Optimization and Computational Fluid Dynamics - Department of ...

2 A Few Illustrative Examples of CFD-based Optimization 55
the in-house Opal library) as well as for the CFD procedure (here, the inhouse
UGC + code). The optimization domain has been explored using EA,
but with a very high computational effort, leading to specific difficulties.
Finally, we have improved the model parameters of a well-known engineering
turbulence model using optimization. The proposed new values predict
more accurately the time-averaged velocity profiles in channel flows, as shown
by a comparison with DNS.
As a whole, this paper has demonstrated that CFD-O can be used for a
variety of complex engineering problems, but is still associated with major
issues. Some of them will be discussed in more detail in further chapters of
this book:
• For complex optimization problems, parallel computations will be absolutely
necessary to reduce user waiting time. CFD-O is indeed very well
suited for parallel computers. The CFD evaluations can be performed in
parallel, or the independent individuals in the optimization can be evaluated
in parallel, as presented in this chapter (Cases A and B). It is of
course possible to combine both and again speed-up the procedure.
• Evolutionary Algorithms require a large number of evaluations (for example
Case C, with more than 5,000 evaluations) to obtain a refined description
of the Pareto front. This is a major problem when the evaluation
is computationally expensive like in Case B. Therefore, the best way to
speed-up optimization is indeed to improve the computational efficiency
of the CFD software!
• If an evaluation is computationally very demanding, as in Case B (burner
computation using detailed chemistry and transport), it is essential to reduce
as far as possible the number and the cost of the evaluations. For
this purpose, concepts like Design of Experiments or approximate evaluations
based, for example, on Artificial Neural Networks appear promising.
As a complement, simplified physical models might be used to get a first
approximation as demonstrated in the present chapter.
• When considering many parameters and many concurrent objectives, the
visualization of the results becomes increasingly difficult. Graphical and
post-processing tools dedicated to optimization would greatly facilitate the
analysis.
Acknowledgements Interesting discussions with D. Thévenin, R. Hilbert and R. Baron
are gratefully acknowledged. This research project has been initially started at the EM2C
Laboratory ( École Centrale Paris, France). The library Opal has been first developed by
R. Baron during his Ph.D., with the financial support of CETIAT and ADEME. The
development of the program UGC + has been mostly carried out by S. Paxion and R. Baron,
financed by different French organizations and companies (DGA, ADEME and CETIAT).
Concerning UGC + the author acknowledges the essential contributions from G. Wittum,
A. Gordner, N. Simus-Paxion, N. Neuss, V. Reichenberger, S. Lang, P. Bastian, B. Fiorina,
R. Hilbert and O. Gicquel.