Optimization and Computational Fluid Dynamics - Department of ...

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∆T
∆P
Position parameters
Master
Worker 1 Worker n
Fig. 2.9 Schematic flow chart showing the multi-objective CFD optimization problem
running on a multi-node PC cluster
An EA optimization procedure can easily be carried out in parallel to
reduce the complete simulation time. The individuals (here the CFD simulations)
are completely independent from each other in one generation. In
this way, several evaluations can be performed at the same time on several
processors (Fig. 2.9). A single simulation for the presented heat exchanger
problem requires only some minutes of CPU time.
The parallel computation can be limited by the required computational
resources, i.e., the number of free computers in the system or the available
licenses when using commercial codes. Most cluster systems are supervised
by a so-called batch system to control and organize the computational work
and the available licenses. Depending on the load of the cluster, it is possible
that at one time no simulation is running or that several computations are
processed at the same time. In this case, the load will not be uniform and
the resulting total wall-clock time may not be representative to measure the
speed-up of the parallel optimization.
Typical results leading to Pareto fronts can be extracted from these calculations
and are presented and discussed next.
2.3.6 Computational Results
2.3.6.1 Pareto Fronts
After several tests, EA optimization was carried out using 40 individuals at
every generation. The applied probabilities are given in Table 2.2.