Bayesian optimization of riser configurations

Abstract

Optimizing the configuration of risers is a challenging task. It requires numerous nonlinear dynamic finite element analyses to evaluate each candidate configuration regarding its structural behavior. In such an optimization procedure, the computational time is commonly dominated by the structural analysis step. Therefore, reducing the number of simulations required to find feasible candidates is paramount to reduce the overall computational cost. In this work, we propose applying the Bayesian Optimization (BO) algorithm to optimize steel risers’ initial configuration efficiently. The performance of BO, measured as the number of objective function evaluations, is shown to be competitive in selected problems of steel lazy-wave risers and catenary risers with hydrodynamic dampers, compared to other optimization methods found in the literature (i.e., MIDACO commercial code, globalized bounded Nelder–Mead and genetic algorithms). In particular, we demonstrate the superior performance of BO compared to genetic algorithms, the most commonly found method in riser literature. Representative examples, which illustrate the capabilities of the proposed strategy, are presented and discussed.