AHA-Kriging surrogate model method for surface partition flaw tolerance assessment of turbine blisk

Author:

Wen Jiongran,Zheng Baiyang,Fei Chengwei

Abstract

Abstract The high-pressure turbine blade in aero-engine power system may experience microstructural degradation due to uncertain flaws, multi-physical fields and loads during manufacturing, processing, installation, and maintenance, leading to serious structure deterioration that affects safety and reliability in service. Therefore, it is necessary to assess the influence of random flaws and loads on the fatigue performance of turbine blades from a probabilistic perspective. In this study, we propose a novel method based on the Artificial Hummingbird Algorithm and Kriging surrogate model (AHA-Kriging), for flaw tolerance assessment in the surface partition of the turbine blade. The results indicate that in the hazardous zone, the flaw tolerance reliability is 0.9984, corresponding to a LCF life of 1520 cycles. In the safe zone, the flaw tolerance reliability is 0.9991, corresponding to a LCF life of 2501 cycles. The primary factor influencing LCF life is flaw size, followed by factors such as the strength coefficient, gas temperature, and fatigue strength exponent. Besides, the AHA-Kriging approach exhibits higher modeling precision and simulation efficiency compared to other methods. This paper presents a practical engineering approach for assessing flaw tolerance in the surface partition of complex components, which is of significant value.

Publisher

IOP Publishing

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3