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Copyright Journal of Ocean Technology 2013

Who should read this paper?This paper will be of interest to marine renewable energy engineers, operatorsand developers; and all those aficionados of the power and grace of vertical(and horizontal) axis turbines, both above and below the water.

Why is it important?Vertical axis turbines (think: eggbeater) are well suited to generating energyfrom ocean tides and currents as they are omnidirectional and require nocomplex yawing or pitching mechanisms in order to face into the flow.However, the not-so-delicate play of water on the blades, struts, hubs andshaft of such turbines results in complex hydrodynamic flows that needto be fully understood in order to optimize turbine output, service lifeand efficiency.

The authors put a new spin on computational fluid dynamics to simulatethe hydrodynamic performance of vertical axis turbines in three dimensions(3D). In particular, they propose a new strut correction factor that results

in more accurate prediction of hydrodynamic flow properties. Improvementsin the prediction of vertical axis turbine performance will result in turbinesthat will last longer while capturing more of the available energy held withinour oceans. Better understanding of the mechanics of vertical axis turbineswill enable the successful development and deployments at reduced scale.Commercialization at larger scales is likely to soon follow, driven by anincreasing appetite for clean and sustainable energy.

About the authorsPhilip Marsh is a PhD student at the National Centre for Maritime Engineeringand Hydrodynamics (NCMEH) at the Australian Maritime College (AMC).His research includes hydrodynamics of vertical axis turbines, fluid-structureinteractions, computational fluid dynamics and ocean renewable energy.Dev Ranmuthugala is the Acting Director, National Centre for Ports andShipping, and Associate Professor in Maritime Engineering at the AMC.His research includes experimental and computational fluid dynamics toinvestigate the hydrodynamic characteristics of underwater vehicles. IrenePenesis is a Senior Lecturer in Mathematics at the NCMEH. Her researchincludes varied applications of numerical and applied modelling techniquesmainly in the area of hydrodynamics, with a special interest in wave andtidal energy. Giles Thomas is an Associate Professor and Acting Director

of the NCMEH. His research and teaching interests include fluid-structureinteraction, hydrodynamics, model testing, surfing and design.

Marsh, Ranmuthugala, Penesis and Thomas explain why

it is not good enough to think in two dimensions (2D)when modelling fluid flow over a vertical axis turbine.

Fifty shades of flow

Philip Marsh

Dev Ranmuthugala

86 The Journal of Ocean Technolog y, V OL. 8, NO. 1, 2013

Irene Penesis

Giles Thomas


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The Journal of Ocean Technolog y, V OL. 8, NO. 1, 2013 87Copyright Journal of Ocean Technology 2013

PERFORMANCE PREDICTIONS OF A STRAIGHT-BLADED VERTICAL AXIS TURBINE USING DOUBLE-MULTIPLE STREAMTUBE ANDCOMPUTATIONAL FLUID DYNAMICS MODELS

P. Marsh1

, D. Ranmuthugala2

, I. Penesis1

, G. Thomas1

1 National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College,University of Tasmania, Australia2 National Centre for Ports and Shipping, Australian Maritime College, University of Tasmania,

Australia

ABSTRACT

Vertical axis turbines are increasingly being utilized to generate power from our oceans. However,due to high levels of dynamic stall and flow interaction effects of the blades, struts, hubs, andshaft, they exhibit complex hydrodynamic flows which need to be fully understood to increaseturbine output, service life, and efficiency. Double-Multiple Streamtube (DMS) andComputational Fluid Dynamics (CFD) models were used to numerically investigate the powergenerated and hydrodynamic properties of these turbines. Three-dimensional (3D) transient CFDsimulations were performed using an Unsteady Reynolds Averaged Navier-Stokes (URANS) solver.The DMS model developed incorporated a new correction factor to account for strut drag effects.

