Application of genetic algorithm methods for water turbine blade shape optimization

Abstract

The use of modern production techniques such as 3D printing brings new requirements for shaping machine parts. In the case of the production of the runner blades of Kaplan micro-turbine using 3D printing technology from plastic, the emphasis is on the mechanical properties of the blade and hydraulic properties of the entire turbine. Achieving the required parameters is conditioned by finding a suitable shape of the runner. Therefore the design, virtual testing, optimization and evaluation process is automated. The paper describes the whole process where virtual testing of hydraulic parameters is performed by CFD simulations, and the methods of genetic algorithms are used for optimization. Selected final geometrical shapes of the blade are subjected to a more detailed analysis of hydraulic parameters in the wider operating range and also to the strength analysis.

Hydraulic Design of an Axial Runner Using Classical Methods and CFD Simulation

“…7). It is one of the most important coefficients that determines the ratio between the lift coefficients of an isolated airfoil in a wind tunnel (𝑐𝑐 𝑧𝑧 ) 1 (14) and in a blade cascade 𝑐𝑐 𝑧𝑧 . We need to know this ratio because experimentally determined values (𝑐𝑐 𝑧𝑧 ) of a NACA profile were obtained on an isolated profile in a wind tunnel.…”

“…An example of the optimization of a Kaplan turbine with a three-blade runner using MOGA algorithms in the CAESES and ANSYS environment is described in the paper [14]. The primary geometry was designed and fully parameterized in the CAESES environment.…”

“…As part of the sensitivity analysis, a total of 300 simulations took place, on the basis of which the dependencies of changes in individual parameters and their mutual correlations were determined. The most important parameters were the angle of the distributor vanes, the inlet and outlet angle of the runner blade and the runner blade chord length [14].…”

“…Approximately 700 simulations were carried out as part of the optimization process. The last phase of the optimization process was the verification of the final geometry in the wider operating range of the heads and rotating speeds of the machine [14].…”

Ant Colony Algorithms For The Vehicle Routing Problem With Time Window, Period And Multiple Depots

“…Ant Colony Optimization is one of the best known metaheuristics after Genetic Algorithm [29], Simulated Annealing [30] and Particle Swarm Optimization [30].…”