Numerical Study of Sediment Erosion Analysis in Francis Turbine

Abstract

Effective hydraulic turbine design prevents sediment and cavitation erosion from impacting the performance and reliability of the machine. Using computational fluid dynamics (CFD) techniques, this study investigated the performance characteristics of sediment and cavitation erosion on a hydraulic Francis turbine by ANSYS-CFX software. For the erosion rate calculation, the particle trajectory Tabakoff–Grant erosion model was used. To predict the cavitation characteristics, the study’s source term for interphase mass transfer was the Rayleigh–Plesset cavitation model. The experimental data acquired by this study were used to validate the existing evaluations of the Francis turbine. Hydraulic results revealed that the maximum difference was only 0.958% compared with the CFD data, and 0.547% compared with the experiment (Korea Institute of Machinery and Materials (KIMM)). The turbine blade region was affected by the erosion rate at the trailing edge because of their high velocity. Furthermore, in the cavitation–erosion simulation, it was observed that abrasion propagation began from the pressure side of the leading edge and continued along to the trailing edge of the runner. Additionally, as sediment flow rates grew within the area of the attached cavitation, they increased from the trailing edge at the suction side, and efficiency was reduced. Cavitation–sand erosion results then revealed a higher erosion rate than of those of the sand erosion condition.

Computation of erosion in a hydro turbine

“…The found results are qualitatively comparable to those obtained by Rakibuzzaman et al (2019), mostly over the pressure side of the runner blade mainly at the leading edge and the trailing edge. Furthermore, Figure 24 provides depictions of the real erosion patterns in the Francis turbine taken from the Cahua hydro-power plant (Eltvik et al , 2011).…”

Development of a Hydropower Turbine Using Seawater from a Fish Farm

“…To reduce the influence of grid number on computational results, a grid dependency study at the rated head (15 m) operating condition (GV 60 • and RV 24 • ) was conducted. This showed that the efficiency deviation was less than 1% [38,39], as shown in Figure 7. The grid independency test was carried out based on the grid convergence index (GCI) method [40][41][42].…”

“…Table 5 illustrates CFD performance and experimental results for the tubular turbine. As shown in Table 6, the average deviation of the head was 0.231%, and the flow rate deviation was only 2.105% at a rotational speed of 496 rpm [39,49]. The power output and efficiency of the turbine were different due to the different runner vane openings in the experiment and simulation.…”

“…The combined standard uncertainty in the hydraulic turbine is the effect of the standard uncertainty of the independent variable on the uncertainty of the flow rate. Unfortunately, only three measurement data for the degree of freedom (DoF) could not illustrate the 95% confidence limit and error propagation from the result (Table 5) [39]. Figure 10 illustrates the experimental layout and devices of the small hydropower plant at the fish farm.…”

“…The cells' finite volume approaches near the wall boundary are irregular, potentially requiring a special procedure. First, prisms create a layer near the wall of regular prisms and then mesh the remaining volume with tetrahedrons [39,43]. This grid approach improves the near walls and provides better solutions and convergence of computational methods [43].…”