All simulations were validated against Experimental Fluid Dynamics (EFD) testing of a three- bladed turbine at the Australian Maritime College Circulating Water Channel. The DMS modelwith a newly developed correction factor for strut drag demonstrated good agreement with theCFD and EFD results for turbine power predictions across the operational tip speed ratio ()range. The 3D CFD model of the full turbine geometry including struts, hubs, and shaft also

provided good agreement with EFD results for turbine power. The 3D CFD model without struts,hubs, and shaft, and the DMS model without strut correction factors overpredicted turbine

performance especially at high , as the resistive torque generated by the struts, which reduces power, was not accounted for. All simulation results demonstrate that strut drag, and the associatedresistive torque, must be modelled if accurate simulations of vertical axis turbine performanceare to be obtained.

KEY WORDS

Vertical axis turbine; Simulation; Modelling; Hydrodynamics; Double-Multiple Streamtube(DMS); Computational Fluid Dynamics (CFD); Experimental Fluid Dynamics (EFD); Strut;Drag; Resistive torque; Ocean power; Tidal power


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88 The Journal of Ocean Technolog y, V OL. 8, NO. 1, 2013 Copyright Journal of Ocean Technology 2013

NOMENCLATURE

AR = blade aspect ratioc = blade chord (m)ca = strut chord (m)

CD = drag coefficientCDf = finite span drag coefficientCDi = induced drag coefficientCD0 = drag coefficient at zero CDStrut = strut drag coefficientCD = freestream drag coefficientCL = lift coefficientCLf = finite span lift coefficientCL

= freestream lift coefficient

C N = normal force coefficientC p = power coefficientC p1 = upstream power coefficientC p2 = downstream power coefficientCQ1 = upstream average torque coefficientCT = tangential force coefficientF = upstream tip loss factor F' = downstream tip loss factor

f up = upwind momentum functionH = turbine height (m)

N = number of turbine bladesr = turbine radius (m)R e = Reynolds number T = blade thickness (m)Ta = average resistive torque (Nm)

Tup = average upstream torque (Nm)Tup () = upstream torque at (Nm)u = interference factor unew = iteration interference factor u' = second interference factor V = local velocity (m/s)Ve = equilibrium velocity (m/s)V' = first induced velocity (m/s)V'' = second induced velocity (m/s)V i = freestream velocity (m/s)W = local relative velocity (m/s) = blade angle of attack (rad) b = effective angle of attack (rad)i = induced angle of attack (rad) = azimuth angle (rad) = tip speed ratio

= kinematic viscosity (m 2/s)

= density of water (kg/m 3) = rotational rate (rad/s)

INTRODUCTION

As global energy requirements increase many

countries are beginning to look to the ocean asan immense source of clean and sustainableenergy to fulfill their energy needs, with manydifferent devices proposed to do so. Verticalaxis turbines, an example of which is shown inFigure 1, are well suited to generating energyfrom ocean tides and currents as they areomnidirectional in nature and require no complexyawing or pitching mechanisms. Furthermore,the electrical generator can be installed out ofthe water, easing installation and servicing.

However, vertical axis turbines exhibitcomplex hydrodynamic flow properties due tohigh levels of dynamic stall [Ferreira et al., 2009],

as well as complex strut [Paraschivoiu, 2002]and wake interactions [Scheurich et al., 2010].To successfully operate vertical axis turbinesand ensure their longevity and efficiency, thesecomplex flow properties need to be fullycomprehended. This study utilizes the Double-Multiple Streamtube (DMS) model,Computational Fluid Dynamics (CFD), andExperimental Fluid Dynamics (EFD) to analyzethe hydrodynamic flow properties and poweroutput of vertical axis turbines.


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Tidal turbines exhibit complex flow propertiesdepending on their geometrical layout, whichhas a significant impact on the power generated

as shown by Rawlings [2008], who notedthrough EFD large variations in the powergenerated across a number of turbines havingthe same blade, hub, and shaft layout, butdifferent strut sections, attachment methods,and strut locations. Any attempt to numericallymodel vertical axis turbines requires thehydrodynamic effects of the struts, hubs, andshaft be accounted for. However, previousDMS and CFD simulations of vertical axisturbines have often been limited to two-

dimensional (2D) analysis models over limitedrotational rates [Lain, 2010; Malipeddi andChatterjee, 2012], which has resulted in the

poor resolution of these complex hydrodynamicflow properties and thus poor power estimatesdue to the lack of strut and tip loss modelling.

