检索结果-内蒙古大学图书馆

HIGH-PERFORMANCE FINITE ELEMENTS WITH MFEM

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Andrej, Julian Atallah, Nabil Bäcker, Jan-Phillip Camier, John Copeland, Dylan Dobrev, Veselin Dudouit, Yohann Duswald, Tobias Keith, Brendan Kim, Dohyun Kolev, Tzanio Lazarov, Boyan Mittal, Ketan Pazner, Will Petrides, Socratis Shiraiwa, Syunichi Stowell, Mark Tomov, Vladimir Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States TU Dortmund University Dortmund Germany CERN and Technical University of Munich Munich Germany Division of Applied Mathematics Brown University ProvidenceRI United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA United States Fariborz Maseeh Department of Mathematics and Statistics Portland State University PortlandOR United States Princeton Plasma Physics Laboratory PrincetonNJ United States

The MFEM (Modular Finite Element Methods) library is a high-performance C++ library for finite element discretizations. MFEM supports numerous types of finite element methods and is the discretization engine powering many computational physics and engineering applications across a number of domains. This paper describes some of the recent research and development in MFEM, focusing on performance portability across leadership-class supercomputing facilities, including exascale supercomputers, as well as new capabilities and functionality, enabling a wider range of applications. Much of this work was undertaken as part of the Department of Energy’s Exascale computing Project (ECP) in collaboration with the Center for Efficient Exascale Discretizations (CEED). Copyright © 2024, The Authors. All rights reserved.

关键词： Finite element method

Neural Networks for Fault diagnosis in Electrical Machine

学校读者我要写书评

暂无评论

Neural Networks for Fault diagnosis in Electrical Machine

Intelligent Technologies (ACOIT), Asian Conference on

作者： R. Vinston Raja J. Raja Sreenivasulu Gogula Atul Katiyar T. Thilagam V. S. Bhagavan Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering (Data Science) Vardhaman College of Engineering Hyderabad Telangana India Department of Electrical Engineering Mahatma Jyotiba Phule Rohilkhand University Bareilly Uttar Pradesh India Department of Science and Engineering S.A Engineering College Chennai Tamil Nadu India Department of Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Finding electrical equipment problems quickly is essential for minimising downtime and ensuring ongoing functioning. When it comes to early defect identification, traditional methods like human inspection or sensor-based surveillance might not cut it. Another alternative to employing machine learning algorithms to analyse complex patterns in sensor data is neural networks. With an emphasis on motor drives, this article lays forth a method for identifying electrical equipment issues using neural networks. The network has been trained to identify various issues, such as imbalanced rotors, broken stator windings, and failing bearings, by examining historical data. The trial findings prove that the suggested method may effectively find issues, make things more reliable, and cut down on maintenance expenses.

关键词： Fault diagnosis Motor drives Machine learning algorithms Surveillance Neural networks Stator windings Rotors Machine learning Maintenance Predictive maintenance

Amplitude Expansion Phase Field Crystal (APFC) Modeling based Efficient Dislocation Simulations using Fourier Pseudospectral Method

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wei, Xinyi Wang, Yangshuai Jiang, Kai Zhang, Lei School of Mathematical Sciences Institute of Natural Sciences and MOE-LSC Shanghai Jiao Tong University Shanghai200240 China Department of Mathematics Faculty of Science National University of Singapore 10 Lower Kent Ridge Road Singapore Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

Crystalline defects play a critical role in determining the properties of crystalline solids, underscoring the need for accurate computational methods to study them. Lattice deformation in dislocation simulations, which involves changes in atomic positions, can be described either microscopically by specific atomic configurations or macroscopically by continuum elasticity, each with inherent limitations. The complex amplitude expansion of the phase field crystal (APFC) model provides a mesoscopic approach that bridges these scales. In this paper, we introduce a Fourier pseudospectral method for efficiently solving the APFC model in the context of crystalline defect simulations. This study marks the first application of the Fourier pseudospectral method to the APFC model. The method fully exploits the system’s periodicity and facilitates the implementation of periodic boundary conditions, thanks to its high accuracy and computational efficiency. Numerical experiments conducted on two-dimensional triangular lattices and three-dimensional body-centered cubic lattices for edge dislocation geometry optimization have produced strain field images that align well with by continuum elasticity predictions. The findings demonstrate the potential of the APFC model to accurately capture the complex strain fields associated with dislocations at the mesoscopic scales, a key step toward modeling more intricate crystalline defect structures and dynamics. © 2024, CC BY.

