Optimization Design and Flow Analysis of Reversible Pump-Turbine

Transcription

1 International Symposium on Current Research in Hydraulic Turbines April 04, 2017, Turbine Testing Lab, Kathmandu University, Dhulikhel, Nepal Optimization Design and Flow Analysis of Reversible Pump-Turbine Baoshan Zhu Department of Thermal Engineering Tsinghua University, Beijing, China Apr. 4, 2017

2 Contents Introduction Optimization Design Flow Analysis Summary

3 Introduction Pump storage plants are necessary in China. The flow of electricity in China

4 Introduction Key equipment of pumped storage plants: Pump-turbine; Efficiency, stability, cavitation.

5 Introduction Complex Geometry; Designed from pump mode, verified with turbine mode; Trial and error in numerical analysis and model tests; Number of input parameters; Hard to obtain high performance at both operating modes.

6 Optimal Design Optimal Design System 3D Inverse Design CFD DoE RSM MOGA Inverse Design: Parametric design Self-made Code, TURBODesign, CFD: Performance estimation ANSYS CFX DoE: Distribution of sample point in design space isight RSM: Relationships between parameters and performances isight MOGA: Optimal solution in design space based on RSM isight

7 3D Inverse Design 3D Inverse Design: The flow field is decomposed into circumferentially averaged components: W W r z 1 V r r z r 1 V z r r z V r V r 1 1 f f r r r z r z r z z r 1 B 2 2 t 2 2 V r tv z r r r z 2r r z 1 E r rf E r rf W 2 z W W r W W r z z r

8 3D Inverse Design Periodic components: 2 s s 2 s 2 2 k k k V r V r 2 c c 2 c 2 2 k 1 k k k B c f f 2cos k kbf 2coskBf r r r z r r r z z 1 r r r z V r V r V r sin kbf kb r r r z 2 2 V r V r k B s f f k 2sin kbf 2sin kbf r r r z z V r V r V r coskbf kb r r r z 2 2

9 DRVTshroud DRVThub 3D Inverse Design Given parameters: (1)Design specification (2)Blade thickness (3)Blockage factor Input parameters: (4)Blade loading (5)Blade stacking (6)Meridional geometry SLOPEshroud SLOPEhub 0.2 NChub NDshroud Hub NCshroud 0.0 Shroud NDhub Meridional distance m Blade loading distribution

10 3D Inverse Design L4 S b 2 HP L4 H Shroud L3 S (C2 S ) Hub L0 S L1 S (C1 S ) L2 S D 2 κ D 1S LP L3 H (C2 H ) LP H (CP H ) D 1H Blade stacking L2 H L0 H L1 H (C1 H ) Meridional channel parameterization

11 Performance Prediction-CFD Total mesh number: Runner mesh number: Turbulence model: RNG k-ε Boundary conditions: Pump mode: Inlet: static pressure 0Pa Outlet: mass flow rate Turbine mode: Inlet: mass flow rate Outlet: static pressure 0Pa

12 DOE RSM and MOGA DOE (Latin Hypercube Sampling): Equal probability distribution of random orthogonal Number of sample point can be decided artificially RSM: MOGA: P j = β 0 j + n i=0 β i j X i Global optimization n + β ii j X i 2 i=0 n + β ik j X i X k i j X i,x k Input parameter P j Optimization target Multi objectives

13 Design of Runners Middle-Head Pump-Turbine Runner Pump mode: Hmax=298m, Hmin=239m, Qmin=70m 3 /s Pmax=270mw, Nr=300rpm Turbine mode: Hr=259m,Pr=255mw,Nr=300rpm Scaled pump-turbine Mode H m (m) Q m (m 3 /s) N m (rpm) B Pump Turbine

14 Design of Runners Ultra-Head Pump-Turbine Runner Pump mode: Hmax=712.46m, Hmin=652.44m Nr=500rpm Turbine mode: Hr=659m,Hmax=693.85m, Hmin=624.66mm, Pr=444.44mw Scaled pump-turbine Mode H m (m) Q m (m 3 /s) N m (rpm) B Pump Turbine

15 DRVTshroud DRVThub Optimization Design Input: Blade loading, Blade lean, Meridional geometry 1.2 b SLOPEshroud L4 S HP L4 H SLOPEhub 0.2 NChub NDshroud Hub NCshroud 0.0 Shroud NDhub Meridional distance m D 1S L0 S Shroud L3 S (C2 S ) Hub L2 S L1 S (C1 S ) κ LP L3 H (C2 H ) LP H (CP H ) D 1H L2 H L0 H L1 H (C1 H ) D 2 Output: Runner efficiency at pump design and turbine rated point, Minimum pressure on the blade surface, Slop of S-shaped characteristic curves

16 Optimization Results Middle-Head Pump-Turbine Runner Turbine runner efficiency/(%) Pump runner efficiency/(%) A Initial runner Runner A Turbine runner efficiency h /(%) B Runner B Pump runner efficiency h /(%) Optimized runners

17 Optimization Results Ultra-Head Pump-Turbine Runner Initial runner Preferred runner Initial runner Preferred runner Optimized runners

18 Flow Analysis-EFD Middle-Head Runner-Model test

19 Flow Analysis-EFD 18 NPSH(m) A s i A s c B s i B s c Operation range Q(m 3 /s) The measured performance in pump mode

20 Flow Analysis-EFD n11/rpm 54 Test Head: 30/m A Q 11 /(m 3 /s) The measured performance in turbine mode B

21 Flow Analysis-EFD ΔH/H(%) B, Hp max B, Hp r B, Hp min A, Hp max A, Hp r A, Hp min Q 11 /(m 3 /s) n11/[r/min] B 1mm B 3mm B 5mm B 7mm B 9mm B 11mm B 13mm B 15mm A 1mm A 3mm A 5mm A 7mm A 9mm A 11mm A 13mm A 15mm B Runaway A Runaway n 11 =50.38 n 11 = Q 11 /[m 3 /s] The measured pressure fluctuations and S-shaped characteristics in turbine mode

22 Flow Analysis-EFD Internal Flow Measurement- Under Way

23 Flow Analysis-CFD Middle-Head Runner Numerical results of efficiency

24 Flow Analysis-CFD Numerical Simulation of pressure fluctuations

25 Flow Analysis-CFD Pump-Turbine Runner

26 Summary A multiobjective optimization strategy is used to develop a reversible pump-turbine runner with high efficiency and stability. Complex flow phenomena are need to investigate. The performance measurements and flow analyses clearly show the effects of parameters on the runner s performance. Design guidelines can be built for different head pump-turbine runners. The strategy can be applied easily for different turbomachines: pump, turbine and so on.

27 International Symposium on Current Research in Hydraulic Turbines April 04, 2017, Turbine Testing Lab, Kathmandu University, Dhulikhel, Nepal Thank You Very Much For Your Attentions!

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