Wind turbine blade design with airfoil shape control using invertible neural networks. Issue 4 (1st May 2022)

• Record Type: Journal Article
• Authors: Jasa, John; Glaws, Andrew; Bortolotti, Pietro; Vijayakumar, Ganesh; Barter, Garrett
• Is Part Of: Journal of physics. Volume 2265:Issue 4(2022)
• Publication Date: 2022-05-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2265/4/042052
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Wind turbine blade design is a highly multidisciplinary process that involves aerodynamics, structures, controls, manufacturing, costs, and other considerations. More efficient blade designs can be found by controlling the airfoil cross-sectional shapes simultaneously with the bulk blade twist and chord distributions. Prior work has focused on incorporating panel-based aerodynamic solvers with a blade design framework to allow for airfoil shape control within the design loop in a tractable manner. Including higher fidelity aerodynamic solvers, such as computational fluid dynamics, makes the design problem computationally intractable. In this work, we couple an invertible neural network trained on high-fidelity airfoil aerodynamic data to a turbine design framework to enable the design of airfoil cross sections within a larger blade design problem. We detail the methodology of this coupled framework and showcase its efficacy by aerostructurally redesigning the IEA 15-MW reference wind turbine blade. The coupled approach reduces the cost of energy by 0.9% compared to a more conventional design approach. This work enables the inclusion of high-fidelity aerodynamic data earlier in the design process, reducing cycle time and increasing certainty in the performance of the optimal design.