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Design and Mixing Analysis of a Passive Micromixer Based on Curly Baffles
A novel passive micromixer based on curly baffles is proposed and optimized through the signal-to-noise analysis of various design parameters. The mixing performance of the proposed design was evaluated across a wide Reynolds number range, from 0.1 to 80. Through the analysis, the most influential p...
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Published in: | Micromachines (Basel) 2023-09, Vol.14 (9), p.1795 |
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creator | Juraeva, Makhsuda Kang, Dong-Jin |
description | A novel passive micromixer based on curly baffles is proposed and optimized through the signal-to-noise analysis of various design parameters. The mixing performance of the proposed design was evaluated across a wide Reynolds number range, from 0.1 to 80. Through the analysis, the most influential parameter was identified, and its value was found to be constant regardless of the mixing mechanism. The optimized design, refined using the signal-to-noise analysis, demonstrated a significant enhancement of mixing performance, particularly in the low Reynolds number range (Re |
doi_str_mv | 10.3390/mi14091795 |
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The mixing performance of the proposed design was evaluated across a wide Reynolds number range, from 0.1 to 80. Through the analysis, the most influential parameter was identified, and its value was found to be constant regardless of the mixing mechanism. The optimized design, refined using the signal-to-noise analysis, demonstrated a significant enhancement of mixing performance, particularly in the low Reynolds number range (Re< 10). The design set obtained at the diffusion dominance range shows the highest degree of mixing (DOM) in the low Reynolds number range of Re< 10, while the design set optimized for the convection dominance range exhibited the least pressure drop across the entire Reynolds number spectrum (Re< 80). The present design approach proved to be a practical tool for identifying the most influential design parameter and achieving excellent mixing and pressure drop characteristics. The enhancement is mainly due to the curvature of the most influential design parameter.</description><identifier>ISSN: 2072-666X</identifier><identifier>EISSN: 2072-666X</identifier><identifier>DOI: 10.3390/mi14091795</identifier><identifier>PMID: 37763958</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Analysis ; Computer software industry ; curly baffles ; degree of mixing (DOM) ; Design analysis ; Design of experiments ; Design optimization ; design parameter ; Design parameters ; Energy consumption ; Fluid flow ; mixing mechanism ; most influential design parameter ; Optimization ; Parameter identification ; Pressure drop ; Reynolds number ; signal-to-noise analysis ; Vortices</subject><ispartof>Micromachines (Basel), 2023-09, Vol.14 (9), p.1795</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. 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The mixing performance of the proposed design was evaluated across a wide Reynolds number range, from 0.1 to 80. Through the analysis, the most influential parameter was identified, and its value was found to be constant regardless of the mixing mechanism. The optimized design, refined using the signal-to-noise analysis, demonstrated a significant enhancement of mixing performance, particularly in the low Reynolds number range (Re< 10). The design set obtained at the diffusion dominance range shows the highest degree of mixing (DOM) in the low Reynolds number range of Re< 10, while the design set optimized for the convection dominance range exhibited the least pressure drop across the entire Reynolds number spectrum (Re< 80). The present design approach proved to be a practical tool for identifying the most influential design parameter and achieving excellent mixing and pressure drop characteristics. 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subjects | Analysis Computer software industry curly baffles degree of mixing (DOM) Design analysis Design of experiments Design optimization design parameter Design parameters Energy consumption Fluid flow mixing mechanism most influential design parameter Optimization Parameter identification Pressure drop Reynolds number signal-to-noise analysis Vortices |
title | Design and Mixing Analysis of a Passive Micromixer Based on Curly Baffles |
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