基于主动学习方法的汽轮机排缸参数分析  

Analysis of Exhaust Hood Parameters of Steam Turbine Based on Active Learning Approaches

作　　者：胡瑞 王晓放 刘庆龙 边超 邰骁 刘海涛 Rui Hu;Xiao-fang Wang;Qing-long Liu;Chao Bian;Xiao Tai;Hai-tao Liu(School of Energy and Power Engineering,Dalian University of Technology;Hangzhou Chinen Steam Turbine Power Co.,Ltd.)

机构地区：[1]School of Energy and Power Engineering,Dalian University of Technology [2]Hangzhou Chinen Steam Turbine Power Co.,Ltd.

出　　处：《风机技术》2023年第3期83-88,共6页Chinese Journal of Turbomachinery

基　　金：National Natural Science Foundation of China(52005074);Natural Science Foundation of Liaoning Province(2022-MS-135)。

摘　　要：It is well known that the efficiency of a steam turbine is affected by the pressure recovery performance of its low-pressure exhaust hood,and therefore,parametric analysis of the exhaust hood is of great importance in the steam turbine design process.In recent years,computationally inexpensive metamodels have been widely used in the parametric analysis of exhaust hood.However,the prediction accuracy of metamodels is highly dependent on the number and distribution of sample points in the design space.The purpose of active learning is selecting informative samples sequentially to obtain an accurate metamodel within a reasonable computational budget.However,the impact of active learning on the accuracy of metamodels such as exhaust hood parameter analysis has not been fully explored.Therefore,this paper investigates and compares four representative active learning methods on the parametric modeling of turbine exhaust hoods,and the comparison results highlight the advantages of active learning and the analysis of the exhaust hood based on the metamodel with the highest accuracy.

关 键 词：Active Learning METAMODEL SteamTurbine Exhaust Hood 

分 类 号：TK261[动力工程及工程热物理—动力机械及工程]