Experimental and numerical validation of a hybrid method for modelling the wake flow of two in-line wind turbines

Abstract

To forecast the wake flow and power reduction affecting downstream turbines reliably and accurately, a hybrid wake model CFD(ALM)-IDWM was improved, in which the forecasting of V_θmax is improved from linear to cubic. Then, a partially overlapping computational domain is added behind the far-wake domain of upstream wind turbine to calculate the aerodynamic performance of a downstream wind turbine, and the numerical model of full CFD(ALM) and CFD(ALM)-IDWM for two in-line wind turbines are developed. Subsequently, a wind tunnel model test on two in-line wind turbines was carried out to validate the full CFD(ALM) model firstly. Subsequently, the hybrid model of CFD(ALM)-IDWM is numerically validated by the full CFD(ALM) simulations. The results show that CFD(ALM)-IDWM cannot only predict the wake characteristics such as vortices and wakes in the wake region more accurately, but can also accurately simulate the average values of the downstream turbine thrust and torque. By ignoring certain flow field details including acceleration, turbulent viscosity, and Reynolds stress during the simulation process, the computational time is reduced. Base on the same CFD(ALM), the computation time of CFD(ALM)-IDWM was approximately 60% of that of full CFD(ALM). The longer the computational domain of IDWM, the greater reduction in computational time.

KEYWORDS:

• In-line wind turbines
• improved hybrid model
• wind tunnel model test
• aerodynamic performance

Acknowledgments

This work is sponsored by the National Natural Science Foundation of China (No.51739001), the RISUD project of the Hong Kong Polytechnic University (no. 1-BBWT). The research was financially supported by the Key Laboratory Fund for Equipment Pre-research (6142223180210). A part of the research related to this paper was carried out when the first author was a visiting student at City, University of London under the support of CSC studentship

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 51739001]; the RISUD project of the Hong Kong Polytechnic University [grant number 1-BBWT]; CSC studentship.