Data driven surrogate-assisted evolutionary fluid dynamic optimisation
This research project aims to permit the application of evolutionary algorithms, a class of global search metaheuristics, to fluid dynamic optimisation of highly complex industrial systems by exploiting surrogate models and modern machine learning techniques. Advanced machine learning techniques such as active learning, on-line incremental learning and semi-supervised learning, will be employed to construct prediction and classification models, which are synergistically combined to assist evolutionary algorithms.
The developed surrogate-assisted evolutionary optimisation algorithms will be applied to important industrial problems including aerodynamic high-lift wing design and drainage flow control.
£1m (Surrey share £395K)