Post-Processing of non gradient-based Topology Optimization with Simulated Annealing

被引:7
|
作者
Najafabadi, Hossein R. [2 ]
Goto, Tiago [1 ,2 ]
Falheiro, Mizael [2 ]
Martins, Thiago C. [2 ]
Barari, Ahmad [3 ]
Tsuzuki, Marcos S. G. [2 ]
机构
[1] Univ Fed Rondonopolis, Inst Ciencias Agr & Tecnol, Rondonopolis, MT, Brazil
[2] Univ Sao Paulo, Escola Politecn, Lab Computat Geometry, Mechatron & Mech Syst Engn Dept, Sao Paulo, Brazil
[3] Univ Ontario Inst Technol Ontario Tech, Fac Engn & Appl Sci, Oshawa, ON, Canada
来源
IFAC PAPERSONLINE | 2021年 / 54卷 / 01期
基金
巴西圣保罗研究基金会;
关键词
Topology Optimization; Simulated Annealing; Density filters; Post-processing; Checkerboard; IMPEDANCE TOMOGRAPHY RECONSTRUCTION; CONTAINERS; PLACEMENT; ALGORITHM; CODE;
D O I
10.1016/j.ifacol.2021.08.184
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Topology optimization (TO) is a mathematical method of determining distribution of material in a design domain to achieve maximum performance for the desired application. Non-gradient-based topology optimization methods are beneficial for the problems in which the derivative of the objective function is not easy or even possible to calculate. The results coming from such methods include gray area and discontinuity based on the optimization algorithm. In this paper, a post-processing algorithm is presented to improve the results from a nongradient topology optimization simulated annealing based process. It has been shown that the results using this post-processing method have less gray areas by fixing the densities of the elements. Therefore, better compliance values obtained for the cantilever and MBB beams problems regarding the results in the literature. The main advantage of post-processing is that the number of iterations can be reduced without sacrificing the quality of the results. This leads to improving the results as well as reducing the calculation costs by the faster convergence. Copyright (C) 2021 The Authors.
引用
收藏
页码:755 / 760
页数:6
相关论文
共 50 条
  • [21] Two-point gradient-based MMA (TGMMA) algorithm for topology optimization
    Li, Lei
    Khandelwal, Kapil
    COMPUTERS & STRUCTURES, 2014, 131 : 34 - 45
  • [22] A gradient-based optimization method with functional principal component analysis for efficient structural topology optimization
    Andrea Montanino
    Gianluca Alaimo
    Ettore Lanzarone
    Structural and Multidisciplinary Optimization, 2021, 64 : 177 - 188
  • [23] A gradient-based optimization method with functional principal component analysis for efficient structural topology optimization
    Montanino, Andrea
    Alaimo, Gianluca
    Lanzarone, Ettore
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2021, 64 (01) : 177 - 188
  • [24] Estimation of Pulmonary Arterial Pressure Using Simulated Non-Invasive Measurements and Gradient-Based Optimization Techniques
    Laubscher, Ryno
    Van der Merwe, Johan
    Herbst, Philip G.
    Liebenberg, Jacques
    MATHEMATICAL AND COMPUTATIONAL APPLICATIONS, 2022, 27 (05)
  • [25] Density gradient-based adaptive refinement of analysis mesh for efficient multiresolution topology optimization
    Mezzadri, Francesco
    Qian, Xiaoping
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2022, 123 (02) : 465 - 504
  • [26] GRADIENT-BASED MULTI-COMPONENT TOPOLOGY OPTIMIZATION FOR ADDITIVE MANUFACTURING (MTO-A)
    Zhou, Yuqing
    Saitou, Kazuhiro
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2017, VOL 2A, 2017,
  • [27] A skeletonization algorithm for gradient-based optimization
    Menten, Martin J.
    Paetzold, Johannes C.
    Zimmer, Veronika A.
    Shit, Suprosanna
    Ezhov, Ivan
    Holland, Robbie
    Probst, Monika
    Schnabel, Julia A.
    Rueckert, Daniel
    2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, : 21337 - 21346
  • [28] A gradient-based direct aperture optimization
    Yang, Jie
    Zhang, Pengcheng
    Zhang, Liyuan
    Gui, Zhiguo
    Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering, 2018, 35 (03): : 358 - 367
  • [29] Gradient-based optimization of spintronic devices
    Imai, Y.
    Liu, S.
    Akashi, N.
    Nakajima, K.
    APPLIED PHYSICS LETTERS, 2025, 126 (08)
  • [30] Catalyst for Gradient-based Nonconvex Optimization
    Paquette, Courtney
    Lin, Hongzhou
    Drusvyatskiy, Dmitriy
    Mairal, Julien
    Harchaoui, Zaid
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 84, 2018, 84