Preprints
https://doi.org/10.5194/wes-2026-66
https://doi.org/10.5194/wes-2026-66
23 Apr 2026
 | 23 Apr 2026
Status: this preprint is currently under review for the journal WES.

Dynamic Geostrophic Nudging (DGN): A Novel Method for Controlling the Background Flow in Large Eddy Simulation

Hai Bui, Mostafa Bakhoday-Paskyabi, and Joachim Reuder

Abstract. Initializing idealized Large-Eddy Simulations (LES) for wind energy applications presents a persistent control problem, typically characterized by slow convergence due to inertial oscillations and the difficulty of matching target height wind targets. To address this, we present Dynamic Geostrophic Nudging (DGN), a method that couples physical fidelity with computational efficiency. Unlike standard velocity nudging, DGN acts on the forcing terms: it dynamically adjusts the geostrophic wind components based on the flow tendency and the error between the mean velocity and the target value. This mechanism allows the controller to efficiently steer the mean wind toward the target while actively damping inertial oscillations in the boundary layer. We employ a one-dimensional model to perform a parameter sweep and investigate the sensitivity of the control parameters before applying the method to a full three-dimensional LES. The results demonstrate that DGN reduces the spin-up time from the standard 12–24 hours to approximately two hours while maintaining the target wind vector with high accuracy. Furthermore, by arresting the unphysical transient growth of the boundary layer, the method allows for the use of vertically optimized domains, representing a significant advancement in the operational efficiency of precursor generation for wind farm simulations.

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Hai Bui, Mostafa Bakhoday-Paskyabi, and Joachim Reuder

Status: open (until 21 May 2026)

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Hai Bui, Mostafa Bakhoday-Paskyabi, and Joachim Reuder
Hai Bui, Mostafa Bakhoday-Paskyabi, and Joachim Reuder
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Latest update: 23 Apr 2026
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Short summary
Computer simulations for wind energy are often slow because they take a long time to reach a stable state, wasting time and resources. We developed a "smart control" technique to speed this up. By dynamically adjusting the forces driving the model, we reduced the setup time from twenty-four hours to just two hours. This method allows researchers to reach desired wind conditions quickly and accurately, making wind energy research and weather modeling much more efficient and cost-effective.
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