ABSTRACT
This chapter presents a method that combines the genetic algorithm (GA) with computational fluid dynamics (CFD) technique, which can efficiently predict and optimize the flow boundary conditions with various objective functions. It describes an approach to effectively predicting the form of a correlation curve: an independent population is arranged to collect globally optimal individuals, and the optimization process stops when every individual of this independent population meets the requirement of the regression. A genetic algorithm uses evolution operations to generate new populations with higher average fitness values. In the coding procedure, a basic genetic algorithm usually encodes multiple variables into one long binary code. A multi-variable case requires large computing memory and other resources. The CFD-based genetic algorithm was able to find several solutions that satisfied all the design objectives after more than 200 solutions had been tried.
