微通道中粘弹性流体的流电势介导电动输运的数据驱动设计优化

中文摘要 AI 辅助

流电势介导的粘弹性流体输运因在电动能量转换和微流体输运中的应用而受到研究关注。现有文献中的解析和半解析模型提供了有价值的物理见解，但需要重复的数值评估来探索大型设计空间并确定最佳操作条件。本研究开发了一种代理辅助框架，用于快速优化狭缝微通道中简化Phan-Thien-Tanner流体的压力驱动电动输运设计。在广泛的控制无量纲参数范围内生成高保真数值数据库，这些参数包括ζ电势、德拜参数、杜金数和粘弹性参数。随后训练机器学习代理模型，以准确近似控制参数与流电势之间的非线性关系，而体积流量和水电能转换效率则通过使用代理预测的流电势的闭式方程计算得出。将该模型与多目标优化策略相结合，以确定能同时最大化能量转换效率和体积流量的操作条件。与针对不同参数重复进行数值模拟相比，所提出的方法可显著加速参数探索，并为电动微流体装置提供实用的设计指南。该研究证明了将计算流体力学与数据驱动代理建模相结合，用于高效工程设计和优化的潜力。

英文摘要

Streaming-potential-mediated transport of viscoelastic fluids has attracted research attention owing to its applications in electrokinetic energy conversion and microfluidic transport. Existing analytical and semi-analytical models in published literature provide valuable physical insights, but require repeated numerical evaluations for exploring large design spaces and identifying the optimal operating conditions. In this work, a surrogate-assisted framework is developed for rapid design optimization of pressure-driven electrokinetic transport of simplified Phan-Thien-Tanner fluids in a slit microchannel. A high-fidelity numerical database is generated over a broad range of governing dimensionless parameters, which includes the zeta potential, the Debye parameter, the Dukhin number, and the viscoelastic parameter. A Machine Learning surrogate model is subsequently trained to accurately approximate the nonlinear relationship between the governing parameters and the streaming potential, while the volumetric flow rate and hydroelectric energy conversion efficiency were calculated from closed form equation by using the streaming potential predicted by the surrogate. This is coupled with a multi-objective optimization strategy to identify operating conditions that simultaneously maximize energy conversion efficiency and volumetric flow rate. The proposed methodology can significantly accelerate parametric exploration compared with repeated numerical simulations across different parameters and provides practical design guidelines for electrokinetic microfluidic devices. The study demonstrates the potential of combining computational fluid mechanics with data-driven surrogate modeling for efficient engineering design and optimization.

发表机构

• Jadavpur University（jadavpur大学）
• Heritage Institute of Technology（Heritage技术学院）

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