Hydrodynamic shape optimization of axisymmetric bodies using multi-fidelity modeling

Abstract

Hydrodynamic shape optimization of axisymmetric bodies is presented. A surrogate-based optimization algorithm is described that exploits a computationally cheap low-fidelity model to construct a surrogate of an accurate but CPU-intensive high-fidelity model. The low-fidelity model is based on the same governing equations as the high-fidelity one, but exploits coarser discretization and relaxed convergence criteria. A multiplicative response correction is applied to the low-fidelity CFD model output to yield an accurate and reliable surrogate model. The approach is implemented for both direct and inverse design. In the direct design approach the optimal hull shape is found by minimizing the drag, whereas in the inverse approach a target pressure distribution is matched. Results show that optimized designs are obtained at substantially lower computational cost (over 94%) when compared to the direct high-fidelity model optimization.