检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Ouyang, Qiumeng Zhuang, Xiaoying Rabczuk, Timon Ning, Youjun Kang, Ge Chen, Pengwan Stake Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing100081 China Department of Geotechnical Engineering Tongji University Shanghai200092 China Chair of Computational Science and Simulation Technology Leibniz University Hannover Hannover30167 Germany Institute of Structural Mechanics Bauhaus-Universität Weimar Weimar99423 Germany School of Mechanical Engineering Southwest Petroleum University Sichuan Chengdu610500 China

Structural impact often accompanies large amounts of contacts and leads to complex mechanical phenomena. In structural mechanics, the numerical manifold method (NMM) has been proposed specially for the solution of continuous-discontinues characteristics based on dual manifold of mathematical and physical covers. In the present work, according to cover-based contact theory, a penalty contact algorithm for 3DNMM is programmed and applied for the impact mechanical problems. The accuracy of the developed contact algorithm is firstly calibrated through free-falling blocks and collision blocks. The influence of contact parameters on contact convergence is systematically studied, and three preliminary criteria for how to set contact parameters are provided. The effectiveness of the contact algorithm is verified based on the conservation of system momentum during two block collision. Subsequently, the contact algorithm is applied to Tylor rod and car-streetlight simulation, which further verifies the effectiveness of the present contact algorithms in modeling high-speed collisions, large displacements, and large deformations of structures. By comparing the 3DNMM results with Abaqus results, the contact algorithm developed here has outstanding performance in solving collision problems and has achieved results that are consistent with commercial software. The research results in the present work verified the applicability and accuracy of the proposed contact algorithm in solving structural dynamic impact problems. The present work also provides a guidance in contact parameter setting in impact problems. © 2024, The Authors. All rights reserved.

关键词： Structural dynamics

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A Phase Field Model for Mixed-Mode Brittle Fractures in Rocks Based on a Unified Driving Force

SSRN

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SSRN 2023年

作者： Zhou, Shuwei Zhuang, Xiaoying Zheng, Hong Rabczuk, Timon Department of Geotechnical Engineering College of Civil Engineering Tongji University Shanghai200092 China Chair of Computational Science and Simulation Technology Faculty of Mathematics and Physics Leibniz University Hannover Hannover30167 Germany Institute of Structural Mechanics Faculty of Civil Engineering Bauhaus University Weimar Weimar99423 Germany Beijing University of Technology Beijing China

We propose a new phase field model (PFM) for mixed-mode brittle fracture in rocks. In the evolution equation of phase field, we establish a unified driving force, which includes three separate energy components induced by the positive volumetric strain, and tensile and compressive principal strains. We introduce two additional parameters to assemble the energy components in the driving force and to reflect the difference between the critical energy release rates under tension and compression. On the basis of the new unified driving force, we subsequently establish a hybrid formulation and use the finite element method to implement the proposed model. We then test the brittle fractures in the uniaxial compression tests on rock specimens with a single inclined flaw and two parallel flaws. It is observed that pure tensile, shear, and mixed-mode cracks and their coalescence in rocks are well captured. In addition, compared with the conventional PFMs, the proposed PFM can well reflect the effects of cohesion and internal friction angle of rocks on fracture pattern, compressive strength, and load-displacement curves. Therefore, the presented numerical results are considerably consistent with previous experimental observations, validating the feasibility and practicability of the proposed PFM for simulating complex brittle fracture pattens in rocks. © 2023, The Authors. All rights reserved.

关键词： Strain

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High tensile strength and thermal conductivity in BeO monolayer: A first-principles study

arXiv

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arXiv 2021年

作者： Mortazavi, Bohayra Shojaei, Fazel Rabczuk, Timon Zhuang, Xiaoying Chair of Computational Science and Simulation Technology Institute of Photonics Department of Mathematics and Physics Leibniz Universität Hannover Appelstraße 11 Hannover30167 Germany Department of Chemistry Faculty of Nano and Bioscience and Technology Persian Gulf University Bushehr75169 Iran College of Civil Engineering Department of Geotechnical Engineering Tongji University 1239 Siping Road Shanghai China

In a latest experimental advance, graphene-like and insulating BeO monolayer was successfully grown over silver surface by molecular beam epitaxy (ACS Nano 15(2021), 2497). Inspired by this accomplishment, in this work we conduct first-principles based simulations to explore the electronic, mechanical properties and thermal conductivity of graphene-like BeO, MgO and CaO monolayers. The considered nanosheets are found to show desirable thermal and dynamical stability. BeO monolayer is found to show remarkably high elastic modulus and tensile strength of 408 and 53.3 GPa, respectively. The electronic band gap of BeO, MgO and CaO monolayers are predicted to be 6.72, 4.79, and 3.80 eV, respectively, using the HSE06 functional. On the basis of iterative solutions of the Boltzmann transport equation, the room temperature lattice thermal conductivity of BeO, MgO and CaO monolayers are predicted to be 385, 64 and 15 W/mK, respectively. Our results reveal substantial decline in the electronic band gap, mechanical strength and thermal conductivity by increasing the weight of metal atoms. This work highlights outstandingly high thermal conductivity, carrier mobility and mechanical strength of insulating BeO nanosheets and suggest them as promising candidates to design strong and insulating components with high thermal conductivities. © 2021, CC BY.

