While data-based approaches were found to be useful for subgrid scale (SGS) modeling in
Reynolds-averaged Navier-Stokes (RANS) simulations, there have not been many attempts …

Successful propagation of information from high-fidelity sources (ie, direct numerical
simulations and large-eddy simulations) into Reynolds-averaged Navier–Stokes (RANS) …

This survey investigates wall modeling in large-eddy simulations (LES) using data-driven
machine-learning (ML) techniques. To this end, we implement three ML wall models in an …

Computational fluid dynamics using the Reynolds-averaged Navier–Stokes (RANS) remains
the most cost-effective approach to study wake flows and power losses in wind farms. The …

To fulfill the increasing need for large power generation by wind turbines, the concept of
multirotor wind turbines has recently received attention as a promising alternative to …

For developing a reliable data-driven Reynold stress tensor (RST) model, successful
reconstruction of the mean velocity field based on high-fidelity information (ie, direct …

Abstract Reynolds-averaged Navier Stokes (RANS) based modeling is considered the
mainstream computational fluid dynamics (CFD) approach for wind energy applications …

Abstract Reynolds-averaged Navier-Stokes (RANS) is one of the most cost-efficient
approaches to simulate wind-farm-atmosphere interactions. However, the applicability of …

We exploit the similarity between the mean momentum equation and the mean energy
equation and derive transformations for mean temperature profiles in compressible wall …

In this study, an analytical wake model for predicting the mean velocity field downstream of a
wind turbine under veering incoming wind is systematically derived and validated. The new …