Výsledky vyhledávání - "Rival, David E."

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Wake dynamics of wind turbines in unsteady streamwise flow conditions

Autor: Wei, Nathaniel J., Makdah, Adnan El, Hu, JiaCheng, Kaiser, Frieder, Rival, David E., Dabiri, John O.

The unsteady flow physics of wind-turbine wakes under dynamic forcing conditions are critical to the modeling and control of wind farms for optimal power density. Unsteady forcing in the streamwise direction may be generated by unsteady inflow condit

Externí odkaz: http://arxiv.org/abs/2406.11693

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Relative Fluid Stretching and Rotation for Sparse Trajectory Observations

Autor: Aksamit, Nikolas O., Encinas-Bartos, Alex P., Haller, George, Rival, David E.

Publikováno v: J. Fluid Mech. 996 (2024) A40

As most mathematically justifiable Lagrangian coherent structure detection methods rely on spatial derivatives, their applicability to sparse trajectory data has been limited. For experimental fluid dynamicists and natural scientists working with Lag

Externí odkaz: http://arxiv.org/abs/2310.05500

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Model form uncertainty quantification of Reynolds-averaged Navier-Stokes modeling of flows over a SD7003 airfoil

Autor: Chu, Minghan, Wu, Xiaohua, Rival, David E.

It is well known that the Boussinesq turbulent viscosity hypothesis can yield inaccurate predictions when complex f low features are involved, e.g. laminar-turbulent transition. The focus of the study is to explore the capability of a physics-based u

Externí odkaz: http://arxiv.org/abs/2207.14376

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Quantification of Reynolds-averaged-Navier-Stokes model form uncertainty in transitional boundary layer and airfoil flows

Autor: Chu, Minghan, Wu, Xiaohua, Rival, David E.

It is well known that Boussinesq turbulent-viscosity hypothesis can introduce uncertainty in predictions for complex flow features such as separation, reattachment, and laminar-turbulent transition. This study adopts a recent physics-based uncertaint

Externí odkaz: http://arxiv.org/abs/2207.07951

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Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data

Autor: Iacobello, Giovanni, Kaiser, Frieder, Rival, David E.

Inspired by biological swimming and flying with distributed sensing, we propose a data-driven approach for load estimation that relies on complex networks. We exploit sparse, real-time pressure inputs, combined with pre-trained transition networks, t

Externí odkaz: http://arxiv.org/abs/2105.04520

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