中国机械工程2025，Vol.36Issue(10)：2171-2178,2189,9.DOI:10.3969/j.issn.1004-132X.2025.10.002
基于预筛选代理模型和直接操纵自由变形参数化的向心涡轮气动优化
Aerodynamic Optimization of Radial Turbines Based on Surrogate Model of Pre-screened Strategies and DFFD Parameterization
摘要
Abstract
There were some problems such as difficult geometric control,many control variables and low optimization efficiency in aerodynamic optimization of three-dimensional complex blade surfaces of ra-dial turbines.To solve these problems,multi-degree-of-freedom parameterization of radial turbine runner and blade multidimensional geometry were implemented based on DFFD method.Then an differential evo-lution algorithm assisted by surrogate models of pre-screened strategies(Pre-SADE)was introduced.Fi-nally,a data-driven three-dimensional aerodynamic optimization platform for centripetal turbines was con-structed by combining python and batch script of process automation.The platform was used to carry out the joint optimization design of flow channel-static/rotating blades for the radial turbines.The results show that after optimization,the adiabatic efficiency and mass-flow of the design point of the centripetal turbines are increased by 1.66%and 1.7%respectively,which effectively reduces the shock intensity in the guide vane channel and the shock loss on the suction surfaces of the guide vane,and the efficiency characteristics of the design rotational speed are improved in all working conditions.Finally,the method and platform may ensure the aerodynamic optimization efficiency,and effectively reduce the optimization variables and sample real evaluation times,significantly improve the optimization efficiency,and meet the rapid and elaborate optimization design requirements of radial turbines.关键词
向心涡轮/气动优化/直接操纵自由变形/预筛选代理模型/差分进化算法Key words
radial turbine/aerodynamic optimization/directly manipulated free-form deformation(DFFD)/surrogate model of pre-screened strategy/differential evolution algorithm引用本文复制引用
王天奇,陈江,向航,宋晓飞..基于预筛选代理模型和直接操纵自由变形参数化的向心涡轮气动优化[J].中国机械工程,2025,36(10):2171-2178,2189,9.基金项目
国家科技重大专项(J2019-Ⅱ-0005-0025) （J2019-Ⅱ-0005-0025）
