City Research Online

Abstract

A framework predicting the rheological (storage and loss moduli, first normal stress coefficient, and relaxation time) and transport (viscosity, diffusivity) properties of non-Newtonian dilute polymer solutions at mesoscales (e.g. to ) from the atomistic-scale molecular behaviour is presented. More specifically, the rheological behaviour differences of OCP and PMA polymer solutions in PAO-2 oil are simulated using both atomistic molecular dynamics (MD) and many-body dissipative particle dynamics (mDPD) within a temperature range of 313–373 K. The simulation methodology described is able to distinguish itself from the standard DPD model by accurately reproducing the shear-thinning with high sensitivity, as seen in the atomistic MD simulations at high shear rates (e.g. s−1). It is shown that the model is well-suited to compute properties such as first normal stress differences and relaxation times that are difficult to estimate at atomistic scales due to the low signal-to-noise ratio. Moreover, the Schmidt numbers () are predicted with high accuracy when compared with the values from atomistic-scale simulations. The proposed model is able to predict relaxation times of dilute polymer mixtures that are difficult to be obtained using state-of-the-art rheometers. Finally, it is found that the terminal relaxation time of PMA polymer chain does not vary monotonically as a function of temperature, unlike in the case of OCP; this is significant for describing viscoelastic behaviour at macroscales where satisfactory constitutive equations are not available.

Publication Type:	Article
Additional Information:	This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Viscoelastic liquids, Multiscale modelling, Many-body DPD, Polymer solution, Rheology
Subjects:	Q Science > QA Mathematics T Technology > TJ Mechanical engineering and machinery
Departments:	School of Science & Technology > Engineering
SWORD Depositor:	Symplectic Administrator

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