关键词： Thermal conductivity

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High thermal conductivity in semiconducting Janus and non-Janus diamanes

arXiv

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arXiv 2020年

作者： Raeisi, Mostafa Mortazavi, Bohayra Podryabinkin, Evgeny V. Shojaei, Fazel Zhuang, Xiaoying Shapeev, Alexander V. Department of Mechanical Engineering Imam Khomeini International University Qazvin Iran Chair of Computational Science and Simulation Technology Department of Mathematics and Physics Leibniz University Hannover Appelstraße 11 Hannover30157 Germany Skolkovo Institute of Science and Technology Skolkovo Innovation Center Nobel St. 3 Moscow143026 Russia Department of Chemistry College of Sciences Persian Gulf University Boushehr75168 Iran

Most recently, F-diamane monolayer was experimentally realized by the fluorination of bilayer graphene. In this work we elaborately explore the electronic and thermal conductivity responses of diamane lattices with homo or hetero functional groups, including: non-Janus C2H, C2F and C2Cl diamane and Janus counterparts of C4HF, C4HCl and C4FCl. Noticeably, C2H, C2F, C2Cl, C4HF, C4HCl and C4FCl diamanes are found to show electronic diverse band gaps of, 3.86, 5.68, 2.42, 4.17, 0.86, and 2.05 eV, on the basis of HSE06 method estimations. The thermal conductivity of diamane nanosheets was acquired using the full iterative solutions of the Boltzmann transport equation, with substantially accelerated calculations by employing machine-learning interatomic potentials in obtaining the anharmonic force constants. According to our results, the room temperature lattice thermal conductivity of graphene and C2H, C2F, C2Cl, C4HF, C4HCl and C4FCl diamane monolayers are estimated to be 3636, 1145, 377, 146, 454, 244 and 196 W/mK, respectively. The underlying mechanisms resulting in significant effects of functional groups on the thermal conductivity of diamane nanosheets were thoroughly explored. Our results highlight the substantial role of functional groups on the electronic and thermal conduction responses of diamane nanosheets. Copyright © 2020, The Authors. All rights reserved.

关键词： Thermal conductivity

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Machine learning assisted multiscale modeling of composite phase change materials for Li-ion batteries' thermal management

arXiv

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arXiv 2021年

作者： Kolodziejczyk, Felix Mortazavi, Bohayra Rabczuk, Timon Zhuang, Xiaoying Institute of Continuum Mechanics Leibniz Universität Hannover Appelstraße 11 Hannover30157 Germany Chair of Computational Science and Simulation Technology Institute of Photonics Department of Mathematics and Physics Leibniz Universität Hannover Appelstraße 11 Hannover30157 Germany College of Civil Engineering Department of Geotechnical Engineering Tongji University Shanghai China

In this work, we develop a combined convolutional neural networks (CNNs) and finite element method (FEM) to examine the effective thermal properties of composite phase change materials (CPCMs) consisting of paraffin and copper foam. In this approach, first the CPCM microstructures are modeled using FEM and next the image dataset with corresponding thermal properties is created. The image dataset is subsequently used to train and test the CNN's performance, which is then compared with the performance of a popular network architecture for image classification tasks. The predicted thermal properties are employed to define the properties of the CPCM material of a battery pack. The heat generation and electrochemical response of a Li-ion cell during the charging/discharging is simulated by applying Newman's battery model. Thermal management is achieved by the latent heat of paraffin, with copper foam for enhancing the thermal conductivity. The multiscale model is finally developed using FEM to investigate the effectiveness of the thermal management of the battery pack. In these models the thermal properties estimated by the FEM and the CNN are employed to define the CPCM materials properties of a battery pack. Our results confirm that the model developed on the basis of a CNN can evaluate the effectiveness of the battery pack's thermal management system with an excellent accuracy in comparison with the original FEM models. © 2021, CC BY.

