利用分类变量编码,基于计算流体力学和人工神经网络分析和预测风对斜平行多建筑模型的影响

Prasenjit Sanyal, Sujit Kumar Dalui
{"title":"利用分类变量编码,基于计算流体力学和人工神经网络分析和预测风对斜平行多建筑模型的影响","authors":"Prasenjit Sanyal, Sujit Kumar Dalui","doi":"10.1002/tal.2105","DOIUrl":null,"url":null,"abstract":"SummaryThis research investigates the influence of wind on four closely spaced parallel building models using computational fluid dynamics (CFD). The buildings are positioned either perpendicular to the wind direction or at various oblique angles. The aerodynamic results obtained for these buildings in an interfering condition are compared to those of an isolated tall building using the interference and obliquity effect (IOE) factor. Graphical comparisons are made among the different models and faces, considering various obliquity angles (OAs). The inner building models exhibit higher pressure and force coefficients at higher OAs. The variation of pressure coefficients along the horizontal peripheral direction is also analyzed, and the trade‐offs of higher and lower OAs are discussed for the different building models. Furthermore, an artificial neural network (ANN) is trained using surface pressure coefficients from approximately 6000 data points distributed over different facets of building models. Categorical encoding is employed using one‐hot encoding‐based dummy variables for different building models, while numerical variables such as OA and X, Y, and Z coordinates are included as input for the ANN. The ANN is trained using a total of 238,340 data points (considering different building models and different OA scenarios), and its parameters are monitored during training to minimize errors and achieve high predictability. Finally, a representative case is used to plot the pressure contour obtained from the trained ANN, which is shown to be highly comparable to the CFD‐based contour.","PeriodicalId":501238,"journal":{"name":"The Structural Design of Tall and Special Buildings","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computational fluid dynamics and artificial neural network‐based analysis and forecasting of wind effects on obliquely parallel multiple building models using categorical variable encoding\",\"authors\":\"Prasenjit Sanyal, Sujit Kumar Dalui\",\"doi\":\"10.1002/tal.2105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"SummaryThis research investigates the influence of wind on four closely spaced parallel building models using computational fluid dynamics (CFD). The buildings are positioned either perpendicular to the wind direction or at various oblique angles. The aerodynamic results obtained for these buildings in an interfering condition are compared to those of an isolated tall building using the interference and obliquity effect (IOE) factor. Graphical comparisons are made among the different models and faces, considering various obliquity angles (OAs). The inner building models exhibit higher pressure and force coefficients at higher OAs. The variation of pressure coefficients along the horizontal peripheral direction is also analyzed, and the trade‐offs of higher and lower OAs are discussed for the different building models. Furthermore, an artificial neural network (ANN) is trained using surface pressure coefficients from approximately 6000 data points distributed over different facets of building models. Categorical encoding is employed using one‐hot encoding‐based dummy variables for different building models, while numerical variables such as OA and X, Y, and Z coordinates are included as input for the ANN. The ANN is trained using a total of 238,340 data points (considering different building models and different OA scenarios), and its parameters are monitored during training to minimize errors and achieve high predictability. Finally, a representative case is used to plot the pressure contour obtained from the trained ANN, which is shown to be highly comparable to the CFD‐based contour.\",\"PeriodicalId\":501238,\"journal\":{\"name\":\"The Structural Design of Tall and Special Buildings\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Structural Design of Tall and Special Buildings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/tal.2105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Structural Design of Tall and Special Buildings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/tal.2105","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

摘要本研究利用计算流体动力学(CFD)技术研究了风对四个间距较近的平行建筑模型的影响。这些建筑物的位置要么与风向垂直,要么呈不同的斜角。利用干扰和斜度效应 (IOE) 因子,将这些建筑物在干扰条件下获得的空气动力学结果与孤立高楼的空气动力学结果进行比较。考虑到不同的斜角 (OA),对不同的模型和面进行了图形比较。内部建筑模型在较高的 OA 下表现出较高的压力和力系数。此外,还分析了压力系数沿水平周边方向的变化,并讨论了不同建筑模型在较高和较低 OA 值之间的权衡。此外,利用分布在不同建筑模型表面的约 6000 个数据点的表面压力系数,对人工神经网络(ANN)进行了训练。针对不同的建筑模型,采用了基于单次编码的虚拟变量进行分类编码,而 OA 和 X、Y、Z 坐标等数值变量则被作为人工神经网络的输入变量。使用总计 238 340 个数据点(考虑到不同的建筑模型和不同的 OA 情景)对方差分析网络进行了训练,并在训练过程中对其参数进行监控,以尽量减少误差并实现高预测性。最后,使用一个具有代表性的案例绘制了由训练有素的 ANN 得出的压力等值线,结果表明该压力等值线与基于 CFD 的等值线具有很高的可比性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational fluid dynamics and artificial neural network‐based analysis and forecasting of wind effects on obliquely parallel multiple building models using categorical variable encoding
SummaryThis research investigates the influence of wind on four closely spaced parallel building models using computational fluid dynamics (CFD). The buildings are positioned either perpendicular to the wind direction or at various oblique angles. The aerodynamic results obtained for these buildings in an interfering condition are compared to those of an isolated tall building using the interference and obliquity effect (IOE) factor. Graphical comparisons are made among the different models and faces, considering various obliquity angles (OAs). The inner building models exhibit higher pressure and force coefficients at higher OAs. The variation of pressure coefficients along the horizontal peripheral direction is also analyzed, and the trade‐offs of higher and lower OAs are discussed for the different building models. Furthermore, an artificial neural network (ANN) is trained using surface pressure coefficients from approximately 6000 data points distributed over different facets of building models. Categorical encoding is employed using one‐hot encoding‐based dummy variables for different building models, while numerical variables such as OA and X, Y, and Z coordinates are included as input for the ANN. The ANN is trained using a total of 238,340 data points (considering different building models and different OA scenarios), and its parameters are monitored during training to minimize errors and achieve high predictability. Finally, a representative case is used to plot the pressure contour obtained from the trained ANN, which is shown to be highly comparable to the CFD‐based contour.
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