In one particular production facility, a corrosive additive
is injected into a flush-mounted injection tee. The additive is introduced
onto the inner wall of the process pipe, and with very little radial flow
in the process flow field, the only mechanism for mixing is diffusion.
At other plants, where similar additive injection methods have caused
corrosion problems and costly shutdowns, engineers have gone to great
lengths to design injection systems to minimize corrosion. For this specific
plant, the engineer wanted to study low-cost options for improving the mixing
of the additive.
Integrated design solution
Because of the ease with which CFD can perform complex parametric
studies involving potentially costly process modifications, all in the
framework of the virtual world of computer analysis, we decided that it
was the ideal tool for optimizing the design of the injector. To achieve
optimal results within a limited project time frame, we selected CFX-5.
We looked at twelve different basic designs, with further
parametric variations on several of these. For each one, the computational
model of the mixer was set up using CFX-5's preprocessor, CFX-Build.
For the particular model presented in the illustration below,
it took about one hour to build up the geometry of the mixer, and five
minutes for the machine to create the 3-D mesh which contains about 272,000
cells. The power of CFX-Build allowed us to build the geometries parametrically
in a very short time and thereby study many possible alternatives.
In the calculations, the flow in the domain has been considered
as single-phase with the additive being introduced as a species of identical
density and viscosity. In the original design, the additive was injected
at the inner wall of the process pipe, but we have considered different
injection methods, including side pipes and centerline injection.
CFX-5 predicted isosurface of additive concentration with velocity
vectors showing the mixing process. The pipe walls and baffles are
also shaded according to the additive concentration.
Accurate prediction of mixing behaviour is essential in
this type of simulation. To minimise numerical diffusion, which can cause
over-prediction of mixing, we used CFX-5's high-resolution bounded discretization
scheme. This ensures high accuracy and good convergence while still maintaining
physical results that are free of spurious spatial overshoots and undershoots
in the solution variables. Minimal numerical diffusion was also achieved
by using CFX-5's grid adaption capability. In this case, the grid was
adapted 3 times according to the additive concentration gradient leading
to a final mesh of about 490,000 elements.
Mesh after 3 adaption cycles.
|CFX-5 dynamically adapts the mesh at
the mixing interface between the additive and the process fluid.
Typical CPU time required for the calculation was two hours
for a 272,000-element run, and two hours more for the calculation with
the three adaption cycles. All simulations were performed on an NT workstation
with a single 500MHz processor
|Mesh adaption refines the contours
of the additive in an axial plane through the pipe. Left, without
adaption; right, with adaption. This shows that the adaption produced
significantly better results by reducing numerical diffusion of the
Our CFD investigations allowed us to identify a number of
options for generating secondary flow and mixing in the process fluid.
To make quantitative comparisons of each option, we used a normalization
parameter, the quotient of the additive reduction factor and the relative
increase in pressure loss.
Now finished, the study has allowed Eastman to develop a
new design which will be implemented shortly, with an estimated added
value from this work of around US$2 million due to reduced corrosion,
and hence frequency of plant shutdowns.
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