Flow Simulation and Efficiency Hill Chart Prediction for Propeller Turbine

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Flow simulation and efficiency hill chart prediction for a Propeller turbine

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2010 IOP Conf. Ser.: Earth Environ. Sci. 12 012040

(http://iopscience.iop.org/1755-1315/12/1/012040)

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geometries. A simple approach which allows us to take into account all the 6 different blade geometries is to averagethem all and create new average blade geometry.

In the present paper we focus on the steady state flow computation. First, we will present the results on theefficiency hill chart prediction of the entire AxialT turbine using the average blade geometry and a discussion on theconsequence of using different blade geometries for the CFD simulation. Secondly, the flow characteristics of theentire turbine will be investigated and compared with experimental data at different measurement planes. Twooperating conditions are selected, OP3 near the best efficiency point and OP1 at part load condition. At the same time,for the same selected operating points, OP1 and OP3, the numerical results for the entire turbine simulation will becompared with flow simulation with our standard stage approach which includes only guide vane, runner and drafttube geometries.

Fig. 1 Locations of flow measurements inCRD AxialT propeller turbine model

Fig. 2Normalized efficiency hill chart of theAxialT propeller turbine model

Fig. 3Contour of geometry deviation of individual blade compared to the main average blade geometry

2.Problem setup

2.1 AxialT runner blade geometry

The geometry of the propeller model runner was measured by IREQ-Hydro Quebec. For more detailedinformation, please see [6]. Using our own runner blade geometry design tool, we have created new bladegeometry by averaging the 6 individual blade geometries. Figure 3 shows the geometry deviation of eachindividual blade compared to the main averaged blade. The scale varies from -0.5% to +0.5% of the throatdiameter. The average deviation of all blades is about 0.3%. The most deviated one is the blade #1 with amaximum value of -0.5% at the blade leading edge. For the model throat diameter of 380mm, this deviation

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corresponds to -1.9mm. The blades #3 and #4 have the least deviation of about 0.15 % which takes place at theleading and trailing edge regions. According to IEC code which allows a maximum of 0.1% deviation [8], wecould not use any geometry among the six blades to simulate accurately the AxialT turbine flow behavior. Besidethe variation on the blade geometry, the six blades have different tip clearances with the shroud. The tipclearance varies from 0.03% to 0.12% of the throat diameter. We keep an average blade tip clearance of 0.07%for all flow simulations in this paper.

2.2 CFD setting for coupled steady-state simulations of entire AxialT propeller turbine model

The computational flow domain for CFD simulation in the entire AxialT turbine model, as shown in Fig. 4,comprises the semi-spiral casing, the stay vanes, one guide vane, one runner blade and the draft tube. Gridgeneration for the spiral casing and stay vanes was made with the commercial grid generator ANSYS ICEM-CFD providing tetrahedral elements with prism layers resolving the boundary layer near the walls. For othercomponents of the turbine, guide vane, runner and draft tube, the grid generation was made with in-houseautomatic mesh generators providing H- and O-type hexahedral meshes. The guide vane is over-hanging from 20degree opening to the maximum opening at full load. The gap configurations due to over-hanging guide vane

and the runner tip clearance are taken care of by the mesh generator. Only one guide vane and one runner bladechannel are generated for the computation. The complete computational grid of the entire propeller turbinesimulations contains about 4106nodes. The generated meshes are intended to be used with k-epsilon turbulencemodel which requires a y+ value varying from 30 to 100 for the first node near solid wall. Meshes for the casingand draft tube have a minimum angle about 10 degrees while meshes for the guide vane and runner have aminimum angle about 5 degrees and a high grid aspect ratio due to the presence of gap of the runner and of theoverhang guide vane. The CFD simulation for efficiency hill chart prediction uses our standard stage approachwhich includes only guide vane, runner and draft tube geometries (Fig. 5). In such case, the inlet region of the guidevane channel is not at the usual stay vane guide vane interface, but it is placed further upstream allowing a uniformincoming flow from the inlet to fully develop. For the sake of simplicity, we call this standard set up Stage2 becausethere are 2 stage interfaces, Guide vane Runner and Runner Draft tube, used in this computation.

