ECN publicatie: facebook
Titel:
Design optimization for wind turbines: optimization algorithms: a stateof the art study
 
Auteur(s):
 
Gepubliceerd door: Publicatie datum:
ECN 1996
 
ECN publicatienummer: Publicatie type:
ECN-C--96-030 ECN rapport
 
Aantal pagina's: Volledige tekst:
31  Niet beschikbaar.

Samenvatting:
The aim of the study on the title subject was to search for a suitablealgorithm for numerical optimizing of the geometry of wind turbine rotor blades. The objectives were to look for a minimization procedure which also minimizes the number of function evaluations. This is mainly due to the fact that function evaluation costs huge amounts of computer time. On a 468 100 MHz computer the evaluation of a specific rotor design still costs a few minutes, and this is only for determining one set of the stationary performance coefficients, e.g. the power coefficient Cp or the rotor torque coefficient Cq or the rotor axial coefficient Cdax. For future extensions of the BLADOPT program, e.g. to HATOPT, with dynamic time simulations with PHATAS or an other aeroelastic code, it is necessary to use these function evaluations as efficiently as possible, because these kind of function evaluations still costs more than 1 hr on a modern work station. The optimization scheme chosen is based on a so called global approximate optimization. With approximate optimization is meant to make an approximate model of the real problem and perform the optimization on that simplified model. Such an approximate model can be made by using e.g. (orthonormal) polynomial curve fitting algorithms. The parameters in the approximate model are the design parameters. Using classical optimization algorithms on the approximate problem, the combination of design parameter values is found to come up with a minimum value of the object function. The design parameters belonging to this solutions are then used in a 'real' function evaluation. With the new function value the approximate model will be updated. This procedure will be performed in a loop until the new function evaluations do not lead to other minima. The foremost virtue of the global approximate model is that it successively includes all real function evaluations, thus not discarding or forgetting any parameter combinations that have been calculated. This prevents repeating the real evaluations of a parameter combination that has already been explored. Such repetitions frequently afflict (very) simple search algorithms. 24 refs.


Terug naar overzicht.