利用深度学习和微流控技术在线估算聚合物熔体流变参数的方法
Methodology for Online Estimation of Rheological Parameters in Polymer Melts Using Deep Learning and Microfluidics
AI总结：
提出融合深度学习、建模与仿真的微流控方法，利用合成数据训练模型，由压降和流量在线识别聚合物熔体流变参数，从而提升黏度测量精度并减少物理原型依赖。
摘要：
微流控装置因其在流体流变估算方面的成本效益，正越来越多地用于生物和化学实验。然而，这些装置在精度、尺寸和成本方面常常面临挑战。本研究提出了一种整合深度学习、建模与仿真的方法，以改进微流控系统的设计，并用于开发一种测量聚合物熔体黏度的创新途径。我们利用仿真生成的合成数据训练深度学习模型，随后该模型根据微流控回路中的压降和流量测量值识别聚合物熔体的流变参数，从而实现流体性质的在线估算。通过提高微流控流变估算的精度和灵活性，我们的方法加快了微流控装置的设计与测试，减少了对物理原型的依赖，并为该领域作出了重要贡献。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
