Performance Prediction of the Francis-99 Hydroturbine with Comparison to Experiment. Chad Custer, PhD Yuvraj Dewan Artem Ivashchenko

Transcription

1 Performance Prediction of the Francis-99 Hydroturbine with Comparison to Experiment Chad Custer, PhD Yuvraj Dewan Artem Ivashchenko Unrestricted Siemens AG 2017 Realize innovation.

2 Agenda Introduction Simulation Methods Results Outlook Page 2

3 Agenda Introduction Simulation Methods Results Outlook Page 3

4 Introduction Presented at the 2 nd Francis-99 workshop hosted by NTNU in December 2016 Paper freely available online as part of the Journal of Physics Conference Series Page 4

5 Introduction Scaled 1:5.1 model of the turbine used at the Tokke power plant The water travels through a 17 kilometer tunnel from Lake Vinjevatn to Lake Bandak Capacity: 430 MW Annual generation: 2,140 GWh Machine Hall at Tokke Building of tunnel Page 5

6 Introduction Test rig housed at the Norwegian University of Science and Technology (NTNU) Water Power Laboratory Experimental Setup Water Power Laboratory Building Page 6

7 Goals Accurate prediction of hydrodynamic performance during: Constant operation at Part Load (PL), Best Efficiency Point (BEP) and High Load (HL) Change from BEP to HL and from BEP to PL Analyze unsteady velocity downstream of runner Provide greater insight into physics and design Provide a platform for design-space exploration Page 7

8 Agenda Introduction Simulation Methods Computational Mesh Physics Modeling Boundary Conditions Results Outlook Page 8

9 Polyhedral + Prism Layer Volume Mesh Total cells: 14 M Spiral casing: 3.5 M Guide vanes: 4 M Runner: 5 M Draft tube: 1.3 M Page 9

10 Computational Mesh Mesh generated to maintain self-similarity Mesh generated for one unique component, then duplicated and transformed Single runner passage Single guide vane Symmetric half of spiral casing Symmetric half of draft tube Page 10

11 Computational Mesh Anisotropic prism layers used to resolve boundary layer gradients Number of prism layers: 16 Runner near-wall thickness: 0.05 mm Runner total thickness: 2.5 mm One prism layer used at stationary/rotating interfaces to ensure cell orthogonality Prism layer thickness set to match volume mesh cell size Page 11

12 Polyhedral Volume Mesh Page 12

13 Boundary Layer Mesh 16 prism layers Runner near-wall thickness: 0.05 mm Runner total thickness: 2.5 mm Average y+ for all wall surfaces: 4.5 Page 13

14 Physics Setup Incompressible Reynolds-Averaged Navier-Stokes Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) solution algorithm 2 nd order spatial gradients SST k-ω turbulence model Quadratic constitutive relation Implicit unsteady time integration Rigid body motion with direct interface communication 2 nd order 1 degree per time step 20 inner iterations Gravity Page 14

15 Boundary Condition Locations Boundary conditions applied away from inlet/outlet measurement locations to reduce the effect of boundary conditions on measurement as: Stagnation inlet Pressure outlet Inlet Boundary Condition (1) Inlet Measurement Plane (2) Outlet Measurement Plane (3) Outlet Boundary Condition (4) Page 15

16 Guide Vane Rotation Guide vane rotation for transient operation is imposed using fully-conservative, computationally efficient mesh morphing Mesh topology remains unchanged Page 16

17 Agenda Introduction Simulation Methods Results Constant Operation Performance Constant Operation Velocity Prediction Variable Operation Performance Outlook Page 17

18 Steady Operation Approximation of net head set as boundary condition Torque prediction agrees with experiment to <1% Discharge under-predicted at all operation points Efficiency over-predicted at all operating points as a result of: Under-prediction of discharge Neglect of secondary flow features Page 18

19 Line Probe Velocity Comparison All data time-averaged for three revolutions Raw data shown No smoothing Cell center values no interpolation Squares: normalized position = 0 Diamonds: normalized position = 1 Page 19

20 Line Probe Comparison BEP Time Average Page 20

21 Line Probe Comparison BEP Time Average Simulation agrees closely with experimental data for all measurements Slight under-prediction of v-velocity along L3 Page 21

22 Line Probe Comparison PL Time Average Page 22

23 Line Probe Comparison PL Time Average Simulation agrees closely with experimental data for measurements along L1 & L2 Moderate under-prediction of v-velocity along L3 Noise observed in u-velocity along L3 Page 23

24 Line Probe Comparison HL Page 24

25 Line Probe Comparison HL V-velocity defect well captured along L1 & L2 Noise observed in u-velocity at center of L1 & L2 Higher level of noise observed along L3 Agreement between simulation and experiment least favorable at HL along L3 Small, strong vortex rope leads to higher level of discrepancy & noise along L3 Page 25

26 Unsteady Velocity Profiles Time-averaged velocity predictions correlate well with experiment Time-accurate simulation shows high level of unsteadiness for off-design conditions Unsteady velocity profiles along L1 will be shown PL BEP HL Page 26

27 Velocity Comparison BEP: Line 1 U Velocity Major flow features steady Large unsteadiness observed within the vortex rope Page 27

28 Velocity Comparison BEP: Line 1 V Velocity Hub wake profile well established with slight oscillation Page 28

29 Best Efficiency Point Quasi-steady flow observed in the draft tube Page 29

30 Velocity Comparison PL: Line 1 U Velocity Very large unsteady flow features Instantaneous provile deviates greatly from the time-average Page 30

31 Velocity Comparison PL: Line 1 V Velocity Multiple unsteady frequencies present Page 31

32 Part Load Low frequency disturbance is prominent in the draft tube Page 32

33 Velocity Comparison HL: Line 1 U Velocity High frequency unsteady flow features along axis Page 33

34 Velocity Comparison HL: Line 1 V Velocity Strong velocity defect along axis Mostly steady response Page 34

35 High Load Quasi-steady flow observed in the draft tube Page 35

36 Vortex Rope Structure Much higher levels of unsteadiness present at part load PL BEP HL Page 36

37 Runtime & Scalability 3.81 hours/revolution on 256 cores Linux cluster Intel Xeon 2.67 GHz Dual octa-core (16 CPUs/node) 80% parallel scaling efficiency at 512 cores Page 40

38 Agenda Introduction Simulation Methods Results Outlook Page 41

39 Outlook Accurate prediction of steady and unsteady operation Efficient time-accurate simulation Full-fidelity geometry capture Robust and automated mesh Predictive CFD simulations can be used as a cost-effective complement to experimental testing A robust and repeatable simulation framework provides access to Design Space Exploration studies such as Design sweeps Parametric optimization Design of experiments Robustness and Reliability Page 42

40 Chad Custer, PhD Technical Specialist, Turbomachinery Page 43