Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
| Hi-index | 31.45 |
A real coded genetic algorithm is implemented for the optimization of actuator parameters for cylinder drag minimization. We consider two types of idealized actuators that are allowed either to move steadily and tangentially to the cylinder surface ("belts") or to steadily blow/suck with a zero net mass constraint. The genetic algorithm we implement has the property of identifying minima basins, rather than single optimum points. The knowledge of the shape of the minimum basin enables further insights into the system properties and provides a sensitivity analysis in a fully automated way. The drag minimization problem is formulated as an optimal regulation problem. By means of the clustering property of the present genetic algorithm, a set of solutions producing drag reduction of up to 50% is identified. A comparison between the two types of actuators, based on the clustering property of the algorithm, indicates that blowing/suction actuation parameters are associated with larger tolerances when compared to optimal parameters for the belt actuators. The possibility of using a few strategically placed actuators to obtain a significant drag reduction is explored using the clustering diagnostics of this method. The optimal belt-actuator parameters obtained by optimizing the two-dimensional case is employed in three-dimensional simulations, by extending the actuators across the span of the cylinder surface. The three-dimensional controlled flow exhibits a strong two-dimensional character near the cylinder surface, resulting in significant drag reduction.