Towards Passive Aeroelastic Tailoring of Large Wind Turbines Using High-Fidelity Multidisciplinary Design Optimization
(2022) AIAA SCITECH 2022 Forum — Location: San Diego, CA, USA (3.January.2022)
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- Abstract
- The decarbonization of the electric grid is a fundamental technological and socio-economical challenge to address the looming threat of climate change. The reduction of the levelized cost of energy is a critical step to expand the application of carbon-free technologies that rely on high-potential, renewable energy sources such as wind power. Advanced computational tools are instrumental to capturing the tightly-coupled, multi-physics interactions that characterize modern highly-flexible wind turbine rotors. In this work, we use our high-fidelity multidisci- plinary design optimization software to perform aerostructural optimization studies of a large wind turbine configuration, using an efficient, highly-scalable, gradient-based approach on a coupled CFD/FEM model. We investigate the trade-offs between steady-state aerodynamic efficiency and structural cost of a benchmark rotor using more than 100 structural and geo- metric design variables. Mass and torque are simultaneously used as performance metrics as we constrain the maximum stress and tip displacement of the rotor at below-rated operating conditions. We discuss both the optimized design features and the general design trends in the form of Pareto front analyses as we sweep over different prescribed torque values and objective weights. The results highlight the benefits of the coupled model with respect to a single-discipline strategy and showcase the additional insight that high-fidelity analysis tools offer to designers. As we work towards a more refined structural model and the inclusion of a larger set of shape variables to extend the optimization capabilities of the tool, the results support the idea that such high-fidelity approaches can complement conventional low-fidelity tools in the design of light and efficient turbine blades.
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Citations
Mangano, M., He, S., Liao, Y., Caprace, D.-G., & Martins, J. R. (2022). Towards Passive Aeroelastic Tailoring of Large Wind Turbines Using High-Fidelity Multidisciplinary Design Optimization. Proceedings of the AIAA SCITECH 2022 Forum. Published. AIAA SCITECH 2022 Forum, San Diego, CA, USA. https://doi.org/10.2514/6.2022-1289