High-performance large-scale simulation of multi-stable metastructures

被引:0
|
作者
Hwang, Myungwon [1 ]
Scalo, Carlo [1 ]
Arrieta, Andres F. [1 ]
机构
[1] Purdue Univ, Sch Mech Engn, W Lafayette, IN 47907 USA
关键词
Metamaterials; Nonlinear waves; Multi-stable systems; MPI applications; TRANSITION WAVES; KINK DYNAMICS; PROPAGATION; SOLITONS;
D O I
10.1016/j.cpc.2022.108365
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper, we have developed a solver based on the message-passing interface (MPI) to enable rapid large-scale simulation of generic metastructures composed of bior multi-stable elements. The in-house solver has been thoroughly validated against a commercial numerical solver (Abaqus) and the well-established serial codes from the previous studies. We can achieve up to 4th-order solution accuracy with fully explicit Runge-Kutta (RK) methods, exceeding what many commercial structural analysis tools provide. With our parallel code dedicated to solving specific problem types, the absolute computational speed can be improved by three orders of magnitude, enabling the investigation of a large parameter space. More importantly, the in-house implementation enables an effective distribution of the computational load following the intrinsic structural periodicity, thus achieving efficient parallel scalability. To demonstrate our code's capability to handle massively large problems previously unattainable with existing solvers, we investigate the amplitude-dependent energy transmissibility of bi-stable metabeams and the stability of the transition wave's propagation speed. The achieved numerical and computational performance gains drastically expand the accessible analysis domains of general nonlinear metamaterial and metastructure architectures, thus opening up the potential to uncover new dynamics and enable practical implementations. Program summary Program Title: NM(boolean AND)3(Nonlinear MetaMaterialsMPI) solver CPC Library link to program files: https://doi.org/10.17632/8f4n99jccf.1 Developer's repository link: https://github.com/wonnie87/NMCube Licensing provisions: MIT Programming language: Fortran Nature of problem: NM(boolean AND)3enables massively parallel simulations of strongly nonlinear metamaterials and metastructures, including 1D multi-stable lattice with coupled pendula (discrete sine-Gordon model), 1D lattice with quartic on-site potentials (discrete f-4 model), and metabeam with a bi-stable microstructure. Solution method: Up to the 4th-order explicit Runge-Kutta (RK) methods are implemented inNM<^>3. The Newmark-ss(implicit) method with constant average acceleration is also available if unconditional numerical stability is desired. Additional comments including restrictions and unusual features: RunningNM(boolean AND)3requires installation ofPython( withNumPylibrary), MPI, andHDF5. APythonscript is used to generate input files. The code useMPIsystem calls to allow a massive parallelization among the compute processes. The code usesHDF5file format for data storage. (c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:13
相关论文
共 50 条
  • [11] Predictive Dynamic Simulation for Large-Scale Power Systems through High-Performance Computing
    Huang, Zhenyu
    Jin, Shuangshuang
    Diao, Ruisheng
    2012 SC COMPANION: HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS (SCC), 2012, : 347 - 354
  • [12] LARGE-SCALE HIGH-PERFORMANCE VIBRATION TABLE FACILITIES AT NUPEC
    OHMORI, T
    OHNO, T
    KUSU, Y
    JOURNAL OF THE ATOMIC ENERGY SOCIETY OF JAPAN, 1983, 25 (02): : 92 - 96
  • [13] High-performance computing for large-scale analysis, optimization, and control
    Adeli, Hojjat, 1600, ASCE, Reston, VA, United States (13):
  • [14] A large-scale study of failures in high-performance computing systems
    Schroeder, Bianca
    Gibson, Garth A.
    DSN 2006 INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS, PROCEEDINGS, 2006, : 249 - 258
  • [15] A High-Performance Accelerator for Large-Scale Convolutional Neural Networks
    Sun, Fan
    Wang, Chao
    Gong, Lei
    Xu, Chongchong
    Zhang, Yiwei
    Lu, Yuntao
    Li, Xi
    Zhou, Xuehai
    2017 15TH IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS AND 2017 16TH IEEE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING AND COMMUNICATIONS (ISPA/IUCC 2017), 2017, : 622 - 629
  • [16] High-Performance Large-Scale Image Recognition Without Normalization
    Brock, Andrew
    De, Soham
    Smith, Samuel L.
    Simonyan, Karen
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, 2021, 139
  • [17] A Large-Scale Study of Failures in High-Performance Computing Systems
    Schroeder, Bianca
    Gibson, Garth A.
    IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2010, 7 (04) : 337 - 350
  • [18] Large-scale linear regression: Development of high-performance routines
    Frank, Alvaro
    Fabregat-Traver, Diego
    Bientinesi, Paolo
    APPLIED MATHEMATICS AND COMPUTATION, 2016, 275 : 411 - 421
  • [19] High-performance computing for large-scale analysis, optimization, and control
    Adeli, H
    JOURNAL OF AEROSPACE ENGINEERING, 2000, 13 (01) : 1 - 10
  • [20] STRATEGIES FOR LARGE-SCALE STRUCTURAL PROBLEMS ON HIGH-PERFORMANCE COMPUTERS
    NOOR, AK
    PETERS, JM
    COMMUNICATIONS IN APPLIED NUMERICAL METHODS, 1991, 7 (06): : 465 - 478