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Optimization of a novel micromixer with fan-shaped obstacles

Narges Jafari Ghahfarokhi, Morteza Bayareh, Amireh Nourbakhsh, and Mohammadreza Baghoolizadeh

Department of Mechanical Engineering, University of Isfahan, Isfahan, Iran



Received: 27 January 2024  Accepted: 18 February 2024


The present paper designs and optimizes a passive micromixer with fan-shaped obstacles using computational fluid dynamics (CFD) and machine learning (ML) algorithms. Two types of micromixers with fan-shaped obstacles, named MFO-I and MFO-II, are compared with a T-shaped micromixer. The results reveal that the MFO-I has better performance compared to the two other ones; therefore, this micromixer is selected to be developed and designed. The CFD simulations of the MFO-I demonstrate that the number of mixing units (m), obstacle radius (r), obstacle angle (α), and inlet velocity (Uin), should be considered to optimize the micromixer. It is found that the mixing efficiency (ME) is improved by augmenting Uin, r, and m. For instance, ME is enhanced from 28.16 to 89.211 by changing m from 1 to 12. Besides, the values of ME are 13.832% and 47.338% for Uin = 0.005 and 1 m/s, respectively, indicating that ME is improved about 3.42 times. Seven ML algorithms are utilized to optimize the micromixer, indicating that the MPR algorithm can provide excellent prediction values for ME and pressure drop (Δp).

Keywords: Micromixer; Fan-shaped obstacles; Machine learning; Optimization; Numerical simulation

Full paper is available at

DOI: 10.1007/s11696-024-03380-y


Chemical Papers 78 (7) 4201–4210 (2024)

Saturday, June 15, 2024

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