To improve simulation accuracy two numericalmethods, DMS models and three-dimensional(3D) CFD, were used to simulate the

performance and hydrodynamic characteristicsof a vertical axis turbine over its entireoperational range. A DMS model for the turbinewas developed and then modified to include a

Figure 1: Three-bladed Experimental Fluid Dynamics vertical axis turbine at the Australian Maritime College.

PHILIP MARSH, 2012


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new strut drag correction factor. Two 3D CFDmodels, with and without struts, hubs, andshaft, were developed. For clarity these 3DCFD models will be referred to henceforth asthe 3D CFD model with or without appendages,

respectively. All simulations were validatedagainst EFD tests conducted at the AustralianMaritime College (AMC) Circulating WaterChannel (CWC) [Australian MaritimeCollege, n.d.].

A key parameter of interest in this study wasthe power coefficient C p given by:

where frontal area S=2rH , r is the turbineradius, H is the turbine height, is the waterdensity (set to 1000 for all simulations), is the rotational speed, V i is the inflow velocity,and Torque is determined by the DMS, CFD,

and EFD methods.

All results were compared across a range of tipspeed ratio , defined as:

LITERATURE REVIEW

Paraschivoiu [2002] conducted numerical studiesof the performance of vertical axis turbinesusing a momentum-based DMS mathematicalmodel, which was an evolution of the singleand multiple streamtube models of Templin[1974] and Strickland [1975]. This methodhas proven to be very expeditious and hasdemonstrated a high level of simulation accuracy[Paraschivoiu, 2002]. However, it is dependent

on the inclusion of correction factors for 3Deffects such as strut drag and finite blade spaneffects, as well as the accuracy of the lift anddrag tables used. In particular, DMS modelswithout strut correction factors overpredict

turbine power for high , as the resistivetorque caused by strut drag is not simulated.

Dai and Lam [2009] performed 2D CFDsimulations using the Shear Stress Transport(SST) turbulence model to investigate the

performance of a three-bladed tidal turbine of0.9 m diameter and found good agreement forC p prediction at a single when comparedwith EFD data from the Ifremer Wave-CurrentCirculation Tank in Boulogne, France. Theyalso performed DMS simulations and againfound good agreement at the given , but didnot perform simulations for the entire range.Lain [2010] also performed 2D CFD simulationsusing similar modelling techniques to Dai andLam for the same turbine geometry and found

similar results; however, again thesesimulations were limited to one .

Nabavi [2008] used 2D CFD methods to simulateturbine C p, finding reasonable agreement againstEFD tests performed by Rawlings [2008] on athree-bladed turbine with a diameter of 0.9144 m.However, Nabavi corrected the 2D CFD results

for 3D effects by subtracting the arm drag C p obtained by EFD testing. Although this

provided good agreement with EFD results, thiscorrection method is not possible without EFDresults, negating the use of CFD alone tosimulate C p.

Malipeddi and Chatterjee [2012] used 2DCFD methods to determine C p for a straight-

bladed turbine using the SST turbulencemodel, and compared C p simulation results

(1)

(2)


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against EFD tests published by Kiho et al.[1996] for a 1.6 m diameter three-bladedturbine. Results at low and high ranges were reasonable, but poor predictions of

peak C p around middle were noted, with

C p prediction errors of more than 50%.

Castelli et al. [2010] compared 2D CFDsimulations of a 1.03 m diameter vertical axisturbine with EFD wind tunnel data, and foundreasonable agreement using the SST and k-turbulence models. These 2D simulationshowever predicted a maximum C p of 0.59compared to an EFD C p of 0.29, as the 2D CFDmodel was unable to account for any strut ortip loss effects. Castelli et al. compared 2D and3D CFD simulation results at one and foundthat the 3D model reduced C p predictions byapproximately 55%, but did not determine 3DC p for more than one .

McLaren [2011] predicted C p at low using

a 2D CFD simulation model of a three-bladed2.8 m turbine with the SST turbulence model.Using this model C p was overpredicted by57% when compared with EFD testingconducted at the University of Waterloo

Fire Research Facility in Ontario, Canada, asno strut drag or tip loss effects were simulated.

