在微流体混合器中通过九网格网络模型进行快速流体速度场预测.
Qian Li1, Yuwei Chen1, Taotao Sun1
1Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
Micromachines
|January 25, 2025
概括
我们使用人工神经网络 (ANN) 开发了一个九网格网络 (NGN) 模型,以预测微流体混合器流体动力学. 与传统方法相比,这种人工智能方法可以加快模拟速度15倍,从而减少设计时间和成本.
科学领域:
- 计算流体动力学的流体动力学.
- 在工程领域的人工智能.
- 微流体和芯片上的实验室技术
背景情况:
- 微流体混合器对于生物工程,化学实验和医学诊断至关重要.
- 传统的模拟方法,如有限元素方法 (FEM) 是计算密集型和耗时的.
- 微流体芯片的设计过程需要高效和快速的模拟工具.
研究的目的:
- 开发一种新的,加速的方法来模拟微流体混合器中的流体动力学.
- 为了利用人工智能 (AI) 和九网格网络 (NGN) 模型实现更快的计算流体动力学 (CFD) 预测.
- 为了减少与微流体芯片设计和优化相关的总体时间和成本.
主要方法:
- 提出了一个九网格网络 (NGN) 模型理论,其中有一个中央对称的结构来分割流体空间.
- 开发和训练基于NGN理论的人工神经网络 (ANN),以预测流体动力学.
- 设计了一种原型微流体混合器,并对传统的有限元法 (FEM) 模拟进行了ANN模型的验证.
主要成果:
- 基于NGN模型的ANN在40秒内实现了流体预测,比FEM的~10分钟显著减少.
- 与FEM相比,开发的AI方法显示了可接受的错误率.
- 在模拟时间中实现了15倍的加速,大大提高了计算效率.
结论:
- 与ANN相结合的NGN模型为模拟微流体混合器流体动力学提供了高效和快速的替代方案.
- 这种人工智能驱动的方法大大减少了微流体芯片设计中的计算时间和成本.
- 经过验证的方法具有显著的潜力,可以加速基于微流体的应用中的创新.
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