L. W. Chew, A. F. Melaku, M. F. Ciarlatani, C. Gorlé
{"title":"利用大涡模拟研究高层建筑入流湍流不确定性对风压的敏感性","authors":"L. W. Chew, A. F. Melaku, M. F. Ciarlatani, C. Gorlé","doi":"10.1111/mice.70016","DOIUrl":null,"url":null,"abstract":"Large eddy simulations (LES) can aid the prediction of wind loading on buildings, provided that representative inflow turbulence properties are prescribed. This study conducts LES to assess the sensitivity of the mean, root mean square (rms) fluctuation, and peak pressure coefficients (<jats:italic>C<jats:sub>p</jats:sub></jats:italic>) on building surfaces to the uncertainties in the incoming flow turbulence. Compared to wind tunnel measurements, the simulated mean <jats:italic>C<jats:sub>p</jats:sub></jats:italic> is well predicted, and the variation in inflow turbulence has a negligible effect. The rms <jats:italic>C<jats:sub>p</jats:sub></jats:italic> increases with increasing turbulence intensities and increasing turbulence length scales. Increasing inlet values of turbulence intensities and turbulence length scales reduces the root mean square errors (RMSE) of rms <jats:italic>C<jats:sub>p</jats:sub></jats:italic> from 0.049 to 0.026 and from 0.047 to 0.024, respectively, on the side surfaces with flow separation. The minimum <jats:italic>C<jats:sub>p</jats:sub></jats:italic> responds similarly, where the RMSE is reduced from 0.382 to 0.280 and from 0.385 to 0.286. The maximum <jats:italic>C<jats:sub>p</jats:sub></jats:italic> on the windward surface achieves the lowest RMSE of 0.089 at nominal inlet values. The agreement between LES and experiment improves significantly after incorporating uncertainties in the input turbulence properties by repeating simulations with smaller and larger values from the estimated turbulence inputs. Wind tunnel experiments often do not measure the complete turbulence properties of the incoming flow, thereby obscuring the validation process of simulation results. The findings recommend wind tunnel experiments to measure and report the complete turbulence properties of the incoming flow for accurate prediction of wind loading.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"6 1","pages":""},"PeriodicalIF":9.1000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sensitivity of inflow turbulence uncertainty on wind pressure on high‐rise buildings using large eddy simulations\",\"authors\":\"L. W. Chew, A. F. Melaku, M. F. Ciarlatani, C. Gorlé\",\"doi\":\"10.1111/mice.70016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Large eddy simulations (LES) can aid the prediction of wind loading on buildings, provided that representative inflow turbulence properties are prescribed. This study conducts LES to assess the sensitivity of the mean, root mean square (rms) fluctuation, and peak pressure coefficients (<jats:italic>C<jats:sub>p</jats:sub></jats:italic>) on building surfaces to the uncertainties in the incoming flow turbulence. Compared to wind tunnel measurements, the simulated mean <jats:italic>C<jats:sub>p</jats:sub></jats:italic> is well predicted, and the variation in inflow turbulence has a negligible effect. The rms <jats:italic>C<jats:sub>p</jats:sub></jats:italic> increases with increasing turbulence intensities and increasing turbulence length scales. Increasing inlet values of turbulence intensities and turbulence length scales reduces the root mean square errors (RMSE) of rms <jats:italic>C<jats:sub>p</jats:sub></jats:italic> from 0.049 to 0.026 and from 0.047 to 0.024, respectively, on the side surfaces with flow separation. The minimum <jats:italic>C<jats:sub>p</jats:sub></jats:italic> responds similarly, where the RMSE is reduced from 0.382 to 0.280 and from 0.385 to 0.286. The maximum <jats:italic>C<jats:sub>p</jats:sub></jats:italic> on the windward surface achieves the lowest RMSE of 0.089 at nominal inlet values. The agreement between LES and experiment improves significantly after incorporating uncertainties in the input turbulence properties by repeating simulations with smaller and larger values from the estimated turbulence inputs. Wind tunnel experiments often do not measure the complete turbulence properties of the incoming flow, thereby obscuring the validation process of simulation results. The findings recommend wind tunnel experiments to measure and report the complete turbulence properties of the incoming flow for accurate prediction of wind loading.\",\"PeriodicalId\":156,\"journal\":{\"name\":\"Computer-Aided Civil and Infrastructure Engineering\",\"volume\":\"6 1\",\"pages\":\"\"},\"PeriodicalIF\":9.1000,\"publicationDate\":\"2025-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer-Aided Civil and Infrastructure Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1111/mice.70016\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.70016","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Sensitivity of inflow turbulence uncertainty on wind pressure on high‐rise buildings using large eddy simulations
Large eddy simulations (LES) can aid the prediction of wind loading on buildings, provided that representative inflow turbulence properties are prescribed. This study conducts LES to assess the sensitivity of the mean, root mean square (rms) fluctuation, and peak pressure coefficients (Cp) on building surfaces to the uncertainties in the incoming flow turbulence. Compared to wind tunnel measurements, the simulated mean Cp is well predicted, and the variation in inflow turbulence has a negligible effect. The rms Cp increases with increasing turbulence intensities and increasing turbulence length scales. Increasing inlet values of turbulence intensities and turbulence length scales reduces the root mean square errors (RMSE) of rms Cp from 0.049 to 0.026 and from 0.047 to 0.024, respectively, on the side surfaces with flow separation. The minimum Cp responds similarly, where the RMSE is reduced from 0.382 to 0.280 and from 0.385 to 0.286. The maximum Cp on the windward surface achieves the lowest RMSE of 0.089 at nominal inlet values. The agreement between LES and experiment improves significantly after incorporating uncertainties in the input turbulence properties by repeating simulations with smaller and larger values from the estimated turbulence inputs. Wind tunnel experiments often do not measure the complete turbulence properties of the incoming flow, thereby obscuring the validation process of simulation results. The findings recommend wind tunnel experiments to measure and report the complete turbulence properties of the incoming flow for accurate prediction of wind loading.
期刊介绍:
Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms.
Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.