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.

优化立管的配置是一项具有挑战性的任务。它需要大量非线性动态有限元分析来评估每个候选配置的结构行为。在这样的优化过程中，计算时间通常由结构分析步骤决定。因此，减少寻找可行候选者所需的模拟次数对于降低整体计算成本至关重要。在这项工作中，我们建议应用贝叶斯优化 (BO) 算法来有效地优化钢立管的初始配置。与文献中发现的其他优化方法相比，以目标函数评估的数量衡量的 BO 的性能在钢制惰性波浪立管和带有流体动力阻尼器的悬链立管的选定问题中具有竞争力（即，MIDACO 商业代码、全球化有界 Nelder-Mead 和遗传算法）。特别是，我们证明了 BO 与遗传算法相比的优越性能，遗传算法是 Riser 文献中最常见的方法。展示并讨论了说明所提议战略的能力的代表性示例。