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Optimization of micromixer with Cantor fractal baffle based on simulated annealing algorithm

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  • Lv, Honglin
  • Chen, Xueye
  • Zeng, Xiangwei

Abstract

To maximize the mixing performance of the micromixer with the Cantor fractal baffle structure, single-objective optimization of the micromixer with different Reynolds (Res) is carried out. The three-dimensional Navier-Stokes equation is used to numerically analyze the fluid flow and mixing in the micromixer. We choose three parameters related to the geometry of the Cantor fractal baffle inside the microchannel as the best design variables. The mixing index at the outlet of the micromixer is used as the objective function. And conduct parameter studies to explore the influence of the design variables on the objective function. For the parameter study of the design space, the Latin hypercube sampling (LHS) method is used as an experimental design technique. It is used to select design points in the design space. We use surrogate modeling of response surface functions to approximate the objective function. When Re is different, the simulated annealing algorithm is used to optimize the objective of the established surrogate modeling, and finally the optimal structure configuration of the micromixer is obtained. In this article, we combine the fractal principle with the simulated annealing algorithm to improve the mixing performance of the micromixer. This is not involved in previous studies. The results show that the mixing performance of the optimized micromixer has indeed been significantly improved. When Re = 0.1, 1, 10, and 100, the mixing efficiency of the optimized micromixer is increased by 7.64%, 17.75%, 14.08%, and 0.91%, respectively, compared with the reference design.

Suggested Citation

  • Lv, Honglin & Chen, Xueye & Zeng, Xiangwei, 2021. "Optimization of micromixer with Cantor fractal baffle based on simulated annealing algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:chsofr:v:148:y:2021:i:c:s0960077921004021
    DOI: 10.1016/j.chaos.2021.111048
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    Citations

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    Cited by:

    1. Zhaoshuang He & Yanhua Chen & Yale Zang, 2024. "Wind Speed Forecasting Based on Phase Space Reconstruction and a Novel Optimization Algorithm," Sustainability, MDPI, vol. 16(16), pages 1-29, August.
    2. Chen, Xueye & Lv, Honglin & Zhang, Yaolong, 2022. "A novel study on separation of particles driven in two steps based on standing surface acoustic waves," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. Liu, Lianggui & Zhang, Rui & Chen, Qiuxia, 2022. "High-performance global peak tracking technique for PV arrays subject to rapidly changing PSC," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    4. Zhao, Kai & Feng, Qiaoyu & Yao, Junzhu & Yang, Bing & Wang, Junsheng, 2024. "An asymmetric orifice-based active micromixer in the microfluidic chip with 3D microelectrode," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    5. Bao, Han & Ding, Ruoyu & Chen, Bei & Xu, Quan & Bao, Bocheng, 2023. "Two-dimensional non-autonomous neuron model with parameter-controlled multi-scroll chaotic attractors," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).

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