Modeling and Multi-objective Optimization of Stall Control on Naca0015 Airfoil with a Synthetic Jet Using Gmdh Type Neural Networks and Genetic Algorithms

This study concerns numerical simulation, modeling and optimization of aerodynamic stall control using a synthetic jet actuator. The numerical simulation was carried out by a large-eddy simulation that employs a RNG-based model as the subgrid-scale model. The flow around a NACA0015 airfoil, including a synthetic jet located at 10 % of the chord, is studied under Reynolds number Re = 12.7 × 10 and the angle-of-attack at 18-deg conditions. Then, group method of data handling (GMDH) type neural networks are used for modeling the effects of the actuators parameters (momentum coefficient, reduced frequency, angle with respect to the wall) on both developed time-averaged lift (CL) and time-averaged drag (CD), using some numerically obtained training and test data. To use the obtained polynomial neural network models, multi-objective genetic algorithms (GAs) (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving the mechanism, which is then used for Pareto based optimization of control parameters considers two conflicting objectives such as lift (CL) and drag (CD). It is shown that some interesting and important relationships as useful optimal design principles are involved in the performance of stall control on NACA0015 airfoil. Using a synthetic jet actuator can be discovered by the Pareto based multi-objective optimization of polynomial models. Such important optimal principles would not have been obtained without the use of both GMDH-type neural network modeling and Pareto optimization approach.