Scholarlink

<p>This study introduces a novel, straightforward acoustic micromixer featuring sharp edges, designed to enhance the mixing index (MI) for practical applications in chemical processes and biomedicine. The device incorporates multiple micropillars with semi-circular tips of varying radii, spaced at specific intervals. Utilizing COMSOL Multiphysics, a finite-element-based software, and the Generalized Lagrangian Mean (GLM) theory, the study assesses how background velocity and sharp edge geometry influence mixing quality. The findings show that the magnitude of MI grows as the tip radius, width, and height of the sharp edges increase; however, it decreases when the background velocity rises, due to the impact of the induced acoustic streaming's strength. For instance, MI values of 99.99%, 99.70%, 95.28%, and 83.71% correspond to inlet velocities of 500&#xa0;μm/s, 700&#xa0;μm/s, 900&#xa0;μm/s, and 1100&#xa0;μm/s, respectively. Additionally, there is an optimal spacing for the sharp edges that maximizes the MI. Due to its simple design, the device can be effectively optimized using machine learning algorithms for practical applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Acoustic micromixer design with novel sharp edges

  • 期刊论文

  • 发表日期：

    2025年6月4日

查看全文
  • Yechun Jin,
  • Jie Li

摘要

This study introduces a novel, straightforward acoustic micromixer featuring sharp edges, designed to enhance the mixing index (MI) for practical applications in chemical processes and biomedicine. The device incorporates multiple micropillars with semi-circular tips of varying radii, spaced at specific intervals. Utilizing COMSOL Multiphysics, a finite-element-based software, and the Generalized Lagrangian Mean (GLM) theory, the study assesses how background velocity and sharp edge geometry influence mixing quality. The findings show that the magnitude of MI grows as the tip radius, width, and height of the sharp edges increase; however, it decreases when the background velocity rises, due to the impact of the induced acoustic streaming's strength. For instance, MI values of 99.99%, 99.70%, 95.28%, and 83.71% correspond to inlet velocities of 500 μm/s, 700 μm/s, 900 μm/s, and 1100 μm/s, respectively. Additionally, there is an optimal spacing for the sharp edges that maximizes the MI. Due to its simple design, the device can be effectively optimized using machine learning algorithms for practical applications.