This monograph is a comprehensive guide to a method of blade profile optimisation for Kaplan-type turbines. This method is based on modelling the interaction between rotor and stator blades. Additionally, the shape of the draft tube is investigated. The influence of the periodic boundary condition vs. full geometry is also discussed. Evolutionary algorithms (EA) are used as an optimisation method together with artificial neural networks (ANN) for objective fitness function approximation. This is due to high computation demand of CFD calculation. ANN allows for significant calculation time reduction. The advantage of EA over other methods is that it seeks for global rather than local optimum. The optimisation criterion (objective fitness function) is the politropic loss coefficient.
Autorzy
Informacje dodatkowe
- Kategoria
- Publikacja monograficzna
- Typ
- książka - monografia autorska/podręcznik w języku o zasięgu międzynarodowym
- Język
- angielski
- Rok wydania
- 2017
Źródło danych: MOSTWiedzy.pl - publikacja "Shape Optimisation of Kaplan Turbine Blades Using Genetic Algorithms" link otwiera się w nowej karcie
