One of the critical modelling tools used across the Aerospace industry is Computational Fluid Dynamics (CFD) which has proved crucial to high performance engineering design. However, CFD can be extremely computationally expensive to run, often making the thousands of simulations necessary for simple automated optimisation processes prohibitive.
Within this group, we work on methods for accelerating CFD-driven design processes through the use of techniques such as mesh-based parameterisation schemes, exploitation of reduced order or surrogate modelling, novel implementations of Evolutionary Optimisation (EA) approaches and the use of Bayesian Optimisation (BO). All of these ideas have been applied to real-world, industrial scale applications ranging from the design of the Bloodhound Land Speed Record vehicle to the aerodynamic optimisation of the Reaction Engines’ Skylon spaceplane.
Despite academic evidence that EA and BO approaches can have a positive impact, automatic optimisation techniques remain underutilised in industrial settings due to a combination of theoretical and practical barriers. This group works closely with industry partners to help break down these barriers and support the implementation of new CFD-driven design and optimisation processes in industry. The group also has interests in innovative ‘human in the loop’ optimisation environments, exploiting augmented reality, to facilitate co-creativity in design.