To improve simulation accuracy DMS modelswith strut correction factors and 3D CFD

models with the full turbine geometryincluding all appendages were developed bythe authors to improve C p predictions.

DOUBLE-MULTIPLE STREAMTUBE(DMS) MODEL

Calculations of turbine torque and powerwere performed using a DMS model basedon the simulation methods outlined byParaschivoiu [2002]. This DMS simulationcode incorporated correction factors forfinite blade span and tip loss effects, butno dynamic stall modelling was performedfor simplicity. The model simulated the

performance of a vertical axis turbine as adouble actuator disk to take into account

the reduction in flow velocity as the flowtravels downstream through the turbine

blades. This actuator disk model and theassociated geometrical notations used areshown in Figure 2.

Figure 2: Rotor geometry for a vertical axis turbine with two actuator disks. PARASCHIVOIU, 2002


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where f up is calculated using Equation 9.

Once completed the upstream turbine bladetorque T up was determined over the span H ofthe blades where:

(14)

From Equation 14 the average upstream halfcycle torque T up was calculated as:

The upstream half-cycle torque coefficient C Q1 and upstream power coefficient C p1 werecalculated as:

and

This method was repeated for the downwindsection to find the downstream C p2, and thusthe total turbine power coefficient C p wascalculated as:

The DMS model was corrected for finite bladespan effects by assuming an elliptical liftdistribution over the blades, where the finite

blade span lift coefficient, C L f , and dragcoefficient, C D f , were determined using:

(15)

(16)

(17)

(18)

(19)

PHILIP MARSH, 2013

Figure 3: Flowchart of Double-Multiple Streamtube iteration process.


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drag on the struts C DStrut as a function of therotational speed of the turbine as:

where C D0 is the drag on the strut section at zero at the inflow Reynolds number, with dragvalues taken from the drag coefficient tables ofSheldahl and Klimas [1981]. This was combinedwith Paraschivoius [2002] strut drag method,where the resistive average torque T a on eachstrut was determined by:

where ca is the chord of the strut.

Once the total torque of the turbine was foundusing the DMS model, the average resistivetorque for all struts was subtracted, thuscorrecting the DMS model results for strut drag.

COMPUTATIONAL FLUID DYNAMICS(CFD)

To simulate and quantify turbine performance,transient 3D CFD models were developedusing the commercial CFD package ANSYSCFX [2010a]. All simulations were performed

using URANS methods with an element-basedfinite volume approach. Turbine torque wascalculated using the inbuilt CFX CEL functionfor torque, which integrates the pressure andshear force distributions over the turbine surfaces.

Computational DomainAll 3D CFD meshes were generated using theANSYS CFX 13.0 mesher [ANSYS, 2010b].Tetrahedral mesh elements were used to ensurerapid meshing while accurately capturing all

hydrodynamic flow properties. Mesh refinementwas performed using either inflation layers or

by refining mesh density for regions aroundthe blades, hubs, struts, and shaft as well as forthe fluid domain encompassing the turbine

wake. Inflation layers were used to accuratelyresolve the laminar sub-layer and buffer layerto capture the flow properties. The first cellheight for all wall surfaces was chosen toensure a y+ < 1, as wall functions were notused by the turbulence model. Figure 4 showsan example of the inflation layers used on theturbine blade surfaces.

Turbine rotation was simulated using a GeneralGrid Interface (GGI), which placed aninterface between the rotating inner domainand the stationary outer domain, over whichflow properties are calculated using anintersection algorithm between the meshes oneither side of the interface. The inside domainwas rotated at the desired corresponding toEFD results. The GGI interface was set at 1.5 D(diameter) from the axis of turbine rotation tominimize any GGI algorithm errors in the

(33)

(34)

Figure 4: Example of mesh inflation layers used on the turbineblade surfaces.

