Application of genetic algorithms to the development of a variable Schmidt number model for jet‐in‐crossflows

Proposes the use of genetic algorithms to assist the development of turbulence models. A variable Schmidt number model for scalar mixing in jet‐in‐crossflows was developed through theoretical analyses. A uniform micro genetic algorithm is implemented to optimize the model. This is the first known application of the genetic algorithm (GA) technique to turbulence model development. Overall, the GA technique worked exceptionally well for this problem in a cost‐effective and time‐efficient manner. A set of experimental data on a single round jet issued into a confined crossflow is selected for calibration and optimization of the model constants using the uniform micro‐genetic optimization algorithm. Three sets of experimental data of jet‐in‐crossflows are used for the validation of the new model. Numerical results show that the proposed scheme of using the genetic algorithms to develop turbulence models produces very promising results.