Abstract
Viscoelastic fluids are a common subclass of rheologically complex materials that are encountered in diverse fields from biology to polymer processing. Often the flows of viscoelastic fluids are unstable in situations where ordinary Newtonian fluids are stable, owing to the nonlinear coupling of the elastic and viscous stresses. Perhaps more surprisingly, the instabilities produce flows with the hallmarks of turbulence—even though the effective Reynolds numbers may be O(1) or smaller. We provide perspectives on viscoelastic flow instabilities by integrating the input from speakers at a recent international workshop: historical remarks, characterization of fluids and flows, discussion of experimental and simulation tools, and modern questions and puzzles that motivate further studies of this fascinating subject. The materials here will be useful for researchers and educators alike, especially as the subject continues to evolve in both fundamental understanding and applications in engineering and the sciences.
| Original language | English |
|---|---|
| Article number | 080701 |
| Pages (from-to) | 1-80 |
| Number of pages | 80 |
| Journal | Physical Review Fluids |
| Volume | 7 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 29 Aug 2022 |
Bibliographical note
Funding Information:We thank the Princeton Center for Theoretical Science at Princeton University for their support of this virtual workshop. In particular, we are grateful for the help provided by Charlene Borsack, both leading up to and during the workshop. We also thank Manuel Alves for helpful feedback on this manuscript. S.S.D. acknowledges the donors of the American Chemical Society Petroleum Research Fund for partial support of this research through grant PRF 59026-DNI9. S.S.D. and H.A.S. acknowledge the NSF for work that was partially supported through Princeton University's Materials Research Science and Engineering Center DMR-2011750. G.H.M. acknowledges the National Science Foundation for funding support under Grant No. CBET-2027870. A.M.A acknowledges support from NSF CBET-2141404. J.E.L.A. acknowledges the support from Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico), Universidad Nacional Autónoma de México UNAM (Grants No. PAPIIT IA105620 and PAIP 5000-9172 Facultad de Química), and Laboratorio Nacional de Cómputo de Alto Desempeo UNAM (Grant No. LANCAD-UNAM-DGTIC-388), for the computational time provided on the Miztli Supercomputer. S.M.F. acknowledges support from the European Research Council under the EU's 7th Framework Programme (FP7/2007-2013)/ERC Grant No. 279365 and under the EU's Horizon 2020 Programme, Grant Agreement No. 885146. M.D.G. acknowledges support from NSF CBET-1510291, AFOSR FA9550-18-1-0174, ONR N00014-18-1-2865, and N00014-17-1-3022. J.S.G. acknowledges support by NSF Awards CBET-1701392 and CAREER-1554095. S.J.H. and A.Q.S. gratefully acknowledge the support of the Okinawa Institute of Science and Technology Graduate University (OIST) with subsidy funding from the Cabinet Office, Government of Japan, and also funding from the Japan Society for the Promotion of Science (JSPS, Grants No. 21K03884 and 18H01135) and the Joint Research Projects (JRPs) supported by the JSPS and the Swiss National Science Foundation (SNSF). R.J.P. would like to acknowledge funding from the UK's Engineering and Physical Sciences Research Council (EPSRC) under Grant No. EP/M025187/1. H.S. acknowledges support from the Deutsche Forschungsgemeinschaft in the framework of the Collaborative Research Center SFB 910.
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