Mean wind flow reconstruction of a high-rise building based on variational data assimilation using sparse pressure measurements

• Published in: Journal of wind engineering and industrial aerodynamics 2022-12, Vol.231 (105204), p.105204, Article 105204
• Main Authors: Ben Ali, M.Y., Tissot, G., Aguinaga, S., Heitz, D., Mémin, E.
• Format: Article
• Language: English
• Subjects: Data assimilation; Engineering Sciences; Fluids mechanics; Mathematical Physics; Mean flow reconstruction; Mechanics; Physics; Turbulence modeling; Wind flow
• ISSN: 0167-6105; 1872-8197
• DOI: 10.1016/j.jweia.2022.105204

The paper investigates the applicability of the variational data assimilation approach to reconstruct three-dimensional wind flows around a high-rise building model, and provides guidelines towards an efficient reconstruction. The objective is to determine the best distributed control parameters based on the Reynolds-averaged-Navier–Stokes (RANS) model coupled with pressure data. The strategy considers a distributed additive forcing control parameter, acting on the momentum equation and/or the transport equation of turbulent dissipations, where the closure is performed. To avoid unphysical solutions and accelerate the optimization convergence a Sobolev gradient descent regularization is proposed and compared with penalty techniques. The proposed data assimilation framework is applied to a realistic wind engineering estimation problem combining sparse wall pressure measurements with a routinely used industrial three-dimensional RANS numerical code. Results demonstrate that wall-pressure data is a meaningful enough piece of information to recover accurately the wake flow extension. A hybrid control parameter on the transport of the dissipation and the momentum equations leads to very precise results, whereas the Sobolev gradient descent direction gives efficient regularization and a fast convergence. These findings can be useful for future application of such a data assimilation technique to wind engineering problems. •Variational data assimilation is well suited for mean wind flow reconstruction.•3D reconstruction with spatially distributed parameters was enabled by adjoint model.•”Hybrid” parameters on the transport of ϵ and the momentum leads to an efficient reconstruction.•Sobolev gradient descent direction leads to efficient regularization and to a fast. convergence.