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

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Franco-Nava, Jose´ Manuel Dorantes-Go´mez, Oscar Rosado-Tamariz, Erik Ferna´ndez-Da´vila, Jose´ Manuel Rangel-Espinosa, Reynaldo
Copyright Year	2009
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.
Sponsorship	Power Division
Starting Page	293
Ending Page	300
Page Count	8
File Format	PDF
ISBN	9780791843505
DOI	10.1115/POWER2009-81201
e-ISBN	9780791838536
Conference Proceedings	ASME 2009 Power Conference
Language	English
Publisher Date	2009-07-21
Publisher Place	Albuquerque, New Mexico, USA
Access Restriction	Subscribed
Subject Keyword	Francis runner Computational fluid dynamics Energy improvement Optimization Hydraulic turbine Cycles Blades Turbomachinery Francis turbines Pressure gradient Genetic algorithms Design Hydraulic turbines Fluids Finite element model Databases Centrifugal force Turbochargers Turbine components Turbines Artificial neural networks Flow (dynamics) Pressure Stress Geometry Computation Boundary-value problems Finite element analysis
Content Type	Text
Resource Type	Article

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CFD Performance Evaluation and Runner Blades Design Optimization in a Francis Turbine

CFD-Based Energy Improvement of a Parametric Blade Model for a Francis Turbine Runner

Flow Induced Stresses in a Francis Turbine Runner Using Computer-Based Design Tools

Multi-Objective Optimization for Francis Turbine Runner Using Genetic Algorithm

Fundamental Energy Transfer Mechanism of Turbomachinery

Development of an Integrated Design System (IDS) for Hydraulic Machinery

Analysis of Heat Transfer in a Radial Turbine Wheel of Turbochargers and Prediction of Heat Transfer Coefficients With an Empirical Method

Application of Multipoint Optimization to the Design of Turbomachinery Blades

Approach to Unidirectional Coupled CFD – FEM Analysis of Axial Turbocharger Turbine Blades

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

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CFD Performance Evaluation and Runner Blades Design Optimization in a Francis Turbine

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