The commercial flow solver ANSYS CFX v12.1 is used for performing the flow analysis. Steady-state time-discretization with a constant pseudo-time step and the so called high-resolution space-discretization (mostly

2nd-order-accurate) has been applied. Turbulence is modeled by the standard k- model. The connectionsbetween different sub-domains from casing to guide vanes, from guide vane to runner and from runner to drafttube have been modeled by circumferential-averaging stage-interfaces. Two operating points at rated net headhave been analyzed with these entire turbine simulations: OP 1 in part load and OP3 near best-efficiency point,see Fig. 2. The flow rate measured in the model test has been specified at the inlet normal to the boundarysurface. Averaged static pressure has been set at the outlet boundary at the end of the draft tube extension box. Inthis setting, the head, the hydraulic power at the rotating runner and so the calculated hydraulic efficiency resultfrom the simulation

Fig. 4Computational flow domainfor full turbine simulation

Fig. 5Computational flow domainfor Stage 2 simulation

Fig. 6 Measurement planelocations near the runner

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3. Efficiency hill chart prediction

Ideally, the computational flow domain for CFD simulation of a hydraulic turbine should comprise the entire

turbine assembly. For steady state simulation, in order to save CPU time, it is preferable that the computationalflow domain would be divided into two flow domains. The first one would include casing and stay vanes. Thesecond domain would include guide vane, runner and draft tube. There is an advantage of dividing the entireturbine into two computational flow domains. The casing and stay vanes assembly, which are fixed components,requires only one simulation for a given operating condition to determine the component head loss. Forsubsequent operating conditions, the head loss of the casing and stay vanes assembly can be calculated simply byassuming that their losses are proportional to the square of the flow rate. For the guide vane, runner and drafttube assembly, the flow analysis has to be performed for all operating conditions of interest with correspondingguide vane opening positions. This approach has been successfully validated for Francis runners and can befound in [7].

We have performed several CFD simulations to compute the efficiency hill chart of the AxialT turbine withdifferent variations of the runner blade geometry. For most of the time, the computation was made for a range ofguide vane opening from 20 to 44 degree with an increment of 2 or 3 degrees. The n 11-value used in the

experimental investigation is 124. At the guide vane inlet, we specify a uniform flow and an inlet flow anglematching the flow orientation at the stay vane exit. The computation starts with a guessed flow rate. There is aprocedure to iterate the flow rate for a specific guide vane opening during the course of the simulation until thecomputed head of the entire turbine matches with the prescribed turbine head. The turbine head is calculated byadding the useful work produced by the runner, and the head losses of all individual turbine components. Theaverage blade geometry runner was chosen for the first calculation. At the beginning, the computation wasperformed for a turbine head of 10m which is our standard turbine head used for low head turbines. Then weperformed a second computation for 7m turbine head which is the turbine head during the test in the laboratory.We obtained the same results from the computations with both turbine heads. Figure 7 shows the comparison ofthe predicted turbine efficiency against the experimental data for a wide range of operating conditions. Thenormalized flow rate varies from 0.75 to 1.15. The numerical prediction matches very well with the lab data interms of the efficiency level and the position of the best efficiency point. This good correlation validates ourapproach of using the average blade geometry to represent a runner with 6 different blades. Figure 7 shows alsothe efficiency prediction for blade #1 which has the largest geometry deviation in the group. It is interesting tonote that the position of the BEP of blade #1 is shifted to the left about 4% compared to the one of the averageblade and the efficiency at the BEP is slightly higher of about 0.25 % compared to the average blade efficiency.For blade #4 which has the least geometry deviation in the group, the shift of the BEP to the left is smaller, about2%. For the last computation, we have chosen blade #2 which has a positive geometric deviation at the bladetrailing edge region as opposed to the two blades #1 and #4, which both have a negative deviation at the bladetrailing edge. This time the BEP location of blade #2 is shifted to the right compared to the one of the averageblade.

