Projects per year
Abstract
Understanding transition and turbulence in the flow of shear-thinning non-Newtonian fluids remains substantially unresolved and additional research is required to develop better computational methods for wall-bounded turbulent flows of these fluids. Previous DNS studies of shear-thinning fluids mainly use purpose-built codes and simple geometries such as pipes and channels. However in practical application, the geometry of mixing vessels, pumps and other process equipment is far more complex, and more flexible computational methods are required. In this paper a general-purpose DNS approach for shear-thinning fluids is undertaken using the OpenFOAM CFD library. DNS of turbulent Newtonian and non-Newtonian flow in a pipe flow are conducted and the accuracy and efficiency of OpenFOAM are assessed against a validated high-order spectral element-Fourier DNS code – Semtex. The results show that OpenFOAM predicts the flow of shear-thinning fluids to be a little more transitional than the predictions from Semtex, with lower radial and azimuthal turbulence intensities and higher axial intensity. Despite this, the first and second order turbulence statistics differ by at most 16%, and usually much less. An assessment of the parallel scaling of OpenFOAM indicates that OpenFOAM scales very well for the CPUs from 8 to 512, but the intranode scalability is poor for less than 8CPUs. The present work shows that OpenFOAM can be used for DNS of shear-thinning fluids in the simple case of pipe flow, and suggests that more complex flows, where flow separation is often important, are likely to be simulated with accuracies that are acceptably good for engineering application.
Original language | English |
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Pages (from-to) | 50-67 |
Number of pages | 18 |
Journal | Applied Mathematical Modelling |
Volume | 72 |
DOIs | |
Publication status | Published - 1 Aug 2019 |
Keywords
- Direct numerical simulation (DNS)
- Generalized Newtonian fluid
- OpenFOAM
- Shear-thinning fluid
- Turbulent pipe flow
Projects
- 1 Finished
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ARC Research Hub for Computational Particle Technology
Yu, A., Zhao, D., Rudman, M., Jiang, X., Selomulya, C., Zou, R., Yan, W., Zhou, Z., Guo, B., Shen, Y., Kuang, S., Chu, K., Yang, R., Zhu, H., Zeng, Q., Dong, K., Strezov, V., Wang, G., Zhao, B., Song, S., Evans, T. J., Mao, X., Zhu, J., Hu, D., Pan, R., Li, J., Williams, S. R. O., Luding, S., Liu, Q., Zhang, J., Huang, H., Jiang, Y., Qiu, T., Hapgood, K. & Chen, W.
Australian Research Council (ARC), Jiangxi University of Science and Technology, Jiangsu Industrial Technology Research Institute, Fujian Longking Co Ltd, Baosteel Group Corporation, Hamersley Iron Pty Limited, Monash University, University of New South Wales (UNSW), University of Queensland , Western Sydney University (WSU), Macquarie University
31/12/16 → 30/12/21
Project: Research