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blade regions. To reduce mesh size, the turbineand its surrounding elements were split alongthe horizontal centreline plane, with the CFDdomain limited to the top half. Due to theresulting symmetry, it is possible to simulate

the full domain by defining the bottom surfaceas a plane of symmetry, as shown in Figure 5.This is possible as no free surface effects weremodelled, and was acceptable for validation

purposes as the turbine was tested in the CWCat an appropriate vertical depth to minimizeany free surface effects. The resulting CFDmesh is shown in Figure 6, which also showsdomain sizes as determined by meshindependence studies.

Boundary conditions are outlined in Table 1for the domain shown in Figure 5, which wereset to simulate freestream operating conditionsas the EFD testing configuration exhibited alow blockage ratio of less than 3.7%.

Turbulence Model and Discretization MethodsThe SST turbulence model was used for allCFD simulations as it has proven to be accuratefor the simulation of flows with high levels ofseparation, adverse pressure gradients, andstalled flow conditions [Lain, 2010], allencountered during the normal operation ofvertical axis turbines due to the changes in

blade angles of attack over each revolution

[Paraschivoiu, 2002]. Turbulence intensity wasset to 5% in line with the flow in the AMCCWC. Convergence for each time step wasdeemed to be achieved when residuals convergedto within 10 -4 for each time step. All simulationswere run for at least five revolutions to ensure

periodic convergence, with most runs startedfrom previous simulations to reduce transient

start-up time, and thus overall simulation time.For advection a high order scheme was used,with the transient terms modelled using asecond order backwards Euler scheme. A timestep that equated to 3.6 of rotation per timestep was used for all CFD simulations, asdetermined by temporal independence studies.

CFD AssumptionsTo simplify the CFD models the followingassumptions were made:

Table 1: Computational Fluid Dynamics (CFD) boundary conditions forall CFD models as shown in Figure 5.

PHILIP MARSH, 2013

Figure 5: Domain boundary condition nomenclature.

Figure 6: Mesh domain for 3D Computational Fluid Dynamics modelwith appendages. All domain sizes in turbine diameters (D).

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Flow was incompressible due to low Machnumber (less than 0.3).

Inlet flow was uniform.

The boundary effects were neglected as the

test rig was located at sufficient distancesaway from the tank boundaries.

No free surface effects were included dueto the EFD testing depth, allowing the useof symmetry to reduce domain size.

Mesh and Domain IndependenceMesh independence was evaluated for the 3D

CFD meshes with and without appendages toensure that the C p obtained was independent ofany meshing factors. Mesh independence wasdetermined when C p variations were less than6%, as this resulted in a suitable balance betweenoverall simulation time and accuracy. All meshindependence tests were conducted by evaluatingthe change in C p for any geometrical or meshfactor changes, which included changes indomain length, width, height, time step, andnumerical discretization method. The ability touse symmetry to reduce mesh size was alsoverified, with minimal change in C p found

between the CFD mesh domain that usedsymmetry and one that modelled the full domain.Boundary layer refinements were performed,

by changing both the number of inflation layers

as well as the height of the boundary layerregions, to ensure that boundary layers were

simulated accurately and to fully resolvehydrodynamic flow near the wall surfaces.

Mesh independence was demonstrated at 3million elements for the 3D model without

appendages as shown in Figure 7. Similarly forthe 3D mesh with appendages mesh independencewas achieved at 9.5 million elements.

Time studies were also performed to ensuretemporal independence. As an implicit solverwas used, there were no strict Courant numberrestrictions due to the high levels of solutionstability. Temporal independence wasdemonstrated at a time step corresponding to3.6 of rotation per time step as shown inTable 2 for the 3D CFD model without

appendages. Similar results were found forthe 3D model with appendages.

Table 2: Temporal independence for the 3D Computational Fluid Dynamics mesh without appendages at of 2.5 at a mesh size of 3 millionelements. Percentage change normalized by the 3.6 case.

Figure 7: Mesh size independence for the 3D Computational Fluid Dynamicsmesh without appendages at of 2.5 at 3.6 of rotation per time step.

