To address the challenges of multiple design variables, long evaluation times, and poor global search capability of traditional surrogate-assisted algorithms that rely on the complete substitution of accurate function evaluations in the turbine aerodynamic optimization process, a pre-screening surrogate-assisted elitist preservation genetic algorithm (pre-SAEGA) optimizer is proposed. Pre-screening strategy in the pre-SAEGA can screen samples instead of directly estimating them, thereby reducing expensive evaluations in each generation. The directly manipulated free-form deformation (DFFD) method is applied to parameterized multi-stage axial turbines, and multi-degree-of-freedom flexible control is realized. Combining the pre-SAEGA with the DFFD method, a data-driven multi-stage axial turbine optimization platform is established. A two-stage axial turbine is the research object, and 44 design variables are selected for the combined optimization design of flow path and blade rows. The results show that the isentropic efficiency and flow rate improve by 1.33 % and 1.81 % respectively, and the pressure ratio decreases by 0.47 % at the turbine design point. The presented optimization platform not only improves the aerodynamic optimization effect but also significantly reduces the number of design variables and real evaluation samples, making it suitable for solving multi-stage turbine optimization problems with multiple degrees of freedom.

中文翻译：

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基于预筛选策略和直接操纵自由变形的多级轴流式涡轮气动优化


针对传统代理辅助算法在涡轮气动优化过程中完全替代精确函数评估的设计变量多、评估时间长、全局搜索能力差等问题，提出了一种预筛选代理辅助精英保存算法。提出了遗传算法（pre-SAEGA）优化器。 pre-SAEGA中的预筛选策略可以筛选样本而不是直接估计样本，从而减少每一代中昂贵的评估。将直接操纵自由变形（DFFD）方法应用于参数化多级轴流式水轮机，实现多自由度柔性控制。将pre-SAEGA与DFFD方法相结合，建立了数据驱动的多级轴流式水轮机优化平台。以两级轴流式水轮机为研究对象，选取44个设计变量进行流道和叶列的联合优化设计。结果表明，在涡轮设计点，等熵效率和流量分别提高了1.33%和1.81%，压力比降低了0.47%。该优化平台不仅提高了气动优化效果，而且显着减少了设计变量和真实评估样本的数量，使其适合解决多自由度的多级涡轮优化问题。