What's inside?
Designing axial turbines requires balancing complex aerodynamic and structural trade-offs. This methodology couples 3D inverse design with genetic algorithms to explore vast design spaces using minimal parameters like blade loading. By relating performance losses and mechanical stresses to geometry, the approach successfully reduces LP rotor stresses while preserving aerodynamic performance.
Genetic algorithms coupled with 3D inverse design enable efficient, multi-disciplinary optimization of axial turbines using very few design parameters.
In this publication, you will:
- Understand how parameterizing turbine blades using loading parameters, spanwise work distribution, and maximum thickness allows for the exploration of a large design space with minimal variables.
- Learn how to couple 3D inverse design with multi-objective genetic algorithms to create a methodology for rapid, multi-disciplinary turbine optimization.
- See how relating blade surface velocities to aerodynamic losses and mechanical stresses enables the redesign of LP rotors to reduce stress without compromising performance.
