Optimization and Computational Fluid Dynamics - Department of ...

2 A Few Illustrative Examples of CFD-based Optimization 47
100%
80%
60%
40%
20%
0%
0.10 [g/s]
0.03 [g/s]
1.6[g/s]
0[g/s]
10 × 10 −3 [g/s]
6 × 10 −3 [g/s]
20 [K]
0[K]
Methane Air CO Temp. variation
Fig. 2.17 Input parameters and objectives of the EA optimization for good configurations
in the laminar burner case
they may fail or lead to inaccurate results. There are well-known issues like
swirl, secondary flow, large pressure gradients, strong streamline curvature,
etc., where model predictions often become poor. In such cases, some modifications
and/or additional terms are needed in the model. Different authors
often propose slightly different parameter values when introducing such modifications.
The determination of the model constants for engineering turbulence models
is indeed a difficult task. The values are often considered as some ad-hoc
values. Changing one parameter in order to observe consequences concerning,
for instance, the time-averaged turbulent velocity distribution or the
shear-stress distribution is easy. But the simultaneous modification of several
parameters of a turbulence model in order to increase accuracy rapidly becomes
a formidable issue. If all the model parameters are changed in small
steps, then the number of possible combinations would yield an enormous –
and probably unnecessary – computational effort to explore the whole domain.
In that case, numerical optimization techniques may help to speed-up
the search procedure in order to find the best possible combination of the
model constants with a minimum computational load, since optimization is
much more efficient than a simple trial and error manual procedure.
The objective in this case is to optimize the prediction of the time-averaged
turbulent velocity distribution in channel flows. Direct numerical simulation
(DNS) data [40, 60] for four different Reynolds numbers are chosen as ref-