1. Efficiency and Cavitation Prediction for
Hydraulic Machines
dr. Dragica Jošt
dr. Aljaž Škerlavaj
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018

2. ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
Presentation:
1. Brief presentation of KOLEKTOR Turboinštitut
2. Flow conditions in turbines
3. Francis turbine, efficiency prediction
4. Francis turbine, prediction of losses in labyrinth seals
5. Francis turbine, pressure pulsations in the draft tube
6. Axial turbines, efficiency and cavitation prediction with advanced
turbulence models
7. Pelton turbines, efficiency and cavitation prediction

3. 3
ACTIVITIES
Turbines: - Development of water turbines
- Model acceptance testing in accordance
with IEC 60193 standard
- Site testing
- Computational Fluid Dynamics
Pumps: - Development of pumps
- Production, refurbishment and consultancy
Small Hydro Power Plants: design, manufacturing and installation
of small turbines and electro mechanical equipment
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018

4. D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
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ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
Model Testing – Test Rigs for Pelton, Axial and Francis Turbines
Site Testing Production of Small Turbines

5. 5
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
Reasons for using CFD in water turbines:
• CFD reduces the number of models and measurements
• CFD gives the insight in flow conditions inside water turbines
• For small projects model tests are too expensive
• Some phenomena on prototype can not be predicted by model tests
• Pressure distribution obtained with CFD is needed as input data for stress analysis
• CFD results can be used for criteria in optimization algorithms

6. Flow conditions in turbomachinery:
• Viscous, incompressible flow
• Reynolds number – 106 , flow is turbulent
• Unsteady flow conditions
• Cavitation
• Dynamic behavior
• Free surface flow in Pelton turbines_____________________________________________________________________________________________
D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018

7. 7
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
Old runner New runner
Q = 47.7 m3/s Q = 54.7 m3/s
Turbine
efficiency
CFD reduces the number of models and measurements

8. CFD gave an insight in flow
conditions.
Separate analysis of tandem
cascade, runner and draft tube
Computational grid had
31x31x71 nodes.
The reason for low efficiency at
large flow rates can be seen
from velocity distribution in the
draft tube.
Refurbishment project - 1994
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines

9. Flow Energy Losses before the Runner
0.0
1.0
2.0
3.0
0.15 0.20 0.25 0.30 0.35
j ( - )
DH/H*100(%)
losses in the spiral casing
losses in the stay vane cascade
losses in the guide vane cascade
Flow energy losses in the draft tube
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
0.18 0.23 0.28 0.33
j ( - )
DH/H*100(%)
Computational mesh with details
Velocity distribution in stay and guide vane cascades
for three guide vane openings
Pressure distribution on runner blades at three
operating regimes
Flow in the draft tube
Francis turbine
Flow energy losses and efficiency prediction
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
Runner efficiency
92
93
94
95
96
0.18 0.23 0.28 0.33
j ( - )
h(%)

10. Grids for Labyrinth seals
Number of nodes Coarse grid Fine grid
Labyrinth seal at hub 10 M
Labyrinth seal at shroud 9 M 63.6 M
Opening condition
with prescribed
static pressure
Opening condition
with prescribed
static pressure
Periodic boundary
condition
Rotating wall – red
Stationary wall – blue
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
Francis turbine
Losses in labyrinth seals
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018

11. Steady state solution,
cavitation not modelled
Steady state analysis,
cavitation modelled
Transient analysis,
cavitation modelled
Transient simulations with cavitation modelling
Steady state, SST, cavitation Transient, SAS SST, cavitation
With steady state simulations with RANS turbulence models (k-ε, k-ω, SST)
usually no vortex rope is obtained.
The correct vortex rope can be obtained only with transient simulations
with more advanced turbulence models, such as SAS SST, RSM, LES or DES.
Results of steady state simulations without cavitation are used as initial for steady state analysis with cavitation.
Results of steady state analysis with cavitation are used as initial for transient simulation with cavitation.
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
Francis turbine
Cavitation prediction
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018

12. 12
SAS SST model, no cavitation modelling, Iso-surface of P = 2700 Pa
Steady-state SST, cavitation modelling, Iso-surface Vapour Volume Fraction = 0.1
Experiment
Francis turbine
Rotating vortex rope at different operating regimes
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines

13. Positions for monitoring of
pressure pulsation
Results of transient simulation: Pressure pulsation at part load operating regime
Francis turbine
Rotating vortex rope at part load – pressure pulsation
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines

14. Kaplan turbine for middle head
Kaplan turbine
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
Number of nodes in grids for all turbine parts
Turbine part Number of nodes
Semi spiral casing with stay
vanes 1,480,999
Guide vane cascade 2,755,496
Runner 1,858,374
Draft tube (Basic grid -BG) 1,786,432
Draft tube prolongation (BG) 398,056
Draft tube (Fine Grid - FG) 6,169,935
Draft tube prolongation (FG) 1,681,992
Total (BG) 8,279,357
Total (FG) 13,946,796
b=120
b=200 b=280

