This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. Our design technique is a significant improvement over the state-of-the-practice linearized blade more » element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. (NREL), Golden, CO (United States) Sponsoring Org.: USDOE Advanced Research Projects Agency - Energy (ARPA-E) USDOE Office of Energy Efficiency and Renewable Energy (EERE) OSTI Identifier: 1866389 Report Number(s): NREL/JA-2C00-79387 Journal ID: ISSN 0001-1452 MainId:33613 UUID:4000e88e-891e-4e88-8fcf-6a939c4dc768 MainAdminID:64443 Grant/Contract Number: AC36-08GO28308 Resource Type: Journal Article: Accepted Manuscript Journal Name: AIAA Journal Additional Journal Information: Journal Volume: 60 Journal Issue: 5 Journal ID: ISSN 0001-1452 Publisher: AIAA Country of Publication: United States Language: English Subject: 17 WIND ENERGY 97 MATHEMATICS AND COMPUTING generative adversarial network airfoil CFD simulation aerodynamic shape optimization flow separation surrogate model wind turbine airfoil turbine blades aerodynamic performance inverse = , Publication Date: Research Org.: National Renewable Energy Lab. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades.
The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. INNs are specialized deep-learning models with well-defined inverse mappings. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure.
Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics.
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