关键词： Edge dislocations

MatterChat: A Multi-Modal LLM for Material Science

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Tang, Yingheng Xu, Wenbin Cao, Jie Gao, Weilu Farrell, Steven Erichson, Benjamin Mahoney, Michael W. Nonaka, Andy Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States NSF National AI Institute for Student-AI Teaming University of Colorado at Boulder Boulder United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States International Computer Science Institute BerkeleyCA United States Department of Statistics University of California at Berkeley BerkeleyCA United States

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great potential to support these efforts by enhancing human–AI interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multi-modal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat employs a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat significantly improves performance in material property prediction and human-AI interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis. © 2025, CC BY.

关键词： Structure (composition)

Multiple-distribution-function finite-difference lattice Boltzmann method for incompressible Navier-Stokes equation

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Chen, Xinmeng Chai, Zhenhua Zhao, Yong Shi, Baochang School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China School of Mathematics and Statistics Changsha University of Science and Technology Hunan Changsha410114 China

In this paper, a multiple-distribution-function finite-difference lattice Boltzmann method (MDF-FDLBM) is proposed for the convection-diffusion system based incompressible Navier-Stokes equations (NSEs). By Chapman Enskog analysis, the convection-diffusion system based incompressible NSEs can be recovered from MDF-FDLBM. Some quantities, including the velocity gradient, velocity divergence, strain rate tensor, shear stress and vorticity, can be computed locally by the first-order moment of the non-equilibrium distribution function. Through the von Neumann analysis, we conduct the stability analysis for the MDF-FDLBM and incompressible finite-difference lattice Boltzmann method (IFDLBM). It is found that the IFDLBM will be more stable than that of MDF-FDLBM with small kinematic viscosity, and the MDF-FDLBM will be more stable than that of IFDLBM with large Courant-Friedrichs-Lewy condition number. Finally, some simulations are conducted to validate the MDF-FDLBM. The results agree well with the analytical solutions and previous results. Through the numerical testing, we find that the MDF-FDLBM has a second-order convergence rate in space and time. The MDF-FDLBMcombined with non-uniform grid also works well. Meanwhile, compared with IFDLBM, it can be found that MDF-FDLBM offers higher accuracy and computational efficiency, reducing computation time by more than 36%. © 2023, CC BY.

关键词： Distribution functions

A damage phase-field model for fractional viscoelastic materials in finite strain

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Da Costa Haveroth, Thaís C. Haveroth, Geovane A. Bittencourt, Marco L. Boldrini, José L. Institute of Mathematics Statistics and Scientific Computing University of Campinas SP 13083-859 Brazil Institute of Mathematics and Computer Science University of São Paulo at São Carlos SP 13560-970 Brazil School of Mechanical Engineering University of Campinas SP 13083-970 Brazil

This paper proposes a thermodynamically consistent phase-field damage model for viscoelastic materials following the strategy developed by Boldrini et al. [7]. Suitable free-energy and pseudo-potentials of dissipation are developed to build a model leading to a stress-strain relation, under the assumption of finite strain, in terms of fractional derivatives. A novel degradation function, which properly couples stress response and damage evolution for viscoelastic materials, is proposed. We obtain a set of differential equations that accounts for the evolution of motion, damage, and temperature. In the present work, for simplicity, this model is numerically solved for isothermal cases by using a semi-implicit/explicit scheme. Several numerical tests, including fitting with experimental data, show that the developed model accounts appropriately for damage in viscoelastic materials for small and finite strains. Non-isothermal numerical simulations will be considered in future *** Codes 74G15 Copyright © 2022, The Authors. All rights reserved.

关键词： Viscoelasticity

Numerical Simulation of Power-Law Fluid Flow in a Trapezoidal Cavity using the Incompressible Finite-Difference Lattice Boltzmann Method

学校读者我要写书评

暂无评论

arXiv 2023年

In this paper, a numerical investigation of power-law fluid flow in the trapezoidal cavity has been conducted by incompressible finite-difference lattice Boltzmann method (IFDLBM). By designing the equilibrium distribution function, the Navier-Stokes equations (NSEs) can be recovered exactly. Through the coordinate transformation method, the body-fitted grid in physical region is transformed into a uniform grid in computational region. The effect of Reynolds (Re) number, the power-law index n and the vertical angle θ on the trapezoidal cavity are investigated. According to the numerical results, we come to some conclusions. For low Re number Re = 100, it can be found that the behavior of power-law fluid flow becomes more complicated with the increase of n. And as vertical angle θ decreases, the flow becomes smooth and the number of vortices decreases. For high Re numbers, the flow development becomes more complex, the number and strength of vortices increase. If the Reynolds number increases further, the power-law fluid will changes from steady flow to periodic flow and then to turbulent flow. For the steady flow, the lager the θ, the more complicated the vortices. And the critical Re number from steady to periodic state decreases with the decrease of power-law index n. © 2023, CC BY.

