wind turbine, blade, fatigue, optimization, reduced order model, surrogate model, loads extrapolation, dimensional analysis
Wind turbine design is a challenging multidisciplinary optimization problem, where the aerodynamic shapes, structural member sizing, and material composition must all be determined and optimized. Some previous blade design methods incorporate static loading with an added safety factor to account for dynamic effects. Others incorporate dynamic loading, but in general limit the evaluation to a few design cases. By not fully incorporating the dynamic loading of the wind turbine, the final turbine blade design is either too conservative by overemphasizing the dynamic effects or infeasible by failing to adequately account for these effects. We propose an iterative method that estimates fatigue effects during the optimization process while quickly converging to the true solution. We also demonstrate an alternate approach where a surrogate model is trained to efficiently estimate the dynamic loading of the wind turbine in the design process. In contrast to the iterative method, there is significant upfront computational cost to construct the surrogate model. However, this surrogate model has been generalized to be used for different rated turbines, and for the scenarios studied in this paper can predict the fatigue damage of a wind turbine with less than 5% error. These methods can be used instead of the more computationally expensive method of calculating the dynamic loading of the turbine within the optimization routine.
Original Publication Citation
Ingersoll, B., and Ning, A., “Efficient Incorporation of Fatigue Damage Constraints in Wind Turbine Blade Optimization,” Wind Energy, Jan. 2020. doi:10.1002/we.2473
BYU ScholarsArchive Citation
Ingersoll, Bryce and Ning, Andrew, "Efficient Incorporation of Fatigue Damage Constraints in Wind Turbine Blade Optimization" (2020). Faculty Publications. 3592.
Ira A. Fulton College of Engineering and Technology
Copyright © 2020 John Wiley & Sons, Ltd. This is the peer reviewed version of the article cited above, which has been published in final form at http://dx.doi.org/10.1002/we.2473. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.