Related papers: Symbolic Regression-Enhanced Dynamic Wake Meanderi…
This work focuses on the validation of the dynamic wake meandering (DWM) model against large eddy simulation (LES). The wake deficit, mean deflection, and meandering under different wind turbine misalignment angles in yaw and tilt, for the…
The rapid expansion of wind power worldwide underscores the critical significance of engineering-focused analytical wake models in both the design and operation of wind farms. These theoretically-derived ana lytical wake models have limited…
Active wake control (AWC) has emerged as a promising strategy for enhancing wind turbine wake recovery, but accurately modelling its underlying fluid mechanisms remains challenging. This study presents a computationally efficient wake model…
Low-fidelity analytical models of turbine wakes have traditionally been used for wind farm planning, performance evaluation, and demonstrating the utility of advanced control algorithms in increasing the annual energy production. In…
So-called engineering or analytical wind farm flow solvers typically build upon two submodels: one for the velocity deficit and one for the wake-added turbulence intensity. While velocity deficit modelling has received considerable…
Wind farm flow control aims to improve wind turbine performance by reducing aerodynamic wake interaction between turbines. Dynamic, physics-based models of wind farm flows have been essential for exploring control strategies such as wake…
The state-of-the-art in wind-farm flow-physics modeling is Large Eddy Simulation (LES) which makes accurate predictions of most relevant physics, but requires extensive computational resources. The next-fidelity model types are…
This study presents a compact data-driven Reynolds-averaged Navier-Stokes (RANS) model for wind turbine wake prediction, built as an enhancement of the standard \(k\)-\(\varepsilon\) formulation. Several candidate models were discovered…
Understanding wind turbine wake mixing and recovery is critical for improving the power generation and structural stability of downwind turbines in a wind farm. In the field, where incoming flow and turbine operation are constantly…
A purely analytical wake model for wind turbines is derived, anchored exclusively in physical interactions between atmospheric turbulence and turbine dynamics, and thus inherently accounting for atmospheric stratification. Unlike empirical…
Computational fluid dynamics (CFD)-driven machine learning frameworks based on symbolic regression offer a promising pathway for turbulence model discovery, but are often hindered by numerical instability, residual stagnation, and…
Accurate, efficient prediction of wind flow with wake effects is crucial for wind-farm layout and power forecasting. Existing approaches-physical measurements, numerical simulations, physics-based models, and data-driven models-face…
Accurate prediction of turbulence kinetic energy (TKE) added by wind-turbine wakes is of significant scientific value for understanding the wake recovery mechanisms. Furthermore, this physical quantity is a critical input for engineering…
This paper solves an approximate form of conservation of mass and momentum for a turbine in a wind farm array. The solution is a fairly simple explicit relationship that predicts the streamwise velocity distribution within a wind farm with…
The development of advanced simulation tools is essential, both presently and in the future, for improving wind-energy design strategies, paving the way for a complete transition to sustainable solutions. The Reynolds-averaged Navier-Stokes…
Dynamic induction control is a wind farm flow control strategy that utilises wind turbine thrust variations to accelerate breakdown of the aerodynamic wake and improve downstream turbine performance. However, when floating wind turbines are…
In this study, we present an improved formulation for the wake-added turbulence to enhance the accuracy of intra-farm and farm-to-farm wake modeling through analytical frameworks. Our goal is to address the tendency of a commonly used…
Super-large-scale particle image velocimetry and flow visualization with natural snowfall is used to collect and analyze multiple datasets in the near wake of a 2.5 MW wind turbine. Each dataset captures the full vertical span of the wake…
To provide automatic generation control (AGC) service, wind farms (WFs) are required to control their operation dynamically to track the time-varying power reference. Wake effects impose significant aerodynamic interactions among turbines,…
Validating engineering wake models under real-world operational conditions is essential for improving wind farm performance predictions. This study uses a unique dataset from the Lillgrund offshore wind farm, collected during the Horizon…