Optimization and Computational Fluid Dynamics - Department of ...

2 A Few Illustrative Examples of CFD-based Optimization 37
∆P [mPa]
∆P [mPa]
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
14 16 18 20 22 24 26
∆T [K]
(a)
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
14 16 18 20 22 24 26
∆T [K]
(c)
∆P [mPa]
∆P [mPa]
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
14 16 18 20 22 24 26
∆T [K]
(b)
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
14 16 18 20 22 24 26
∆T [K]
(d)
Fig. 2.12 The non-dominated individuals for the heat-exchanger problem (elite). (a)The
non-dominated individuals after one generation. (b) The non-dominated individuals after
two generations. (c) The non-dominated individuals after five generations. (d) The nondominated
individuals after 20 generations
very early, i.e., already after 2 or 3 generations. Nevertheless, the quality
increases with the number of generations and the POF becomes more refined.
There are two reasons for this progressive improvement: first, we have more
and more individuals in the elite group. Secondly, Opal favors individuals
that are very different. For example, if we have 5 non-dominated individuals
with the same fitness value in the group of the parent, with 4 of them very
close to each other in parameter space and the last one quite different, this
last individual will be favored for reproduction to enhance diversity, which
improves the spatial extension of the POF.
2.3.6.3 Speed-up Obtained Through Parallelization
In order to reduce the waiting time for the user, the process has been parallelized
and carried out on a multi-node Linux PC cluster with 16 worker
PCs. Each node is a 2.6 GHz/2 GB-RAM Pentium-IV Linux PC. The communications
are performed via a Fast Ethernet (1 Gb/s) network connection.