TY - JOUR
T1 - Wind turbine wake computation with the ST-VMS method, isogeometric discretization and multidomain method
T2 - II. Spatial and temporal resolution
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 downstream of the lead turbine in their computations [].
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 second part of a two-part article, we present extensive studies on spatial and temporal resolution in wind turbine wake computation with the computational framework presented in the first part. The framework, which is made of the Space–Time Variational Multiscale (ST-VMS) method, ST isogeometric discretization, and the Multidomain Method (MDM), enables accurate representation of the turbine long-wake vortex patterns in a computationally efficient way. Because of the ST context, the framework has higher-order accuracy to begin with; because of the VMS feature of the ST-VMS, it addresses the computational challenges associated with the multiscale nature of the flow; with the isogeometric discretization, it provides increased accuracy in the flow solution; and with the MDM, a long wake can be computed over a sequence of subdomains, instead of a single, long domain, thus reducing the computational cost. Also 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. In the computations presented here, 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 resolution studies involve three different spatial resolutions and two different temporal resolutions. The studies show that the computational framework provides, with a practical level of efficiency, high-fidelity solutions in wind turbine long-wake computations.
AB - In this second part of a two-part article, we present extensive studies on spatial and temporal resolution in wind turbine wake computation with the computational framework presented in the first part. The framework, which is made of the Space–Time Variational Multiscale (ST-VMS) method, ST isogeometric discretization, and the Multidomain Method (MDM), enables accurate representation of the turbine long-wake vortex patterns in a computationally efficient way. Because of the ST context, the framework has higher-order accuracy to begin with; because of the VMS feature of the ST-VMS, it addresses the computational challenges associated with the multiscale nature of the flow; with the isogeometric discretization, it provides increased accuracy in the flow solution; and with the MDM, a long wake can be computed over a sequence of subdomains, instead of a single, long domain, thus reducing the computational cost. Also 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. In the computations presented here, 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 resolution studies involve three different spatial resolutions and two different temporal resolutions. The studies show that the computational framework provides, with a practical level of efficiency, high-fidelity solutions in wind turbine long-wake computations.
KW - Isogeometric discretization
KW - Long-wake vortex patterns
KW - Multidomain Method
KW - Space–Time Variational Multiscale Method
KW - Spatial and temporal resolution
KW - Temporal periodicity
KW - Wind turbine wake
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U2 - 10.1007/s00466-021-02025-1
DO - 10.1007/s00466-021-02025-1
M3 - Article
AN - SCOPUS:85106068197
SN - 0178-7675
VL - 68
SP - 175
EP - 184
JO - Computational Mechanics
JF - Computational Mechanics
IS - 1
ER -