用深度学习+微流控实现聚合物熔体粘度在线估算
Methodology for Online Estimation of Rheological Parameters in Polymer Melts Using Deep Learning and Microfluidics
- 用仿真生成数据训练深度学习模型，从压差和流量反推流变参数
- 实现聚合物熔体粘度的在线实时估计，精度优于传统方法
- 适合材料研发与微流控器件快速迭代的工程场景
微流控装置因其成本低，被广泛用于流体流变特性估算。但其常面临精度不足、尺寸大和成本高的问题。本研究提出一种融合深度学习、建模与仿真的方法，用于优化微流控系统设计，开发出一种新型聚合物熔体粘度测量技术。利用仿真生成的合成数据训练深度学习模型，使其能够根据微流控回路中的压降和流量测量值，识别流变参数，实现流体性质的在线估计。该方法提升了微流控流变测量的准确性与灵活性，加速了器件的设计与测试过程，减少对物理原型的依赖，对相关领域具有重要贡献。
原文摘要 · Abstract (English)
Microfluidic devices are increasingly used in biological and chemical experiments due to their cost-effectiveness for rheological estimation in fluids. However, these devices often face challenges in terms of accuracy, size, and cost. This study presents a methodology, integrating deep learning, modeling and simulation to enhance the design of microfluidic systems, used to develop an innovative approach for viscosity measurement of polymer melts. We use synthetic data generated from the simulations to train a deep learning model, which then identifies rheological parameters of polymer melts from pressure drop and flow rate measurements in a microfluidic circuit, enabling online estimation of fluid properties. By improving the accuracy and flexibility of microfluidic rheological estimation, our methodology accelerates the design and testing of microfluidic devices, reducing reliance on physical prototypes, and offering significant contributions to the field.
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