The final phase of this design project included using automatic optimization to improve the stage efficiency. The blade geometry in TURBOdesign1 is parameterised by using the blade loading and TURBOdesign Optima is used to run Multi Objective Genetic Algorithm (MOGA) to explore the design space and create a pareto front of contrasting design objectives.
TURBOdesign1 parametrises a fully 3D blade shape with only a few design parameters and also provides detailed 3D inviscid surface pressure and velocity information, using ADT’s correlations based on the 3D inviscid flow fields it is possible to run hundreds of designs in less than one hour and achieve a well-defined Pareto Front (Fig. 4 and 5) clearly showing the trade-off between loss mechanisms.
The turbine stator and rotor blades were parameterised using 8 blade loading parameters and also including the number of blades, the optimization objectives were to minimise the profile losses and diffusion ratio for the stator
and secondary flows and diffusion ratio while maintaining profile losses below the baseline blade for the rotor. The resulting Pareto Fronts are shown in Fig. 4 and 5 for the Stator and Rotor respectively.
The optimized stage geometries, compared to the original stage design, resulted in a turbodrill turbine stage that produced 19.3% more torque, provided a 6% higher Total-to-Static stage efficiency and also resulting in 10.5% reduction in axial thrust while presenting the same level of Total-to-Total Pressure Drop.