Effects of inflow condition on RANS and LES predictions of the flow around a high-rise building

Giulio Vita*, Simone Salvadori, Daniela Anna Misul, Hassan Hemida

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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An increasing number of engineering applications require accurate predictions of the flow around buildings to guarantee performance and safety. This paper investigates the effects of variations in the turbulent inflow, as predicted in different numerical simulations, on the flow pattern prediction around buildings, compared to wind tunnel tests. Turbulence characteristics were assessed at several locations around a model square high-rise building, namely, above the roof region, at the pedestrian level, and in the wake. Both Reynolds-averaged Navier–Stokes (RANS, where turbulence is fully modelled) equations and large-eddy simulation (LES, where turbulence is partially resolved) were used to model an experimental setup providing validation for the roof region. The performances of both techniques were compared in ability to predict the flow features. It was found that RANS provides reliable results in regions of the flow heavily influenced by the building model, and it is unreliable where the flow is influenced by ambient conditions. In contrast, LES is generally reliable, provided that a suitable turbulent inflow is included in the simulation. RANS also benefits when a turbulent inflow is provided in simulations. In general, LES should be the methodology of choice if engineering applications are involved with the highly separated and turbulent flow features around the building, and RANS provides reliable information when regions of high wind speed and low turbulence are investigated.

Original languageEnglish
Article number233
Number of pages21
Issue number4
Publication statusPublished - 7 Dec 2020

Bibliographical note

Funding Information:
Funding: The support of the European Commission’s Framework Program Horizon 2020 through the Marie Skłodowska-Curie Innovative Training Networks (ITN) AEOLUS4FUTURE—Efficient harvesting of the wind energy (H2020-MSCA-ITN-2014: grant agreement number 643167) is acknowledged together with the plethora of data and expertise provided by the COST Action TU1804 WINERCOST—Wind Energy to enhance the concept of Smart cities.

Publisher Copyright:
© 2020 by the authors.


  • Building aerodynamics
  • Large eddy simulation
  • Turbulent inflow
  • Urban wind

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes


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