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Radial Inflow Turbine Design Through Multi-Disciplinary Optimisation Technique

[+] Author Affiliations
Dario Barsi, Andrea Perrone, Luca Ratto, Daniele Simoni, Pietro Zunino

Università di Genova, Genova, Italy

Paper No. GT2015-42702, pp. V008T23A009; 12 pages
  • ASME Turbo Expo 2015: Turbine Technical Conference and Exposition
  • Volume 8: Microturbines, Turbochargers and Small Turbomachines; Steam Turbines
  • Montreal, Quebec, Canada, June 15–19, 2015
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5679-6
  • Copyright © 2015 by ASME


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 aeromechanical 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 Reynolds Averaged Navier-Stokes (RANS) solver is used for the aerodynamic optimisation, while an open source Finite Element Analysis (FEA) 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 FEA mesh is built by means of a 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.

Copyright © 2015 by ASME



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