Abstract
This paper presents the development and application of the continuous adjoint method for the shape optimization of wind turbine blades aiming at maximum power output. A RANS solver, coupled with the Spalart-Allmaras turbulence model, is the flow (primal) model based on which the adjoint system of equations is derived. The latter includes the adjoint to the turbulence model equation. The primal and adjoint fields are used for the computation of the objective function gradient w.r.t. the design variables. A volumetric Non-Uniform Rational B-Splines (NURBS) model is used to parameterize the shape to be designed. The latter is also used for deforming the computational mesh at each optimization cycle. In order to reduce the computational cost, the aforementioned tools, developed in the CUDA environment, run on a cluster of Graphics Processing Units (GPUs) using the MPI protocol. Optimized GPU memory handling and GPU dedicated algorithmic techniques make the overall optimization process up to 50x faster than the same process running on a CPU. The developed software is used for the shape optimization of an horizontal axis wind turbine blade for maximum power output.
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Acknowledgements
This study has been co-financed by the European Union (European Social Fund-ESF) and Greek national funds through the Operational Program Education and Lifelong Learning of the National Strategic Reference Framework (NSRF) Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.
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Tsiakas, K.T., Trompoukis, X.S., Asouti, V.G., Giannakoglou, K.C. (2019). Shape Optimization of Wind Turbine Blades Using the Continuous Adjoint Method and Volumetric NURBS on a GPU Cluster. In: Minisci, E., Vasile, M., Periaux, J., Gauger, N., Giannakoglou, K., Quagliarella, D. (eds) Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Computational Methods in Applied Sciences, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-89988-6_8
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DOI: https://doi.org/10.1007/978-3-319-89988-6_8
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