Abstract

This study presents the use of the wall y+ approach as a form of guidance for reliable selection of mesh and turbulence models in bent pipe flow investigations. The research builds on previous studies recommended by Salim et al.[1]–[3] for using the wall y+ approach to balance between the computational cost and time. This method is proposed as an effective tool for selecting an appropriate near wall treatment and corresponding turbulence model and remove the necessity of physical validation when experimental data is unavailable or difficult to obtain. Flow in a 90-degree pipe elbow is modelled using the ANSYS FLUENT CFD solver to evaluate the performance of different Reynolds-Averaged Navier-Stokes (RANS) turbulence models. The RANS models tested are the standard k-ε, the Reynolds Stress Model (RSM), the k–ω Shear Stress Transport (SST) and the Spalart–Allmaras. A range of near wall spatial resolutions is used to determine the effectiveness of near wall modelling techniques when used in conjunction with each of the turbulence models. The near-wall treatments are investigated by solving the y+ values for the first layer of cells are in the viscous sublayer (y+ ≈ 3), buffer region (y+ ≈ 19) and log law region (y+ ≈ 39). The achieved results in this current study using the wall y+ approach are compared against experimental data published by Sudo et al.[4] and numerical simulations published by Kim et al.[5]. Qualitative analysis and quantitative assessment are carried out to identify which turbulence model agrees best with the published data. It is observed that the near wall models provide better results when the y+ values for the first layer of near wall cells are within viscous sublayer in comparison to simulations where it is in the buffer and log-low regions. The RSM predicts the flow field most accurately when compared against the reference data. This in turn will allow pipeline designers to assess the effectiveness of their design, and any potential problems with it, before the manufacturing stage.

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