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Design Tools for the Performance Improvement of a 76 MW Francis Turbine Runner

[+] Author Affiliations
José Manuel Franco-Nava, Oscar Dorantes-Gómez, Erik Rosado-Tamariz

Instituto de Investigaciones Eléctricas, Cuernavaca, MOR, México

José Manuel Fernández-Dávila, Reynaldo Rangel-Espinosa

Comisión Federal de Electricidad, Mexico City, México

Paper No. POWER2009-81201, pp. 293-300; 8 pages
doi:10.1115/POWER2009-81201
From:
  • ASME 2009 Power Conference
  • ASME 2009 Power Conference
  • Albuquerque, New Mexico, USA, July 21–23, 2009
  • Conference Sponsors: Power Division
  • ISBN: 978-0-7918-4350-5 | eISBN: 978-0-7918-3853-6
  • Copyright © 2009 by ASME

abstract

Application of two mayor design tools, Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD), for the performance improvement of a 76 MW Francis turbine runner is presented. In order to improve the performance of the runner, not only a CFD based optimization for the runner but also its structural integrity evaluation was carried out. In this paper, a number of analyses included within the design tools-based runner optimization process are presented. Initially, a reference condition for the fluid behaviour through turbine components was carried out by means of the computation of fluid conditions through the spiral case and stays vanes, followed by CFD-based fluid behaviour for the wicket so as to include the flow effects induced by these components in the final CFD analysis for the runner. All CFD computations were generated within the three dimensional Navier-Stoke commercial turbomachinery oriented CFD code FINE™/Turbo from NUMECA. The whole hydraulic turbine performance was then compared against actual data from a medium-head Francis type hydro turbine (76 MW). Then, CFD-based flow induced stresses in the turbine runner were computed by using a three dimensional finite element model built within the FEA commercial code ANSYS. Appropriate boundary conditions were set in order to obtain the results due to the different type loads (pressure and centrifugal force). The FEM model was able to capture the pressure gradients on the blade surfaces obtained from the CFD results. Improvement of efficiency and power for the runner was computed by using a parametric model built within 3D CFD code integrated environment FINETM/Design3D from NUMECA which combines genetic algorithms and a trained artificial neural network. During the optimization process the artificial neural network is trained with a database of geometries and their respective CFD computations in order to determine the optimum geometry for a given objective function. The optimisation process and the trend curve of the optimization or design cycle that included 29 parameters (corresponding to the control points of runner blade primary sections) which could vary during the process is presented. Finally, the flow induced stresses of the optimized Francis turbine runner was computed so as to evaluate the final blade geometry modifications related to the efficiency and power improvement.

Copyright © 2009 by ASME

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