Minimum momentum flux ratio required to prevent air curtain breakthrough in case of cross-curtain pressure gradients: CFD versus analytical equation

Khayrullina, AA Adelya; Hooff, T. van; Ruiz, Claudio Alanis; Blocken, Bert; Heijst, Gert Jan van

doi:10.1007/s12273-020-0633-2

Cited by 9 publications

(1 citation statement)

References 36 publications

(58 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…R-squared (R 2 ), factor of 1.3, 1.5, and 2 observations (FAC1.3, FAC1.5, and FAC2, respectively) [35] [36] have been selected as the performance metrics to evaluate the accuracy of the proposed method:…”

Section: Resultsmentioning

confidence: 99%

A method for fast and accurate prediction of wind turbine thrust coefficients using classical momentum theory and power curve

Tai,

Tan,

Moey

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

The planning and development of windfarms require accurate prediction of the thrust coefficient (cT ) of wind turbines, which significantly affects the downstream wake. Traditional methods, such as blade element momentum theory (BEMT), often necessitate detailed geometric information of wind turbines for cT computation, information that is not frequently available, especially in the early stages of windfarm planning. This paper aims to address this challenge by presenting a novel and efficient approach to predict cT for horizontal-axis wind turbines (HAWTs). The proposed method integrates classical momentum theory with power curve data to estimate the average axial induction factor (a), thereby enabling the calculation of cT without requiring detailed geometric information of HAWTs. The method was validated against thirty-five existing pitch-controlled HAWTs, with R2 values ranging from 0.9604 to 0.9989. This validation confirms the accuracy of the method, making it a viable alternative to traditional techniques that demand comprehensive wind turbine geometric details. The method has demonstrated both rapidity and precision in cT computation for turbine wake analysis, ensuring high levels of prediction accuracy and potentially lowering the barrier to entry for windfarm development. Unlike existing models predominantly focused on wind turbine power curves, cT modelling has largely been overlooked. This study makes a unique contribution to the field by proposing a novel method for cT prediction, thereby filling a critical gap in windfarm planning and development. However, while the study shows promising results, further research is warranted to explore its applicability in diverse windfarm scenarios and turbine configurations.

show abstract

Section: Resultsmentioning

confidence: 99%