scispace - formally typeset
J

Joseph D. Peterson

Researcher at University of Cambridge

Publications -  19
Citations -  179

Joseph D. Peterson is an academic researcher from University of Cambridge. The author has contributed to research in topics: Shear rate & Population. The author has an hindex of 6, co-authored 17 publications receiving 100 citations. Previous affiliations of Joseph D. Peterson include University of California, Santa Barbara.

Papers
More filters
Journal ArticleDOI

Nonlinear rheology of polydisperse blends of entangled linear polymers: Rolie-Double-Poly models

TL;DR: In this paper, the authors proposed a pair of simplified tube models for polydisperse melts of entangled linear polymers that combine the success of the double reptation approximation [des Cloizeaux, Europhys. Lett. 5, 437-442 (1988)] in the linear regime with the success in the Rolie-Poly constitutive equation [Likhtman et al., J. Non Newtonian Fluid Mech. Rheol. 56, 823-873 (2012)], which is able to predict the linear and nonlinear rheology
Journal ArticleDOI

Shear banding predictions for the two-fluid Rolie-Poly model

TL;DR: In this paper, a two-fluid Rolie-poly approximation for entangled polymer solutions is studied and the authors show that the time to reach steady state becomes very long as the gap width increases.
Journal ArticleDOI

Constitutive Model for Time-Dependent Flows of Shear-Thickening Suspensions.

TL;DR: In this article, a tensorial constitutive model for dense, shear-thickening particle suspensions subjected to time-dependent flow is developed, whereby friction proliferates among compressive contacts at large particle stresses.
Journal ArticleDOI

Constitutive model for shear-thickening suspensions: Predictions for steady shear with superposed transverse oscillations

TL;DR: Lin et al. as discussed by the authors developed a tensorial constitutive model for dense, shear-thickening particle suspensions that combines rate-independent microstructural evolution with a stress-dependent jamming threshold.
Posted Content

Inference, prediction and optimization of non-pharmaceutical interventions using compartment models: the PyRoss library

TL;DR: It is argued that such compartment models, by allowing social data of arbitrary granularity to be combined with Bayesian parameter estimation for poorly-known disease variables, could enable more powerful and robust prediction than other approaches to detailed epidemic modelling.