CFD-Based Design Optimization for Hydro Turbines

Abstract

A computational fluid dynamics-based design system with the integration of three blade design approaches, automatic mesh generator and CFD codes enables a quick and efficient design optimization of turbine components. It is applied to a Francis turbine rehabilitation project with strict customer requirements to provide over 3% increase in peak efficiency, 13% upgrade in power, and improved cavitation characteristics. Extensive turbulent flow simulations are performed for both the existing and new turbines at design and off design conditions. In order to take into account the interactions between different components, particularly the effects between the rotating and stationary parts, coupling calculations based on the implicit coupling method under multiple frames of reference are carried out for the entire turbine model. As a result, the runner and guide vanes are optimized to the greatest extent, and the stay vanes are locally modified with a possible minimum cost under the geometrical constraints of the existing machine. The performance of the new design is verified by model tests, and exceeds required improvements.

An Intelligent Optimization Method for Vortex-Induced Vibration Reducing and Performance Improving in a Large Francis Turbine

“…For example, Viviano and Musumeci et al [7][8][9] proposed simplified numerical models, which simulate turbulence by means of eddy viscosity formulation, to study the mechanism of waves. But when it comes to optimal design, computational fluid dynamics (CFD) turns out to be a better choice because it is more practical and it can overcome the limitations of experimental conditions [10][11][12][13][14][15][16][17][18]. For instance, Zhang et al [10] performed an investigation of two degrees of freedom on vortex-induced vibration under the wakes interference of an oscillating airfoil.…”

Fluid flow modelling through an axial-flow microhydro turbine

“…Fig. 14 shows that the maximum efficiency of the draft tube is 75 %, while the minimum efficiency of the draft tube is 70 % in the worst design of Kaplan turbine [16]. Hence, design of the draft tube has some problem.…”

“…4 [15]. The Y + of N 3 grid density varies in the range of 1 to 30, which is the acceptable range for an automatic wall function treatment in the boundary layer of SST turbulence model [16]. …”

Francis Turbine Blade Design on the Basis of Port Area and Loss Analysis

“…Computational fluid dynamics (CFD) analysis is a very useful tool for predicting hydro machinery performance at various operating conditions [12][13][14][15]. Commercial code of ANSYS CFX [16] was employed to predict the characteristics of the Francis turbine.…”