15. Streamlines and velocity contours in the draft tube
(a) steady state SST, HRS, FG, (b) transient SST, HRS, FG,
(c) SAS, BCDS, FG, (d) ZLES, BCDS, FG.
a) b)
c) d)
Kaplan turbine
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines

16. (a) steady state SST, HRS, (b) transient SST, HRS, (c) SAS, HRS,
(d) SAS, bounded CDS, (e) ZLES, bounded CDS, (f) ZLES, bounded CDS, BG.
Isosurfaces of velocity invariant Q=0, coloured by viscosity ratio
a) b) c)
d) e) f)
Kaplan turbine
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines

17. 0 – measurement, 1 - steady state SST HR scheme, FG,
2 - transient SST, HRS, FG, 3 - SAS, HRS, FG
4 - SAS, bounded CDS, FG, 5 - ZLES, bounded CDS, FG,
6 - ZLES, bounded CDS, BG
OP3 OP3 OP3
Cavitation at OP4
σ = 0.52 ≪ σpl.
a) Photo from the test rig ,
b) Steady-state simulation,
SST
c) Transient simulation,
SAS SST.
Kaplan turbine
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018

18. Transient simulation with the SAS SST ZLES model on the basic grid
Kaplan turbine
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018

19. Sliding interfaces
• Frozen rotor:
• the position of runner blades is fixed relative to the
stationary parts,
• no mixing due to runner rotation is taken into account.
• Stage condition:
• all quantities are averaged in circumferential direction,
• stage analysis is most appropriate when the
circumferential variation of the flow is small.
• Transient:
• the transient relative motion between the components on
each side of the GGI connection is simulated. The interface
position is updated each time step.
• all interaction effects between components that are in
relative motion to each other are taken into account.
Velocity on the interface
between the runner and
the draft tube
Stage condition
Frozen rotor condition
Transient condition
Rotor stator interaction
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018

20. 6 - jet Pelton turbine
Computational meshes
Nodes Elements
Distributor, Basic grid 12.5M 14.2M
Distributor, Refined grid 20.4M 31.9M
Runner 20.9M 45.7M
Quality of the distributor:
• Flow energy losses in distributor
• Distribution of flow rate between injectors
• Shape of the jets
• Secondary velocity in the jet
22
sec yx vvv 
Shape of the jet and secondary velocity
a) basic grid, steady state SST,
b) refined grid, transient SAS SST
a b
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines

21. Cavitation prediction for Pelton turbines ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
Cavitation in Pelton turbines:
Three-component flow: water, air and water vapour
Homogeneous model with free surface and cavitation modelling
Conditions for cavitation pitting on Pelton buckets:
1. Vapour cavity is sticking to the bucket surface.
2. Water vapour is condensed in a very short time.
3. The condensation of water vapour is developed in absence of air.
Locations, where cavitation pitting can be expected.
The picture is from the paper: A. Rossetti; G. Pavesi; G.
Ardizzon; A. Santolin, Numerical Analyses of Cavitating
Flow in a Pelton Turbine, J. Fluids Eng. 2014;
136(8):081304-081304-10.
Computational mesh
An example of
material erosion due
to cavitation

22. Cavitation prediction for Pelton turbines
Cavitation at the inner side
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
1
3
5
2
4 6
3
5
Water is presented with iso-surface of Water Volume Fraction = 0.8 in transparent red.
Water vapour is presented with iso-surfaces of Vapour Volume Fraction = 0.2 coloured with Wall Distance (dark blue).
At the inner side of the bucket a small cavity with less than 40% of water vapour can be observed. The condensation is very
slow therefore the conditions for cavitation pitting are not fulfilled.

23. 23
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
t*
Cavitation on the back side of the buckets
t*+1.85E-4
t*+2.96E-4 s
t*+4.33E-4 s
t*+6.296E-4s
The condensation is faster than at the inner
side of the bucket but the water vapour is in
contact with air, therefore no erosion of
material is expected.
Cavitation prediction for Pelton turbines
Cavitation at the inner side
ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
Water is presented with iso-surface of Water Volume
Fraction = 0.8 in transparent red.
Water vapour is presented with iso-surfaces of Vapour
Volume Fraction = 0.2 coloured with Wall Distance (dark
blue).

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ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
Conclusions
• Numerical flow analysis with efficiency and cavitation prediction is an
indispensable tool for design and development of water turbines and
pumps.
• Correct qualitative results can be obtained even with steady-state
simulations in a short time.
• For accurate values of energetic, cavitation and dynamic characteristics
transient simulations with advanced turbulence models have to be
performed.

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ANSYS Convergence Virtual Conference
SE Europe, 5th July 2018
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D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
Thank you for your attention !