Abstract
This work presents and compares two formulations for the co-design optimization of a wind turbine blade under non-linear transient loads: the Nested Analysis and Design (NAND) and the Simultaneous Analysis and Design (SAND) approaches. Analytic sensitivies are used in order to ensure the convergence of the optimization within reasonable computational resources. The two formulations are compared on a mass minimization problem with dynamic constraints, solved with the interior-point method in IPOPT, for a gust input and a turbulent input. Results shows that the NAND and SAND approaches converge towards the same optimum with similar performances. The SAND approach benefits from a simpler design sensitivity analysis and a sparse jacobian of the constraints.
Highlights
One key challenge of wind turbine blade optimization is to take in account the complexity of loads on the blade and to estimate the corresponding gradients with good accuracy
This study presents and compares two possible formulations of the same co-design optimization problem for wind turbine blades submitted to dynamic loads
Results show that the two formulations converge to the same optimal design, and the state equations are correctly solved in the Simultaneous Analysis and Design (SAND) approach
Summary
One key challenge of wind turbine blade optimization is to take in account the complexity of loads on the blade and to estimate the corresponding gradients with good accuracy. In the optimization framework HAWTOpt2 [1], the aero-elastic couplings are taken in account by using the aero-elastic tool HAWC2 [2] to run dynamic simulations and estimate the gradient of the optimization constraints. Another well-known optimization framework, Cp-max [3], formulates optimization sub-problems to tackle the uncoupled structural and aeroelastic aspects. The aero-elastic coupling is captured by doing iterations until convergence is reached When it comes to control design in the optimization, a common practice in the field of wind energy is to update the control law or control parameters at each optimization iteration by solving a nested problem [4] [5]. They show that with the co-design approach, the optimizer has more freedom in the design space and gives a better optimum design
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