TY - JOUR
T1 - Wind turbine wake computation with the ST-VMS method, isogeometric discretization and multidomain method
T2 - I. Computational framework
AU - Kuraishi, Takashi
AU - Zhang, Fulin
AU - Takizawa, Kenji
AU - Tezduyar, Tayfun E.
N1 - Funding Information:
This work was supported in part by Rice–Waseda research agreement. The work was also supported in part by ARO Grant W911NF-17-1-0046 (first and fourth authors), top Global University Project of Waseda University (fourth author), and China Scholarship Council (No. 201906710089) (second author). We are grateful to Artem Korobenko (University of Calgary), Jinhu Yan (University of Illinois at Urbana-Champaign) and Yuri Bazilevs (Brown University) for providing us the velocity data at a plane 10 m downstream of the lead turbine in their computations [].
Funding Information:
This work was supported in part by Rice?Waseda research agreement. The work was also supported in part by ARO Grant W911NF-17-1-0046 (first and fourth authors), top Global University Project of Waseda University (fourth author), and China Scholarship Council (No. 201906710089) (second author). We are grateful to Artem Korobenko (University of Calgary), Jinhu Yan (University of Illinois at Urbana-Champaign) and Yuri Bazilevs (Brown University) for providing us the velocity data at a plane 10?m downstream of the lead turbine in their computations [5 ].
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2021/7
Y1 - 2021/7
N2 - In this first part of a two-part article, we present a framework for wind turbine wake computation. The framework is made of the Space–Time Variational Multiscale (ST-VMS) method, ST isogeometric discretization, and the Multidomain Method (MDM). The ST context provides higher-order accuracy in general, and the VMS feature of the ST-VMS addresses the computational challenges associated with the multiscale nature of the flow. The ST isogeometric discretization enables increased accuracy in the flow solution. With the MDM, a long wake can be computed over a sequence of subdomains, instead of a single, long domain, thus somewhat reducing the computational cost. Furthermore, with the MDM, the computation over a downstream subdomain can start several turbine rotations after the computation over the upstream subdomain starts, thus reducing the computational cost even more. All these good features of the framework, in combination, enable accurate representation of the turbine long-wake vortex patterns in a computationally efficient way. In the computations we present, the velocity data on the inflow plane comes from a previous wind turbine computation, extracted by projection from a plane located 10 m downstream of the turbine, which has a diameter of 126 m. The results show the effectiveness of the framework in wind turbine long-wake computation.
AB - In this first part of a two-part article, we present a framework for wind turbine wake computation. The framework is made of the Space–Time Variational Multiscale (ST-VMS) method, ST isogeometric discretization, and the Multidomain Method (MDM). The ST context provides higher-order accuracy in general, and the VMS feature of the ST-VMS addresses the computational challenges associated with the multiscale nature of the flow. The ST isogeometric discretization enables increased accuracy in the flow solution. With the MDM, a long wake can be computed over a sequence of subdomains, instead of a single, long domain, thus somewhat reducing the computational cost. Furthermore, with the MDM, the computation over a downstream subdomain can start several turbine rotations after the computation over the upstream subdomain starts, thus reducing the computational cost even more. All these good features of the framework, in combination, enable accurate representation of the turbine long-wake vortex patterns in a computationally efficient way. In the computations we present, the velocity data on the inflow plane comes from a previous wind turbine computation, extracted by projection from a plane located 10 m downstream of the turbine, which has a diameter of 126 m. The results show the effectiveness of the framework in wind turbine long-wake computation.
KW - Isogeometric discretization
KW - Long-wake vortex patterns
KW - Multidomain Method
KW - Space–Time Variational Multiscale Method
KW - Temporal periodicity
KW - Wind turbine wake
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U2 - 10.1007/s00466-021-02022-4
DO - 10.1007/s00466-021-02022-4
M3 - Article
AN - SCOPUS:85105589469
SN - 0178-7675
VL - 68
SP - 113
EP - 130
JO - Computational Mechanics
JF - Computational Mechanics
IS - 1
ER -