{"title":"RANS-based fast computational methods for indoor flows: a framework-driven performance assessment for a simple benchmark","authors":"Eugene Mamulova , Marcel Loomans , Twan van Hooff","doi":"10.1016/j.dibe.2025.100716","DOIUrl":null,"url":null,"abstract":"<div><div>Reliable indoor climate prediction requires reliable tools. Complex airflow predictions often involve the use of physical or numerical models. Computational fluid dynamics (CFD) is a popular, high-fidelity method that can produce reliable predictions. Conventional CFD methods like steady Reynolds-averaged Navier-Stokes (RANS) are time-consuming. Certain methods, called fast computational methods, may be faster and easier to use. However, a framework for addressing the speed, accuracy and software trade-off of these methods is lacking. In this paper, a unified framework is used to compare conventional, two-equation RANS (RANS-2) to four, RANS-based fast computational methods: coarse-grid modelling (c-RANS-2), zero-equation modelling (RANS-0), one-equation modelling (RANS-1), and GPU-based modelling (gpu-RANS-2). This framework is applied to a simple case, as a preliminary step towards understanding how fast computational methods may be applied in practice. This research concludes that gpu-RANS-2 and c-RANS-2 offer the best trade-off for simple mixing ventilation, and makes suggestions for simulating complex flows.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"23 ","pages":"Article 100716"},"PeriodicalIF":6.2000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Developments in the Built Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666165925001164","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Reliable indoor climate prediction requires reliable tools. Complex airflow predictions often involve the use of physical or numerical models. Computational fluid dynamics (CFD) is a popular, high-fidelity method that can produce reliable predictions. Conventional CFD methods like steady Reynolds-averaged Navier-Stokes (RANS) are time-consuming. Certain methods, called fast computational methods, may be faster and easier to use. However, a framework for addressing the speed, accuracy and software trade-off of these methods is lacking. In this paper, a unified framework is used to compare conventional, two-equation RANS (RANS-2) to four, RANS-based fast computational methods: coarse-grid modelling (c-RANS-2), zero-equation modelling (RANS-0), one-equation modelling (RANS-1), and GPU-based modelling (gpu-RANS-2). This framework is applied to a simple case, as a preliminary step towards understanding how fast computational methods may be applied in practice. This research concludes that gpu-RANS-2 and c-RANS-2 offer the best trade-off for simple mixing ventilation, and makes suggestions for simulating complex flows.
期刊介绍:
Developments in the Built Environment (DIBE) is a recently established peer-reviewed gold open access journal, ensuring that all accepted articles are permanently and freely accessible. Focused on civil engineering and the built environment, DIBE publishes original papers and short communications. Encompassing topics such as construction materials and building sustainability, the journal adopts a holistic approach with the aim of benefiting the community.