正弦收敛-发散微通道中非牛顿流体微混合的数据驱动代理建模
Data-Driven Surrogate Modeling for Micromixing of Non-Newtonian Fluids in Sinusoidal Converging-Diverging Microchannels
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中文总结 AI 辅助
本研究针对正弦收敛-发散微通道中非牛顿流体微混合难题，通过高保真模拟构建GPR代理模型并耦合NSGA-II算法，实现了兼顾混合性能与压降的微通道多目标优化设计。
中文摘要 AI 辅助
非牛顿流体的微混合仍具挑战性，因为微尺度下的层流将横向输运主要限制为分子扩散。本研究探究二维正弦收敛-发散微通道中Carreau-Yasuda流体被动微混合的输运机制，并开发用于其多目标设计的代理辅助框架。在 creeping-flow（蠕动流）条件下，通过系统改变壁面振幅比、相位偏移和波数，开展高保真有限体积模拟。结果表明，连续收缩-扩张单元通过界面拉伸、剪切速率升高及剪切稀化诱导的粘度降低的组合效应增强混合；这些机制提升标量输运，但同时增大压降，在混合性能与水力阻力间形成固有权衡。为高效探索多维设计空间，基于高保真数值模拟构建代理模型，确定高斯过程回归（GPR）为混合指数和压降的最准确预测器。将验证后的GPR代理与非支配排序遗传算法II（NSGA-II）耦合，可准确复现高保真模拟得到的帕累托前沿，并识别平衡混合增强与压力损失的最优微通道几何结构。所提出的机器学习框架为非牛顿流体被动微混合器的多目标设计提供了快速、准确且物理一致的策略。
英文摘要
Micromixing of non-Newtonian fluids remains challenging because laminar flow at microscales restricts transverse transport primarily to molecular diffusion. In this study, we investigate the transport mechanisms governing passive micromixing of a Carreau--Yasuda fluid in two-dimensional sinusoidal converging--diverging microchannels and develop a surrogate-assisted framework for their multi-objective design. We perform high-fidelity finite-volume simulations by systematically varying the wall-amplitude ratio, phase offset, and wave count under creeping-flow conditions. The results show that successive contraction--expansion units enhance mixing through the combined effects of interface stretching, elevated shear rates, and shear-thinning-induced viscosity reduction. These mechanisms improve scalar transport but simultaneously increase pressure drop, creating an inherent trade-off between mixing performance and hydraulic resistance. To efficiently explore the multidimensional design space, we construct surrogate models from high-fidelity numerical simulations and identify Gaussian Process Regression (GPR) as the most accurate predictor of both the mixing index and pressure drop. Coupling the validated GPR surrogate with Non-dominated Sorting Genetic Algorithm II (NSGA-II) accurately reproduces the Pareto front obtained from the high-fidelity simulations and identifies optimal microchannel geometries that balance mixing enhancement against pressure loss. The proposed machine learning framework provides a fast, accurate, and physically consistent strategy for the multi-objective design of passive micromixers for non-Newtonian fluids.
