Multidisciplinary Optimisation of High Pressure Turbine Blade Tips

High pressure turbines are characterised by increasing turbine entry temperatures
and unshrouded rotor blades, resulting in thermal stresses at the turbine blades due to
elevated heat load. Removal of the shroud improves turbine performance by allowing
over tip leakage flow driven by the turbine propulsive pressure gradient. This thesis
discusses the multidisciplinary optimisation of high pressure turbine blade tips, where
both heat load and turbine performance are assessed simultaneously.
Optimising winglet tips, the thesis shows that by using a locally corrected heat
transfer coefficient, good representation of heat load can be achieved taking into
account the change of local flow with wall temperature. Focusing on the tip leakage,
performance losses are identified, and their changes are analysed for a set of selected
winglet tips. It is presented that winglet tips have a great potential for increasing the
stage efficiency through the tip leakage mass flow and shearing losses reduction.
By utilising a design space defined through the use of the radial basis function
surfaces, the thesis demonstrates that both topology and in-topology shape optimisation
can be simultaneously performed. Because of the non-linearity of the design
space, conventional gradient based methods are shown to struggle regressing a design
space and finding an optimum. To resolve that, approach using subdivision of
design space has been examined. Novel response surface method that uses artificial
neural networks is also successfully applied to this kind of a design space. Finally,
the thesis provides results of genetic based optimisation and Pareto front has been
generated and analysed. Discussion shows that treating a turbine tip optimisation as
a topology problem can significantly increase the range of a potential improvement,
both aerodynamically and in the terms of heat load.