Details

Autor(en) / Beteiligte

• Diop, Moussa
• Dubois, Pierre
• Toubin, Hélène
• Planckaert, Laurent
• Roy, Jean-François Le
• Garnier, Eric

Titel

Reconstruction of flow around a high-rise building from wake measurements using Machine Learning techniques

Ist Teil von

• Journal of wind engineering and industrial aerodynamics, 2022-11, Vol.230, p.105149, Article 105149

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• This paper investigates the unsteady flow around a high-rise building using OpenFoam. A Vortex Method is developed to generate upstream unsteady fluctuations that are validated considering the numerical simulation of a neutral atmospheric boundary layer around a high-rise building. The Vortex Method parameters are tuned to produce an upstream velocity profile whose turbulence characteristics are comparable to experimental data. Comparison between mean and fluctuating velocity profiles of the numerical data measured in the building’s wake with experimental data shows satisfactory results. The resulting database is used to reconstruct the flow from limited velocity measurements inside the wake and static wall pressure measurements on the building surface. Machine learning techniques such as linear regressions (Ridge, Lasso, ElastikNet, MultiTaskLasso regression) and Artificial Neural Network are tested and compared. The flow reconstruction using velocity measurements inside the wake leads to a better result compared to the flow reconstruction from the wall pressure measurements. At the same time, it was noticed that the Artificial Neural Network regression does not lead to more satisfactory results compared to linear regression techniques. •Reconstruction of unsteady flow field using limited measurements data.•Linear and non-linear regressions for flow reconstruction.•Prediction of a high-rise building’s wake.