Numerical simulation of transonic flows by a double loop flexible evolution

Abstract

The chapter presents a methodology that uses evolutionary algorithms to obtain fluid velocities for potential flows inside a nozzle. A genetic algorithm with real encoding which, with the technique of partial sampling of the solution nodes, presents a number of advantages over other evolutionary algorithms is proposed in the chapter. These include the avoidance of a rigid connectivity to discretize the domain, thus making it a meshless method. The chapter also proposes a double-loop strategy with EAs which enables improvement of the methodology. The amount of computer storage is low and convergence behavior is good as the qualitative characteristics of the solution are taken into account in the algorithm. The artificial intelligence based on evolutionary algorithms can bring together lessons learned by developers and decision tools into advanced software developments, which can facilitate their use to solve complex challenging problems in industry.