高压汽轮机叶片寿命数字孪生的在役负荷计算替代模型

IF 0.7 4区 工程技术 Q4 ENGINEERING, AEROSPACE
Chunhua Li, Jianzhong Sun, Bowen Wang, Jinchen Nian
{"title":"高压汽轮机叶片寿命数字孪生的在役负荷计算替代模型","authors":"Chunhua Li, Jianzhong Sun, Bowen Wang, Jinchen Nian","doi":"10.1515/tjj-2023-0040","DOIUrl":null,"url":null,"abstract":"Abstract There are developed methods for high-pressure turbine (HPT) blade loads and remaining useful life (RUL) prediction; however, they are ineffective and time-consuming for in-service HPT blades under actual operating conditions. Hence, it is necessary to use an acceptable computational effort to predict the HPT blade’s load and in-service lifetime. Drawing from the idea of the usage-based life (UBL) prediction method, this paper first proposes a framework for the life digital twin (LDT) to characterize and track the in-service life consumption of the HPT blades under actual operating conditions. The second work mainly focuses on developing the steady-state and transient load calculation surrogate models for the HPT blade’s LDT. Using the developed surrogate models, it can now calculate the steady-state and transient loads of the HPT blade in an acceptable time with the necessary accuracy. The proposed approach is demonstrated on an HPT blade of a typical commercial turbofan engine. Because the operating load of the HPT blade severely affects its in-service lifetime, the application of this approach enables the construction of an LDT of the HPT blade. It can reduce the uncertainty and variability associated with the in-service life prediction of the HPT blade under actual operating conditions.","PeriodicalId":50284,"journal":{"name":"International Journal of Turbo & Jet-Engines","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"In-service load calculation surrogate models for high-pressure turbine blade life digital twin\",\"authors\":\"Chunhua Li, Jianzhong Sun, Bowen Wang, Jinchen Nian\",\"doi\":\"10.1515/tjj-2023-0040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract There are developed methods for high-pressure turbine (HPT) blade loads and remaining useful life (RUL) prediction; however, they are ineffective and time-consuming for in-service HPT blades under actual operating conditions. Hence, it is necessary to use an acceptable computational effort to predict the HPT blade’s load and in-service lifetime. Drawing from the idea of the usage-based life (UBL) prediction method, this paper first proposes a framework for the life digital twin (LDT) to characterize and track the in-service life consumption of the HPT blades under actual operating conditions. The second work mainly focuses on developing the steady-state and transient load calculation surrogate models for the HPT blade’s LDT. Using the developed surrogate models, it can now calculate the steady-state and transient loads of the HPT blade in an acceptable time with the necessary accuracy. The proposed approach is demonstrated on an HPT blade of a typical commercial turbofan engine. Because the operating load of the HPT blade severely affects its in-service lifetime, the application of this approach enables the construction of an LDT of the HPT blade. It can reduce the uncertainty and variability associated with the in-service life prediction of the HPT blade under actual operating conditions.\",\"PeriodicalId\":50284,\"journal\":{\"name\":\"International Journal of Turbo & Jet-Engines\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Turbo & Jet-Engines\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1515/tjj-2023-0040\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, AEROSPACE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Turbo & Jet-Engines","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1515/tjj-2023-0040","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
引用次数: 0

摘要

摘要:高压涡轮机(HPT)叶片载荷和剩余使用寿命(RUL)的预测方法已得到发展;然而,在实际操作条件下,它们对于在役HPT叶片来说是无效且耗时的。因此,有必要使用可接受的计算工作来预测HPT叶片的载荷和使用寿命。借鉴基于使用寿命(UBL)预测方法的思想,本文首先提出了一个寿命数字孪生(LDT)框架,用于表征和跟踪实际运行条件下HPT叶片的使用寿命消耗。第二项工作主要是开发HPT叶片LDT的稳态和瞬态载荷计算代理模型。使用开发的替代模型,它现在可以在可接受的时间内以必要的精度计算HPT叶片的稳态和瞬态载荷。所提出的方法在一个典型的商用涡扇发动机的HPT叶片上进行了演示。由于HPT叶片的工作负载严重影响其使用寿命,因此采用这种方法可以构建HPT叶片的LDT。它可以减少在实际操作条件下与HPT叶片使用寿命预测相关的不确定性和可变性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In-service load calculation surrogate models for high-pressure turbine blade life digital twin
Abstract There are developed methods for high-pressure turbine (HPT) blade loads and remaining useful life (RUL) prediction; however, they are ineffective and time-consuming for in-service HPT blades under actual operating conditions. Hence, it is necessary to use an acceptable computational effort to predict the HPT blade’s load and in-service lifetime. Drawing from the idea of the usage-based life (UBL) prediction method, this paper first proposes a framework for the life digital twin (LDT) to characterize and track the in-service life consumption of the HPT blades under actual operating conditions. The second work mainly focuses on developing the steady-state and transient load calculation surrogate models for the HPT blade’s LDT. Using the developed surrogate models, it can now calculate the steady-state and transient loads of the HPT blade in an acceptable time with the necessary accuracy. The proposed approach is demonstrated on an HPT blade of a typical commercial turbofan engine. Because the operating load of the HPT blade severely affects its in-service lifetime, the application of this approach enables the construction of an LDT of the HPT blade. It can reduce the uncertainty and variability associated with the in-service life prediction of the HPT blade under actual operating conditions.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Journal of Turbo & Jet-Engines
International Journal of Turbo & Jet-Engines 工程技术-工程:宇航
CiteScore
1.90
自引率
11.10%
发文量
36
审稿时长
6 months
期刊介绍: The Main aim and scope of this Journal is to help improve each separate components R&D and superimpose separated results to get integrated systems by striving to reach the overall advanced design and benefits by integrating: (a) Physics, Aero, and Stealth Thermodynamics in simulations by flying unmanned or manned prototypes supported by integrated Computer Simulations based on: (b) Component R&D of: (i) Turbo and Jet-Engines, (ii) Airframe, (iii) Helmet-Aiming-Systems and Ammunition based on: (c) Anticipated New Programs Missions based on (d) IMPROVED RELIABILITY, DURABILITY, ECONOMICS, TACTICS, STRATEGIES and EDUCATION in both the civil and military domains of Turbo and Jet Engines. The International Journal of Turbo & Jet Engines is devoted to cutting edge research in theory and design of propagation of jet aircraft. It serves as an international publication organ for new ideas, insights and results from industry and academic research on thermodynamics, combustion, behavior of related materials at high temperatures, turbine and engine design, thrust vectoring and flight control as well as energy and environmental issues.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信