[EN] The increase in emissions associated with aviation requires deeper research into novel sensing and flow-control strategies to obtain improved aerodynamic performances. In this context, data-driven methods are suitable ...
Lazpita, Eneko; Martínez-Sánchez, Álvaro; Corrochano, Adrián; Hoyas, S; Le clainche, Soledad; Vinuesa, Ricardo(American Institute of Physics, 2022-05)
[EN] This study uses higher-order dynamic mode decomposition to analyze a high-fidelity database of the turbulent flow in an urban environment consisting of two buildings separated by a certain distance. We recognize the ...
Yu, Linqui; Yousif, Mustafa Z.; Zhang, Meng; Hoyas, S; Vinuesa, Ricardo; Lim, Hee-Chang(American Institute of Physics, 2022-12)
[EN] Turbulence is a complicated phenomenon because of its chaotic behavior with multiple spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting and reconstructing turbulence more ...
Eivazi, Hamidreza; Le Clainche, Soledad; Hoyas, S; Vinuesa, Ricardo(Elsevier, 2022-09-15)
[EN] Modal-decomposition techniques are computational frameworks based on data aimed at identifying a low-dimensional space for capturing dominant flow features: the so-called modes. We propose a deep probabilistic-neural-network ...
[EN] In fluid mechanics, the bi-Laplacian operator with Neumann homogeneous boundary conditions emerges when transforming the Navier-Stokes equations to the vorticity-velocity formulation. In the case of problems with a ...