关键词： Reynolds number

Defining myocardial fiber bundle architecture in atrial digital twins

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Piersanti, Roberto Bradley, Ryan Ali, Syed Yusuf Quarteroni, Alfio Dede, Luca Trayanova, Natalia A. MOX - Laboratory of Modeling and Scientific Computing Dipartimento di Matematica Politecnico di Milano Milano Italy ADVANCE - Alliance for Cardiovascular Diagnostic and Treatment Innovation Johns Hopkins University Baltimore United States Research Computing Lehigh University BethlehemPA United States Department of Biomedical Engineering Johns Hopkins University Baltimore United States Mathematics Institute École Polytechnique Fédérale de Lausanne Lausanne Switzerland

A key component in developing atrial digital twins (ADT) - virtual representations of patients’ atria - is the accurate prescription of myocardial fibers which are essential for the tissue characterization. Due to the difficulty of reconstructing atrial fibers from medical imaging, a widely used strategy for fiber generation in ADT relies on mathematical models. Existing methodologies utilze semi-automatic approaches, are tailored to specific morphologies, and lack rigorous validation against imaging fiber data. In this study, we introduce a novel atrial Laplace-Dirichlet-Rule-Based Method (LDRBM) for prescribing highly detailed myofiber orientations and providing robust regional annotation in bi-atrial morphologies of any complexity. The robustness of our approach is verified in eight extremely detailed bi-atrial geometries, derived from a sub-millimiter Diffusion-Tensor-Magnetic-Resonance Imaging (DTMRI) human atrial fiber dataset. We validate the LDRBM by quantitatively recreating each of the DTMRI fiber architectures: a comprehensive comparison with DTMRI ground truth data is conducted, investigating differences between electrophysiology (EP) simulations provided by either LDRBM and DTMRI fibers. Finally, we demonstrate that the novel LDRBM outperforms current state-of-the-art fiber models, confirming the exceptional accuracy of our methodology and the critical importance of incorporating detailed fiber orientations in EP simulations. Ultimately, this work represents a fundamental step toward the development of physics-based digital twins of the human atria, establishing a new standard for prescribing fibers in ADT. © 2024, CC BY.

关键词： Magnetic resonance imaging

A Robust Remote Client Password Authentication Technique for Smart Cities E-Governance Applications

学校读者我要写书评

暂无评论

A Robust Remote Client Password Authentication Technique for...

2023 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2023

作者： Obaidat, Mohammad S. Meshram, Chandrashekhar Kartikey, Deepak Hsiao, Kuei-Fang Imoize, Agbotiname Lucky Barve, Sharad Kumar Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing Srm University Department of Computational Intelligence TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Jayawanti Haksar Government Post Graduation College College of Barkatullah University Department of Post Graduation Studies and Research in Mathematics M.P. Betul460001 India S.S.P. Govt. College Waraseoni Department of Mathematics M.P. Balaghat India University of Texas-Permian Basin Computer Science Department OdessaTX79762 United States University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Lagos100213 Nigeria Institute of Digital Communication Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany Pandit S. N. Shukla University Shahdol Department of Mathematics Madhya Pradesh Shahdol484001 India

ISBN: (纸本)9798350302561

Smart cities are fast proliferating the global space in recent times. In order to achieve the envisioned next-generation cities, the smart city paradigm aspires to develop a completely integrated network-based infrastructure. With the development of innovative smart cities, ICT-based e-governance has made it possible for services to be delivered intelligently and interactively via online platforms. However, the communication between government agencies and other stakeholders in the smart city ecosystem is through open channels, which poses serious security concerns. In order to address this problem, this work proposes a robust remote client password authentication technique for e-governance applications in smart cities. The proposed authentication technique is lightweight, resistant to several attacks, and achieves low computational overhead. © 2023 IEEE.

关键词： Smart cards