English(EN) Data-Driven Design Optimization of Streaming-Potential-Mediated Electrokinetic Transport of Viscoelastic Fluids in Microchannels

机器学习加速用于能量转换的微流控器件设计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一个数据驱动的框架，用于优化粘弹性流体传输的微流控器件设计。该方法使用机器学习创建一个代理模型，该模型可以根据各种物理参数快速预测性能，与传统模拟方法相比，显著加速了设计探索过程。该框架旨在确定最大化能量转换效率和体积流量的最佳操作条件，为电渗微流控器件提供实用的设计指南。 AI

影响通过实现快速参数探索，加速了微流控器件的工程设计和优化。

排序理由学术论文，详细介绍了使用机器学习优化微流控器件的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Ankan Basu, Sumanta Banerjee · 2026-09-01 04:00

微通道内粘弹性流体流体电输运的基于数据的设计优化

arXiv:2608.29939v1 Announce Type: cross Abstract: 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 model…