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EXPERIMENTAL FLUID DYNAMICS (EFD)

In 2012, a straight three-bladed vertical axisturbine was tested in the AMC CWC, an 11 m

by 5 m by 2.5 m tank capable of maximum flow

speeds of 1.5 m/s [Australian Maritime College,n.d.]. Measurements of torque and rotation speedwere made at an inflow velocity of 1 m/s, usinga combined torque and rotation speed sensormounted to the turbine shaft. To control turbinespeed a variable speed motor drive unit wasutilized, enabling testing at different from 1to 3.5. Turbine dimensions are outlined inTable 3, and the EFD test rig is shown inFigure 8. Only preliminary EFD results are

presented in this paper and are used to validate both the DMS and CFD numerical models.

RESULTS AND DISCUSSION

Results for C p predictions by the DMS andCFD simulations against EFD testing results

are shown in Figure 9 at an inflow velocity of1 m/s.

Although at low there are good correlations between all numerical C p predictions and the

EFD results, at higher they differ significantlyin accuracy depending on the inclusion of strutloss modelling. This is due to the strut draggenerating significant resistive torque at thehigher , which in turn reduces the C p. Allsimulations resulted in C p - curves thatfollowed the expected shape, but were shiftedto higher C p and locations depending on theapplication of strut correction modellingtechniques.

The DMS model without a strut correctionfactor, labelled DMS in Figure 9, failed toaccurately predict turbine C p when comparedagainst the EFD results. At low ranges, C p was predicted reasonably accurately, with a

prediction error of less than 10% at = 1.75.

Figure 8: Vertical axis turbine installed in the Australian Maritime College Circulating Water Channel showing the gearbox, torque and rotationspeed sensor, turbine, and support frame.

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However, there was a significant reduction insimulation accuracy as increased, due tohydrodynamic effects caused by the struts,hubs, and shaft which were not included in thenumerical model. The accuracy at low wasinfluenced by the incorporation of correctionfactors for finite blade span and tip losses, andthe induction factors that account for thereduced velocity in the downstream turbineareas. The shape of the C p - curve was similarto the EFD results, but the entire curve was

shifted to higher . Thus, a DMS strut correctionmodel was proposed by the authors to accountfor the additional resistive torque caused bystrut drag to improve C p simulation resultsfor all .

The DMS model with strut drag correctionfactors, labelled DMS-S in Figure 9, was ableto predict C p performance well when comparedwith the CFD results. Across the entire rangegood agreement was found between the new

DMS model with strut correction factors(curve DMS-S) and the 3D CFD results withappendages (curve CFD-S), with a C p predictiondifference of less than 10% at = 2 between thetwo models. Prediction accuracy was improvedat high due to the simulation of resistivetorque by the modified DMS model. Both themaximum value and location of C p were

predicted well when compared to the 3DCFD-S curve. When compared to EFD results(curve EFD in Figure 9), there was reasonable

agreement at low , with a C p prediction errorof less than 25% at =2; however, as increasedthe simulation error increased. This may bedue to the significant bearing losses in the EFDresults at higher , as they were not accountedfor in the experimental setup. Above =3 theDMS model predicts negative C p, as the turbinewas no longer generating power. The locationwas overpredicted, but when compared to

previous numerical simulations [Castelli et al.,2010; Malipeddi and Chatterjee, 2012] this

Table 3: Turbine geometry, blade and strut section, and construction materials used for the Experimental Fluid Dynamics turbine.


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method results in a more accurate representationof the overall location and shape of theC p - curve.

Lift and drag coefficient data for the NACA63 4221 section were not available at thelow operational Reynolds numbers of the EFD

turbine, and NACA0025 data were used as thesections exhibit similar lift and drag coefficientsat high Reynolds numbers above 2 million[Abbott and Von Doenhoff, 1959; Sheldahl andKlimas, 1981]. The rationale for the substitutionof NACA63 4221 blade section data with

NACA0025 data is demonstrated in Figure 9when comparing all CFD and DMS C p results.The DMS model C

p predictions using the

NACA0025 section show good agreementwith CFD simulations that use the

NACA63 4221 data. Although there are smalldifferences in the hydrodynamic lift and dragcoefficients of the two blade sections, thesedifferences do not significantly reduce the C p

prediction accuracy of the DMS models. Inaddition, at high the effects of strut drag onturbine C p far outweighs the small differences

in lift and drag coefficients between the twosections. Previous numerical simulations havefailed to account for this additional drag [Daiand Lam, 2009; Lain, 2010; Malipeddi andChatterjee, 2012], which has a much greatereffect on C p prediction accuracy than the bladesection substitution used here.