Fig. 7AxialT turbine efficiency with different bladerunner geometries

Fig. 8Head losses of individual components withdifferent blade runner geometries

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The head loss of the entire turbine can be broken into head losses of individual components as shown in Fig.8 for the whole range of the turbine operating conditions. It indicates clearly that the shape of the efficiency hillchart of a low head turbine is governed by the performance curve of the draft tube. While the runner loss variessmoothly over the wide range of operating condition, the head loss of the draft tube varies sharply near the BEP.We can find that the position of the lowest energy loss in the draft tube corresponds with the location of the BEPof the turbine as shown in Fig. 7. The location of the lowest draft tube loss associated with the Blade #1 runner isalso shifted 4% to the left compared to the lowest draft tube loss associated with the average blade runner. Thehead loss of the blade #1 runner is overall slightly smaller then the one of the average blade runner. This explainsthe higher efficiency of the blade #1 runner. The loss of the casing-stay vane assembly, which is similar for allblade runner geometries, increases with the flow rate Q11. On the contrary, the guide vane head loss decreaseswith flow rate Q11due to a small flow passage between the guide vanes at part load condition. The guide vaneloss is quite similar for all blade runner geometries and it is shown here only for the average blade computation.

3.

CFD simulations of the entire AxialT propeller turbine model

The CFD simulation in the entire turbine model geometry including casing, stay vanes, guide vanes, runner anddraft tube is performed for two selected operating points, OP1 at part load condition and OP3 near the best efficiencypoint. The OP1 condition corresponds to the guide vane opening of 25 degrees and the OP3 condition corresponds tothe guide vane opening at 33 degrees. For this computation, we imposed the turbine flow rate obtained from themodel test with a uniform velocity distribution at the casing inlet. The turbulence intensity was set to 2% at the inlet.Concurrently with the full turbine computation, we performed CFD simulations with the Stage2 computation domainas described above for the same operating conditions, OP3 and OP1. We used the same flow rate imposed by themodel test with a uniform flow angle of 45 degrees at the inlet of the guide vane. The turbulence intensity was set to1% at the guide vane inlet.The following is the comparison of the CFD results obtained from both setups against the experimental data. Figure6 shows the location of several planes upstream and downstream of the runner used for comparison: the STV-GVinterface (r = 0.25m), the GV-RN Interface (corresponding to the measurement plane #3) and the runneroutlet/draft tube cone planes (Planes #5a, #5b and #5c). In all figures, the velocity has been normalized by theaverage axial velocity at the turbine throat.

4.1 Results and discussion for operating point OP1 at part load (= 25)

At the STV-GV interface (Fig. 9), the velocity profiles from the two flow simulations are plotted in order toverify if the imposed uniform flow at the inlet for the Stage2 calculation is valid. A good correlation is obtainedfor the radial component distribution suggesting that the position of guide vane inlet of the Stage2 simulation isadequate.

Fig. 9 Axial and tangential velocity profiles atthe STV-GV interface OP1

Fig. 10 Axial and tangential velocity profiles at theGV-RN interface Plane 3 OP1

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Fig. 11Axial and tangential velocity profiles atthe DT inlet Plane 5a OP1

Fig. 12Axial and tangential velocity profiles underthe hub Plane 5b OP1

Fig. 13Axial and tangential velocity profiles insidethe DT cone Plane 5c OP1

Fig. 14Turbulence intensity profiles inside theDT cone Plane 5c OP1

Overall, for the OP1 condition, the results from the full turbine and Stage2 simulations match quite well withthe phase-averaged velocity measurements. At the GV-RN interface (Fig. 10), the full turbine simulationcorrectly predicts the phase-averaged axial velocity profile, while slightly under-predicting the tangentialvelocity of the flow. The Stage2 simulation predicts the velocity profiles quite well, although it tends toovershoot the tangential velocity and to under-predict the axial velocity near the hub, with the reversephenomenon at the shroud.

The velocity profiles at the 5a, 5b and 5c measurement planes (Fig. 11, 12 and 13) show that neither type ofsimulation can be said to be better predicting the flow in the draft tube cone. At 5a (Fig. 11), the CFD velocityprofiles more or less match up to the measured data. However, downstream planes 5b and 5c show that thepredicted tangential velocities are lower than measured. Also, while the axial component profiles match up prettywell over most of the 5b and 5c planes, the predicted behavior under the hub did not match up very well tomeasured data, even to the point where the CFD results show a large region of counter-flow under the hub, at the

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5c plane. Figure 14 shows the distribution of the turbulence intensity at the plane 5c. The numerical results fromboth flow simulations are about 40% of the experimental data.Finally, the velocity contours at the draft tube outlet obtained with both simulations are very similar and comparewell with the experimental velocity contour (Fig. 15 and 16). The measured mass flow distribution for the twodraft tube channels is 23.1% and 79%. We obtain a distribution of about 22% - 78% for the full turbine and21.5% - 78.5 % for the Stage2 simulation.

