Radial Inflow Turbine Design Through Multi-Disciplinary Optimization Techniques

In the last years micro-gas turbines used in cogeneration power plants have been proved to be a promising technical solution for distributed combined production of electricity and heat, especially due to their low emissions and fuel flexibility. An effort should be made to improve performance of the microturbine in order to enhance cycle efficiency, taking it closer to internal combustion engines one. In particular, components efficiency heavily affects plant performance and aerodynamic design of novel geometries has to consider mechanical constraints in order to pursue this target without compromising machine integrity. Multidisciplinary design optimisation (MDO) is nowadays widely employed to obtain advanced turbomachines design. The aim of this work is to provide a complete tool for the aero-mechanical design of a radial inflow gas turbine. The high rotational speed of such machines, especially if used for micro cogenerative power plants, coupled with high exhaust gas temperature, exposes blades to really high centrifugal and thermal stresses; thus the aerodynamics optimisation has to be necessarily coupled with the mechanical one. Such an approach involves two different computational tools: a fully 3D RANS solver is used for the aerodynamic optimisation, while an open source FEM solver is employed for the mechanical integrity assessment. The geometry parameterization is handled with a commercial tool that employs b-spline advanced curve for blades and vanes definition. The aerodynamic mesh generation is managed via dedicated tools provided by the CFD software and it is a fully structured hexahedral multi-block grid. The FEM mesh is built by means of an harmonic map approach which is able to provide high quality second order unstructured grid preserving geometrical features starting from boundary surfaces of the fluid domain. The finite element calculation provides stresses, displacements and eigenmodes, that are used for mechanical integrity assessments while the CFD solver provides performance parameters and local thermodynamic quantities. Due to the high computational cost of both these two solvers, a metamodel, such as an artificial neural network, is employed to speed up the process. The interaction between two codes, the mesh generation and the post processing of the results is obtained via in-house developed scripting modules. Results obtained are presented and discussed.