In this paper, we propose a shape optimization pipeline for propeller blades, applied to naval applications. The geometrical features of a blade are exploited to parametrize it, allowing to obtain deformed blades by perturbating their parameters. The optimization is performed using a genetic algorithm that exploits the computational speed-up of reduced order models to maximize the efficiency of a given propeller. A standard offline-online procedure is exploited to construct the reduced-order model. In an expensive offline phase, the full order model, which reproduces an open water test, is set up in the open-source software OpenFOAM and the same full order setting is used to run the CFD simulations for all the deformed propellers. The collected high-fidelity snapshots and the deformed parameters are used in the online stage to build the non-intrusive reduced-order model. This paper provides a proof of concept of the pipeline proposed, where the optimized propeller improves the efficiency of the original propeller.

翻译：本文针对船用螺旋桨叶片提出了一种形状优化流程。利用叶片的几何特征进行参数化，通过扰动参数可生成变形叶片。优化过程采用遗传算法，利用降阶模型的计算加速优势，以最大化给定螺旋桨的效率。采用标准的离线-在线流程构建降阶模型。在计算密集的离线阶段，基于开源软件OpenFOAM建立全阶模型（模拟敞水试验），并沿用相同的全阶设置对所有变形螺旋桨进行CFD仿真。在线阶段利用采集的高保真快照与变形参数构建非侵入式降阶模型。本文对所提流程进行了概念验证，优化后的螺旋桨相比原始螺旋桨显著提升了效率。