Sep 2026· Journal of Physics, Conference Series· Vol 3305, pp. 012018· 0 citations· 4 references
Physics
TL;DR
This study presents a baseline for optimization research on physical performance in power enhancement, followed by a multi-output neural network surrogate and wind-tunnel experimental measurements, which reduces the number of high-fidelity simulation test runs by 40–60 percent.
Abstract
It is challenging to advance the design system for wind turbine blades. In the design, the blades must be well optimized for the energy-capturing stage, balancing aerodynamic stability, efficiency, and manufacturability. This study presents a baseline for optimization research on physical performance in power enhancement, which is followed by a multi-output neural network surrogate and wind-tunnel experimental measurements. Compared with a simulation analysis that would require at least 300 high-fidelity evaluations to achieve a comparable Pareto front, the proposed workflow reduces the number of high-fidelity simulation test runs by 40–60 percent. The optimized blades achieve 5–10 percent higher lift-to-drag ratio near the design tip-speed ratio and reduce broadband noise by 2–5 dB at the design condition.
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