The C p

- curve for the 3D CFD model withoutappendages, labelled CFD in Figure 9,demonstrated reasonable agreement with the

Figure 9: Comparisons of Cp for 3D Computational Fluid Dynamics results with appendages (CFD-S), and without appendages (CFD), DMS resultswith (DMS-S), and without strut correction factors (DMS), and the EFD results (EFD).

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EFD results at low , with a C p predictionerror of less than 30% at =1.5. However, itoverpredicted C p at high as the draggenerated by these additional components wasnot accounted for. When compared with EFDresults, the 3D CFD model with appendages(curve CFD-S) predicted both the C p - curvetrend and its maximum value with a higherdegree of accuracy than the CFD modelwithout appendages (curve CFD) and againstresults from previous simulation models forvertical axis turbines [Castelli et al., 2010;Malipeddi and Chatterjee, 2012]. This resultreveals the need to model the full turbinegeometry to ensure that strut drag effects onC p are simulated.

The results from the EFD testing, labelledEFD in Figure 9, revealed low overall C p

performance across when compared toturbines of similar sizing tested by Rawlings[2008]. As increased above 2.5, the EFDtesting C p was negative as no power was

produced due to the increase in resistive torquegenerated by strut drag overcoming the positivetorque generated by the turbine blades. Toimprove C p, the struts could be redesigned toreduce this effect.

As increases, the C p predicted by all DMSand CFD models moves away from the EFDresults, as shown in Figure 9. This may be dueto bearing losses that were not accounted for,which would significantly reduce C p as increases. Work to estimate bearing losses isunderway in order to correct all EFD C p results.The effect of performing EFD testing in aCWC has yet to be investigated but may alsoeffect C p measurements.

The 3D CFD model with appendages hasrevealed significant w velocities of more than0.5 m/s as shown in Figure 10, which areassociated with the vortices generated by the

blade tips, struts, hubs, and shaft, shown inFigure 11. Clearly visible in Figure 11 are vortex

structures emanating from the blade tips, blade-strut intersection points, struts, hubs, and shaft.These vortices will reduce turbine performancedue to interference of the flow over the turbine

blades, reducing the total torque generated.

The DMS model developed is suitable forturbine optimization studies not only of bladesection but also strut section. Turbine C p wasfound to be greatly dependent on strut sectioneffects, and thus any optimization studies should

Figure 10 (left): Flow velocityw (m/s) for the 3D Computational Fluid Dynamics model with appendages at of 2.Figure 11 (right): Vortex visualization of 3D Computational Fluid Dynamics model with appendages. Helicity of 6 m/s2 at of 2.

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account for the resistive torque generated by thestrut drag. Due to its accuracy and solution speedwhen compared to CFD methods, the DMSmodel could also be used to perform time-efficient Fluid Structure Interaction studies of

vertical axis turbines to investigate their loadingcharacteristics. Validation of the new DMS modelstrut correction factor using different turbinegeometries and inflow velocities is underway toensure its global applicability.

CONCLUSIONS

This study has demonstrated that turbine strutsmust be modelled to accurately predict C p andthe shape of the C p - curve. This can be

performed either by using DMS models withstrut correction factors or by using full 3DCFD models. The use of 2D CFD methods orDMS models without strut correction factorswill result in C p overpredictions as increases,since the resistive torque generated by strut

drag is not simulated.

The DMS model with the new strut correctionfactor has accurately simulated vertical axisturbine C p, improving simulation accuracywhen compared to those without strut correctionfactors. This DMS model offers an alternativemethod to CFD for the simulation of vertical

axis turbines, with the benefits of reducedsimulation time and computational requirements.However, CFD enables numerical investigationsto be extended to include flow visualizationthat enhances the understanding of verticalaxis turbine hydrodynamics.

