Autonomous in silico optimization framework for high-performance micromixers
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Effective mixing at the microscale is essential for lab-on-a-chip systems, yet designing micromixers that achieve both high mixing efficiency and low pressure drop remains challenging and resource-intensive. Here, we introduce an autonomous in silico framework for designing obstacle-based micromixers through integrating 3D geometry generation, computational fluid dynamics (CFD) simulations and a multi-objective artificial intelligence optimization algorithm within a fully automated close-loop workflow. A constraint-aware NSGA-II variant is used, incorporating a repair operator to ensure design feasibility. Experimentally validated against hyperspectral imaging-based mixing characterization and pressure-drop measurements, the framework eliminates manual trial-and-error workload and alleviates researchers from the tedious tasks of navigating across 3D modeling, CFD simulations, and optimization algorithms, reducing optimization time by 48% compared to the conventional simulation-assisted approach. By autonomously screening hundreds of designs, it identifies Pareto-optimal micromixers and generates an extensive database that supports inverse design and reveals the mixing structure-performance relationship, facilitating the establishment of general design guidelines. The framework is generally applicable to a wide range of passive micromixers.
Date Issued
2026-02-01
Date Acceptance
2026-01-12
Citation
Chemical Engineering Journal, 2026, 529
ISSN
1385-8947
Publisher
Elsevier BV
Journal / Book Title
Chemical Engineering Journal
Volume
529
Copyright Statement
© 2026 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
172957
Date Publish Online
2026-01-13