关键词： Machine learning

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Applications of Scientific Machine Learning for the Analysis of Functionally Graded Porous Beams

arXiv

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arXiv 2024年

作者： Eshaghi, Mohammad Sadegh Bamdad, Mostafa Anitescu, Cosmin Wang, Yizheng Zhuang, Xiaoying Rabczuk, Timon Chair of Computational Science and Simulation Technology Institute of Photonics Department of Mathematics and Physics Leibniz University Hannover Hannover30167 Germany Department of Geotechnical Engineering College of Civil Engineering Tongji University Siping Road 1239 Shanghai200092 China Institute of Structural Mechanics Bauhaus-Universität Weimar Germany Department of Engineering Mechanics Tsinghua University Beijing China

This study investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded (FG) porous beams and compares them under a new framework. The beam material properties are assumed to vary as an arbitrary continuous function. The methods consider the output of a neural network/operator as an approximation to the displacement fields and derive the equations governing beam behavior based on the continuum formulation. The methods are implemented in the framework and formulated by three approaches: (a) the vector approach leads to a Physics-Informed Neural Network (PINN), (b) the energy approach brings about the Deep Energy Method (DEM), and (c) the data-driven approach, which results in a class of Neural Operator methods. Finally, a neural operator has been trained to predict the response of the porous beam with functionally graded material under any porosity distribution pattern and any arbitrary traction condition. The results are validated with analytical and numerical reference solutions. The data and code accompanying this manuscript will be publicly available at https://***/eshaghi-ms/DeepNetBeam. © 2024, CC BY.

关键词： Functionally graded materials

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Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential

arXiv

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arXiv 2024年

作者： Rajabpour, Ali Mortazavi, Bohayra Mirchi, Pedram Hajj, Julien El Guo, Yangyu Zhuang, Xiaoying Merabia, Samy Mechanical Engineering Department Imam Khomeini International University Qazvin34148-96818 Iran Institut Lumière Matière Université Claude Bernard Lyon 1 CNRS Université de Lyon Villeurbanne69622 France Chair of Computational Science and Simulation Technology Institute of Photonics Department of Mathematics and Physics Leibniz Universität Hannover Appelstraße 11 Hannover30167 Germany School of Energy Science and Engineering Harbin Institute of Technology Harbin150001 China

Thermal management at silicon-diamond interface is critical for advancing high-performance electronic and optoelectronic devices. In this study, we calculate the interfacial thermal conductance between silicon and diamond using machine learning (ML) interatomic potentials trained on density functional theory (DFT) data. Using non-equilibrium molecular dynamics (NEMD) simulations, we compute the interfacial thermal conductance (ITC) for various system sizes. Our results show a closer agreement with experimental data than those obtained using traditional semi-empirical potentials such as Tersoff and Brenner which overestimate ITC by a factor of about 3. In addition, we analyze the frequency-dependent heat transfer spectrum, providing insights into the contributions of different phonon modes to the interfacial thermal conductance. The ML potential accurately captures the phonon dispersion relations and lifetimes, in good agreement with DFT calculations and experimental observations. It is shown that the Tersoff potential predicts higher phonon group velocities and phonon lifetimes compared to the DFT results. Furthermore, it predicts higher interfacial bonding strength, which is consistent with higher interfacial thermal conductance as compared to the ML potential. This study highlights the use of the ML interatomic potential to improve the accuracy and computational efficiency of thermal transport simulations in complex material systems. © 2024, CC BY.

关键词： Density functional theory

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Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution

arXiv

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arXiv 2020年

作者： Mortazavi, Bohayra Podryabinkin, Evgeny V. Novikov, Ivan S. Rabczuk, Timon Zhuang, Xiaoying Shapeev, Alexander V. Chair of Computational Science and Simulation Technology Department of Mathematics and Physics Leibniz Universität Hannover Appelstraße 11 Hannover30157 Germany Gottfried Wilhelm Leibniz Universität Hannover Hannover Germany Skolkovo Institute of Science and Technology Skolkovo Innovation Center Nobel St. 3 Moscow143026 Russia Institute of Materials Science University of Stuttgart Pfaffenwaldring 55 Stuttgart70569 Germany Division of Computational Mechanics Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam

Accurate evaluation of the thermal conductivity of a material can be a challenging task from both experimental and theoretical points of view. In particular for the nanostructured materials, the experimental measurement of thermal conductivity is associated with diverse sources of uncertainty. As a viable alternative to experiment, the combination of density functional theory (DFT) simulations and the solution of Boltzmann transport equation is currently considered as the most trusted approach to examine thermal conductivity. The main bottleneck of the aforementioned method is to acquire the anharmonic interatomic force constants using the computationally demanding DFT calculations. In this work we propose a substantially accelerated approach for the evaluation of anharmonic interatomic force constants via employing machine-learning interatomic potentials (MLIPs) trained over short ab-initio molecular dynamics trajectories. The remarkable accuracy of the proposed accelerated method is confirmed by comparing the estimated thermal conductivities of several bulk and two-dimensional materials with those computed by the full-DFT approach. The MLIP-based method proposed in this study can be employed as a standard tool, which would substantially accelerate and facilitate the estimation of lattice thermal conductivity in comparison with the commonly used full-DFT solution. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Nanoporous C3N4, C3N5 and C3N6 nanosheets;Novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties

arXiv

引用

arXiv 2020年

作者： Mortazavi, Bohayra Shojaei, Fazel Shahrokhi, Masoud Azizi, Maryam Rabczuk, Timon Shapeev, Alexander V. Zhuang, Xiaoying Chair of Computational Science and Simulation Technology Department of Mathematics and Physics Leibniz Universität Hannover Appelstraße 11 Hannover30157 Germany Gottfried Wilhelm Leibniz Universität Hannover Hannover Germany Department of Chemistry College of Sciences Persian Gulf University Boushehr75168 Iran Tehran Iran Department of Physics Faculty of Science University of Kurdistan Sanandaj Iran College of Civil Engineering Department of Geotechnical Engineering Tongji University Shanghai China Skolkovo Institute of Science and Technology Skolkovo Innovation Center Nobel St. 3 Moscow143026 Russia

Carbon nitride two-dimensional (2D) materials are among the most attractive class of nanomaterials, with wide range of application prospects. As a continuous progress, most recently, two novel carbon nitride 2D lattices of C3N5 and C3N4 have been successfully experimentally realized. Motivated by these latest accomplishments and also by taking into account the well-known C3N4 triazine-based graphitic carbon nitride structures, we predicted two novel C3N6 and C3N4 counterparts. We then conducted extensive density functional theory simulations to explore the thermal stability, mechanical, electronic and optical properties of these novel nanoporous carbon-nitride nanosheets. According to our results all studied nanosheets were found to exhibit desirable thermal stability and mechanical properties. Non-equilibrium molecular dynamics simulations on the basis of machine learning interatomic potentials predict ultralow thermal conductivities for these novel nanosheets. Electronic structure analyses confirm direct band gap semiconducting electronic character and optical calculations reveal the ability of these novel 2D systems to adsorb visible range of light. Extensive first-principles based results by this study provide a comprehensive vision on the stability, mechanical, electronic and optical responses of C3N4, C3N5 and C3N6 as novel 2D semiconductors and suggest them as promising candidates for the design of advanced nanoelectronics and energy storage/conversion systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Nanosheets

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Converse Flexoelectricity of Low-Dimensional Bismuth Selenite (Bi2Se3) Revealed by Piezoresponse Force Microscopy (PFM)

arXiv

引用

arXiv 2023年

作者： Liu, Qiong Nanthakumar, S.S. Li, Bin Cheng, Teresa Bittner, Florian Ma, Chenxi Ding, Fei Zheng, Lei Roth, Bernhard Zhuang, Xiaoying Chair of Computational Science and Simulation Technology Faculty of Mathematics and Physics Leibniz University Hannover Hannover Germany Department of Geotechnical Engineering College of Civil Engineering Tongji University Shanghai China Faculty of Mechanical Engineering Leibniz University Hannover Hannover Germany Institute of Solid State Physics Faculty of Mathematics and Physics Leibniz University Hannover Hannover Germany Hannover Centre for Optical Technologies Leibniz University Hannover Hannover Germany

Many kinds of two-dimensional (2D) van der Waals (vdW) have been demonstrated to exhibit electromechanical coupling effects, which makes them promising candidates for next-generation devices, such as piezotronics and nanogenerators. Recently, flexoelectricity was found to account for the out-of-plane electromechanical coupling in many 2D transition metal dichalcogenides (TMDs) who only exhibit in-plane piezoelectricity. However, low dimensional vdW three-dimensional (3D) topological insulators (TIs) have been overlooked regarding their electromechanical properties. In this study, for the first time, we experimentally investigate the electromechanical coupling of low dimensional 3D TIs with a centrosymmetric crystal structure, where a binary compound, bismuth selenite (Bi2Se3), is taken as an example. The results of piezoresponse force microscope (PFM) tests on the Bi2Se3 nanoflakes show that the material exhibits both out-of-plane and in-plane electromechanical responses. The Bi2Se3 nanoflake with a thickness of 37 nm possesses an effective out-of-plane piezoelectric coefficient of ~0.65 pm V-1. With careful analyses, the electromechanical responses are verified to arise from the converse flexoelectricity. The measured effective out-of-plane piezoelectric coefficient is mainly contributed by flexoelectric coefficient, μ39 which is estimated to be approximately 0.13 nC m-1. However it is rather difficult to obtain the in-plane component of the flexoelectric tensor from the in-plane PFM measurements since the direction of the in-plane stress is always not normal to the AFM cantilever axis. The results provide useful guidance for understanding the flexoelectric effect of low dimensional vdW materials with centrosymmetric crystal structures. Moreover the work can pave to way to explore the electromechanical devices based on the flexoelectricity of vdW TIs. © 2023, CC BY-NC-ND.

关键词： Bismuth compounds

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