The performance of an EFD vertical axis turbinehas been accurately simulated using 3D CFDmodels with all appendages included. This workhas demonstrated that transient 3D CFD

simulations are now feasible without the useof excessive computational resources orunreasonable solution times. When using CFDmodelling, it is essential to use a 3D model inorder to capture the strut and 3D effects to ensure

sufficient accuracy, especially at higher .

ACKNOWLEDGEMENTS

The authors wish to thank Christopher Hawtone,Matthew Skledar, Rowan Frost and AlanFaulkner for conducting and providing theEFD results.

REFERENCES

Abbott, I.H. and Von Doenhoff, A.E. [1959].Theory of wing sections: including a

summary of airfoil data . New York:Dover Publications.

ANSYS [2010a]. ANSYS CFX-solver theory guide: release 13.0 . Canonsburg: ANSYS Inc.

ANSYS [2010b]. ANSYS meshing users guide:release 13.0 . Canonsburg: ANSYS Inc.

Australian Maritime College [n.d.]. CirculatingWater Channel . Retrieved from https://www.amc.edu.au/maritime-engineering-and- hydrodynamics/circulating-water-channel.

Castelli, M.R., Ardizzon, G., Battisti, L.,

Benini, E., and Pavesi, G. [2010]. Modeling strategy and numerical validation for a Darrieus vertical axis micro-wind turbine .Proceedings of the ASME 2010 InternationalMechanical Engineering Congress andExpositions. Vancouver: AMSE.

Dai, Y. and Lam, W. [2009]. Numerical studyof straight-bladed Darrieus-type tidalturbin e. ICE-Energy, Vol. 162, pp. 67-76.

Ferreira, C.S., Kuik, G.N., Busel, G., andScarano, F. [2009]. Visualisation by PIV of


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dynamic stall on a vertical axis windturbine . Journal of Experimental Fluids,Vol. 46, pp. 97-108.

Kiho, S., Shiono, M., and Suzuki, K. [1996].The power generation from tidal currents

by Darrieus turbines . Proceedings of theWorld Renewable Energy Congress, Vol. 2,

pp. 1242-1255.Lain, S. [2010]. Simulation and evaluation of a

straight-bladed Darrieus-type cross flowmarine turbine . Journal of Scientific andIndustrial Research, Vol. 69, pp. 906-912.

Malipeddi, A.R. and Chatterjee, D. [2012]. Influence of duct geometry on the performance of Darrieus hydroturbine .Journal of Renewable Energy, Vol. 43,

pp. 292-300.McLaren, K. [2011]. A numerical and

experimental study of unsteady loading ofhigh solidity vertical axis wind turbines (Doctoral dissertation). Retrieved fromhttp://digitalcommons.mcmaster.ca/

opendissertations/6091. Nabavi, Y. [2008]. Numerical study of the duct

shape effect on the performance of a ductedvertical axis tidal turbine (Masters thesis).Retrieved from https://circle.ubc.ca/handle/2429/14704.

Paraschivoiu, I. [2002]. Wind turbine design:with emphasis on Darrieus concept .

Quebec: Polytechnic International Press.Rawlings, G. [2008]. Parametric

characterization of an experimental verticalaxis hydro turbine (Masters thesis).Retrieved from https://circle.ubc.ca/handle/2429/14706.

Scheurich, F., Fletcher, T.M., and Brown, R.[2010]. Simulating the aerodynamic

performance and wake dynamics of avertical-axis wind turbine . Journal of WindEnergy, Vol. 14, pp. 159-177.

Sheldahl, R. and Klimas, P. [1981]. Aerodynamic characteristics of seen symmetrical airfoil sections through180-degree angle of attack for use inaerodynamic analysis of vertical axis wind

turbines (SAND80-2114). Albuquerque:Sandia National Laboratories.

Strickland, J.H. [1975]. The Darrieus turbine:a performance prediction model usingmultiple streamtubes (SAND 75-0430).Albuquerque: Sandia National Laboratories.

Templin, R.J. [1974]. Aerodynamic performancetheory for the NRC vertical-axis windturbine (LTR-LA-160). Ottawa: NationalResearch Council of Canada.

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