Reynolds number effects on horizontal axis wind turbine performance and optimal pitch angle
ORAL
Abstract
Modern wind turbines exceed 200 m in diameter and generate up to 15 MW power with their performance depending on the diameter-based Reynolds number (ReD). A recent study by Miller et al. (2019) observed that the turbine performance becomes Reynolds number invariant beyond ReD ∼ 107. However, the effect of the optimal blade pitch angle on this Reynolds number trend is unknown.
Traditional wind tunnels cannot reach such values without increasing flow speed, which leads to compressibility effects and makes it difficult to match the tip speed ratio (TSR). To resolve this issue, we use the Variable Density Turbulence Tunnel (VDTT) at the MPI-DS, which enables 1 × 104 ≤ ReD ≤ 2 ×107 by pressurizing sulfur hexafluoride (SF6), allowing for high TSRs at realistic rotation rates. This setup bridges the gap between lab and field studies. A model turbine (MoWiTO 0.6) is used. We record power output and determine the optimal pitch angle. Results show that the power coefficient Cp becomes Reynolds number invariant above a chord-based Reynolds number of Rec ∼ 1 × 106. We also investigate hysteresis in the Cp data depending on blade pitch direction.
Traditional wind tunnels cannot reach such values without increasing flow speed, which leads to compressibility effects and makes it difficult to match the tip speed ratio (TSR). To resolve this issue, we use the Variable Density Turbulence Tunnel (VDTT) at the MPI-DS, which enables 1 × 104 ≤ ReD ≤ 2 ×107 by pressurizing sulfur hexafluoride (SF6), allowing for high TSRs at realistic rotation rates. This setup bridges the gap between lab and field studies. A model turbine (MoWiTO 0.6) is used. We record power output and determine the optimal pitch angle. Results show that the power coefficient Cp becomes Reynolds number invariant above a chord-based Reynolds number of Rec ∼ 1 × 106. We also investigate hysteresis in the Cp data depending on blade pitch direction.
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Presenters
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Arun Iyer
Princeton University
Authors
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Arun Iyer
Princeton University
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Akhileshwar Borra
Max Planck Institute for Dynamics and Self-Organization
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Yuna Hattori
Max Planck Institute for Dynamics and Self-Organization
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Julian Jüchter
Carl von Ossietzky Universität Oldenburg
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Claudia E Brunner
Max Planck Institute for Dynamics and Self-Organization