通过基于相似性的样本处理进行燃气轮机燃烧空间模拟的局部代用建模

Junjie Geng, Haiying Qi, Jialu Li, Xingjian Wang
{"title":"通过基于相似性的样本处理进行燃气轮机燃烧空间模拟的局部代用建模","authors":"Junjie Geng, Haiying Qi, Jialu Li, Xingjian Wang","doi":"10.1115/1.4065994","DOIUrl":null,"url":null,"abstract":"\n The present work proposes an accurate and efficient surrogate modeling method for predicting combustion field in a gas-turbine combustor. The method integrates proper orthogonal decomposition-based dimensional reduction, and Gaussian process regression, in conjunction with the similarity-based sample processing technique. The design parameters of concern include fuel mass flow rate and swirler vane angle. Global surrogate models (GSMs) based on proper orthogonal decomposition and kriging produce significant errors for spatial emulation of methane concentration and turbulent kinetic energy (TKE), which is found to be largely attributed to the feature disparity of sample data at different design points. The Tanimoto coefficient is introduced to identify the similarity relation of the sample design points. The similarity-based sample processing method leverages the techniques of radial partitioning, azimuthal rotation, and sample similarity clustering to enhance the similarity among samples. The radial partitioning divides the physical fields into subzones according to the peak and trough characteristics along the radial direction. Local surrogate models (LSMs) are then adaptively constructed in the subzones, through azimuthal rotation for the methane concentration field and sample similarity clustering for the TKE field. The results show that the LSMs reduced the average prediction error of the CH4 concentration field from 19.56% to 8.16% and the TKE field from 93.75% to 9.12% compared to the GSMs. The present method can effectively support the surrogate modeling of combustors with complex variations of geometric structures and flow physics.","PeriodicalId":508252,"journal":{"name":"Journal of Engineering for Gas Turbines and Power","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Local Surrogate Modeling for Spatial Emulation of Gas-Turbine Combustion via Similarity-Based Sample Processing\",\"authors\":\"Junjie Geng, Haiying Qi, Jialu Li, Xingjian Wang\",\"doi\":\"10.1115/1.4065994\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The present work proposes an accurate and efficient surrogate modeling method for predicting combustion field in a gas-turbine combustor. The method integrates proper orthogonal decomposition-based dimensional reduction, and Gaussian process regression, in conjunction with the similarity-based sample processing technique. The design parameters of concern include fuel mass flow rate and swirler vane angle. Global surrogate models (GSMs) based on proper orthogonal decomposition and kriging produce significant errors for spatial emulation of methane concentration and turbulent kinetic energy (TKE), which is found to be largely attributed to the feature disparity of sample data at different design points. The Tanimoto coefficient is introduced to identify the similarity relation of the sample design points. The similarity-based sample processing method leverages the techniques of radial partitioning, azimuthal rotation, and sample similarity clustering to enhance the similarity among samples. The radial partitioning divides the physical fields into subzones according to the peak and trough characteristics along the radial direction. Local surrogate models (LSMs) are then adaptively constructed in the subzones, through azimuthal rotation for the methane concentration field and sample similarity clustering for the TKE field. The results show that the LSMs reduced the average prediction error of the CH4 concentration field from 19.56% to 8.16% and the TKE field from 93.75% to 9.12% compared to the GSMs. The present method can effectively support the surrogate modeling of combustors with complex variations of geometric structures and flow physics.\",\"PeriodicalId\":508252,\"journal\":{\"name\":\"Journal of Engineering for Gas Turbines and Power\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Engineering for Gas Turbines and Power\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4065994\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering for Gas Turbines and Power","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4065994","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本研究提出了一种准确、高效的代用建模方法,用于预测燃气轮机燃烧器的燃烧场。该方法将基于正交分解的适当降维、高斯过程回归与基于相似性的样本处理技术相结合。设计参数包括燃料质量流量和漩涡叶片角度。基于适当正交分解和克里格法的全局代理模型(GSM)在甲烷浓度和湍流动能(TKE)的空间模拟方面产生了显著误差,这在很大程度上归因于不同设计点样本数据的特征差异。为了识别样本设计点的相似性关系,引入了 Tanimoto 系数。基于相似性的样本处理方法利用径向分割、方位旋转和样本相似性聚类技术来增强样本间的相似性。径向划分法根据沿径向的峰谷特征将物理场划分为子区。然后,通过甲烷浓度场的方位角旋转和 TKE 场的样本相似性聚类,在子区内自适应地构建局部替代模型(LSM)。结果表明,与 GSM 相比,LSM 将甲烷浓度场的平均预测误差从 19.56% 降低到 8.16%,将 TKE 场的平均预测误差从 93.75% 降低到 9.12%。本方法可有效支持几何结构和流动物理变化复杂的燃烧器的替代建模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Local Surrogate Modeling for Spatial Emulation of Gas-Turbine Combustion via Similarity-Based Sample Processing
The present work proposes an accurate and efficient surrogate modeling method for predicting combustion field in a gas-turbine combustor. The method integrates proper orthogonal decomposition-based dimensional reduction, and Gaussian process regression, in conjunction with the similarity-based sample processing technique. The design parameters of concern include fuel mass flow rate and swirler vane angle. Global surrogate models (GSMs) based on proper orthogonal decomposition and kriging produce significant errors for spatial emulation of methane concentration and turbulent kinetic energy (TKE), which is found to be largely attributed to the feature disparity of sample data at different design points. The Tanimoto coefficient is introduced to identify the similarity relation of the sample design points. The similarity-based sample processing method leverages the techniques of radial partitioning, azimuthal rotation, and sample similarity clustering to enhance the similarity among samples. The radial partitioning divides the physical fields into subzones according to the peak and trough characteristics along the radial direction. Local surrogate models (LSMs) are then adaptively constructed in the subzones, through azimuthal rotation for the methane concentration field and sample similarity clustering for the TKE field. The results show that the LSMs reduced the average prediction error of the CH4 concentration field from 19.56% to 8.16% and the TKE field from 93.75% to 9.12% compared to the GSMs. The present method can effectively support the surrogate modeling of combustors with complex variations of geometric structures and flow physics.
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
自引率
0.00%
发文量
0
×
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