Engineering and optimization of a 3D helical lattice microchannel using machine learning

WENG Junqi; LIU Haoxin; WANG Ju; CHU Bozhao

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Engineering and optimization of a 3D helical lattice microchannel using machine learning

更新时间：2026-04-28

- Engineering and optimization of a 3D helical lattice microchannel using machine learning
- CIESC Journal (2026)
- 作者机构：
  
  中石化（上海）石油化工研究院有限公司，上海201208
- 作者简介：
- 基金信息：
- DOI：
  CLC： TQ 021
- Received：31 March 2026，
  
  Revised：2026-04-28，
  
  Accepted：28 April 2026，
- 稿件说明：
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WENG Junqi, LIU Haoxin, WANG Ju, et al. Engineering and optimization of a 3D helical lattice microchannel using machine learning[J/OL]. CIESC Journal, 2026.
DOI：

WENG Junqi, LIU Haoxin, WANG Ju, et al. Engineering and optimization of a 3D helical lattice microchannel using machine learning[J/OL]. CIESC Journal, 2026. DOI：

摘要

提出了一种新型三维螺旋晶格穿孔微通道，采用计算流体力学与深度神经网络相结合的方法对其结构参数进行了优化，以在实现高混合性能的同时降低压降。首先，通过CFD模拟对比了SMC、LMC和HLMC三种结构的混合性能与压降特性；随后，研究了HLMC的五个结构参数（

、

和

）对压降和混合指数的影响；最后，利用CFD模拟数据训练了深度神经网络代理模型，对混合指数与压降进行了多目标优化。结果表明，HLMC的混合指数达到0.985，分别比SMC和LMC提高了121%和15.2%。

、

和

存在最优取值，而

和

越大，HLMC性能越高。获得压降和混合指数的Pareto前沿，拐点压降为396 Pa/m、混合指数为0.985。研究结果可为微混合器的设计优化提供理论指导和可靠的数据驱动方法。

Abstract

Micromixers are core devices for achieving highly efficient fluid mixing in micro-chemical processes

and their internal structures directly determine mixing performance and flow pressure drop. This paper proposes a novel three-dimensional helical lattice perforated microchannel and systematically optimizes its structural parameters using a combination of computational fluid dynamics (CFD) and deep neural networks (DNN) to achieve high mixing performance while minimizing pressure drop. First

the mixing performance and pressure drop characteristics of three configurations (straight microchannel

lattice microchannel

and helical lattice microchannel) were compared via CFD simulations. Subsequently

the effects of five structural parameters (

and

) on the pressure drop and mixing index were investigated

. Finally

a DNN surrogate model was trained using CFD simulation data to perform multi-objective optimization of the mixing index and pressure drop. The results demonstrate that the mixing index of the HLMC reaches 0.985

representing increases of 121% and 15.2% compared to the SMC and LMC

respectively. Optimal values exist for

and

whereas larger values of

and

correlate with higher HLMC performance. The Pareto front for pressure drop and mixing index was obtained

identifying a knee point with a pressure drop of 396 Pa/m and a mixing index of 0.985. These findings provide theoretical guidance and a robust data-driven methodology for the design and optimization of micromixers.

关键词

Keywords

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