TY - JOUR
T1 - Wind turbine wake computation with the ST-VMS method and isogeometric discretization
T2 - Directional preference in spatial refinement
AU - Zhang, Fulin
AU - Kuraishi, Takashi
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 (second and fourth authors), Top Global University Project of Waseda University (fourth author), and Ministry of Science and Technology of China (No. 2019YFE0105200) and National Natural Science Foundation of China (No. 52106239) (first 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 (second and fourth authors), Top Global University Project of Waseda University (fourth author), and Ministry of Science and Technology of China (No. 2019YFE0105200) and National Natural Science Foundation of China (No. 52106239) (first 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 [7].
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2022/4
Y1 - 2022/4
N2 - In this sequel to a two-part article on wind turbine wake computation with the Space–Time Variational Multiscale (ST-VMS) method and ST isogeometric discretization, we study directional preference in spatial refinement. We evaluate the wake computation accuracy of different combinations of mesh resolutions in the free-stream and cross-flow directions. We also evaluate the accuracy of different combinations of B-spline polynomial orders in those directions. The computational framework is the same as in the two-part article. It is made of, in addition to the ST-VMS and ST isogeometric discretization, the Multidomain Method (MDM). It enables accurate representation of the turbine long-wake vortex patterns in a computationally efficient way. Because of the ST context, the computational framework has higher-order accuracy to begin with; because of the VMS feature, 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, as in the two-part article, 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 directional-refinement studies involve four different spatial resolutions, two different B-spline polynomial orders, and two different temporal resolutions. The studies show that there is some preference for refinement in the cross-flow directions.
AB - In this sequel to a two-part article on wind turbine wake computation with the Space–Time Variational Multiscale (ST-VMS) method and ST isogeometric discretization, we study directional preference in spatial refinement. We evaluate the wake computation accuracy of different combinations of mesh resolutions in the free-stream and cross-flow directions. We also evaluate the accuracy of different combinations of B-spline polynomial orders in those directions. The computational framework is the same as in the two-part article. It is made of, in addition to the ST-VMS and ST isogeometric discretization, the Multidomain Method (MDM). It enables accurate representation of the turbine long-wake vortex patterns in a computationally efficient way. Because of the ST context, the computational framework has higher-order accuracy to begin with; because of the VMS feature, 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, as in the two-part article, 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 directional-refinement studies involve four different spatial resolutions, two different B-spline polynomial orders, and two different temporal resolutions. The studies show that there is some preference for refinement in the cross-flow directions.
KW - Directional preference in spatial refinement
KW - Isogeometric discretization
KW - Long-wake vortex patterns
KW - Multidomain Method
KW - Space–Time Variational Multiscale method
KW - Spatial and temporal resolution
KW - Wind turbine wake
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U2 - 10.1007/s00466-021-02129-8
DO - 10.1007/s00466-021-02129-8
M3 - Article
AN - SCOPUS:85123866001
SN - 0178-7675
VL - 69
SP - 1031
EP - 1040
JO - Computational Mechanics
JF - Computational Mechanics
IS - 4
ER -