Multiple surrogates for the shape optimization of bluff body-facilitated mixing

Plausible alternative surrogate models can lead to different results in surrogate-based optimization. Since the cost of constructing surrogates is small compared to the cost of the simulations, using multiple surrogates may offer advantages compared to the use of a single surrogate. This idea is explored for a complex design space encountered when shape optimization of a bluff body is performed to facilitate mixing while minimizing the total pressure loss. It is shown that the design space has small islands where mixing is very effective compared to the rest of the design space. It is difficult to use a single surrogate model to capture such local but critical features. Both polynomial response surfaces and radial basis neural networks are used as surrogates. The former are more accurate away from the high-mixing regions while the latter are more accurate near these regions. A combined use of both models is beneficial. The surrogates are also used to perform global sensitivity analysis and bi-objective optimization. The former help rank the design variables in terms of their influence on the objectives, while the latter elucidates the tradeoffs between mixing efficiency and total pressure loss.