerospace Engineering Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics Department of Engineering Structure and Mechanics School of Science Wuhan University of Technology Wuhan430070 China Institute of Advanced Structure Technology Beijing Institute of Technology Beijing100081 China

Nature have provided abundant unexpected inspirations for the innovative design of dual-phase composite metamaterials, which were generally composed of the matrix phase (MP) and the reinforcement phase (RP). Improvement in mechanical properties and regulation in deformation processes could be implemented on this kind of metamaterials simultaneously. This study innovated three different bioinspired design methods for dual-phase composite metamaterials based on the phase boundary sliding effect of magnesium nanocrystals, hierarchical effect of cauliflowers and interpenetrating hierarchical effect of nanocages, respectively. Through systematic design and finite element analysis, we studied the mechanical properties of various metamaterials under the quasi-static loading condition. Results indicated that increasing the connectivity, phase boundary area and continuous distribution characteristics of the reinforcement phase were the effective approaches in improving the mechanical properties (e.g. 96.90% increase in specific stiffness) and energy absorption characteristics (e.g. 67.70% increase in energy absorption) of the metamaterials. Besides, introducing the interpenetrating matrix lattice at the specific positions was effective in regulating the deformation processes and failure modes. This study offered a novel pathway to manipulate the mechanical properties and deformation modes through bioinspired dual-phase pattern, which would markedly expand the design space for the development of future materials. © 2023, The Authors. All rights reserved.

关键词： Energy absorption

Functionally Graded Triply Periodic Minimal Surface Scaffold of Ha-Al2o3 Via Vat Photopolymerization 3d Printing

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SSRN 2023年

作者： Liu, Qiuyu Tang, Qinchao Huang, Zhifeng Li, Zhiwei Wang, Xuye Wen, Pin Bai, Yi Chen, Fei Hubei Longzhong Laboratory Wuhan University of Technology Xiangyang Demonstration Zone Xiangyang441000 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China International School of Materials Science and Engineering Wuhan University of Technology Wuhan430070 China State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration Key Laboratory of Oral Biomedicine Ministry of Education Hubei Key Laboratory of Stomatology School & Hospital of Stomatology Wuhan430079 China Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of Science Wuhan University of Technology Wuhan430070 China

Hydroxyapatite (HA) is widely used in scaffolds due to its high similarity in the composition to natural bone. However, the weak and fragile characteristics of synthetic HA limit its application. The strength of HA can be increased by the incorporation of alumina (Al2O3) to obtain composites with extraordinary compressive strength and biocompatibility. HA-Al2O3 composites with different ratios were prepared by Vat photopolymerization 3D printing, and their shrinkage, porosity, phase composition and biocompatibility were systematically investigated. In addition, the Diamond structure of the scaffold was chosen to achieve the trade-off relationship between compressive strength and porosity. The results suggest that the HA-Al2O3 composite with 20 vol% HA possesses the maximum compressive strength and 60 vol% HA has optimal biocompatibility. A compositional gradient scaffold from 60 vol% HA and 20 vol% HA was proposed to satisfy different needs of the different parts in scaffold for biocompatibility and compressive strength. The HA-Al2O3 gradient scaffold achieves a preferable compressive strength of 13.7 MPa while possessing gradient biocompatibility. © 2023, The Authors. All rights reserved.

关键词： Hydroxyapatite

Click-Through Rate Prediction Models based on Interest Modeling 5

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Click-Through Rate Prediction Models based on Interest Model...

5th International Conference on Big Data Engineering, BDE 2023

作者： Luo, Zhen Zhang, Yingfang Hu, Chengxuan Xia, Yuxuan Zhu, Shengxin Department of Statistical Science University College London United Kingdom Department of Statistics and Actuarial Science University of Hong Kong Hong Kong Hong Kong Department of Industrial and Systems Engineering Polytechnic University of Hong Kong Hong Kong Hong Kong School of Mathematics University of Southampton United Kingdom Research Center for Mathematics Advanced Institute of Natural Science Beijing Normal University Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College China

ISBN: (纸本)9798400708695

Deep learning frameworks have achieved extraordinary results in popular technological frontiers, which also greatly inspires considerable researchers in recommender system. As one of the mainstream research frontiers, click-through rate (CTR) predictions have received increasing attention from both industrial and academic circles. Recently, Alibaba group has come up with novel idea of interest modeling that can effectively overcome the limitations brought by previous deep learning CTR prediction models, which is also the key to improve performance. For the past few years, more and more relevant works begin to explore approaches taking advantage of the learning, representation, and interpretation ability from neural networks. However, there are few comprehensive surveys targeted at this topic. In this paper, we systematically review and summarize recent CTR prediction models based on interest modeling. First, we elaborate their different design ideas and pay additional attention to their intrinsic relationship including improvements and extensions, similarities and differences, advantages and disadvantages and so on. Next, we deploy them separately according to official open-source code and compare their performance assessment in terms of accuracy and speed, looking for best optimizers and other hyperparameters. Furthermore, we briefly discuss some possible refinement directions and future research trends. © 2023 ACM.

关键词： Recommender systems