Volume fraction (liquid)
Radial plane view along Train-1
Radial plane view along Train-2
1.00e–01 9.00e–02 0.00e+00 1.00e–02 2.00e–02 3.00e–02 4.00e–02 5.00e–02 6.00e–02 7.00e–02 8.00e–02
Train-1
Train-2
Mid plane slice-Front view
Figure 4 Radial profiles of liquid phase volume fraction along the length of recommended piping configuration leading to heat exchanger Train-1 and Train-2
distribution improved substantially to a nearly uniform split of 53% and 47% between Train-1 and Train-2, respectively (see Figure 4 ). The results highlight the critical role of CFD in diagnosing complex flow phenomena and enabling the design and validation of robust engineering solutions. Case study 3: Dosing of additive in storage tank In the refining industry, accurate prediction and control of mixing profiles are vital for ensuring product quality, mini - mising chemical usage, and preventing operational issues such as dead zones and short-circuiting. CFD has become an indispensable tool in this context because it resolves three-dimensional flow fields, concentration distributions, and turbulence effects under realistic operating conditions. These capabilities go far beyond traditional one-dimen- sional or empirical design correlations. Such detailed flow characterisation is critical for processes such as fuel blending, additive injection, and storage tank homogenisation, where mixing non-idealities directly impact product specifications. From a modelling perspective, turbulence plays a cen- tral role in mixing phenomena in most refinery equipment. Reynolds-averaged Navier–Stokes (RANS) models such as the k- ε and k- ω SST turbulence models are widely used in industrial CFD to predict average flow structures and mixing indices. The choice of turbulence model significantly affects the predicted mixing profiles, and validation with experimen - tal or high-fidelity data remains essential.
As computational power and modelling techniques con - tinue to advance, CFD increasingly provides predictive insights that reduce experimental requirements, shorten development cycles, and enhance reliability across down- stream refining processes. In the present study, CFD simulations were performed to evaluate the mixing and dispersion behaviour of a viscous additive within a storage tank for different additive injection locations. The analysis focused on velocity fields and additive concentration distributions to quantify the extent of homoge - nisation achieved under each scenario. These CFD-generated flow and scalar profiles were used as objective criteria to compare the effectiveness of available injection points prior to demonstration at commercial scale. Accordingly, additive dosing at the suction of the centrifugal pump in the tank recir- culation line was opted for demonstration. The high shear and turbulence generated at the pump suc- tion promote rapid breakup of the viscous additive stream, resulting in improved initial distribution before re-entry into the tank. Furthermore, CFD results confirmed that the exist - ing internal jet circulation is sufficient to achieve bulk tank mixing, with path line and velocity vectors showing flow circulation (see Figure 5 ), limited dead zones, and uniform additive dispersion within an acceptable mixing time. Building on these findings, the pump-suction location was identified as the most suitable point for additive injection. The combined effects of pump-induced turbulence and existing
Velocity magnitude (m/s)
Time (s)
1.00e+03 9.00e+02 0.00e+00 1.00e+02 2.00e+02 3.00e+02 4.00e+02 5.00e+02 6.00e+02 7.00e+02 8.00e+02
1.00e+00 9.00e–01 0.00e+00 1.00e–01 2.00e–01 3.00e–01 4.00e–01 5.00e–01 6.00e–01 7.00e–01 8.00e–01
Figure 5 Pathlines and velocity vector profiles of additive mixing in the tank
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PTQ Q3 2026
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