Phase 1: GRNN Training
- Trained on 90% of DoE simulation data
- Normalised parameters (0 to 1)
- Chose spread value for balance of error vs smoothness
- Trained Generalized Regression Neural Network
- Output: pressure increase
Phase 2: Genetic Algorithm
- Fitness function defined as difference from target pressure
- Implemented modified NSGA-II in MATLAB
- Searched for blade parameters that matched required head
- Final solution verified via full CFD Setup simulation