Photo by jule_42 from unsplash
Significance Science-based data-driven methods that can describe the rheological behavior of complex fluids can be transformative across many disciplines. Digital rheometer twins, which are developed here, can significantly reduce the… Click to show full abstract
Significance Science-based data-driven methods that can describe the rheological behavior of complex fluids can be transformative across many disciplines. Digital rheometer twins, which are developed here, can significantly reduce the cost, time, and energy required to characterize complex fluids and predict their future behavior. This is made possible by combining two different methods of informing neural networks with the rheological underpinnings of a system, resulting in quantitative recovery of a gel’s response to different flow protocols. The platform developed here is general enough that it can be extended to areas well beyond complex fluids modeling.
Journal Title: Proceedings of the National Academy of Sciences of the United States of America
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!