Reprint

Advances in Experimental and Computational Rheology, Volume II

Edited by
November 2020
192 pages
  • ISBN978-3-03943-565-4 (Hardback)
  • ISBN978-3-03943-566-1 (PDF)

This book is a reprint of the Special Issue Advances in Experimental and Computational Rheology, Volume II that was published in

Engineering
Physical Sciences
Summary
Rheology, defined as the science of deformation and flow of matter, is a multidisciplinary scientific field, covering both fundamental and applied approaches. The study of rheology includes both experimental and computational methods, which are not mutually exclusive. Its practical importance embraces many processes, from daily life, like preparing mayonnaise or spreading an ointment or shampooing, to industrial processes like polymer processing and oil extraction, among several others. Practical applications include also formulations and product development. Following a successful first volume, we are now launching this second volume to continue to present the latest advances in the fields of experimental and computational rheology applied to the most diverse classes of materials (foods, cosmetics, pharmaceuticals, polymers and biopolymers, multiphasic systems, and composites) and processes.
Format
  • Hardback
License
© 2021 by the authors; CC BY-NC-ND license
Keywords
graphene oxide; polyethylene glycol; rheological characterization; human milk; tube feeding; breastfeeding; viscosity; complex modulus; density; rheological measurements; non-viscometric geometries; Couette analogy; shear thinning fluids; suspensions; bread; whey; complex fluids; experimental rheology; breadmaking; yield stress; grout; polypropylene fiber; masonry; consolidation; rheology; drop impact; elasto-viscoplastic material; free surface; gravitational effects; rheology; MRSA; S. aureus; antibiotics; oxacillin; bactericidal; injection molding; filling stage; Cross-WLF model; Tait model; finite volume method; openInjMoldSim; OpenFOAM®; Boger fluids; circular contraction flow; lip vortex; pressure-drops; vortex-enhancement; first normal-stress difference; swIM model; reactive extrusion; data-driven; machine learning; artificial engineering; polymer processing; digital twin; n/a