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Energy Science & Engineering
Article . 2023 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Energy Science & Engineering
Article . 2023
Data sources: DOAJ
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A linear wake expansion function for the double‐Gaussian analytical wake model

Authors: Qidun Maulana Binu Soesanto; Tsukasa Yoshinaga; Akiyoshi Iida;

A linear wake expansion function for the double‐Gaussian analytical wake model

Abstract

AbstractThe double‐Gaussian (DG) approach for analytical wake modeling leads to a better understanding of the wake transition mechanism within a full‐wake region behind a non‐yawed horizontal‐axis wind turbine (HAWT). To date, a key parameter of the wake expansion in the DG model still has yet to be defined explicitly instead of tuning, thus limiting its usability for practical applications. The present work aims to overcome this limitation by proposing a simple linear wake expansion function for the DG model constructed from the existing parameters based on the conservation of mass and momentum. Considering the physical and statistical approaches, the proposed function is specifically intended to approximate the wake expansion downstream of a non‐yawed HAWT under turbulence inflow. Seven case studies from wind tunnel measurements and large eddy simulations under different inflow conditions were used to examine the effectiveness of the proposed function. In general, the evaluation results in the present study show the effectiveness of the proposed expansion function for the DG wake model to predict the wake expansion and its recovery behind a non‐yawed HAWT without a prior adjustment or tuning of the wake expansion parameter.

Keywords

Technology, T, Science, Q, analytical wake model, double‐Gaussian approach, HAWT, linear wake expansion

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Top 10%
gold