Full turbine simulation Stage2 simulation

Fig. 15Experimental velocitycontour at Draft Tube Outlet OP1

Fig. 16Computed velocity contour at Draft Tube Outlet OP1

4.2 Results and discussion for operating point OP3 near BEP (

= 33)

At the STV-GV interface for the operating point OP3 (Fig. 17), we find the same similarity as observed forthe operating point OP1. Figure 18 shows the predicted axial and tangential velocity profiles at the GV-RNinterface. While both simulations are very close to the measured axial velocity profile, neither simulationpredicts with precision the measured tangential velocity profile. The full turbine solution under-predicts whilethe Stage2 over-predicts.

The velocity profiles at the 5a plane (Fig. 19) show that the full turbine simulation is a better predictor ofboth the axial and tangential measured velocity profiles than the Stage2 simulation. The Stage2 solution underpredicts the tangential velocity profile near the draft tube wall. The same tangential velocity defect is observedfor the planes 5b and 5c as shown in Fig. 20 and 21. At the same planes 5b and 5c, the full turbine velocityprofiles match up well with the measured velocity profiles near the draft tube wall while it is rather the Stage2simulation that seems to be better at predicting the velocity profiles near the hub region. One noteworthydifference between the two simulations is the inability for the full turbine simulation to predict the surge intangential velocity near the center (at about r=0.02-0.03 m), which the Stage2 simulation has no trouble catching.Also, the full turbine simulation predicts a large area of flow recirculation directly under the hub (Fig. 20), whichwe found surprising because of the measurement planes proximity to the hub. Figure 22 shows the distributionof the turbulence intensity at the plane 5c for the OP3 condition near the BEP. It is surprising to see that theturbulence intensity profile obtained from the full turbine computation is several times smaller compared to theexperimental data and the result from Stage2 simulation. This could explain the different results from the twosimulations at the hub region.

Fig. 17Axial and tangential velocity profiles at theSTV-GV interface OP3

Fig. 18 Axial and tangential velocity profiles atthe GV-RN interface Plane 3 OP3

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Fig. 19 Axial and tangential velocity profiles at theDT Inlet Plane 5a OP3

Fig. 20 Axial and tangential velocity profiles Plane 5b OP3

Fig. 21Axial and tangential velocity profiles Plane 5c- OP3

Fig. 22 Turbulence intensity profiles inside the DTCone

Plane 5c OP3

Figures 23 and 24 show a comparison of the velocity contours at the draft tube outlet. The measured massflow distribution for the two draft tube channels is 38.1% and 52.8%. The values are the percentage of mass flowin one channel (measured and integrated from LDV data) compared to total mass flow measured on test rig.Since LDV measurements did not cover the full cross-section, the sum of both values is not 100%. While the twotypes of simulation are moderately close in terms of mass flow distribution, about 36% - 64% for the full turbineand 32% - 68 % for the Stage2, the flow pattern show little similitude between the 2 flow simulations and theexperimental data.

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Full turbine simulation Stage2 simulation

Fig. 23 Experimental velocitycontour at Draft Tube Outlet OP3

Fig. 24Computed velocity contour at Draft Tube Outlet OP3

4.Conclusion

In the present paper, we have presented flow simulations of a low head Propeller turbine at various designand off-design conditions. We have demonstrated that creating an average blade runner to represent a modelrunner with different geometry variation is a valid and simple approach allowing us to predict correctly theefficiency hill chart of this particular runner and we have shown the consequence of geometry deviation on theefficiency hill chart prediction. It is crucial to model the correct geometry, even small deviations in runner bladegeometry could lead to inaccurate results (e.g. when taking blade 1 instead of the averaged blade). Obviously, wewill perform comparative CFD simulations with a computational domain including 6 different blade geometriesfor further validation. Also, we have performed comparative simulations with the full turbine and Stage2 flowdomains. Both computations give relatively similar results, but the difference found in the draft tube flow predictionin OP3 has to be investigated. One of the possible reasons is the difference in the turbulence intensity level developedat the runner outlet which leads to different results in the velocity profile below the hub.The steady state CFD analysis shows reliable results for the analysis of global turbine characteristics for a range of -25% to +15% of the flow rate from the BEP, as demonstrated in our efficiency hill chart prediction, given that theappropriate geometry is used. However, details of flow patterns (e.g. swirl and backflow in hub wake) are not exactlypredicted. In such case, an unsteady simulation is necessary, especially in the draft tube, where time-dependent flowphenomena with different timescales exist, which has major impact on the performance of low head water turbines.However, results of these investigations will be presented in a subsequent publication.

