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Jul. 11, 2018•0 likes•1,414 views

Jul. 11, 2018•0 likes•1,414 views

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Engineering

Dragica Jošt, Aljaž Škerlavaj, Kolektor Turboinštitut d.o.o., Slovenia.

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- 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 4 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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. _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines
- 14. Kaplan turbine for middle head Kaplan turbine _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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 _____________________________________________________________________________________________ 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).
- 24. 24 ANSYS Convergence Virtual Conference SE Europe, 5th July 2018 _____________________________________________________________________________________________ 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.
- 25. 25 ANSYS Convergence Virtual Conference SE Europe, 5th July 2018 _____________________________________________________________________________________________ D. Jošt, A. Škerlavaj: Efficiency and Cavitation Prediction for Hydraulic Machines Thank you for your attention !