Abstract
We present and investigate techniques for optimizing particle dispersions in all kinds of Reynolds number flows. In particular, we show the range of application, power and efficiency of space mapping approaches that are based on a hierarchy of models ranging from a complex (fine, accurate, costly) to simple (coarse, rough, cheap) model. Space mapping turns out to be a reasonable approximation to optimal control and a competitive alternative to instantaneous control regarding speed and memory demands when dealing with complex, non-stationary problems. Moreover it allows the easy and efficient treatment of stochastic design problems. To control random particle dynamics in a turbulent flow, we suggest a Monte-Carlo aggressive space mapping algorithm which yields very convincing numerical results.
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