Acknowledgments

The authors would like to thank the participants on the Consortium on Hydraulic Machines for their supportand contribution to this research project: Alstom Hydro Canada Inc., Andritz Hydro, Edelca, Hydro-Quebec,Laval University, NRCan, Voith Hydro Inc. Our gratitude goes as well to the Canadian Natural Sciences and

Engineering Research Council who provided funding for this research. A special thanks to IREQ HydroQuebec having provided us the AxialT geometry in a requested specific format. The in-house automatic meshgenerators for distributor, runner and draft tube are from the project Gmath, a collaborative R&D projectbetween cole Polytechnique de Montral and Andritz Hydro Ltd.

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Nomenclature

Aref

BEPcx

crefCS

DDTGVn11OPOx

Oz

Area of draft tube outlet section [m2]

Best efficiency pointHorizontal velocity component in direction of Ox[m/s]Reference velocity at DT outlet cref= Q/Aref[m/s]Spiral casingThroat diameter of the turbine [m]Draft tubeGuide vanesUnit speed n11= nD/H

0.5Operating pointHorizontal reference axis pointing towards tailwaterVertical reference axis, turbine axis

Q

Q11rRN

STVvr

vavtw

ref

Flow rate [m3/s]

Unit flow rate Q11= Q/D2H0.5Radius [m]RunnerStay vanesRadial velocity component[m/s]Axial velocity component[m/s]Circumferential velocity component[m/s]Axial velocity component[m/s]Guide vane opening angle []Hydraulic efficiencyIndex referring to the operating point nearbest-efficiency point

References

[1] Deschnes C, Ciocan G D, De Henau V, Flemming F, Huang J, Koller M, Arloza F N, Page M, Qian R andVu T C 2010 General overview of the AxialT Project: a partnership for low head turbine developments25thIAHR Symp. on Hydr. Mach. and Syst(Timisoara, Romania)

[2] Gagnon J M, Iliescu M, Ciocan G D and Deschnes C 2008 Experimental Investigation of Runner OutletFlow in Axial Turbine with LDV and Stereoscopic PIV 24 thIAHR Symp. on Hydr. Mach. and Syst.(Foz do Iguassu, Brazil)

[3] Beaulieu S, Deschnes C, Iliescu M and Fraser R 2009 Flow Field Measurement Through the Runner of aPropeller Turbine Using Stereoscopic PIV 8th Int. Symp. on Particle Image Velocimetry PIV09(Melbourne, Australia)

[4]

Gouin P, Deschnes C, Iliescu M and Ciocan G D 2009 Experimental Investigation of Draft Tube Flow ofan Axial Turbine by Laser Doppler Velocimetry 3rdIAHR Int. Meeting of the Workgroup on Cavitationand Dynamic Problems in Hydr. Mach. and Syst.(Brno, Czech Republic)

[5] Duquesne P, Iliescu M, Fraser R, Deschnes C and Ciocan G D 2010 Monitoring of velocity and pressurefields within an axial turbine 25thIAHR Symp. on Hydr. Mach. and Syst.(Timisoara, Romania)

[6] Nicolle J, Labb P, Gauthier G and Lussier M 2010 Impact of blade geometry differences from CFDperformance analysis of existing turbines 25th IAHR Symp. on Hydr. Mach. and Syst. (Timisoara,Romania)

[7] Thi C Vu and Safia Retieb 2002 Accuracy assessment of current CFD tools to predict hydraulic turbineefficiency hill chart 21stIAHR Symp. on Hydr.Mach.and Syst.(Lausanne, Switzerland)

[8] IEC Code 60193 - Hydraulic turbines, storage pumps and pump-turbines Model acceptance tests 2ndedition

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