检索结果-内蒙古大学图书馆

Bending analysis of magnetoelectroelastic nanoplates resting on Pasternak elastic foundation based on nonlocal theory

学校读者我要写书评

暂无评论

Applied Mathematics and Mechanics(English Edition) 2020年第12期41卷 1769-1786页

作者： Wenjie FENG ZhenYAN JiLIN CZZHANG Department of Engineering Mechanics Shijiazhuang Tiedao UniversityShijiazhuang 050043China Hebei Key Laboratory of Smart Material and Structure Mechanics Shijiazhuang Tiedao UniversityShijiazhuang 050043China Department of Civil Engineering University of SiegenSiegen D-57068Germany China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering International Center for Simulation Software in Engineering and SciencesCollege of Mechanics and MaterialsHohai UniversityNanjing 211100China

Based on the nonlocal theory and Mindlin plate theory,the governing equations(i.e.,a system of partial differential equations(PDEs)for bending problem)of magnetoelectroelastic(MEE)nanoplates resting on the Pasternak elastic foundation are first derived by the variational *** polynomial particular solutions corresponding to the established model are then obtained and further employed as basis functions with the method of particular solutions(MPS)to solve the governing equations *** is confirmed that for the present bending model,the new solution strategy possesses more general applicability and superior flexibility in the selection of collocation *** effects of different boundary conditions,applied loads,and geometrical shapes on the bending properties of MEE nanoplates are evaluated by using the developed *** important conclusions are drawn,which should be helpful for the design and applications of electromagnetic nanoplate structures.

关键词： magnetoelectroelastic(MEE)nanoplate bending nonlocal theory Mindlin plate theory method of particular solution(MPS) polynomial basis function

Unravelling spontaneous Bloch-type skyrmion in centrosymmetric two-dimensional magnets

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Pang, Jingman Niu, Xiaohang Zhao, Hong Jian Zhang, Yun Bellaiche, Laurent College of Physics and Optoelectronic Technology Collaborative Innovation Center of Rare-Earth Functional Materials and Devices Development Baoji University of Arts and Sciences Baoji721016 China Faculty of Chemistry and Chemical Engineering Baoji University of Arts and Sciences 1 Hi-Tech Avenue Shaanxi Baoji China Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of Physics Jilin University Changchun130012 China Physics Department Institute for Nanoscience and Engineering University of Arkansas FayettevilleAR72701 United States

The realization of magnetic skyrmions in two-dimensional (2D) magnets holds great promise for both fundamental research and device applications. Despite recent progress, two-dimensional skyrmions hosts are still limited, due to the fact that most 2D magnets are centrosymmetric and thus lack Dzyaloshinskii−Moriya interaction (DMI). We show here, using a general analysis based on symmetry, that Bloch-type skyrmions can, in fact, be stabilized in 2D magnets, due to the interplay between in-plane component (dx) of second nearest-neighbor DMI and magnetic anisotropy. Its validity is demonstrated in Cr2Ge2Te6 monolayer, which is also verified by recent experiments. Our work gives a clear direction for experimental studies of 2D magnetic materials to stabilize skyrmions and should greatly enrich the research on magnetic skyrmions in 2D lattices. © 2023, CC BY.

关键词： Magnetic anisotropy

Physics-informed neural networks for elliptic partial differential equations on 3D manifolds

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Tang, Zhuochao Fu, Zhuojia Key Laboratory of Ministry of Education for Coastal Disaster and Protection Hohai University Nanjing210098 China Center for Numerical Simulation Software in Engineering and Sciences College of Mechanics and Materials Hohai University Nanjing211100 China State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing210098 China

Motivated by recent research on Physics-Informed Neural Networks (PINNs), we make the first attempt to introduce the PINNs for numerical simulation of the elliptic Partial Differential Equations (PDEs) on 3D manifolds. PINNs are one of the deep learning-based techniques. Based on the data and physical models, PINNs introduce the standard feedforward neural networks (NNs) to approximate the solutions to the PDE systems. By using automatic differentiation, the PDEs system could be explicitly encoded into NNs and consequently, the sum of mean squared residuals from PDEs could be minimized with respect to the NN parameters. In this study, the residual in the loss function could be constructed validly by using the automatic differentiation because of the relationship between the surface differential operators ∇S/∆S and the standard Euclidean differential operators ∇/∆. We first consider the unit sphere as surface to investigate the numerical accuracy and convergence of the PINNs with different training example sizes and the depth of the NNs. Another examples are provided with different complex manifolds to verify the robustness of the PINNs. Copyright © 2021, The Authors. All rights reserved.

关键词： Mathematical operators

The design of JLAMT: An aided tool for large-scale complex physical modeling 18th

学校读者我要写书评

暂无评论

The design of JLAMT: An aided tool for large-scale complex p...

18th International Conference on Geometry and Graphics, ICGG 2018

作者： Ma, Yan Fu, Yuanguang Qin, GuiMing The Institute of Applied Physics and Computational Mathematics Beijing China China Academy of Engineering Physics Software Center of High Performance Numerical Simulation Beijing China

ISBN: (纸本)9783319955872

To create calculation model easily and efficiently can essentially reduce time cost. Currently, lots of visual modeling programs have been developed based on different CAD systems. In this article, the developing idea of a visual modeling tool based on field oriented development was introduced;Considering the feature of physical modeling, fast and convenient operation modules were developed;in order to solve the storage and conversion problems of large-scale models, the data structure and conversional algorithm based on the hierarchical geometry tree were designed;several functional modules were developed, which can provide the template of the complex model components and material parameters;The automatic conversion and generation of physical model input file for some MC codes. By using this modeling tool, some reactor whole-core physical model was created, and the transformed file was delivered to MC code for transport calculation. The results were compared and validate the correctness of the visual modeling tool. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Digital storage

DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Mao, Runze Lin, Minqi Zhang, Yan Zhang, Tianhan John Xu, Zhi-Qin Chen, Zhi X. State Key Laboratory of Turbulence and Complex Systems Aeronautics and Astronautics College of Engineering Peking University Beijing100871 China Ai for Science Institute Beijing100080 China Caep Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100088 China Department of Mechanics and Aerospace Engineering SUSTech Shenzhen518055 China Institute of Natural Sciences School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China MOE-LSC Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai200240 China

Recent developments in deep learning have brought many inspirations for the scientific computing community and it is perceived as a promising method in accelerating the computationally demanding reacting flow simulations. In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip - deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware. © 202

关键词： Graphics processing unit

A priori subcell limiting based on compact nonuniform nonlinear weighted schemes of high-order CPR method for hyperbolic conservation laws

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhu, Huajun Liu, Huayong Yan, Zhen-Guo Shi, Guoquan Deng, Xiaogang State Key Laboratory of Aerodynamics Sichuan Mianyang621000 China Tianfu Engineering-Oriented Numerical Simulation & Software Innovation Center Sichuan University Sichuan Chengdu610000 China College of Aerospace Science and Engineering National University of Defense Technology Hunan Changsha410073 China Chinese Academy of Military Science Beijing100071 China

This paper develops a shock capturing approach for high-order correction procedure via reconstruction (CPR) method with Legendre-Gauss solution points. Shock regions are treated by novel compact nonuniform nonlinear weighted (CNNW) schemes, which have the same solution points as the CPR method. CNNW schemes are constructed by discretizing flux derivatives based on Riemann fluxes at flux points in one cell and using nonuniform nonlinear weighted (NNW) interpolations to obtain the left and right values at flux points. Then, a priori subcell p-adaptive CNNW limiting of the CPR method is proposed for hyperbolic conservation laws. Firstly, a troubled cell indicator is used to detect shock regions and to quantify solution smoothness. Secondly, according to the magnitude of the indicator, CNNW schemes with varying accuracy orders are chosen adaptively for the troubled cells. The spectral property and discrete conservation laws are mathematically analyzed. Various numerical experiments show that the CPR method with subcell CNNW limiting has superiority in satisfying discrete conservation laws and in good balance between resolution and shock capturing robustness. Copyright © 2021, The Authors. All rights reserved.

关键词： numerical methods

Layer-splitting methods for time-dependent Schrödinger equations of incommensurate systems

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Wang, Ting Chen, Huajie Zhou, Aihui Zhou, Yuzhi School of Mathematical Sciences Beijing Normal University Beijing100875 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100094 China

This work considers numerical methods for the time-dependent Schrödinger equation of incommensurate systems. By using a plane wave method for spatial discretization, the incommensurate problem is lifted to a higher dimension that results in semidiscrete differential equations with extremely demanding computational cost. We propose several fully discrete time stepping schemes based on the idea of "layer-splitting", which decompose the semidiscrete problem into sub-problems that each corresponds to one of the periodic layers. Then these schemes handle only some periodic systems in the original lower dimension at each time step, which reduces the computational cost significantly and is natural to involve stochastic methods and parallel computing. Both theoretical analysis and numerical experiments are provided to support the reliability and efficiency of the algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： numerical methods

The modeling for numerical simulation of a benchmark model based on CAD system 18th

学校读者我要写书评

暂无评论

The modeling for numerical simulation of a benchmark model b...

18th International Conference on Geometry and Graphics, ICGG 2018

作者： Xu, MinLe Fu, YuanGuang The Experimental High School Attached to Beijing Normal University Beijing China China Academy of Engineering Physics Software Center of High Performance Numerical Simulation Beijing China

ISBN: (纸本)9783319955872

The Constructive Solid Geometry (CSG) allows modelers to create models by using Boolean operations to combine different types of basic solids. CSG has been adopted by lots of numerical simulation codes as geometric description. In the research of reactor numerical simulation, Monte Carlo (MC) is a common method for neutronics analysis, which needs to calculate the intersections between the random flight paths of neutrons and the geometry. A lot of widely used MC codes (such as Geant4, MCNP) use CSG to describe geometry. The prerequisite to run MC code is to do geometric modeling, which however in many MC codes, is finished by manually writing text files, it is very hard to view the model and it is inefficient to build. In this work, a MC modeling tool (JLAMT) based on UG software is used to build the benchmark model. The tool supplies geometric modeling functions, physical parameter setting functions, and other assistant tools. With a flexible GUI, it helps users to achieve quick modeling. © 2019, Springer International Publishing AG, part of Springer Nature.

关键词： Geometry

A Hybrid Method of FEBI and PO for Scattering Analysis of Inhomogeneous Structures with Large-Scale Platform 5

学校读者我要写书评

暂无评论

A Hybrid Method of FEBI and PO for Scattering Analysis of In...

5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019

作者： Liu, Yang Zheng, Yu-Teng Zhou, Hai-Jing Chen, Xiao-Jie Guo, Xing-Yue Institute of Applied Physics and Computational Mathematics Beijing China CAEP Software Center for High Performance Numerical Simulation Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China

ISBN: (纸本)9781538671115

In the paper, a hybrid of finite element boundary integral (FEBI) method and physical optics (PO) is discussed for analyzing scattering problems of inhomogeneous structures with electrically large-scale platform. The hybrid of these two methods is realized by an iterative process. The hybrid method can efficiently reduce the number of unknowns and the computational complexity while preserving acceptable accuracy. Some numerical results are displayed to illuminate the efficiency of the method. © 2019 IEEE.

关键词： Physical optics