Prediction of normal stresses under large amplitude oscillatory shear flow》.pdfVIP

Prediction of normal stresses under large amplitude oscillatory shear flow》.pdf

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Prediction of normal stresses under large amplitude oscillatory shear flow》.pdf

J. Non-Newtonian Fluid Mech. 150 (2008) 1–10 Prediction of normal stresses under large amplitude oscillatory shear flow Jung Gun Nam a , Kyu Hyunb , Kyung Hyun Ahn a,∗, Seung Jong Lee a a School of Chemical and Biological Engineering, Seoul National University, Seoul 151-744, Republic of Korea b Max-Planck-Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany Received 9 September 2006; received in revised form 26 September 2007; accepted 2 October 2007 Abstract The dynamic response of viscoelastic fluids under large amplitude oscillatory shear (LAOS) has been a subject of long history. In the LAOS flow, the analysis has been mostly focused on shear stress, possibly due to the lack of accurate measurement of normal stress. However, the normal stress may become larger than shear stress at high-strain amplitudes, and thus it is important that we have a good understanding of the normal stress behavior. Furthermore, with the advancement in the instrumentation, it has become possible to get more reliable data. The purpose of this paper is to develop a research platform to analyze and to understand the normal stress behavior of complex fluids under LAOS flow. In this study, we utilized the Giesekus model as a representative constitutive model, and investigated its diverse responses. We defined the dynamic properties corresponding to normal stress, in a similar way to define dynamic moduli from shear stress, and examine their behavior with various analyzing tools. Experimental data were also compared with model predictions. Despite the fact that it is not yet possible to compare all of the predictions because of instrumental limitation, the prediction has been found to fit well with the experimental data. This study is expected to provide a useful framework for further understa

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