Topology optimization method based on the Wray-Agarwal turbulence model

被引:6
|
作者
Alonso, Diego Hayashi [1 ]
Romero Saenz, Juan Sergio [2 ]
Picelli, Renato [3 ]
Nelli Silva, Emilio Carlos [1 ]
机构
[1] Univ Sao Paulo, Dept Mechatron & Mech Syst Engn, Polytech Sch, Sao Paulo, SP, Brazil
[2] Univ Fed Espirito Santo, Dept Mech Engn, Vitoria, ES, Brazil
[3] Univ Sao Paulo, Dept Min & Petr Engn, Polytech Sch, Sao Paulo, SP, Brazil
基金
巴西圣保罗研究基金会;
关键词
Fluid topology optimization; Turbulence; Wray-Agarwal model; 2D swirl flow; Integer linear programming; FLOW; ADJOINT; IMPLEMENTATION; DESIGN; SHAPE;
D O I
10.1007/s00158-021-03106-8
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
One of the current challenges in fluid topology optimization is to address these turbulent flows such that industrial or more realistic fluid flow devices can be designed. Therefore, there is a need for considering turbulence models in more efficient ways into the topology optimization framework. From the three possible approaches (DNS, LES, and RANS), the RANS approach is less computationally expensive. However, when considering the RANS models that have already been considered in fluid topology optimization (Spalart-Allmaras, k-epsilon, and k-omega models), they all include the additional complexity of having at least two more topology optimization coefficients (normally chosen in a "trial and error" approach). Thus, in this work, the topology optimization method is formulated based on the Wray-Agarwal model ("WA2018"), which combines modeling advantages of the k-epsilon model ("freestream" modeling) and the k-omega model ("near-wall" modeling), and relies on the solution of a single equation, also not requiring the computation of the wall distance. Therefore, this model requires the selection of less topology optimization parameters, while also being less computationally demanding in a topology optimization iterative framework than previously considered turbulence models. A discrete design variable configuration from the TOBS approach is adopted, which enforces a binary variables solution through a linearization, making it possible to achieve clearly defined topologies (solid-fluid) (i.e., with clearly defined boundaries during the topology optimization iterations), while also lessening the dependency of the material model penalization in the optimization process (Souza et al. 2021) and possibly reducing the number of topology optimization iterations until convergence. The traditional pseudo-density material model for topology optimization is adopted with a nodal (instead of element-wise) design variable, which enables the use of a PDE-based (Helmholtz) pseudo-density filter alongside the TOBS approach. The formulation is presented for axisymmetric flows with rotation around an axis ("2D swirl flow model"). Numerical examples are presented for some turbulent 2D swirl flow configurations in order to illustrate the approach.
引用
收藏
页数:24
相关论文
共 50 条
  • [41] MQ quasi-interpolation-based level set method for structural topology optimization
    Yang, Chen-Dong
    Feng, Jian-Hu
    Ren, Jiong
    Shen, Ya-Dong
    JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2024, : 3521 - 3532
  • [42] A Comprehensive Review of Explicit Topology Optimization Based on Moving Morphable Components (MMC) Method
    Li, Zhao
    Xu, Hongyu
    Zhang, Shuai
    ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2024, 31 (05) : 2507 - 2536
  • [43] Efficient, high-resolution topology optimization method based on convolutional neural networks
    Xue, Liang
    Liu, Jie
    Wen, Guilin
    Wang, Hongxin
    FRONTIERS OF MECHANICAL ENGINEERING, 2021, 16 (01) : 80 - 96
  • [44] A topology optimization method based on element independent nodal density
    Yi Ji-Jun
    Zeng Tao
    Rong Jian-hua
    Li Yan-mei
    JOURNAL OF CENTRAL SOUTH UNIVERSITY, 2014, 21 (02) : 558 - 566
  • [45] Robust topology optimization considering load uncertainty based on a semi-analytical method
    Zheng, Yongfeng
    Gao, Liang
    Xiao, Mi
    Li, Hao
    Luo, Zhen
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2018, 94 (9-12) : 3537 - 3551
  • [46] An explicit structural topology optimization method based on the descriptions of areas
    Yang, Hang
    Huang, Jinying
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2020, 61 (03) : 1123 - 1156
  • [47] A Parallel Level-Set Based Method for Topology Optimization
    Wu, Tao
    Xu, Hao
    Hu, Qiangwen
    Zhao, Yansong
    Peng, Ying
    Chen, Lvjie
    Fu, Yu
    PROCEEDINGS OF THE 2014 IEEE 18TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD), 2014, : 505 - 509
  • [48] A stress-based topology optimization method for heterogeneous structures
    Conlan-Smith, Cian
    James, Kai A.
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2019, 60 (01) : 167 - 183
  • [49] Topology Optimization of Passive Micromixers Based on Lagrangian Mapping Method
    Guo, Yuchen
    Xu, Yifan
    Deng, Yongbo
    Liu, Zhenyu
    MICROMACHINES, 2018, 9 (03):
  • [50] Shape and topology optimization based on the convected level set method
    Yaji, Kentaro
    Otomori, Masaki
    Yamada, Takayuki
    Izui, Kazuhiro
    Nishiwaki, Shinji
    Pironneau, Olivier
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2016, 54 (03) : 659 - 672